diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS new file mode 100644 index 000000000..6e32ade9a --- /dev/null +++ b/.github/CODEOWNERS @@ -0,0 +1,34 @@ +* @temporalio/sdk + +# SDK & Nexus own the README, pyproject.toml, and uv.lock +/README.md @temporalio/sdk @temporalio/nexus +/pyproject.toml @temporalio/sdk @temporalio/nexus +/uv.lock @temporalio/sdk @temporalio/nexus + +# The Nexus team owns any folder whose name starts or ends with "nexus", +# both at the repo root and under tests/ +/nexus*/ @temporalio/nexus +/*nexus/ @temporalio/nexus +/tests/nexus*/ @temporalio/nexus +/tests/*nexus/ @temporalio/nexus + +# The AI SDK team owns the AI integration samples and their tests. We add +# @temporalio/sdk too, so the SDK team can continue to manage repo-wide concerns. +/deepagents_plugin/ @temporalio/sdk @temporalio/ai-sdk +/google_adk_agents/ @temporalio/sdk @temporalio/ai-sdk +/google_genai/ @temporalio/sdk @temporalio/ai-sdk +/langfuse_tracing/ @temporalio/sdk @temporalio/ai-sdk +/langgraph_plugin/ @temporalio/sdk @temporalio/ai-sdk +/langsmith_tracing/ @temporalio/sdk @temporalio/ai-sdk +/litellm_activity/ @temporalio/sdk @temporalio/ai-sdk +/openai_agents/ @temporalio/sdk @temporalio/ai-sdk +/strands_plugin/ @temporalio/sdk @temporalio/ai-sdk +/tests/deepagents_plugin/ @temporalio/sdk @temporalio/ai-sdk +/tests/google_adk_agents/ @temporalio/sdk @temporalio/ai-sdk +/tests/google_genai/ @temporalio/sdk @temporalio/ai-sdk +/tests/langfuse_tracing/ @temporalio/sdk @temporalio/ai-sdk +/tests/langgraph_plugin/ @temporalio/sdk @temporalio/ai-sdk +/tests/langsmith_tracing/ @temporalio/sdk @temporalio/ai-sdk +/tests/litellm_activity/ @temporalio/sdk @temporalio/ai-sdk +/tests/openai_agents/ @temporalio/sdk @temporalio/ai-sdk +/tests/strands_plugin/ @temporalio/sdk @temporalio/ai-sdk diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index ff07e2afa..e85fbe40d 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -1,4 +1,7 @@ name: Continuous Integration +permissions: + contents: read + actions: write on: # rebuild any PRs and main branch changes pull_request: push: @@ -12,31 +15,58 @@ jobs: strategy: fail-fast: true matrix: - python: ["3.8", "3.12"] - os: [ubuntu-latest, macos-latest, windows-latest] - runs-on: ${{ matrix.os }} + python: ["3.10", "3.14"] + os: [ubuntu-latest, macos-intel, macos-arm, windows-latest] + include: + - os: macos-intel + runsOn: macos-15-intel + - os: macos-arm + runsOn: macos-latest + runs-on: ${{ matrix.runsOn || matrix.os }} steps: + - uses: astral-sh/setup-uv@cec208311dfd045dd5311c1add060b2062131d57 # v8 - name: Print build information run: "echo head_ref: ${{ github.head_ref }}, ref: ${{ github.ref }}, os: ${{ matrix.os }}, python: ${{ matrix.python }}" - - uses: actions/checkout@v2 + - uses: actions/checkout@9c091bb21b7c1c1d1991bb908d89e4e9dddfe3e0 # v7.0.0 with: submodules: recursive - - uses: actions/setup-python@v1 + - uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6 with: python-version: ${{ matrix.python }} - # Using fixed Poetry version until - # https://github.com/python-poetry/poetry/pull/7694 is fixed - - run: python -m pip install --upgrade wheel "poetry==1.4.0" poethepoet - - run: poetry install --with pydantic --with dsl + # protoc is needed whenever uv builds temporalio from a git source + # (e.g. when [tool.uv.sources] pins a branch while a feature is in + # development). Kept on by default so we don't have to add and remove + # this step every time we iterate on a new plugin. + - uses: arduino/setup-protoc@c65c819552d16ad3c9b72d9dfd5ba5237b9c906b # v3 + with: + version: "23.x" + repo-token: ${{ secrets.GITHUB_TOKEN }} + - run: uv tool install poethepoet + - run: uv sync --group=dsl --group=encryption --group=trio-async - run: poe lint - - run: poe test -s -o log_cli_level=DEBUG - - run: poe test -s -o log_cli_level=DEBUG --workflow-environment time-skipping + - run: mkdir junit-xml + - run: poe test -s --junit-xml=junit-xml/${{ matrix.python }}--${{ matrix.os }}.xml + - run: poe test -s --workflow-environment time-skipping --junit-xml=junit-xml/${{ matrix.python }}--${{ matrix.os }}--time-skipping.xml + # This must remain the last step since it downgrades pydantic + - name: Uninstall pydantic + shell: bash + run: | + echo y | uv run pip uninstall pydantic + echo y | uv run pip uninstall pydantic-core + uv run pip install pydantic==1.10 + poe test -s --junit-xml=junit-xml/${{ matrix.python }}--${{ matrix.os }}--pydantic-v1.xml tests/pydantic_converter_v1/workflow_test.py # On latest, run gevent test - name: Gevent test if: ${{ matrix.python == '3.12' }} run: | - poetry install --with gevent - poetry run python gevent_async/test/run_combined.py - + uv sync --group gevent + uv run gevent_async/test/run_combined.py + - name: Upload junit-xml artifacts + uses: actions/upload-artifact@bbbca2ddaa5d8feaa63e36b76fdaad77386f024f # v7 + if: always() + with: + name: junit-xml--${{github.run_id}}--${{github.run_attempt}}--${{ matrix.python }}--${{ matrix.os }} + path: junit-xml + retention-days: 14 diff --git a/.gitignore b/.gitignore index 033df5fb6..6f9d58c21 100644 --- a/.gitignore +++ b/.gitignore @@ -1,2 +1,11 @@ .venv +.idea __pycache__ +.vscode +.DS_Store +.claude +.mypy_cache/ +**/client.key +**/client.pem +*.egg-info/ +.env diff --git a/README.md b/README.md index df72efc7b..164263995 100644 --- a/README.md +++ b/README.md @@ -1,22 +1,28 @@ # Temporal Python SDK Samples -This is the set of Python samples for the [Python SDK](https://github.com/temporalio/sdk-python). +This is a collection of samples showing how to use the [Python SDK](https://github.com/temporalio/sdk-python). ## Usage Prerequisites: -* Python >= 3.8 -* [Poetry](https://python-poetry.org) -* [Local Temporal server running](https://docs.temporal.io/application-development/foundations#run-a-development-cluster) +* [uv](https://docs.astral.sh/uv/) +* [Temporal CLI](https://docs.temporal.io/cli) with a local dev server running: + ``` + temporal server start-dev + ``` + +The SDK requires Python >= 3.10. You can install Python using uv. For example, + + uv python install 3.13 With this repository cloned, run the following at the root of the directory: - poetry install + uv sync -That loads all required dependencies. Then to run a sample, usually you just run it in Python. For example: +That loads all required dependencies. Then to run a sample, usually you just run it under uv. For example: - poetry run python hello/hello_activity.py + uv run hello/hello_activity.py Some examples require extra dependencies. See each sample's directory for specific instructions. @@ -37,6 +43,7 @@ Some examples require extra dependencies. See each sample's directory for specif * [hello_async_activity_completion](hello/hello_async_activity_completion.py) - Complete an activity outside of the function that was called. * [hello_cancellation](hello/hello_cancellation.py) - Manually react to cancellation inside workflows and activities. + * [hello_change_log_level](hello/hello_change_log_level.py) - Change the level of workflow task failure from WARN to ERROR. * [hello_child_workflow](hello/hello_child_workflow.py) - Execute a child workflow from a workflow. * [hello_continue_as_new](hello/hello_continue_as_new.py) - Use continue as new to restart a workflow. * [hello_cron](hello/hello_cron.py) - Execute a workflow once a minute. @@ -50,30 +57,66 @@ Some examples require extra dependencies. See each sample's directory for specif * [hello_search_attributes](hello/hello_search_attributes.py) - Start workflow with search attributes then change while running. * [hello_signal](hello/hello_signal.py) - Send signals to a workflow. + * [hello update](hello/hello_update.py) - Send a request to and a response from a client to a workflow execution. * [activity_worker](activity_worker) - Use Python activities from a workflow in another language. +* [batch_sliding_window](batch_sliding_window) - Batch processing with a sliding window of child workflows. +* [bedrock](bedrock) - Orchestrate a chatbot with Amazon Bedrock. +* [cloud_export_to_parquet](cloud_export_to_parquet) - Set up schedule workflow to process exported files on an hourly basis +* [context_propagation](context_propagation) - Context propagation through workflows/activities via interceptor. * [custom_converter](custom_converter) - Use a custom payload converter to handle custom types. * [custom_decorator](custom_decorator) - Custom decorator to auto-heartbeat a long-running activity. +* [custom_metric](custom_metric) - Custom metric to record the workflow type in the activity schedule to start latency. +* [deepagents_plugin](deepagents_plugin) - Make LangChain Deep Agents durable: each LLM/tool/backend call becomes a Temporal Activity while the agent loop replays in the Workflow. * [dsl](dsl) - DSL workflow that executes steps defined in a YAML file. +* [eager_wf_start](eager_wf_start) - Run a workflow using Eager Workflow Start * [encryption](encryption) - Apply end-to-end encryption for all input/output. +* [env_config](env_config) - Load client configuration from TOML files with programmatic overrides. +* [external_storage](external_storage) - Offload large payloads to S3-compatible object storage, plus a codec server for the Web UI and CLI. +* [external_storage_redis](external_storage_redis) - Redis driver for external storage * [gevent_async](gevent_async) - Combine gevent and Temporal. +* [google_adk_agents](google_adk_agents) - Run Google ADK agents as durable Temporal workflows (model calls, tools, multi-agent, MCP, streaming). +* [google_genai](google_genai) - Run the Google Gemini SDK inside durable Temporal workflows. +* [hello_nexus](hello_nexus) - Define a Nexus service, implement operation handlers, and call them from a workflow. +* [hello_standalone_nexus](hello_standalone_nexus) - Use Nexus Operations without using a workflow. +* [hello_standalone_activity](hello_standalone_activity) - Use activities without using a workflow. +* [lambda_worker](lambda_worker) - Run a Temporal Worker inside an AWS Lambda function. +* [langfuse_tracing](langfuse_tracing) - Trace Temporal workflows in Langfuse with the OpenTelemetry plugin and OTLP export. +* [langgraph_plugin](langgraph_plugin) - Run LangGraph workflows as durable Temporal workflows (Graph API and Functional API). +* [langsmith_tracing](langsmith_tracing) - Trace Temporal workflows with LangSmith via the LangSmith plugin. +* [litellm_activity](litellm_activity) - Call LLM providers through LiteLLM from a Temporal Activity. +* [message_passing/introduction](message_passing/introduction/) - Introduction to queries, signals, and updates. +* [message_passing/safe_message_handlers](message_passing/safe_message_handlers/) - Safely handling updates and signals. +* [message_passing/update_with_start/lazy_initialization](message_passing/update_with_start/lazy_initialization/) - Use update-with-start to update a Shopping Cart, starting it if it does not exist. +* [nexus_cancel](nexus_cancel) - Fan out concurrent Nexus operations, take the first result, and cancel the rest. +* [Nexus Messaging](nexus_messaging): Demonstrates how send signal, update and query messages through Nexus. + This contains two samples, one sending messages to an existing workflow and a second that creates a workflow through Nexus + and sends messages to it. +* [nexus_multiple_args](nexus_multiple_args) - Map a Nexus operation to a handler workflow that takes multiple arguments. +* [nexus_standalone_operations](nexus_standalone_operations) - Execute Nexus operations directly from client code, +without wrapping them in a workflow. * [open_telemetry](open_telemetry) - Trace workflows with OpenTelemetry. +* [openai_agents](openai_agents) - Run OpenAI Agents SDK agents as durable Temporal workflows. * [patching](patching) - Alter workflows safely with `patch` and `deprecate_patch`. * [polling](polling) - Recommended implementation of an activity that needs to periodically poll an external resource waiting its successful completion. * [prometheus](prometheus) - Configure Prometheus metrics on clients/workers. * [pydantic_converter](pydantic_converter) - Data converter for using Pydantic models. +* [pydantic_converter_v1](pydantic_converter_v1) - Data converter for Pydantic v1 models (prefer pydantic_converter for v2). +* [replay](replay) - Verify that workflow code changes are compatible with existing histories. +* [resource_pool](resource_pool) - Allocate a pool of shared resources across workflows. * [schedules](schedules) - Demonstrates a Workflow Execution that occurs according to a schedule. * [sentry](sentry) - Report errors to Sentry. +* [sleep_for_days](sleep_for_days) - A workflow that runs forever, sending an email every 30 days. +* [strands_plugin](strands_plugin) - Run Strands Agents as durable Temporal workflows (model calls, tools, MCP, HITL). +* [trio_async](trio_async) - Use asyncio Temporal in Trio-based environments. +* [updatable_timer](updatable_timer) - A timer that can be updated while sleeping. +* [worker_multiprocessing](worker_multiprocessing) - Leverage Python multiprocessing to parallelize workflow tasks and other CPU bound operations by running multiple workers. * [worker_specific_task_queues](worker_specific_task_queues) - Use unique task queues to ensure activities run on specific workers. * [worker_versioning](worker_versioning) - Use the Worker Versioning feature to more easily version your workflows & other code. - +* [workflow_streams](workflow_streams) - Workflow-hosted durable event stream via `temporalio.contrib.workflow_streams`. **Experimental** ## Test -Running the tests requires `poe` to be installed. - - python -m pip install poethepoet - -Once you have `poe` installed you can run: +To run the tests: - poe test + uv run poe test diff --git a/activity_worker/README.md b/activity_worker/README.md index 8b8212819..068d7e990 100644 --- a/activity_worker/README.md +++ b/activity_worker/README.md @@ -6,8 +6,8 @@ First run the Go workflow worker by running this in the `go_workflow` directory go run . -Then in another terminal, run the sample from this directory: +Then in another terminal, run the sample from the root directory: - poetry run python activity_worker.py + uv run activity_worker/activity_worker.py The Python code will invoke the Go workflow which will execute the Python activity and return. \ No newline at end of file diff --git a/activity_worker/activity_worker.py b/activity_worker/activity_worker.py index 3e613169a..986b0ae94 100644 --- a/activity_worker/activity_worker.py +++ b/activity_worker/activity_worker.py @@ -4,6 +4,7 @@ from temporalio import activity from temporalio.client import Client +from temporalio.envconfig import ClientConfig from temporalio.worker import Worker task_queue = "say-hello-task-queue" @@ -18,7 +19,9 @@ async def say_hello_activity(name: str) -> str: async def main(): # Create client to localhost on default namespace - client = await Client.connect("localhost:7233") + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) # Run activity worker async with Worker(client, task_queue=task_queue, activities=[say_hello_activity]): diff --git a/batch_sliding_window/README.md b/batch_sliding_window/README.md new file mode 100644 index 000000000..8d573ca33 --- /dev/null +++ b/batch_sliding_window/README.md @@ -0,0 +1,21 @@ +# Batch Sliding Window + +This sample demonstrates a batch processing workflow that maintains a sliding window of record processing workflows. + +A `SlidingWindowWorkflow` starts a configured number (sliding window size) of `RecordProcessorWorkflow` children in parallel. Each child processes a single record. When a child completes, a new child is started. + +The `SlidingWindowWorkflow` calls continue-as-new after starting a preconfigured number of children to keep its history size bounded. A `RecordProcessorWorkflow` reports its completion through a signal to its parent, which allows notification of a parent that called continue-as-new. + +A single instance of `SlidingWindowWorkflow` has limited window size and throughput. To support larger window size and overall throughput, multiple instances of `SlidingWindowWorkflow` run in parallel. + +### Running This Sample + +To run, first see [README.md](../README.md) for prerequisites. Then, run the following from root directory to start the worker: + + uv run batch_sliding_window/worker.py + +This will start the worker. Then, in another terminal, run the following to execute the workflow: + + uv run batch_sliding_window/starter.py + +The workflow will process 90 records using a sliding window of 10 parallel workers across 3 partitions, with a page size of 5 records per continue-as-new iteration. diff --git a/batch_sliding_window/__init__.py b/batch_sliding_window/__init__.py new file mode 100644 index 000000000..959ab031e --- /dev/null +++ b/batch_sliding_window/__init__.py @@ -0,0 +1,40 @@ +"""Sliding Window Batch Processing Sample. + +This sample demonstrates a batch processing workflow that maintains a sliding window +of record processing workflows. It includes: + +- ProcessBatchWorkflow: Main workflow that partitions work across multiple sliding windows +- SlidingWindowWorkflow: Implements the sliding window pattern with continue-as-new +- RecordProcessorWorkflow: Processes individual records +- RecordLoader: Activity for loading records from external sources +""" + +from batch_sliding_window.batch_workflow import ( + ProcessBatchWorkflow, + ProcessBatchWorkflowInput, +) +from batch_sliding_window.record_loader_activity import ( + GetRecordsInput, + GetRecordsOutput, + RecordLoader, + SingleRecord, +) +from batch_sliding_window.record_processor_workflow import RecordProcessorWorkflow +from batch_sliding_window.sliding_window_workflow import ( + SlidingWindowState, + SlidingWindowWorkflow, + SlidingWindowWorkflowInput, +) + +__all__ = [ + "ProcessBatchWorkflow", + "ProcessBatchWorkflowInput", + "SlidingWindowWorkflow", + "SlidingWindowWorkflowInput", + "SlidingWindowState", + "RecordProcessorWorkflow", + "RecordLoader", + "GetRecordsInput", + "GetRecordsOutput", + "SingleRecord", +] diff --git a/batch_sliding_window/batch_workflow.py b/batch_sliding_window/batch_workflow.py new file mode 100644 index 000000000..a8d1d4882 --- /dev/null +++ b/batch_sliding_window/batch_workflow.py @@ -0,0 +1,110 @@ +import asyncio +from dataclasses import dataclass +from datetime import timedelta +from typing import List + +from temporalio import workflow +from temporalio.common import WorkflowIDReusePolicy +from temporalio.exceptions import ApplicationError + +from batch_sliding_window.record_loader_activity import RecordLoader +from batch_sliding_window.sliding_window_workflow import ( + SlidingWindowWorkflow, + SlidingWindowWorkflowInput, +) + + +@dataclass +class ProcessBatchWorkflowInput: + """Input for the ProcessBatchWorkflow. + + A single input structure is preferred to multiple workflow arguments + to simplify backward compatible API changes. + """ + + page_size: int # Number of children started by a single sliding window workflow run + sliding_window_size: int # Maximum number of children to run in parallel + partitions: int # How many sliding windows to run in parallel + + +@workflow.defn +class ProcessBatchWorkflow: + """Sample workflow that partitions the data set into continuous ranges. + + A real application can choose any other way to divide the records + into multiple collections. + """ + + @workflow.run + async def run(self, input: ProcessBatchWorkflowInput) -> int: + # Get total record count + record_count: int = await workflow.execute_activity_method( + RecordLoader.get_record_count, + start_to_close_timeout=timedelta(seconds=5), + ) + + if input.sliding_window_size < input.partitions: + raise ApplicationError( + "SlidingWindowSize cannot be less than number of partitions" + ) + + partitions = self._divide_into_partitions(record_count, input.partitions) + window_sizes = self._divide_into_partitions( + input.sliding_window_size, input.partitions + ) + + workflow.logger.info( + f"ProcessBatchWorkflow started", + extra={ + "input": input, + "record_count": record_count, + "partitions": partitions, + "window_sizes": window_sizes, + }, + ) + + # Start child workflows for each partition + tasks = [] + offset = 0 + + for i in range(input.partitions): + # Make child id more user-friendly + child_id = f"{workflow.info().workflow_id}/{i}" + + # Define partition boundaries + maximum_partition_offset = offset + partitions[i] + if maximum_partition_offset > record_count: + maximum_partition_offset = record_count + + child_input = SlidingWindowWorkflowInput( + page_size=input.page_size, + sliding_window_size=window_sizes[i], + offset=offset, # inclusive + maximum_offset=maximum_partition_offset, # exclusive + progress=0, + current_records=None, + ) + + task = workflow.execute_child_workflow( + SlidingWindowWorkflow.run, + child_input, + id=child_id, + id_reuse_policy=WorkflowIDReusePolicy.ALLOW_DUPLICATE, + ) + tasks.append(task) + offset += partitions[i] + + # Wait for all child workflows to complete + results = await asyncio.gather(*tasks) + return sum(results) + + def _divide_into_partitions(self, number: int, n: int) -> List[int]: + """Divide a number into n partitions as evenly as possible.""" + base = number // n + remainder = number % n + partitions = [base] * n + + for i in range(remainder): + partitions[i] += 1 + + return partitions diff --git a/batch_sliding_window/record_loader_activity.py b/batch_sliding_window/record_loader_activity.py new file mode 100644 index 000000000..26ae14b1e --- /dev/null +++ b/batch_sliding_window/record_loader_activity.py @@ -0,0 +1,59 @@ +from dataclasses import dataclass +from typing import List + +from temporalio import activity + + +@dataclass +class GetRecordsInput: + """Input for the GetRecords activity.""" + + page_size: int + offset: int + max_offset: int + + +@dataclass +class SingleRecord: + """Represents a single record to be processed.""" + + id: int + + +@dataclass +class GetRecordsOutput: + """Output from the GetRecords activity.""" + + records: List[SingleRecord] + + +class RecordLoader: + """Activities for loading records from an external data source.""" + + def __init__(self, record_count: int): + self.record_count = record_count + + @activity.defn + async def get_record_count(self) -> int: + """Get the total record count. + + Used to partition processing across parallel sliding windows. + The sample implementation just returns a fake value passed during worker initialization. + """ + return self.record_count + + @activity.defn + async def get_records(self, input: GetRecordsInput) -> GetRecordsOutput: + """Get records loaded from an external data source. + + The sample returns fake records. + """ + if input.max_offset > self.record_count: + raise ValueError( + f"max_offset({input.max_offset}) > record_count({self.record_count})" + ) + + limit = min(input.offset + input.page_size, input.max_offset) + records = [SingleRecord(id=i) for i in range(input.offset, limit)] + + return GetRecordsOutput(records=records) diff --git a/batch_sliding_window/record_processor_workflow.py b/batch_sliding_window/record_processor_workflow.py new file mode 100644 index 000000000..8921a808d --- /dev/null +++ b/batch_sliding_window/record_processor_workflow.py @@ -0,0 +1,33 @@ +import asyncio +import random + +from temporalio import workflow + +from batch_sliding_window.record_loader_activity import SingleRecord + + +@workflow.defn +class RecordProcessorWorkflow: + """Workflow that implements processing of a single record.""" + + @workflow.run + async def run(self, record: SingleRecord) -> None: + await self._process_record(record) + + # Notify parent about completion via signal + parent = workflow.info().parent + + # This workflow is always expected to have a parent. + # But for unit testing it might be useful to skip the notification if there is none. + if parent: + # Don't specify run_id as parent calls continue-as-new + handle = workflow.get_external_workflow_handle(parent.workflow_id) + await handle.signal("report_completion", record.id) + + async def _process_record(self, record: SingleRecord) -> None: + """Simulate application specific record processing.""" + # Use workflow.random() to get a random number to ensure workflow determinism + sleep_duration = workflow.random().randint(1, 10) + await workflow.sleep(sleep_duration) + + workflow.logger.info(f"Processed record {record}") diff --git a/batch_sliding_window/sliding_window_workflow.py b/batch_sliding_window/sliding_window_workflow.py new file mode 100644 index 000000000..a416fb995 --- /dev/null +++ b/batch_sliding_window/sliding_window_workflow.py @@ -0,0 +1,167 @@ +import asyncio +from dataclasses import dataclass +from datetime import timedelta +from typing import Dict, List, Optional, Set + +from temporalio import workflow +from temporalio.common import WorkflowIDReusePolicy + +from batch_sliding_window.record_loader_activity import ( + GetRecordsInput, + GetRecordsOutput, + RecordLoader, + SingleRecord, +) +from batch_sliding_window.record_processor_workflow import RecordProcessorWorkflow + + +@dataclass +class SlidingWindowWorkflowInput: + """Contains SlidingWindowWorkflow arguments.""" + + page_size: int + sliding_window_size: int + offset: int # inclusive + maximum_offset: int # exclusive + progress: int = 0 + # The set of record ids currently being processed + current_records: Optional[Set[int]] = None + + +@dataclass +class SlidingWindowState: + """Used as a 'state' query result.""" + + current_records: List[int] # record ids currently being processed + children_started_by_this_run: int + offset: int + progress: int + + +@workflow.defn +class SlidingWindowWorkflow: + """Workflow processes a range of records using a requested number of child workflows. + + As soon as a child workflow completes a new one is started. + """ + + def __init__(self): + self.current_records: Set[int] = set() + self.children_started_by_this_run = [] + self.offset = 0 + self.progress = 0 + self._completion_signals_received = 0 + + @workflow.run + async def run(self, input: SlidingWindowWorkflowInput) -> int: + workflow.logger.info( + f"SlidingWindowWorkflow started", + extra={ + "sliding_window_size": input.sliding_window_size, + "page_size": input.page_size, + "offset": input.offset, + "maximum_offset": input.maximum_offset, + "progress": input.progress, + }, + ) + + # Initialize state from input + self.current_records = input.current_records or set() + self.offset = input.offset + self.progress = input.progress + + # Set up query handler + workflow.set_query_handler("state", self._handle_state_query) + + # Set up signal handler for completion notifications + workflow.set_signal_handler("report_completion", self._handle_completion_signal) + + return await self._execute(input) + + async def _execute(self, input: SlidingWindowWorkflowInput) -> int: + """Main execution logic.""" + # Get records for this page if we haven't reached the end + records = [] + if self.offset < input.maximum_offset: + get_records_input = GetRecordsInput( + page_size=input.page_size, + offset=self.offset, + max_offset=input.maximum_offset, + ) + get_records_output: GetRecordsOutput = ( + await workflow.execute_activity_method( + RecordLoader.get_records, + get_records_input, + start_to_close_timeout=timedelta(seconds=5), + ) + ) + records = get_records_output.records + + workflow_id = workflow.info().workflow_id + + # Process records + for record in records: + # Wait until we have capacity in the sliding window + await workflow.wait_condition( + lambda: len(self.current_records) < input.sliding_window_size + ) + + # Start child workflow for this record + child_id = f"{workflow_id}/{record.id}" + self.current_records.add(record.id) + child_handle = await workflow.start_child_workflow( + RecordProcessorWorkflow.run, + record, + id=child_id, + id_reuse_policy=WorkflowIDReusePolicy.ALLOW_DUPLICATE, + parent_close_policy=workflow.ParentClosePolicy.ABANDON, + ) + + self.children_started_by_this_run.append(child_handle) + + return await self._continue_as_new_or_complete(input) + + async def _continue_as_new_or_complete( + self, input: SlidingWindowWorkflowInput + ) -> int: + """Continue-as-new after starting page_size children or complete if done.""" + # Update offset based on children started in this run + new_offset = input.offset + len(self.children_started_by_this_run) + + if new_offset < input.maximum_offset: + # In Python, await start_child_workflow() already waits until + # the start has been accepted by the server, so no additional wait needed + + # Continue-as-new with updated state + new_input = SlidingWindowWorkflowInput( + page_size=input.page_size, + sliding_window_size=input.sliding_window_size, + offset=new_offset, + maximum_offset=input.maximum_offset, + progress=self.progress, + current_records=self.current_records, + ) + + workflow.continue_as_new(new_input) + + # Last run in the continue-as-new chain + # Wait for all children to complete + await workflow.wait_condition(lambda: len(self.current_records) == 0) + return self.progress + + def _handle_completion_signal(self, record_id: int) -> None: + """Handle completion signal from child workflow.""" + # Check for duplicate signals + if record_id in self.current_records: + self.current_records.remove(record_id) + self.progress += 1 + + def _handle_state_query(self) -> SlidingWindowState: + """Handle state query for monitoring.""" + current_record_ids = sorted(list(self.current_records)) + return SlidingWindowState( + current_records=current_record_ids, + children_started_by_this_run=len(self.children_started_by_this_run), + offset=self.offset, + progress=self.progress, + ) diff --git a/batch_sliding_window/starter.py b/batch_sliding_window/starter.py new file mode 100644 index 000000000..7e3b1fb30 --- /dev/null +++ b/batch_sliding_window/starter.py @@ -0,0 +1,63 @@ +#!/usr/bin/env python3 +"""Starter for the batch sliding window sample.""" + +import asyncio +import datetime +import logging + +from temporalio.client import Client +from temporalio.envconfig import ClientConfig + +from batch_sliding_window.batch_workflow import ( + ProcessBatchWorkflow, + ProcessBatchWorkflowInput, +) + + +async def main(): + """Start the ProcessBatchWorkflow.""" + # Set up logging + logging.basicConfig(level=logging.INFO) + + # Create client + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) + + # Create unique workflow ID with timestamp + timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S") + workflow_id = f"batch_sliding_window_example_{timestamp}" + + # Define workflow input + workflow_input = ProcessBatchWorkflowInput( + page_size=5, + sliding_window_size=10, + partitions=3, + ) + + print(f"Starting workflow with ID: {workflow_id}") + print(f"Input: {workflow_input}") + + # Start workflow + handle = await client.start_workflow( + ProcessBatchWorkflow.run, + workflow_input, + id=workflow_id, + task_queue="batch_sliding_window_task_queue", + ) + + print(f"Workflow started with ID: {handle.id}") + print(f"Waiting for workflow to complete...") + + # Wait for result + try: + result = await handle.result() + print(f"Workflow completed successfully!") + print(f"Total records processed: {result}") + except Exception as e: + print(f"Workflow failed with error: {e}") + raise + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/batch_sliding_window/worker.py b/batch_sliding_window/worker.py new file mode 100644 index 000000000..a72ed96c9 --- /dev/null +++ b/batch_sliding_window/worker.py @@ -0,0 +1,51 @@ +#!/usr/bin/env python3 +"""Worker for the batch sliding window sample.""" + +import asyncio +import logging + +from temporalio import worker +from temporalio.client import Client +from temporalio.envconfig import ClientConfig + +from batch_sliding_window.batch_workflow import ProcessBatchWorkflow +from batch_sliding_window.record_loader_activity import RecordLoader +from batch_sliding_window.record_processor_workflow import RecordProcessorWorkflow +from batch_sliding_window.sliding_window_workflow import SlidingWindowWorkflow + + +async def main(): + """Run the worker that registers all workflows and activities.""" + # Set up logging + logging.basicConfig(level=logging.INFO) + + # Create client + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) + + # Create RecordLoader activity with sample data + record_loader = RecordLoader(record_count=90) + + # Create worker + temporal_worker = worker.Worker( + client, + task_queue="batch_sliding_window_task_queue", + workflows=[ + ProcessBatchWorkflow, + SlidingWindowWorkflow, + RecordProcessorWorkflow, + ], + activities=[ + record_loader.get_record_count, + record_loader.get_records, + ], + ) + + print("Starting worker...") + # Run the worker + await temporal_worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/bedrock/README.md b/bedrock/README.md new file mode 100644 index 000000000..5b294d64e --- /dev/null +++ b/bedrock/README.md @@ -0,0 +1,29 @@ +# AI Chatbot example using Amazon Bedrock + +Demonstrates how Temporal and Amazon Bedrock can be used to quickly build bulletproof AI applications. + +## Samples + +* [basic](basic) - A basic Bedrock workflow to process a single prompt. +* [signals_and_queries](signals_and_queries) - Extension to the basic workflow to allow multiple prompts through signals & queries. +* [entity](entity) - Full multi-Turn chat using an entity workflow.. + +## Pre-requisites + +1. An AWS account with Bedrock enabled. +2. A machine that has access to Bedrock. +3. A local Temporal server running on the same machine, started with the [Temporal CLI](https://docs.temporal.io/cli): + + ``` + temporal server start-dev + ``` + +These examples use Amazon's Python SDK (Boto3). To configure Boto3 to use your AWS credentials, follow the instructions in [the Boto3 documentation](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html). + +## Running the samples + +For these sample, the optional `bedrock` dependency group must be included. To include, run: + + uv sync --group bedrock + +There are 3 Bedrock samples, see the README.md in each sub-directory for instructions on running each. \ No newline at end of file diff --git a/bedrock/__init__.py b/bedrock/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/bedrock/basic/README.md b/bedrock/basic/README.md new file mode 100644 index 000000000..4f3f7430e --- /dev/null +++ b/bedrock/basic/README.md @@ -0,0 +1,10 @@ +# Basic Amazon Bedrock workflow + +A basic Bedrock workflow. Starts a workflow with a prompt, generates a response and ends the workflow. + +To run, first see `samples-python` [README.md](../../README.md), and `bedrock` [README.md](../README.md) for prerequisites specific to this sample. Once set up, run the following from the root directory: + +1. Run the worker: `uv run bedrock/basic/run_worker.py` +2. In another terminal run the client with a prompt: + + e.g. `uv run bedrock/basic/send_message.py 'What animals are marsupials?'` \ No newline at end of file diff --git a/bedrock/basic/__init__.py b/bedrock/basic/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/bedrock/basic/run_worker.py b/bedrock/basic/run_worker.py new file mode 100644 index 000000000..085b695c3 --- /dev/null +++ b/bedrock/basic/run_worker.py @@ -0,0 +1,39 @@ +import asyncio +import concurrent.futures +import logging + +from temporalio.client import Client +from temporalio.envconfig import ClientConfig +from temporalio.worker import Worker +from workflows import BasicBedrockWorkflow + +from bedrock.shared.activities import BedrockActivities + + +async def main(): + # Create client connected to server at the given address + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) + + activities = BedrockActivities() + + # Run the worker + with concurrent.futures.ThreadPoolExecutor(max_workers=100) as activity_executor: + worker = Worker( + client, + task_queue="bedrock-task-queue", + workflows=[BasicBedrockWorkflow], + activities=[activities.prompt_bedrock], + activity_executor=activity_executor, + ) + await worker.run() + + +if __name__ == "__main__": + print("Starting worker") + print("Then run 'python send_message.py \"\"'") + + logging.basicConfig(level=logging.INFO) + + asyncio.run(main()) diff --git a/bedrock/basic/send_message.py b/bedrock/basic/send_message.py new file mode 100644 index 000000000..692a4927a --- /dev/null +++ b/bedrock/basic/send_message.py @@ -0,0 +1,32 @@ +import asyncio +import sys + +from temporalio.client import Client +from temporalio.envconfig import ClientConfig +from workflows import BasicBedrockWorkflow + + +async def main(prompt: str) -> str: + # Create client connected to server at the given address + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) + + # Start the workflow + workflow_id = "basic-bedrock-workflow" + handle = await client.start_workflow( + BasicBedrockWorkflow.run, + prompt, # Initial prompt + id=workflow_id, + task_queue="bedrock-task-queue", + ) + return await handle.result() + + +if __name__ == "__main__": + if len(sys.argv) != 2: + print("Usage: python send_message.py ''") + print("Example: python send_message.py 'What animals are marsupials?'") + else: + result = asyncio.run(main(sys.argv[1])) + print(result) diff --git a/bedrock/basic/workflows.py b/bedrock/basic/workflows.py new file mode 100644 index 000000000..0ebf02160 --- /dev/null +++ b/bedrock/basic/workflows.py @@ -0,0 +1,23 @@ +from datetime import timedelta + +from temporalio import workflow + +with workflow.unsafe.imports_passed_through(): + from bedrock.shared.activities import BedrockActivities + + +@workflow.defn +class BasicBedrockWorkflow: + @workflow.run + async def run(self, prompt: str) -> str: + workflow.logger.info("Prompt: %s" % prompt) + + response = await workflow.execute_activity_method( + BedrockActivities.prompt_bedrock, + prompt, + schedule_to_close_timeout=timedelta(seconds=20), + ) + + workflow.logger.info("Response: %s" % response) + + return response diff --git a/bedrock/entity/README.md b/bedrock/entity/README.md new file mode 100644 index 000000000..3f8e90dab --- /dev/null +++ b/bedrock/entity/README.md @@ -0,0 +1,19 @@ +# Multi-turn chat with Amazon Bedrock Entity Workflow + +Multi-Turn Chat using an Entity Workflow. The workflow runs forever unless explicitly ended. The workflow continues as new after a configurable number of chat turns to keep the prompt size small and the Temporal event history small. Each continued-as-new workflow receives a summary of the conversation history so far for context. + +To run, first see `samples-python` [README.md](../../README.md), and `bedrock` [README.md](../README.md) for prerequisites specific to this sample. Once set up, run the following from the root directory: + +1. Run the worker: `uv run bedrock/entity/run_worker.py` +2. In another terminal run the client with a prompt. + + Example: `uv run bedrock/entity/send_message.py 'What animals are marsupials?'` + +3. View the worker's output for the response. +4. Give followup prompts by signaling the workflow. + + Example: `uv run bedrock/entity/send_message.py 'Do they lay eggs?'` +5. Get the conversation history summary by querying the workflow. + + Example: `uv run bedrock/entity/get_history.py` +6. To end the chat session, run `uv run bedrock/entity/end_chat.py` diff --git a/bedrock/entity/__init__.py b/bedrock/entity/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/bedrock/entity/end_chat.py b/bedrock/entity/end_chat.py new file mode 100644 index 000000000..199842028 --- /dev/null +++ b/bedrock/entity/end_chat.py @@ -0,0 +1,25 @@ +import asyncio +import sys + +from temporalio.client import Client +from temporalio.envconfig import ClientConfig +from workflows import EntityBedrockWorkflow + + +async def main(): + # Create client connected to server at the given address + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) + + workflow_id = "entity-bedrock-workflow" + + handle = client.get_workflow_handle_for(EntityBedrockWorkflow.run, workflow_id) + + # Sends a signal to the workflow + await handle.signal(EntityBedrockWorkflow.end_chat) + + +if __name__ == "__main__": + print("Sending signal to end chat.") + asyncio.run(main()) diff --git a/bedrock/entity/get_history.py b/bedrock/entity/get_history.py new file mode 100644 index 000000000..ffcfa31e4 --- /dev/null +++ b/bedrock/entity/get_history.py @@ -0,0 +1,35 @@ +import asyncio + +from temporalio.client import Client +from temporalio.envconfig import ClientConfig +from workflows import EntityBedrockWorkflow + + +async def main(): + # Create client connected to server at the given address + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) + + workflow_id = "entity-bedrock-workflow" + + handle = client.get_workflow_handle(workflow_id) + + # Queries the workflow for the conversation history + history = await handle.query(EntityBedrockWorkflow.get_conversation_history) + + print("Conversation History") + print( + *(f"{speaker.title()}: {message}\n" for speaker, message in history), sep="\n" + ) + + # Queries the workflow for the conversation summary + summary = await handle.query(EntityBedrockWorkflow.get_summary_from_history) + + if summary is not None: + print("Conversation Summary:") + print(summary) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/bedrock/entity/run_worker.py b/bedrock/entity/run_worker.py new file mode 100644 index 000000000..ecc76c52f --- /dev/null +++ b/bedrock/entity/run_worker.py @@ -0,0 +1,39 @@ +import asyncio +import concurrent.futures +import logging + +from temporalio.client import Client +from temporalio.envconfig import ClientConfig +from temporalio.worker import Worker +from workflows import EntityBedrockWorkflow + +from bedrock.shared.activities import BedrockActivities + + +async def main(): + # Create client connected to server at the given address + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) + + activities = BedrockActivities() + + # Run the worker + with concurrent.futures.ThreadPoolExecutor(max_workers=100) as activity_executor: + worker = Worker( + client, + task_queue="bedrock-task-queue", + workflows=[EntityBedrockWorkflow], + activities=[activities.prompt_bedrock], + activity_executor=activity_executor, + ) + await worker.run() + + +if __name__ == "__main__": + print("Starting worker") + print("Then run 'python send_message.py \"\"'") + + logging.basicConfig(level=logging.INFO) + + asyncio.run(main()) diff --git a/bedrock/entity/send_message.py b/bedrock/entity/send_message.py new file mode 100644 index 000000000..be7897f0f --- /dev/null +++ b/bedrock/entity/send_message.py @@ -0,0 +1,33 @@ +import asyncio +import sys + +from temporalio.client import Client +from temporalio.envconfig import ClientConfig +from workflows import BedrockParams, EntityBedrockWorkflow + + +async def main(prompt): + # Create client connected to server at the given address + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) + + workflow_id = "entity-bedrock-workflow" + + # Sends a signal to the workflow (and starts it if needed) + await client.start_workflow( + EntityBedrockWorkflow.run, + BedrockParams(None, None), + id=workflow_id, + task_queue="bedrock-task-queue", + start_signal="user_prompt", + start_signal_args=[prompt], + ) + + +if __name__ == "__main__": + if len(sys.argv) != 2: + print("Usage: python send_message.py ''") + print("Example: python send_message.py 'What animals are marsupials?'") + else: + asyncio.run(main(sys.argv[1])) diff --git a/bedrock/entity/workflows.py b/bedrock/entity/workflows.py new file mode 100644 index 000000000..710f09160 --- /dev/null +++ b/bedrock/entity/workflows.py @@ -0,0 +1,164 @@ +import asyncio +from collections import deque +from dataclasses import dataclass +from datetime import timedelta +from typing import Deque, List, Optional, Tuple + +from temporalio import workflow + +with workflow.unsafe.imports_passed_through(): + from bedrock.shared.activities import BedrockActivities + + +@dataclass +class BedrockParams: + conversation_summary: Optional[str] = None + prompt_queue: Optional[Deque[str]] = None + + +@workflow.defn +class EntityBedrockWorkflow: + def __init__(self) -> None: + # List to store prompt history + self.conversation_history: List[Tuple[str, str]] = [] + self.prompt_queue: Deque[str] = deque() + self.conversation_summary: Optional[str] = None + self.continue_as_new_per_turns: int = 6 + self.chat_ended: bool = False + + @workflow.run + async def run( + self, + params: BedrockParams, + ) -> str: + if params and params.conversation_summary: + self.conversation_history.append( + ("conversation_summary", params.conversation_summary) + ) + + self.conversation_summary = params.conversation_summary + + if params and params.prompt_queue: + self.prompt_queue.extend(params.prompt_queue) + + while True: + workflow.logger.info("Waiting for prompts...") + + # Wait for a chat message (signal) or timeout + await workflow.wait_condition( + lambda: bool(self.prompt_queue) or self.chat_ended + ) + + if self.prompt_queue: + # Fetch next user prompt and add to conversation history + prompt = self.prompt_queue.popleft() + self.conversation_history.append(("user", prompt)) + + workflow.logger.info("Prompt: " + prompt) + + # Send prompt to Amazon Bedrock + response = await workflow.execute_activity_method( + BedrockActivities.prompt_bedrock, + self.prompt_with_history(prompt), + schedule_to_close_timeout=timedelta(seconds=20), + ) + + workflow.logger.info(f"{response}") + + # Append the response to the conversation history + self.conversation_history.append(("response", response)) + + # Continue as new every x conversational turns to avoid event + # history size getting too large. This is also to avoid the + # prompt (with conversational history) getting too large for + # AWS Bedrock. + + # We summarize the chat to date and use that as input to the + # new workflow + if len(self.conversation_history) >= self.continue_as_new_per_turns: + # Summarize the conversation to date using Amazon Bedrock + self.conversation_summary = await workflow.start_activity_method( + BedrockActivities.prompt_bedrock, + self.prompt_summary_from_history(), + schedule_to_close_timeout=timedelta(seconds=20), + ) + + workflow.logger.info( + "Continuing as new due to %i conversational turns." + % self.continue_as_new_per_turns, + ) + + workflow.continue_as_new( + args=[ + BedrockParams( + self.conversation_summary, + self.prompt_queue, + ) + ] + ) + + continue + + # If end chat signal was sent + if self.chat_ended: + # The workflow might be continued as new without any + # chat to summarize, so only call Bedrock if there + # is more than the previous summary in the history. + if len(self.conversation_history) > 1: + # Summarize the conversation to date using Amazon Bedrock + self.conversation_summary = await workflow.start_activity_method( + BedrockActivities.prompt_bedrock, + self.prompt_summary_from_history(), + schedule_to_close_timeout=timedelta(seconds=20), + ) + + workflow.logger.info( + "Chat ended. Conversation summary:\n" + + f"{self.conversation_summary}" + ) + + return f"{self.conversation_history}" + + @workflow.signal + async def user_prompt(self, prompt: str) -> None: + # Chat ended but the workflow is waiting for a chat summary to be generated + if self.chat_ended: + workflow.logger.warn(f"Message dropped due to chat closed: {prompt}") + return + + self.prompt_queue.append(prompt) + + @workflow.signal + async def end_chat(self) -> None: + self.chat_ended = True + + @workflow.query + def get_conversation_history(self) -> List[Tuple[str, str]]: + return self.conversation_history + + @workflow.query + def get_summary_from_history(self) -> Optional[str]: + return self.conversation_summary + + # Helper method used in prompts to Amazon Bedrock + def format_history(self) -> str: + return " ".join(f"{text}" for _, text in self.conversation_history) + + # Create the prompt given to Amazon Bedrock for each conversational turn + def prompt_with_history(self, prompt: str) -> str: + history_string = self.format_history() + return ( + f"Here is the conversation history: {history_string} Please add " + + "a few sentence response to the prompt in plain text sentences. " + + "Don't editorialize or add metadata like response. Keep the " + + f"text a plain explanation based on the history. Prompt: {prompt}" + ) + + # Create the prompt to Amazon Bedrock to summarize the conversation history + def prompt_summary_from_history(self) -> str: + history_string = self.format_history() + return ( + "Here is the conversation history between a user and a chatbot: " + + f"{history_string} -- Please produce a two sentence summary of " + + "this conversation." + ) diff --git a/bedrock/shared/__init__.py b/bedrock/shared/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/bedrock/shared/activities.py b/bedrock/shared/activities.py new file mode 100644 index 000000000..98c8d055a --- /dev/null +++ b/bedrock/shared/activities.py @@ -0,0 +1,39 @@ +import json + +import boto3 +from botocore.config import Config +from temporalio import activity + +config = Config(region_name="us-west-2") + + +class BedrockActivities: + def __init__(self) -> None: + self.bedrock = boto3.client(service_name="bedrock-runtime", config=config) + + @activity.defn + def prompt_bedrock(self, prompt: str) -> str: + # Model params + modelId = "meta.llama2-70b-chat-v1" + accept = "application/json" + contentType = "application/json" + max_gen_len = 512 + temperature = 0.1 + top_p = 0.2 + + body = json.dumps( + { + "prompt": prompt, + "max_gen_len": max_gen_len, + "temperature": temperature, + "top_p": top_p, + } + ) + + response = self.bedrock.invoke_model( + body=body, modelId=modelId, accept=accept, contentType=contentType + ) + + response_body = json.loads(response.get("body").read()) + + return response_body.get("generation") diff --git a/bedrock/signals_and_queries/README.md b/bedrock/signals_and_queries/README.md new file mode 100644 index 000000000..9eb5df8d0 --- /dev/null +++ b/bedrock/signals_and_queries/README.md @@ -0,0 +1,19 @@ +# Amazon Bedrock workflow using Signals and Queries + +Adding signals & queries to the [basic Bedrock sample](../1_basic). Starts a workflow with a prompt, allows follow-up prompts to be given using Temporal signals, and allows the conversation history to be queried using Temporal queries. + +To run, first see `samples-python` [README.md](../../README.md), and `bedrock` [README.md](../README.md) for prerequisites specific to this sample. Once set up, run the following from the root directory: + +1. Run the worker: `uv run bedrock/signals_and_queries/run_worker.py` +2. In another terminal run the client with a prompt. + + Example: `uv run bedrock/signals_and_queries/send_message.py 'What animals are marsupials?'` + +3. View the worker's output for the response. +4. Give followup prompts by signaling the workflow. + + Example: `uv run bedrock/signals_and_queries/send_message.py 'Do they lay eggs?'` +5. Get the conversation history by querying the workflow. + + Example: `uv run bedrock/signals_and_queries/get_history.py` +6. The workflow will timeout after inactivity. diff --git a/bedrock/signals_and_queries/__init__.py b/bedrock/signals_and_queries/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/bedrock/signals_and_queries/get_history.py b/bedrock/signals_and_queries/get_history.py new file mode 100644 index 000000000..2bd6049f9 --- /dev/null +++ b/bedrock/signals_and_queries/get_history.py @@ -0,0 +1,35 @@ +import asyncio + +from temporalio.client import Client +from temporalio.envconfig import ClientConfig +from workflows import SignalQueryBedrockWorkflow + + +async def main(): + # Create client connected to server at the given address + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) + + workflow_id = "bedrock-workflow-with-signals" + + handle = client.get_workflow_handle(workflow_id) + + # Queries the workflow for the conversation history + history = await handle.query(SignalQueryBedrockWorkflow.get_conversation_history) + + print("Conversation History") + print( + *(f"{speaker.title()}: {message}\n" for speaker, message in history), sep="\n" + ) + + # Queries the workflow for the conversation summary + summary = await handle.query(SignalQueryBedrockWorkflow.get_summary_from_history) + + if summary is not None: + print("Conversation Summary:") + print(summary) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/bedrock/signals_and_queries/run_worker.py b/bedrock/signals_and_queries/run_worker.py new file mode 100644 index 000000000..9d611588d --- /dev/null +++ b/bedrock/signals_and_queries/run_worker.py @@ -0,0 +1,39 @@ +import asyncio +import concurrent.futures +import logging + +from temporalio.client import Client +from temporalio.envconfig import ClientConfig +from temporalio.worker import Worker +from workflows import SignalQueryBedrockWorkflow + +from bedrock.shared.activities import BedrockActivities + + +async def main(): + # Create client connected to server at the given address + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) + + activities = BedrockActivities() + + # Run the worker + with concurrent.futures.ThreadPoolExecutor(max_workers=100) as activity_executor: + worker = Worker( + client, + task_queue="bedrock-task-queue", + workflows=[SignalQueryBedrockWorkflow], + activities=[activities.prompt_bedrock], + activity_executor=activity_executor, + ) + await worker.run() + + +if __name__ == "__main__": + print("Starting worker") + print("Then run 'python send_message.py \"\"'") + + logging.basicConfig(level=logging.INFO) + + asyncio.run(main()) diff --git a/bedrock/signals_and_queries/send_message.py b/bedrock/signals_and_queries/send_message.py new file mode 100644 index 000000000..35a9df1cf --- /dev/null +++ b/bedrock/signals_and_queries/send_message.py @@ -0,0 +1,34 @@ +import asyncio +import sys + +from temporalio.client import Client +from temporalio.envconfig import ClientConfig +from workflows import SignalQueryBedrockWorkflow + + +async def main(prompt): + # Create client connected to server at the given address + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) + + workflow_id = "bedrock-workflow-with-signals" + inactivity_timeout_minutes = 1 + + # Sends a signal to the workflow (and starts it if needed) + await client.start_workflow( + SignalQueryBedrockWorkflow.run, + inactivity_timeout_minutes, + id=workflow_id, + task_queue="bedrock-task-queue", + start_signal="user_prompt", + start_signal_args=[prompt], + ) + + +if __name__ == "__main__": + if len(sys.argv) != 2: + print("Usage: python send_message.py ''") + print("Example: python send_message.py 'What animals are marsupials?'") + else: + asyncio.run(main(sys.argv[1])) diff --git a/bedrock/signals_and_queries/workflows.py b/bedrock/signals_and_queries/workflows.py new file mode 100644 index 000000000..0c9147d79 --- /dev/null +++ b/bedrock/signals_and_queries/workflows.py @@ -0,0 +1,110 @@ +import asyncio +from collections import deque +from datetime import timedelta +from typing import Deque, List, Optional, Tuple + +from temporalio import workflow + +with workflow.unsafe.imports_passed_through(): + from bedrock.shared.activities import BedrockActivities + + +@workflow.defn +class SignalQueryBedrockWorkflow: + def __init__(self) -> None: + # List to store prompt history + self.conversation_history: List[Tuple[str, str]] = [] + self.prompt_queue: Deque[str] = deque() + self.conversation_summary = "" + self.chat_timeout: bool = False + + @workflow.run + async def run(self, inactivity_timeout_minutes: int) -> str: + while True: + workflow.logger.info( + "Waiting for prompts... or closing chat after " + + f"{inactivity_timeout_minutes} minute(s)" + ) + + # Wait for a chat message (signal) or timeout + try: + await workflow.wait_condition( + lambda: bool(self.prompt_queue), + timeout=timedelta(minutes=inactivity_timeout_minutes), + ) + # If timeout was reached + except asyncio.TimeoutError: + self.chat_timeout = True + workflow.logger.info("Chat closed due to inactivity") + # End the workflow + break + + while self.prompt_queue: + # Fetch next user prompt and add to conversation history + prompt = self.prompt_queue.popleft() + self.conversation_history.append(("user", prompt)) + + workflow.logger.info(f"Prompt: {prompt}") + + # Send the prompt to Amazon Bedrock + response = await workflow.execute_activity_method( + BedrockActivities.prompt_bedrock, + self.prompt_with_history(prompt), + schedule_to_close_timeout=timedelta(seconds=20), + ) + + workflow.logger.info(f"{response}") + + # Append the response to the conversation history + self.conversation_history.append(("response", response)) + + # Generate a summary before ending the workflow + self.conversation_summary = await workflow.start_activity_method( + BedrockActivities.prompt_bedrock, + self.prompt_summary_from_history(), + schedule_to_close_timeout=timedelta(seconds=20), + ) + + workflow.logger.info(f"Conversation summary:\n{self.conversation_summary}") + + return f"{self.conversation_history}" + + @workflow.signal + async def user_prompt(self, prompt: str) -> None: + # Chat timed out but the workflow is waiting for a chat summary to be generated + if self.chat_timeout: + workflow.logger.warn(f"Message dropped due to chat closed: {prompt}") + return + + self.prompt_queue.append(prompt) + + @workflow.query + def get_conversation_history(self) -> List[Tuple[str, str]]: + return self.conversation_history + + @workflow.query + def get_summary_from_history(self) -> str: + return self.conversation_summary + + # Helper method used in prompts to Amazon Bedrock + def format_history(self) -> str: + return " ".join(f"{text}" for _, text in self.conversation_history) + + # Create the prompt given to Amazon Bedrock for each conversational turn + def prompt_with_history(self, prompt: str) -> str: + history_string = self.format_history() + return ( + f"Here is the conversation history: {history_string} Please add " + + "a few sentence response to the prompt in plain text sentences. " + + "Don't editorialize or add metadata like response. Keep the " + + f"text a plain explanation based on the history. Prompt: {prompt}" + ) + + # Create the prompt to Amazon Bedrock to summarize the conversation history + def prompt_summary_from_history(self) -> str: + history_string = self.format_history() + return ( + "Here is the conversation history between a user and a chatbot: " + + f"{history_string} -- Please produce a two sentence summary of " + + "this conversation." + ) diff --git a/cloud_export_to_parquet/README.md b/cloud_export_to_parquet/README.md new file mode 100644 index 000000000..3079c9f14 --- /dev/null +++ b/cloud_export_to_parquet/README.md @@ -0,0 +1,23 @@ +# Cloud Export to parquet sample + +This is an example workflow to convert exported file from proto to parquet file. The workflow is an hourly schedule. + +Please make sure your python version is 3.10 or above. For this sample, run: + + uv sync --group=cloud-export-to-parquet + +Before you start, please modify workflow input in `create_schedule.py` with your s3 bucket and namespace. Also make sure you've the right AWS permission set up in your environment to allow this workflow read and write to your s3 bucket. + +To run, first see [README.md](../README.md) for prerequisites. Then, run the following from the root directory to start the worker: + +```bash +uv run cloud_export_to_parquet/run_worker.py +``` + +This will start the worker. Then, in another terminal, run the following to execute the schedule: + +```bash +uv run cloud_export_to_parquet/create_schedule.py +``` + +The workflow should convert exported file in your input s3 bucket to parquet in your specified location. diff --git a/cloud_export_to_parquet/__init__.py b/cloud_export_to_parquet/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/cloud_export_to_parquet/create_schedule.py b/cloud_export_to_parquet/create_schedule.py new file mode 100644 index 000000000..1f40a3ed4 --- /dev/null +++ b/cloud_export_to_parquet/create_schedule.py @@ -0,0 +1,68 @@ +import asyncio +import traceback +from datetime import datetime, timedelta + +from temporalio.client import ( + Client, + Schedule, + ScheduleActionStartWorkflow, + ScheduleIntervalSpec, + ScheduleSpec, + WorkflowFailureError, +) +from temporalio.envconfig import ClientConfig + +from cloud_export_to_parquet.workflows import ( + ProtoToParquet, + ProtoToParquetWorkflowInput, +) + + +async def main() -> None: + """Main function to run temporal workflow.""" + # Create client connected to server at the given address + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) + + # TODO: update s3_bucket and namespace to the actual usecase + wf_input = ProtoToParquetWorkflowInput( + num_delay_hour=2, + export_s3_bucket="test-input-bucket", + namespace="test.namespace", + output_s3_bucket="test-output-bucket", + ) + + # Run the workflow + # try: + # await client.start_workflow( + # ProtoToParquet.run, + # wf_input, + # id = f"proto-to-parquet-{datetime.now()}", + # task_queue="DATA_TRANSFORMATION_TASK_QUEUE", + # ) + # except WorkflowFailureError: + # print("Got exception: ", traceback.format_exc()) + + # Create the schedule + try: + await client.create_schedule( + "hourly-proto-to-parquet-wf-schedule", + Schedule( + action=ScheduleActionStartWorkflow( + ProtoToParquet.run, + wf_input, + id=f"proto-to-parquet-{datetime.now()}", + task_queue="DATA_TRANSFORMATION_TASK_QUEUE", + ), + spec=ScheduleSpec( + intervals=[ScheduleIntervalSpec(every=timedelta(hours=1))] + ), + ), + ) + except WorkflowFailureError: + print("Got exception: ", traceback.format_exc()) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/cloud_export_to_parquet/data_trans_activities.py b/cloud_export_to_parquet/data_trans_activities.py new file mode 100644 index 000000000..b91f6b1a3 --- /dev/null +++ b/cloud_export_to_parquet/data_trans_activities.py @@ -0,0 +1,123 @@ +import json +import uuid +from dataclasses import dataclass +from typing import List + +import boto3 +import pandas as pd +import temporalio.api.export.v1 as export +from google.protobuf.json_format import MessageToJson +from temporalio import activity + + +@dataclass +class GetObjectKeysActivityInput: + bucket: str + path: str + + +@dataclass +class DataTransAndLandActivityInput: + export_s3_bucket: str + object_key: str + output_s3_bucket: str + write_path: str + + +@activity.defn +def get_object_keys(activity_input: GetObjectKeysActivityInput) -> List[str]: + """Function that list objects by key.""" + object_keys = [] + s3 = boto3.client("s3") + response = s3.list_objects_v2( + Bucket=activity_input.bucket, Prefix=activity_input.path + ) + for obj in response.get("Contents", []): + object_keys.append(obj["Key"]) + if len(object_keys) == 0: + raise FileNotFoundError( + f"No files found in {activity_input.bucket}/{activity_input.path}" + ) + + return object_keys + + +@activity.defn +def data_trans_and_land(activity_input: DataTransAndLandActivityInput) -> str: + """Function that convert proto to parquet and save to S3.""" + key = activity_input.object_key + data = get_data_from_object_key(activity_input.export_s3_bucket, key) + activity.logger.info("Convert proto to parquet for file: %s", key) + parquet_data = convert_proto_to_parquet_flatten(data) + activity.logger.info("Finish transformation for file: %s", key) + return save_to_sink( + parquet_data, activity_input.output_s3_bucket, activity_input.write_path + ) + + +def get_data_from_object_key( + bucket_name: str, object_key: str +) -> export.WorkflowExecutions: + """Function that get object by key.""" + v = export.WorkflowExecutions() + + s3 = boto3.client("s3") + try: + data = s3.get_object(Bucket=bucket_name, Key=object_key)["Body"].read() + except Exception as e: + activity.logger.error(f"Error reading object: {e}") + raise e + v.ParseFromString(data) + return v + + +def convert_proto_to_parquet_flatten(wfs: export.WorkflowExecutions) -> pd.DataFrame: + """Function that convert flatten proto data to parquet.""" + dfs = [] + for wf in wfs.items: + start_attributes = wf.history.events[ + 0 + ].workflow_execution_started_event_attributes + histories = wf.history + json_str = MessageToJson(histories) + row = { + "WorkflowID": start_attributes.workflow_id, + "RunID": start_attributes.original_execution_run_id, + "Histories": json.loads(json_str), + } + dfs.append(pd.DataFrame([row])) + df = pd.concat(dfs, ignore_index=True) + rows_flatten = [] + for _, row in df.iterrows(): + wf_histories_raw = row["Histories"]["events"] + worfkow_id = row["WorkflowID"] + run_id = row["RunID"] + for history_event in wf_histories_raw: + row_flatten = pd.json_normalize(history_event, sep="_") + skip_name = ["payloads", "."] + columns_to_drop = [ + col for col in row_flatten.columns for skip in skip_name if skip in col + ] + row_flatten.drop(columns_to_drop, axis=1, inplace=True) + row_flatten.insert(0, "WorkflowId", worfkow_id) + row_flatten.insert(1, "RunId", run_id) + rows_flatten.append(row_flatten) + df_flatten = pd.concat(rows_flatten, ignore_index=True) + return df_flatten + + +def save_to_sink(data: pd.DataFrame, s3_bucket: str, write_path: str) -> str: + """Function that save object to s3 bucket.""" + write_bytes = data.to_parquet(None, compression="snappy", index=False) + uuid_name = uuid.uuid1() + file_name = f"{uuid_name}.parquet" + activity.logger.info("Writing to S3 bucket: %s", file_name) + + s3 = boto3.client("s3") + try: + key = f"{write_path}/{file_name}" + s3.put_object(Bucket=s3_bucket, Key=key, Body=write_bytes) + return key + except Exception as e: + activity.logger.error(f"Error saving to sink: {e}") + raise e diff --git a/cloud_export_to_parquet/run_worker.py b/cloud_export_to_parquet/run_worker.py new file mode 100644 index 000000000..6062abcd6 --- /dev/null +++ b/cloud_export_to_parquet/run_worker.py @@ -0,0 +1,41 @@ +import asyncio +from concurrent.futures import ThreadPoolExecutor + +from temporalio.client import Client +from temporalio.envconfig import ClientConfig +from temporalio.worker import Worker +from temporalio.worker.workflow_sandbox import ( + SandboxedWorkflowRunner, + SandboxRestrictions, +) + +from cloud_export_to_parquet.data_trans_activities import ( + data_trans_and_land, + get_object_keys, +) +from cloud_export_to_parquet.workflows import ProtoToParquet + + +async def main() -> None: + """Main worker function.""" + # Create client connected to server at the given address + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) + + # Run the worker + worker: Worker = Worker( + client, + task_queue="DATA_TRANSFORMATION_TASK_QUEUE", + workflows=[ProtoToParquet], + activities=[get_object_keys, data_trans_and_land], + workflow_runner=SandboxedWorkflowRunner( + restrictions=SandboxRestrictions.default.with_passthrough_modules("boto3") + ), + activity_executor=ThreadPoolExecutor(100), + ) + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/cloud_export_to_parquet/workflows.py b/cloud_export_to_parquet/workflows.py new file mode 100644 index 000000000..3c9fe2702 --- /dev/null +++ b/cloud_export_to_parquet/workflows.py @@ -0,0 +1,74 @@ +from datetime import timedelta + +from temporalio import workflow +from temporalio.common import RetryPolicy +from temporalio.exceptions import ActivityError + +with workflow.unsafe.imports_passed_through(): + from cloud_export_to_parquet.data_trans_activities import ( + DataTransAndLandActivityInput, + GetObjectKeysActivityInput, + data_trans_and_land, + get_object_keys, + ) +from dataclasses import dataclass + + +@dataclass +class ProtoToParquetWorkflowInput: + num_delay_hour: int + export_s3_bucket: str + namespace: str + output_s3_bucket: str + + +@workflow.defn +class ProtoToParquet: + """Proto to parquet workflow.""" + + @workflow.run + async def run(self, workflow_input: ProtoToParquetWorkflowInput) -> str: + """Run proto to parquet workflow.""" + retry_policy = RetryPolicy( + maximum_attempts=10, maximum_interval=timedelta(seconds=5) + ) + + # Read from export S3 bucket and given at least 2 hour delay to ensure the file has been uploaded + read_time = workflow.now() - timedelta(hours=workflow_input.num_delay_hour) + common_path = f"{workflow_input.namespace}/{read_time.year}/{read_time.month:02}/{read_time.day:02}/{read_time.hour:02}/00" + path = f"temporal-workflow-history/export/{common_path}" + get_object_keys_input = GetObjectKeysActivityInput( + workflow_input.export_s3_bucket, path + ) + + # Read Input File + object_keys_output = await workflow.execute_activity( + get_object_keys, + get_object_keys_input, + start_to_close_timeout=timedelta(minutes=5), + retry_policy=retry_policy, + ) + + write_path = f"temporal-workflow-history/parquet/{common_path}" + + try: + # Could create a list of corountine objects to process files in parallel + for key in object_keys_output: + data_trans_and_land_input = DataTransAndLandActivityInput( + workflow_input.export_s3_bucket, + key, + workflow_input.output_s3_bucket, + write_path, + ) + # Convert proto to parquet and save to S3 + await workflow.execute_activity( + data_trans_and_land, + data_trans_and_land_input, + start_to_close_timeout=timedelta(minutes=15), + retry_policy=retry_policy, + ) + except ActivityError as output_err: + workflow.logger.error(f"Data transformation failed: {output_err}") + raise output_err + + return write_path diff --git a/context_propagation/README.md b/context_propagation/README.md new file mode 100644 index 000000000..fc79f80b6 --- /dev/null +++ b/context_propagation/README.md @@ -0,0 +1,16 @@ +# Context Propagation Interceptor Sample + +This sample shows how to use an interceptor to propagate contextual information through workflows and activities. For +this example, [contextvars](https://docs.python.org/3/library/contextvars.html) holds the contextual information. + +To run, first see [README.md](../README.md) for prerequisites. Then, run the following from the root directory to start the +worker: + + uv run context_propagation/worker.py + +This will start the worker. Then, in another terminal, run the following to execute the workflow: + + uv run context_propagation/starter.py + +The starter terminal should complete with the hello result and the worker terminal should show the logs with the +propagated user ID contextual information flowing through the workflows/activities. \ No newline at end of file diff --git a/context_propagation/__init__.py b/context_propagation/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/context_propagation/activities.py b/context_propagation/activities.py new file mode 100644 index 000000000..77cde0159 --- /dev/null +++ b/context_propagation/activities.py @@ -0,0 +1,9 @@ +from temporalio import activity + +from context_propagation import shared + + +@activity.defn +async def say_hello_activity(name: str) -> str: + activity.logger.info(f"Activity called by user {shared.user_id.get()}") + return f"Hello, {name}" diff --git a/context_propagation/interceptor.py b/context_propagation/interceptor.py new file mode 100644 index 000000000..b90585484 --- /dev/null +++ b/context_propagation/interceptor.py @@ -0,0 +1,193 @@ +from __future__ import annotations + +from contextlib import contextmanager +from typing import Any, Mapping, Protocol, Type + +import temporalio.activity +import temporalio.api.common.v1 +import temporalio.client +import temporalio.converter +import temporalio.worker +import temporalio.workflow + +with temporalio.workflow.unsafe.imports_passed_through(): + from context_propagation.shared import HEADER_KEY, user_id + + +class _InputWithHeaders(Protocol): + headers: Mapping[str, temporalio.api.common.v1.Payload] + + +def set_header_from_context( + input: _InputWithHeaders, payload_converter: temporalio.converter.PayloadConverter +) -> None: + user_id_val = user_id.get() + if user_id_val: + input.headers = { + **input.headers, + HEADER_KEY: payload_converter.to_payload(user_id_val), + } + + +@contextmanager +def context_from_header( + input: _InputWithHeaders, payload_converter: temporalio.converter.PayloadConverter +): + payload = input.headers.get(HEADER_KEY) + token = ( + user_id.set(payload_converter.from_payload(payload, str)) if payload else None + ) + try: + yield + finally: + if token: + user_id.reset(token) + + +class ContextPropagationInterceptor( + temporalio.client.Interceptor, temporalio.worker.Interceptor +): + """Interceptor that propagates a value through client, workflow and activity calls. + + This interceptor implements methods `temporalio.client.Interceptor` and `temporalio.worker.Interceptor` so that + + (1) a user ID key is taken from context by the client code and sent in a header field with outbound requests + (2) workflows take this value from their task input, set it in context, and propagate it into the header field of + their outbound calls + (3) activities similarly take the value from their task input and set it in context so that it's available for their + outbound calls + """ + + def __init__( + self, + payload_converter: temporalio.converter.PayloadConverter = temporalio.converter.default().payload_converter, + ) -> None: + self._payload_converter = payload_converter + + def intercept_client( + self, next: temporalio.client.OutboundInterceptor + ) -> temporalio.client.OutboundInterceptor: + return _ContextPropagationClientOutboundInterceptor( + next, self._payload_converter + ) + + def intercept_activity( + self, next: temporalio.worker.ActivityInboundInterceptor + ) -> temporalio.worker.ActivityInboundInterceptor: + return _ContextPropagationActivityInboundInterceptor(next) + + def workflow_interceptor_class( + self, input: temporalio.worker.WorkflowInterceptorClassInput + ) -> Type[_ContextPropagationWorkflowInboundInterceptor]: + return _ContextPropagationWorkflowInboundInterceptor + + +class _ContextPropagationClientOutboundInterceptor( + temporalio.client.OutboundInterceptor +): + def __init__( + self, + next: temporalio.client.OutboundInterceptor, + payload_converter: temporalio.converter.PayloadConverter, + ) -> None: + super().__init__(next) + self._payload_converter = payload_converter + + async def start_workflow( + self, input: temporalio.client.StartWorkflowInput + ) -> temporalio.client.WorkflowHandle[Any, Any]: + set_header_from_context(input, self._payload_converter) + return await super().start_workflow(input) + + async def query_workflow(self, input: temporalio.client.QueryWorkflowInput) -> Any: + set_header_from_context(input, self._payload_converter) + return await super().query_workflow(input) + + async def signal_workflow( + self, input: temporalio.client.SignalWorkflowInput + ) -> None: + set_header_from_context(input, self._payload_converter) + await super().signal_workflow(input) + + async def start_workflow_update( + self, input: temporalio.client.StartWorkflowUpdateInput + ) -> temporalio.client.WorkflowUpdateHandle[Any]: + set_header_from_context(input, self._payload_converter) + return await self.next.start_workflow_update(input) + + +class _ContextPropagationActivityInboundInterceptor( + temporalio.worker.ActivityInboundInterceptor +): + async def execute_activity( + self, input: temporalio.worker.ExecuteActivityInput + ) -> Any: + with context_from_header(input, temporalio.activity.payload_converter()): + return await self.next.execute_activity(input) + + +class _ContextPropagationWorkflowInboundInterceptor( + temporalio.worker.WorkflowInboundInterceptor +): + def init(self, outbound: temporalio.worker.WorkflowOutboundInterceptor) -> None: + self.next.init(_ContextPropagationWorkflowOutboundInterceptor(outbound)) + + async def execute_workflow( + self, input: temporalio.worker.ExecuteWorkflowInput + ) -> Any: + with context_from_header(input, temporalio.workflow.payload_converter()): + return await self.next.execute_workflow(input) + + async def handle_signal(self, input: temporalio.worker.HandleSignalInput) -> None: + with context_from_header(input, temporalio.workflow.payload_converter()): + return await self.next.handle_signal(input) + + async def handle_query(self, input: temporalio.worker.HandleQueryInput) -> Any: + with context_from_header(input, temporalio.workflow.payload_converter()): + return await self.next.handle_query(input) + + def handle_update_validator( + self, input: temporalio.worker.HandleUpdateInput + ) -> None: + with context_from_header(input, temporalio.workflow.payload_converter()): + self.next.handle_update_validator(input) + + async def handle_update_handler( + self, input: temporalio.worker.HandleUpdateInput + ) -> Any: + with context_from_header(input, temporalio.workflow.payload_converter()): + return await self.next.handle_update_handler(input) + + +class _ContextPropagationWorkflowOutboundInterceptor( + temporalio.worker.WorkflowOutboundInterceptor +): + async def signal_child_workflow( + self, input: temporalio.worker.SignalChildWorkflowInput + ) -> None: + set_header_from_context(input, temporalio.workflow.payload_converter()) + return await self.next.signal_child_workflow(input) + + async def signal_external_workflow( + self, input: temporalio.worker.SignalExternalWorkflowInput + ) -> None: + set_header_from_context(input, temporalio.workflow.payload_converter()) + return await self.next.signal_external_workflow(input) + + def start_activity( + self, input: temporalio.worker.StartActivityInput + ) -> temporalio.workflow.ActivityHandle: + set_header_from_context(input, temporalio.workflow.payload_converter()) + return self.next.start_activity(input) + + async def start_child_workflow( + self, input: temporalio.worker.StartChildWorkflowInput + ) -> temporalio.workflow.ChildWorkflowHandle: + set_header_from_context(input, temporalio.workflow.payload_converter()) + return await self.next.start_child_workflow(input) + + def start_local_activity( + self, input: temporalio.worker.StartLocalActivityInput + ) -> temporalio.workflow.ActivityHandle: + set_header_from_context(input, temporalio.workflow.payload_converter()) + return self.next.start_local_activity(input) diff --git a/context_propagation/shared.py b/context_propagation/shared.py new file mode 100644 index 000000000..faae59d81 --- /dev/null +++ b/context_propagation/shared.py @@ -0,0 +1,6 @@ +from contextvars import ContextVar +from typing import Optional + +HEADER_KEY = "__my_user_id" + +user_id: ContextVar[Optional[str]] = ContextVar("user_id", default=None) diff --git a/context_propagation/starter.py b/context_propagation/starter.py new file mode 100644 index 000000000..4d141dc0f --- /dev/null +++ b/context_propagation/starter.py @@ -0,0 +1,39 @@ +import asyncio +import logging + +from temporalio.client import Client +from temporalio.envconfig import ClientConfig + +from context_propagation import interceptor, shared, workflows + + +async def main(): + logging.basicConfig(level=logging.INFO) + + # Set the user ID + shared.user_id.set("some-user") + + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + + # Connect client + client = await Client.connect( + **config, + # Use our interceptor + interceptors=[interceptor.ContextPropagationInterceptor()], + ) + + # Start workflow, send signal, wait for completion, issue query + handle = await client.start_workflow( + workflows.SayHelloWorkflow.run, + "Temporal", + id=f"context-propagation-workflow-id", + task_queue="context-propagation-task-queue", + ) + await handle.signal(workflows.SayHelloWorkflow.signal_complete) + result = await handle.result() + logging.info(f"Workflow result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/context_propagation/worker.py b/context_propagation/worker.py new file mode 100644 index 000000000..70ffa3689 --- /dev/null +++ b/context_propagation/worker.py @@ -0,0 +1,45 @@ +import asyncio +import logging + +from temporalio.client import Client +from temporalio.envconfig import ClientConfig +from temporalio.worker import Worker + +from context_propagation import activities, interceptor, workflows + +interrupt_event = asyncio.Event() + + +async def main(): + logging.basicConfig(level=logging.INFO) + + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + + # Connect client + client = await Client.connect( + **config, + # Use our interceptor + interceptors=[interceptor.ContextPropagationInterceptor()], + ) + + # Run a worker for the workflow + async with Worker( + client, + task_queue="context-propagation-task-queue", + activities=[activities.say_hello_activity], + workflows=[workflows.SayHelloWorkflow], + ): + # Wait until interrupted + logging.info("Worker started, ctrl+c to exit") + await interrupt_event.wait() + logging.info("Shutting down") + + +if __name__ == "__main__": + loop = asyncio.new_event_loop() + try: + loop.run_until_complete(main()) + except KeyboardInterrupt: + interrupt_event.set() + loop.run_until_complete(loop.shutdown_asyncgens()) diff --git a/context_propagation/workflows.py b/context_propagation/workflows.py new file mode 100644 index 000000000..e9c120b37 --- /dev/null +++ b/context_propagation/workflows.py @@ -0,0 +1,28 @@ +from datetime import timedelta + +from temporalio import workflow + +with workflow.unsafe.imports_passed_through(): + from context_propagation.activities import say_hello_activity + from context_propagation.shared import user_id + + +@workflow.defn +class SayHelloWorkflow: + def __init__(self) -> None: + self._complete = False + + @workflow.run + async def run(self, name: str) -> str: + workflow.logger.info(f"Workflow called by user {user_id.get()}") + + # Wait for signal then run activity + await workflow.wait_condition(lambda: self._complete) + return await workflow.execute_activity( + say_hello_activity, name, start_to_close_timeout=timedelta(minutes=5) + ) + + @workflow.signal + async def signal_complete(self) -> None: + workflow.logger.info(f"Signal called by user {user_id.get()}") + self._complete = True diff --git a/custom_converter/README.md b/custom_converter/README.md index d4c9f5886..4ded41985 100644 --- a/custom_converter/README.md +++ b/custom_converter/README.md @@ -2,14 +2,14 @@ This sample shows how to make a custom payload converter for a type not natively supported by Temporal. -To run, first see [README.md](../README.md) for prerequisites. Then, run the following from this directory to start the +To run, first see [README.md](../README.md) for prerequisites. Then, run the following from the root directory to start the worker: - poetry run python worker.py + uv run custom_converter/worker.py This will start the worker. Then, in another terminal, run the following to execute the workflow: - poetry run python starter.py + uv run custom_converter/starter.py The workflow should complete with the hello result. If the custom converter was not set for the custom input and output classes, we would get an error on the client side and on the worker side. \ No newline at end of file diff --git a/custom_converter/shared.py b/custom_converter/shared.py index 7ac454951..2aa71f169 100644 --- a/custom_converter/shared.py +++ b/custom_converter/shared.py @@ -54,8 +54,7 @@ def __init__(self) -> None: # Just add ours as first before the defaults super().__init__( GreetingEncodingPayloadConverter(), - # TODO(cretz): Make this list available without instantiation - https://github.com/temporalio/sdk-python/issues/139 - *DefaultPayloadConverter().converters.values(), + *DefaultPayloadConverter.default_encoding_payload_converters, ) diff --git a/custom_converter/starter.py b/custom_converter/starter.py index 54fdf1624..cc500ae48 100644 --- a/custom_converter/starter.py +++ b/custom_converter/starter.py @@ -1,6 +1,7 @@ import asyncio from temporalio.client import Client +from temporalio.envconfig import ClientConfig from custom_converter.shared import ( GreetingInput, @@ -11,9 +12,12 @@ async def main(): + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + # Connect client client = await Client.connect( - "localhost:7233", + **config, # Without this we get: # TypeError: Object of type GreetingInput is not JSON serializable data_converter=greeting_data_converter, diff --git a/custom_converter/worker.py b/custom_converter/worker.py index 96214cdd6..17186aee4 100644 --- a/custom_converter/worker.py +++ b/custom_converter/worker.py @@ -1,6 +1,7 @@ import asyncio from temporalio.client import Client +from temporalio.envconfig import ClientConfig from temporalio.worker import Worker from custom_converter.shared import greeting_data_converter @@ -10,9 +11,12 @@ async def main(): + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + # Connect client client = await Client.connect( - "localhost:7233", + **config, # Without this, when trying to run a workflow, we get: # KeyError: 'Unknown payload encoding my-greeting-encoding data_converter=greeting_data_converter, diff --git a/custom_decorator/README.md b/custom_decorator/README.md index 65f594824..24cd516de 100644 --- a/custom_decorator/README.md +++ b/custom_decorator/README.md @@ -3,14 +3,14 @@ This sample shows a custom decorator can help with Temporal code reuse. Specifically, this makes a `@auto_heartbeater` decorator that automatically configures an activity to heartbeat twice as frequently as the heartbeat timeout is set to. -To run, first see [README.md](../README.md) for prerequisites. Then, run the following from this directory to start the +To run, first see [README.md](../README.md) for prerequisites. Then, run the following from the root directory to start the worker: - poetry run python worker.py + uv run custom_decorator/worker.py This will start the worker. Then, in another terminal, run the following to execute the workflow: - poetry run python starter.py + uv run custom_decorator/starter.py The workflow will be started, and then after 5 seconds will be sent a signal to cancel its forever-running activity. The activity has a heartbeat timeout set to 2s, so since it has the `@auto_heartbeater` decorator set, it will heartbeat diff --git a/custom_decorator/starter.py b/custom_decorator/starter.py index 98bf542fa..aff675da9 100644 --- a/custom_decorator/starter.py +++ b/custom_decorator/starter.py @@ -1,13 +1,16 @@ import asyncio from temporalio.client import Client +from temporalio.envconfig import ClientConfig from custom_decorator.worker import WaitForCancelWorkflow async def main(): # Connect client - client = await Client.connect("localhost:7233") + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) # Start the workflow handle = await client.start_workflow( diff --git a/custom_decorator/worker.py b/custom_decorator/worker.py index 7d0d25ca1..0d25145bb 100644 --- a/custom_decorator/worker.py +++ b/custom_decorator/worker.py @@ -4,6 +4,7 @@ from temporalio import activity, workflow from temporalio.client import Client from temporalio.common import RetryPolicy +from temporalio.envconfig import ClientConfig from temporalio.worker import Worker from custom_decorator.activity_utils import auto_heartbeater @@ -51,7 +52,9 @@ def cancel_activity(self) -> None: async def main(): # Connect client - client = await Client.connect("localhost:7233") + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) # Run a worker for the workflow async with Worker( diff --git a/custom_metric/README.md b/custom_metric/README.md new file mode 100644 index 000000000..de1d51d5d --- /dev/null +++ b/custom_metric/README.md @@ -0,0 +1,36 @@ +# Custom Metric + +This sample deminstrates two things: (1) how to make a custom metric, and (2) how to use an interceptor. +The custom metric in this sample is an activity schedule-to-start-latency with a workflow type tag. + +Please see the top-level [README](../README.md) for prerequisites such as Python, uv, starting the local temporal development server, etc. + +1. Run the worker with `uv run custom_metric/worker.py` +2. Request execution of the workflow with `uv run custom_metric/starter.py` +3. Go to `http://127.0.0.1:9090/metrics` in your browser + +You'll get something like the following: + +```txt +custom_activity_schedule_to_start_latency_bucket{activity_type="print_and_sleep",namespace="default",service_name="temporal-core-sdk",task_queue="custom-metric-task-queue",workflow_type="StartTwoActivitiesWorkflow",le="100"} 1 +custom_activity_schedule_to_start_latency_bucket{activity_type="print_and_sleep",namespace="default",service_name="temporal-core-sdk",task_queue="custom-metric-task-queue",workflow_type="StartTwoActivitiesWorkflow",le="500"} 1 +custom_activity_schedule_to_start_latency_bucket{activity_type="print_and_sleep",namespace="default",service_name="temporal-core-sdk",task_queue="custom-metric-task-queue",workflow_type="StartTwoActivitiesWorkflow",le="1000"} 1 +custom_activity_schedule_to_start_latency_bucket{activity_type="print_and_sleep",namespace="default",service_name="temporal-core-sdk",task_queue="custom-metric-task-queue",workflow_type="StartTwoActivitiesWorkflow",le="5000"} 2 +custom_activity_schedule_to_start_latency_bucket{activity_type="print_and_sleep",namespace="default",service_name="temporal-core-sdk",task_queue="custom-metric-task-queue",workflow_type="StartTwoActivitiesWorkflow",le="10000"} 2 +custom_activity_schedule_to_start_latency_bucket{activity_type="print_and_sleep",namespace="default",service_name="temporal-core-sdk",task_queue="custom-metric-task-queue",workflow_type="StartTwoActivitiesWorkflow",le="100000"} 2 +custom_activity_schedule_to_start_latency_bucket{activity_type="print_and_sleep",namespace="default",service_name="temporal-core-sdk",task_queue="custom-metric-task-queue",workflow_type="StartTwoActivitiesWorkflow",le="1000000"} 2 +custom_activity_schedule_to_start_latency_bucket{activity_type="print_and_sleep",namespace="default",service_name="temporal-core-sdk",task_queue="custom-metric-task-queue",workflow_type="StartTwoActivitiesWorkflow",le="+Inf"} 2 +custom_activity_schedule_to_start_latency_sum{activity_type="print_and_sleep",namespace="default",service_name="temporal-core-sdk",task_queue="custom-metric-task-queue",workflow_type="StartTwoActivitiesWorkflow"} 1010 +custom_activity_schedule_to_start_latency_count{activity_type="print_and_sleep",namespace="default",service_name="temporal-core-sdk",task_queue="custom-metric-task-queue",workflow_type="StartTwoActivitiesWorkflow"} 2 +... +temporal_activity_schedule_to_start_latency_bucket{namespace="default",service_name="temporal-core-sdk",task_queue="custom-metric-task-queue",le="100"} 1 +temporal_activity_schedule_to_start_latency_bucket{namespace="default",service_name="temporal-core-sdk",task_queue="custom-metric-task-queue",le="500"} 1 +temporal_activity_schedule_to_start_latency_bucket{namespace="default",service_name="temporal-core-sdk",task_queue="custom-metric-task-queue",le="1000"} 1 +temporal_activity_schedule_to_start_latency_bucket{namespace="default",service_name="temporal-core-sdk",task_queue="custom-metric-task-queue",le="5000"} 2 +temporal_activity_schedule_to_start_latency_bucket{namespace="default",service_name="temporal-core-sdk",task_queue="custom-metric-task-queue",le="10000"} 2 +temporal_activity_schedule_to_start_latency_bucket{namespace="default",service_name="temporal-core-sdk",task_queue="custom-metric-task-queue",le="100000"} 2 +temporal_activity_schedule_to_start_latency_bucket{namespace="default",service_name="temporal-core-sdk",task_queue="custom-metric-task-queue",le="1000000"} 2 +temporal_activity_schedule_to_start_latency_bucket{namespace="default",service_name="temporal-core-sdk",task_queue="custom-metric-task-queue",le="+Inf"} 2 +temporal_activity_schedule_to_start_latency_sum{namespace="default",service_name="temporal-core-sdk",task_queue="custom-metric-task-queue"} 1010 +temporal_activity_schedule_to_start_latency_count{namespace="default",service_name="temporal-core-sdk",task_queue="custom-metric-task-queue"} 2 +``` diff --git a/custom_metric/__init__.py b/custom_metric/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/custom_metric/activity.py b/custom_metric/activity.py new file mode 100644 index 000000000..7f2ee1163 --- /dev/null +++ b/custom_metric/activity.py @@ -0,0 +1,9 @@ +import time + +from temporalio import activity + + +@activity.defn +def print_and_sleep(): + print("In the activity.") + time.sleep(1) diff --git a/custom_metric/starter.py b/custom_metric/starter.py new file mode 100644 index 000000000..aeb6d2b73 --- /dev/null +++ b/custom_metric/starter.py @@ -0,0 +1,23 @@ +import asyncio +import uuid + +from temporalio.client import Client +from temporalio.envconfig import ClientConfig + +from custom_metric.workflow import StartTwoActivitiesWorkflow + + +async def main(): + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) + + await client.start_workflow( + StartTwoActivitiesWorkflow.run, + id="execute-activity-workflow-" + str(uuid.uuid4()), + task_queue="custom-metric-task-queue", + ) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/custom_metric/worker.py b/custom_metric/worker.py new file mode 100644 index 000000000..21dd9c93a --- /dev/null +++ b/custom_metric/worker.py @@ -0,0 +1,73 @@ +import asyncio +from concurrent.futures import ThreadPoolExecutor + +from temporalio import activity +from temporalio.client import Client +from temporalio.envconfig import ClientConfig +from temporalio.runtime import PrometheusConfig, Runtime, TelemetryConfig +from temporalio.worker import ( + ActivityInboundInterceptor, + ExecuteActivityInput, + Interceptor, + Worker, +) + +from custom_metric.activity import print_and_sleep +from custom_metric.workflow import StartTwoActivitiesWorkflow + + +class SimpleWorkerInterceptor(Interceptor): + def intercept_activity( + self, next: ActivityInboundInterceptor + ) -> ActivityInboundInterceptor: + return CustomScheduleToStartInterceptor(next) + + +class CustomScheduleToStartInterceptor(ActivityInboundInterceptor): + async def execute_activity(self, input: ExecuteActivityInput): + schedule_to_start = ( + activity.info().started_time + - activity.info().current_attempt_scheduled_time + ) + # Could do the original schedule time instead of current attempt + # schedule_to_start_second_option = activity.info().started_time - activity.info().scheduled_time + + meter = activity.metric_meter() + histogram = meter.create_histogram_timedelta( + "custom_activity_schedule_to_start_latency", + description="Time between activity scheduling and start", + unit="duration", + ) + histogram.record( + schedule_to_start, {"workflow_type": activity.info().workflow_type or ""} + ) + return await self.next.execute_activity(input) + + +async def main(): + runtime = Runtime( + telemetry=TelemetryConfig(metrics=PrometheusConfig(bind_address="0.0.0.0:9090")) + ) + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect( + **config, + runtime=runtime, + ) + worker = Worker( + client, + task_queue="custom-metric-task-queue", + interceptors=[SimpleWorkerInterceptor()], + workflows=[StartTwoActivitiesWorkflow], + activities=[print_and_sleep], + # only one activity executor with two concurrently scheduled activities + # to force a nontrivial schedule to start times + activity_executor=ThreadPoolExecutor(1), + max_concurrent_activities=1, + ) + + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/custom_metric/workflow.py b/custom_metric/workflow.py new file mode 100644 index 000000000..cf37823bd --- /dev/null +++ b/custom_metric/workflow.py @@ -0,0 +1,25 @@ +import asyncio +from datetime import timedelta + +from temporalio import workflow + +with workflow.unsafe.imports_passed_through(): + from custom_metric.activity import print_and_sleep + + +@workflow.defn +class StartTwoActivitiesWorkflow: + @workflow.run + async def run(self): + # Request two concurrent activities with only one task slot so + # we can see nontrivial schedule to start times. + activity1 = workflow.execute_activity( + print_and_sleep, + start_to_close_timeout=timedelta(seconds=5), + ) + activity2 = workflow.execute_activity( + print_and_sleep, + start_to_close_timeout=timedelta(seconds=5), + ) + await asyncio.gather(activity1, activity2) + return None diff --git a/deepagents_plugin/README.md b/deepagents_plugin/README.md new file mode 100644 index 000000000..885384a20 --- /dev/null +++ b/deepagents_plugin/README.md @@ -0,0 +1,134 @@ +# Deep Agents Samples + +These samples demonstrate the [Temporal Deep Agents plugin](https://github.com/temporalio/sdk-python/tree/main/temporalio/contrib/deepagents), +which makes [LangChain Deep Agents](https://github.com/langchain-ai/deepagents) +durable. Build your agent with `create_deep_agent(...)` inside a +`@workflow.defn` and add `DeepAgentsPlugin()` to your client — each LLM call and +each I/O tool/backend operation becomes a Temporal Activity, while the agent's +control loop runs (and deterministically replays) inside the Workflow. + +> **Experimental.** The `temporalio.contrib.deepagents` plugin is experimental +> and its API may change. + +`DeepAgentsPlugin` is a **client-level** plugin: add it to `Client.connect(...)` +and the SDK propagates it to any Worker built from that client. Add it on exactly +one side. + +## Samples + +| Sample | Description | +|--------|-------------| +| [hello_world](hello_world) | Minimal single-shot Deep Agent; a bare `model=` string auto-routed through the model activity. Start here. | +| [react_agent](react_agent) | Tool-calling loop showing the explicit per-tool choice: `activity_as_tool` for an existing activity, `tool_as_activity` for an I/O tool, plus per-agent `activity_options` via `create_temporal_deep_agent`. | +| [human_in_the_loop](human_in_the_loop) | Pause on `interrupt_on` and resume via the native LangGraph protocol, mapped to a Temporal Query + Update. | +| [continue_as_new](continue_as_new) | Long-running agent that carries messages and the model/tool result cache across continue-as-new via `run_deep_agent`. | +| [filesystem_backend](filesystem_backend) | Durable real filesystem I/O by wrapping a `FilesystemBackend` in `TemporalBackend`. | +| [subagents](subagents) | Durability propagates across the agent tree — sub-agent model calls become activities with no per-sub-agent wiring. | +| [streaming](streaming) | Stream model chunks to external subscribers via `streaming_topic` + `WorkflowStream`, keeping the durable result identical. | +| [langsmith_tracing](langsmith_tracing) | Compose `DeepAgentsPlugin` with `LangSmithPlugin` for durable execution + LLM tracing. | + +## Prerequisites + +> **Python ≥ 3.11 required.** `deepagents` (and therefore the plugin) does not +> support older interpreters. On Python 3.10 the `deepagents` dependency group +> resolves to nothing, so `uv sync` silently installs none of the dependencies +> below. + +1. Install dependencies: + + ```bash + uv sync --group deepagents + ``` + + > The Deep Agents plugin ships as the `temporalio[deepagents]` extra. It + > is merged to `sdk-python` `main` but the current PyPI release (1.31.0) + > predates the merge and does not carry the extra, so the `deepagents` + > group above does not include it yet. Until a release ships the extra + > (> 1.31.0), install it from main: + > + > ```bash + > uv pip install "temporalio[deepagents] @ git+https://github.com/temporalio/sdk-python.git" + > ``` + > + > This builds the SDK from source (including its Rust core), so expect a + > few minutes on first install. Once a release with the extra is on PyPI + > this step goes away: `temporalio[deepagents]` joins the `deepagents` + > group and a plain `uv sync --group deepagents` is all you need. + +2. Configure a model provider. The samples use + `anthropic:claude-sonnet-4-5`, which needs an Anthropic API key: + + ```bash + export ANTHROPIC_API_KEY=... + ``` + + To use a different provider, change the `model=` string in the sample's + `workflow.py` and set that provider's credentials (the plugin resolves the + model worker-side via LangChain's `init_chat_model`). + +3. Start a [Temporal dev server](https://docs.temporal.io/cli#start-dev-server): + + ```bash + temporal server start-dev + ``` + +## Running a Sample + +> **Use `uv run --no-sync`.** Because the plugin is installed out-of-band +> from sdk-python main (see Prerequisites) and is not yet in any dependency +> group, a bare `uv run` or `uv sync` re-syncs the environment to the lockfile +> first and uninstalls it. `--no-sync` runs against the environment as-is. +> (Once a released `temporalio[deepagents]` joins the `deepagents` group, the +> flag becomes unnecessary.) + +Most samples have two scripts. Start the Worker first, then the Workflow starter +in a separate terminal: + +```bash +# Terminal 1: start the Worker +uv run --no-sync deepagents_plugin//run_worker.py + +# Terminal 2: start the Workflow +uv run --no-sync deepagents_plugin//run_workflow.py +``` + +For example, to run the hello world sample: + +```bash +# Terminal 1 +uv run --no-sync deepagents_plugin/hello_world/run_worker.py + +# Terminal 2 +uv run --no-sync deepagents_plugin/hello_world/run_workflow.py +``` + +The `langsmith_tracing` sample instead bundles the worker and starter into a +single driver: + +```bash +uv run --no-sync deepagents_plugin/langsmith_tracing/main.py +``` + +## Key Features Demonstrated + +- **Durable model invocation** — every LLM call runs in an `invoke_model` + activity with configurable timeouts and retries; a bare `model=` string is + auto-routed, or use `create_temporal_deep_agent(..., activity_options=...)` + to scope model-call options per agent (recommended). +- **Explicit Workflow-vs-Activity tool choice** — `activity_as_tool`, + `tool_as_activity`, and `TemporalBackend` move I/O out of workflow code. +- **Human-in-the-loop** — the native LangGraph `interrupt_on` return value + mapped to a Temporal Query and Update. +- **Long-lived agents** — `run_deep_agent(...)` carries messages and the result + cache across server-suggested (or explicitly thresholded) continue-as-new. +- **Sub-agent durability** — sub-agents inherit the durable model object with no + extra wiring. +- **Streaming** — forward model chunks to external subscribers while keeping the + durable result unchanged. +- **Observability** — compose with `LangSmithPlugin` for tracing. + +## Related + +- [Temporal Deep Agents plugin](https://github.com/temporalio/sdk-python/tree/main/temporalio/contrib/deepagents) +- [LangChain Deep Agents](https://github.com/langchain-ai/deepagents) +- [langgraph_plugin](../langgraph_plugin) — for agents built directly as LangGraph graphs diff --git a/deepagents_plugin/__init__.py b/deepagents_plugin/__init__.py new file mode 100644 index 000000000..99ad713af --- /dev/null +++ b/deepagents_plugin/__init__.py @@ -0,0 +1 @@ +"""Temporal Deep Agents plugin samples.""" diff --git a/deepagents_plugin/continue_as_new/README.md b/deepagents_plugin/continue_as_new/README.md new file mode 100644 index 000000000..7fb5eb380 --- /dev/null +++ b/deepagents_plugin/continue_as_new/README.md @@ -0,0 +1,53 @@ +# Continue as New + +A long-running research agent whose conversation could outgrow Temporal's +workflow-history limit. `run_deep_agent(agent, input, state_snapshot=...)` keeps +the run bounded: once a turn ends with pending todos and the server recommends +continuing (`workflow.info().is_continue_as_new_suggested()` — the default and +recommended mode, which accounts for both history length and size), it snapshots +the accumulated messages **and** the model/tool result cache and continues into +a fresh run. Completed model/tool calls are reused from the carried cache rather +than re-run. To trigger on a fixed history-event count instead, pass an explicit +`continue_as_new_after=N`. + +The `@workflow.run` signature is `run(self, input, state_snapshot=None)`, where +`input` is the messages mapping and `state_snapshot` is how `run_deep_agent` +threads carried state into the continued run. On a continue-as-new the workflow +is re-invoked with `args=[input, snapshot]`, so `input` must be passed straight +into `run_deep_agent` — re-wrapping it would nest a dict where a message is +expected and corrupt the carried conversation. Durability rides on the default +in-workflow `InMemorySaver` (rehydrated by replay); a database-backed +checkpointer would do I/O from workflow code and is not replay-safe. + +## What This Sample Demonstrates + +- `run_deep_agent(agent, input, state_snapshot=...)` in the default + server-suggested mode (with `continue_as_new_after=N` as the explicit override) +- The `run(self, input, state_snapshot=None)` continue-as-new contract +- Carrying both messages and the result cache across continue-as-new + +## Running the Sample + +Prerequisites: Python >= 3.11 with the [suite setup](../README.md#prerequisites) +applied (interim plugin install), an `ANTHROPIC_API_KEY` in your +environment, and a running Temporal dev server (`temporal server start-dev`). + +> The experimental plugin is not in the `deepagents` group — install it as shown +> in the [suite README](../README.md#prerequisites) and run with `--no-sync`, or +> a bare `uv run`/`uv sync` re-syncs the environment and uninstalls it. + +```bash +# Terminal 1 +uv run --no-sync deepagents_plugin/continue_as_new/run_worker.py + +# Terminal 2 +uv run --no-sync deepagents_plugin/continue_as_new/run_workflow.py +``` + +## Files + +| File | Description | +|------|-------------| +| `workflow.py` | `LongResearchAgent` driven by `run_deep_agent` | +| `run_worker.py` | Adds `DeepAgentsPlugin`, starts the worker | +| `run_workflow.py` | Executes the workflow and prints the result | diff --git a/deepagents_plugin/continue_as_new/__init__.py b/deepagents_plugin/continue_as_new/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/deepagents_plugin/continue_as_new/run_worker.py b/deepagents_plugin/continue_as_new/run_worker.py new file mode 100644 index 000000000..c9a7e8747 --- /dev/null +++ b/deepagents_plugin/continue_as_new/run_worker.py @@ -0,0 +1,29 @@ +"""Worker for the continue-as-new sample.""" + +import asyncio +import os + +from temporalio.client import Client +from temporalio.contrib.deepagents import DeepAgentsPlugin +from temporalio.worker import Worker + +from deepagents_plugin.continue_as_new.workflow import LongResearchAgent + + +async def main() -> None: + client = await Client.connect( + os.environ.get("TEMPORAL_ADDRESS", "localhost:7233"), + plugins=[DeepAgentsPlugin()], + ) + + worker = Worker( + client, + task_queue="deepagents-continue-as-new", + workflows=[LongResearchAgent], + ) + print("Worker started. Ctrl+C to exit.") + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/deepagents_plugin/continue_as_new/run_workflow.py b/deepagents_plugin/continue_as_new/run_workflow.py new file mode 100644 index 000000000..9eb1acc90 --- /dev/null +++ b/deepagents_plugin/continue_as_new/run_workflow.py @@ -0,0 +1,37 @@ +"""Start the long-running research agent workflow.""" + +import asyncio +import os + +from temporalio.client import Client + +from deepagents_plugin.continue_as_new.workflow import LongResearchAgent + + +async def main() -> None: + client = await Client.connect(os.environ.get("TEMPORAL_ADDRESS", "localhost:7233")) + + result = await client.execute_workflow( + LongResearchAgent.run, + # The workflow's first arg is the messages mapping (run_deep_agent's + # continue-as-new contract), not a bare question string. + { + "messages": [ + { + "role": "user", + "content": ( + "Research the tradeoffs between Raft and Paxos and " + "summarize them." + ), + } + ] + }, + id="deepagents-continue-as-new", + task_queue="deepagents-continue-as-new", + ) + + print(f"Result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/deepagents_plugin/continue_as_new/workflow.py b/deepagents_plugin/continue_as_new/workflow.py new file mode 100644 index 000000000..d56aa6578 --- /dev/null +++ b/deepagents_plugin/continue_as_new/workflow.py @@ -0,0 +1,59 @@ +"""Long-running research agent that carries state across continue-as-new. + +A long conversation would bloat workflow history until it hits Temporal's limit. +``run_deep_agent(agent, input, state_snapshot=...)`` solves this: once a turn +finishes with pending work and the server recommends continuing +(``workflow.info().is_continue_as_new_suggested()`` — the default and +recommended mode, accounting for both history length and size), it snapshots the +accumulated messages **and** the model/tool result cache and continues into a +fresh run — so completed model/tool calls are reused, not re-run, after the +continue-as-new. Pass ``continue_as_new_after=N`` instead to trigger on a fixed +history-event count. + +The contract ``run_deep_agent`` requires is that the ``@workflow.run`` method +accepts the carried state, i.e. its signature is +``run(self, input, state_snapshot=None)`` where ``input`` is the messages +mapping. On a continue-as-new, ``run_deep_agent`` re-invokes the workflow with +``args=[input, snapshot]``, so ``input`` must be passed straight through — not +re-wrapped — or the carried conversation is corrupted. Only an in-workflow +``InMemorySaver`` (the default) is replay-safe; a durable checkpointer would do +I/O from workflow code. +""" + +# @@@SNIPSTART python-deepagents-continue-as-new-workflow +from typing import Any + +from deepagents import create_deep_agent +from temporalio import workflow +from temporalio.contrib.deepagents import run_deep_agent + + +@workflow.defn +class LongResearchAgent: + @workflow.run + async def run( + self, input: dict[str, Any], state_snapshot: dict | None = None + ) -> str: + agent = create_deep_agent( + model="anthropic:claude-sonnet-4-5", + system_prompt=( + "You are a research agent. Break large tasks into todos and work " + "through them until the research is complete." + ), + ) + result = await run_deep_agent( + agent, + # ``input`` is the messages mapping. Pass it through unchanged: on a + # continue-as-new, run_deep_agent re-invokes this method with the + # carried input as its first arg, so re-wrapping it here would nest a + # dict where a message is expected and corrupt the conversation. + input, + # No threshold: continue-as-new fires when the agent still has + # pending todos and the server suggests continuing — the recommended + # mode. Pass continue_as_new_after=N to use a fixed event count. + state_snapshot=state_snapshot, + ) + return result["messages"][-1].content + + +# @@@SNIPEND diff --git a/deepagents_plugin/filesystem_backend/README.md b/deepagents_plugin/filesystem_backend/README.md new file mode 100644 index 000000000..a8e3198f4 --- /dev/null +++ b/deepagents_plugin/filesystem_backend/README.md @@ -0,0 +1,50 @@ +# Filesystem Backend + +Give a Deep Agent durable, real filesystem access. The agent's built-in file +tools (`write_file`, `read_file`, `ls`, …) delegate to a *backend*. Wrapping a +`FilesystemBackend` with `TemporalBackend(inner, activity_options=...)` routes +each file operation through a `deepagents.backend_op` activity, so real disk I/O +happens in an activity worker instead of in workflow code. + +Contrast this with the default `StateBackend`, whose "files" live in agent state +— that is pure workflow state and correctly stays in the workflow with no +wrapping. `TemporalBackend` is only for backends that do real I/O. + +The scratch directory (`root_dir`) is chosen client-side and passed in as a +workflow argument, so the workflow never reads the environment or the disk +directly. + +## What This Sample Demonstrates + +- `TemporalBackend` wrapping a real-I/O `FilesystemBackend` +- The agent's built-in file tools running their I/O as `backend_op` activities +- Keeping the workflow deterministic by passing `root_dir` in as an argument + +## Running the Sample + +Prerequisites: Python >= 3.11 with the [suite setup](../README.md#prerequisites) +applied (interim plugin install), an `ANTHROPIC_API_KEY` in your +environment, and a running Temporal dev server (`temporal server start-dev`). + +> The experimental plugin is not in the `deepagents` group — install it as shown +> in the [suite README](../README.md#prerequisites) and run with `--no-sync`, or +> a bare `uv run`/`uv sync` re-syncs the environment and uninstalls it. + +```bash +# Terminal 1 +uv run --no-sync deepagents_plugin/filesystem_backend/run_worker.py + +# Terminal 2 +uv run --no-sync deepagents_plugin/filesystem_backend/run_workflow.py +``` + +By default the starter creates a temporary scratch directory; set +`DEEPAGENTS_WORKDIR` to point the agent at a directory of your choice. + +## Files + +| File | Description | +|------|-------------| +| `workflow.py` | `FilesystemAgent` wrapping a `FilesystemBackend` in `TemporalBackend` | +| `run_worker.py` | Adds `DeepAgentsPlugin`, starts the worker | +| `run_workflow.py` | Chooses a scratch dir, executes the workflow, prints the result | diff --git a/deepagents_plugin/filesystem_backend/__init__.py b/deepagents_plugin/filesystem_backend/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/deepagents_plugin/filesystem_backend/run_worker.py b/deepagents_plugin/filesystem_backend/run_worker.py new file mode 100644 index 000000000..722fc1584 --- /dev/null +++ b/deepagents_plugin/filesystem_backend/run_worker.py @@ -0,0 +1,29 @@ +"""Worker for the filesystem backend sample.""" + +import asyncio +import os + +from temporalio.client import Client +from temporalio.contrib.deepagents import DeepAgentsPlugin +from temporalio.worker import Worker + +from deepagents_plugin.filesystem_backend.workflow import FilesystemAgent + + +async def main() -> None: + client = await Client.connect( + os.environ.get("TEMPORAL_ADDRESS", "localhost:7233"), + plugins=[DeepAgentsPlugin()], + ) + + worker = Worker( + client, + task_queue="deepagents-filesystem-backend", + workflows=[FilesystemAgent], + ) + print("Worker started. Ctrl+C to exit.") + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/deepagents_plugin/filesystem_backend/run_workflow.py b/deepagents_plugin/filesystem_backend/run_workflow.py new file mode 100644 index 000000000..2f1c1361c --- /dev/null +++ b/deepagents_plugin/filesystem_backend/run_workflow.py @@ -0,0 +1,43 @@ +"""Start the filesystem backend workflow against a scratch directory. + +The workflow's agent writes a file and reads it back; each file operation runs +as a ``deepagents.backend_op`` activity, so the real disk write happens in an +activity worker, not in workflow code. +""" + +import asyncio +import os +import tempfile + +from temporalio.client import Client + +from deepagents_plugin.filesystem_backend.workflow import FilesystemAgent + + +async def main() -> None: + client = await Client.connect(os.environ.get("TEMPORAL_ADDRESS", "localhost:7233")) + + # Choose the scratch directory here (client side), then pass it in — the + # workflow itself never reads the environment or the filesystem. + root_dir = os.environ.get("DEEPAGENTS_WORKDIR") or tempfile.mkdtemp( + prefix="deepagents-fs-" + ) + print(f"Agent working directory: {root_dir}") + + result = await client.execute_workflow( + FilesystemAgent.run, + args=[ + root_dir, + "Write a short haiku about durability to notes.txt, then read it " + "back and tell me what it says.", + ], + id="deepagents-filesystem-backend", + task_queue="deepagents-filesystem-backend", + ) + + print(f"Result: {result}") + print(f"Files written under: {root_dir}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/deepagents_plugin/filesystem_backend/workflow.py b/deepagents_plugin/filesystem_backend/workflow.py new file mode 100644 index 000000000..67e43775e --- /dev/null +++ b/deepagents_plugin/filesystem_backend/workflow.py @@ -0,0 +1,53 @@ +"""Durable real filesystem I/O via ``TemporalBackend``. + +A Deep Agent's built-in file tools (``write_file``, ``read_file``, ``ls``, …) +delegate to a *backend*. The default ``StateBackend`` keeps files in agent state +— pure workflow state, replay-safe, no wrapping needed. A ``FilesystemBackend``, +by contrast, touches real disk, which must not happen from workflow code. + +``TemporalBackend(inner, activity_options=...)`` wraps such a backend so each +file operation the agent's tools invoke becomes a ``deepagents.backend_op`` +activity instead of running in the workflow. The agent code is unchanged; only +the backend is wrapped. + +``root_dir`` is passed in as a workflow argument (rather than read from the +environment inside the workflow) to keep the workflow deterministic. +""" + +# @@@SNIPSTART python-deepagents-filesystem-backend-workflow +from datetime import timedelta + +from deepagents import create_deep_agent +from deepagents.backends import FilesystemBackend +from temporalio import workflow +from temporalio.contrib.deepagents import TemporalBackend + + +@workflow.defn +class FilesystemAgent: + @workflow.run + async def run(self, root_dir: str, instruction: str) -> str: + # Wrap the real-I/O backend so every file op runs in an activity. + backend = TemporalBackend( + # virtual_mode roots every path the agent uses under root_dir, so the + # agent's file tools stay sandboxed to this working directory. + FilesystemBackend(root_dir=root_dir, virtual_mode=True), + activity_options={"start_to_close_timeout": timedelta(seconds=30)}, + ) + agent = create_deep_agent( + model="anthropic:claude-sonnet-4-5", + # TemporalBackend delegates the backend protocol to the wrapped + # backend at runtime, which the static type can't see through. + backend=backend, # type: ignore[arg-type] + system_prompt=( + "You are a file-savvy assistant. Use the write_file and " + "read_file tools to complete the task." + ), + ) + result = await agent.ainvoke( + {"messages": [{"role": "user", "content": instruction}]} + ) + return result["messages"][-1].content + + +# @@@SNIPEND diff --git a/deepagents_plugin/hello_world/README.md b/deepagents_plugin/hello_world/README.md new file mode 100644 index 000000000..16ea9ed21 --- /dev/null +++ b/deepagents_plugin/hello_world/README.md @@ -0,0 +1,40 @@ +# Hello World + +The simplest Deep Agents + Temporal sample: build a Deep Agent with +`create_deep_agent(...)` and invoke it once. The agent code is unchanged from a +non-Temporal program — adding `DeepAgentsPlugin()` to the client is what makes +the single LLM call run as a durable `deepagents.invoke_model` activity, with +Temporal-managed retries and timeouts. + +## What This Sample Demonstrates + +- Wiring `DeepAgentsPlugin` onto the client (it auto-propagates to the worker) +- Building a Deep Agent from a bare `model="anthropic:claude-sonnet-4-5"` string, + which the plugin auto-routes through the model activity +- Driving the agent with `await agent.ainvoke(...)` from a `@workflow.defn` + +## Running the Sample + +Prerequisites: Python >= 3.11 with the [suite setup](../README.md#prerequisites) +applied (interim plugin install), an `ANTHROPIC_API_KEY` in your +environment, and a running Temporal dev server (`temporal server start-dev`). + +> The experimental plugin is not in the `deepagents` group — install it as shown +> in the [suite README](../README.md#prerequisites) and run with `--no-sync`, or +> a bare `uv run`/`uv sync` re-syncs the environment and uninstalls it. + +```bash +# Terminal 1 +uv run --no-sync deepagents_plugin/hello_world/run_worker.py + +# Terminal 2 +uv run --no-sync deepagents_plugin/hello_world/run_workflow.py +``` + +## Files + +| File | Description | +|------|-------------| +| `workflow.py` | `HelloWorldAgent`: one Deep Agent, one `ainvoke` | +| `run_worker.py` | Adds `DeepAgentsPlugin` to the client, starts the worker | +| `run_workflow.py` | Executes the workflow and prints the result | diff --git a/deepagents_plugin/hello_world/__init__.py b/deepagents_plugin/hello_world/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/deepagents_plugin/hello_world/run_worker.py b/deepagents_plugin/hello_world/run_worker.py new file mode 100644 index 000000000..442f0af4b --- /dev/null +++ b/deepagents_plugin/hello_world/run_worker.py @@ -0,0 +1,37 @@ +"""Worker for the hello world sample. + +``DeepAgentsPlugin`` is a client-level plugin: add it to ``Client.connect(...)`` +and the SDK propagates it to every Worker built from that client. The plugin +registers the ``deepagents.*`` activities and installs the LangChain-aware data +converter, so the worker needs no other wiring. +""" + +# @@@SNIPSTART python-deepagents-hello-world-worker +import asyncio +import os + +from temporalio.client import Client +from temporalio.contrib.deepagents import DeepAgentsPlugin +from temporalio.worker import Worker + +from deepagents_plugin.hello_world.workflow import HelloWorldAgent + + +async def main() -> None: + client = await Client.connect( + os.environ.get("TEMPORAL_ADDRESS", "localhost:7233"), + plugins=[DeepAgentsPlugin()], + ) + + worker = Worker( + client, + task_queue="deepagents-hello-world", + workflows=[HelloWorldAgent], + ) + print("Worker started. Ctrl+C to exit.") + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) +# @@@SNIPEND diff --git a/deepagents_plugin/hello_world/run_workflow.py b/deepagents_plugin/hello_world/run_workflow.py new file mode 100644 index 000000000..a0a87343c --- /dev/null +++ b/deepagents_plugin/hello_world/run_workflow.py @@ -0,0 +1,27 @@ +"""Start the hello world workflow and print the agent's answer.""" + +# @@@SNIPSTART python-deepagents-hello-world-run-workflow +import asyncio +import os + +from temporalio.client import Client + +from deepagents_plugin.hello_world.workflow import HelloWorldAgent + + +async def main() -> None: + client = await Client.connect(os.environ.get("TEMPORAL_ADDRESS", "localhost:7233")) + + result = await client.execute_workflow( + HelloWorldAgent.run, + "What is Temporal in one sentence?", + id="deepagents-hello-world", + task_queue="deepagents-hello-world", + ) + + print(f"Result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) +# @@@SNIPEND diff --git a/deepagents_plugin/hello_world/workflow.py b/deepagents_plugin/hello_world/workflow.py new file mode 100644 index 000000000..f99ea6a39 --- /dev/null +++ b/deepagents_plugin/hello_world/workflow.py @@ -0,0 +1,34 @@ +"""Minimal single-shot Deep Agent, made durable by the plugin. + +The workflow builds a vanilla ``create_deep_agent(...)`` and drives it with +``await agent.ainvoke(...)`` — exactly the code you would write outside Temporal. +The only reason it is durable is that a ``DeepAgentsPlugin`` is wired onto the +client (see ``run_worker.py``): the bare ``model="anthropic:..."`` string is +auto-routed through the ``deepagents.invoke_model`` activity, so the LLM call +gets Temporal-managed retries and timeouts while the agent's control loop +replays deterministically in the workflow. +""" + +# @@@SNIPSTART python-deepagents-hello-world-workflow +# No `workflow.unsafe.imports_passed_through()` guard is needed: the plugin +# configures the workflow sandbox to pass the deepagents / LangChain import +# tree through, so workflow files import them like any other module. +from deepagents import create_deep_agent +from temporalio import workflow + + +@workflow.defn +class HelloWorldAgent: + @workflow.run + async def run(self, question: str) -> str: + agent = create_deep_agent( + model="anthropic:claude-sonnet-4-5", + system_prompt="You are a helpful assistant. Answer concisely.", + ) + result = await agent.ainvoke( + {"messages": [{"role": "user", "content": question}]} + ) + return result["messages"][-1].content + + +# @@@SNIPEND diff --git a/deepagents_plugin/human_in_the_loop/README.md b/deepagents_plugin/human_in_the_loop/README.md new file mode 100644 index 000000000..c688f7a60 --- /dev/null +++ b/deepagents_plugin/human_in_the_loop/README.md @@ -0,0 +1,58 @@ +# Human in the Loop + +Pause a Deep Agent for human approval before it runs a guarded tool, then resume +it with the human's decision — using the **native LangGraph interrupt protocol** +(the plugin adds no shim of its own). + +`create_deep_agent(..., interrupt_on={"book_trip": True})` plus an in-workflow +`InMemorySaver` checkpointer make the agent pause before calling `book_trip`. +With a checkpointer configured, `ainvoke` *returns* the pending approval under +the SDK-native `__interrupt__` key rather than raising. Because the loop runs in +the workflow, the pause surfaces in workflow code, where it is mapped to +Temporal messaging: + +- a **`@workflow.query`** (`pending_approval`) exposes the pending prompt; +- a **`@workflow.update`** (`resume`) feeds the decision back via + `Command(resume={"decisions": [{"type": decision}]})`. + +The `InMemorySaver` is replay-safe (its state is workflow memory rehydrated by +replay); the `thread_id` is the workflow id. + +For clarity this sample handles a single interrupt. A production workflow would +loop — re-checking `__interrupt__` after each resume — since the model may +request another guarded tool call. + +## What This Sample Demonstrates + +- `interrupt_on` + an in-workflow `InMemorySaver` checkpointer +- Reading the native `__interrupt__` return value in workflow code +- Mapping the pause to a Temporal Query and the resume to a Temporal Update + +## Running the Sample + +Prerequisites: Python >= 3.11 with the [suite setup](../README.md#prerequisites) +applied (interim plugin install), an `ANTHROPIC_API_KEY` in your +environment, and a running Temporal dev server (`temporal server start-dev`). + +> The experimental plugin is not in the `deepagents` group — install it as shown +> in the [suite README](../README.md#prerequisites) and run with `--no-sync`, or +> a bare `uv run`/`uv sync` re-syncs the environment and uninstalls it. + +```bash +# Terminal 1 +uv run --no-sync deepagents_plugin/human_in_the_loop/run_worker.py + +# Terminal 2 +uv run --no-sync deepagents_plugin/human_in_the_loop/run_workflow.py +``` + +The starter polls the query until the agent pauses, prints the approval prompt, +sends an `approve` decision via the update, and prints the final result. + +## Files + +| File | Description | +|------|-------------| +| `workflow.py` | `HumanInTheLoopAgent` with `interrupt_on`, a Query, and an Update | +| `run_worker.py` | Adds `DeepAgentsPlugin`, starts the worker | +| `run_workflow.py` | Starts the workflow, polls the query, sends the resume update | diff --git a/deepagents_plugin/human_in_the_loop/__init__.py b/deepagents_plugin/human_in_the_loop/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/deepagents_plugin/human_in_the_loop/run_worker.py b/deepagents_plugin/human_in_the_loop/run_worker.py new file mode 100644 index 000000000..1eced08df --- /dev/null +++ b/deepagents_plugin/human_in_the_loop/run_worker.py @@ -0,0 +1,29 @@ +"""Worker for the human-in-the-loop sample.""" + +import asyncio +import os + +from temporalio.client import Client +from temporalio.contrib.deepagents import DeepAgentsPlugin +from temporalio.worker import Worker + +from deepagents_plugin.human_in_the_loop.workflow import HumanInTheLoopAgent + + +async def main() -> None: + client = await Client.connect( + os.environ.get("TEMPORAL_ADDRESS", "localhost:7233"), + plugins=[DeepAgentsPlugin()], + ) + + worker = Worker( + client, + task_queue="deepagents-human-in-the-loop", + workflows=[HumanInTheLoopAgent], + ) + print("Worker started. Ctrl+C to exit.") + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/deepagents_plugin/human_in_the_loop/run_workflow.py b/deepagents_plugin/human_in_the_loop/run_workflow.py new file mode 100644 index 000000000..298d8626b --- /dev/null +++ b/deepagents_plugin/human_in_the_loop/run_workflow.py @@ -0,0 +1,45 @@ +"""Start the HITL workflow, wait for the approval prompt, then approve it. + +Starts the agent, polls the ``pending_approval`` query until the agent pauses on +the guarded ``book_trip`` tool, then sends the ``resume`` update with a decision. +In a real app the query result would be shown to a person and the update sent +from a UI. +""" + +import asyncio +import os + +from temporalio.client import Client + +from deepagents_plugin.human_in_the_loop.workflow import HumanInTheLoopAgent + + +async def main() -> None: + client = await Client.connect(os.environ.get("TEMPORAL_ADDRESS", "localhost:7233")) + + handle = await client.start_workflow( + HumanInTheLoopAgent.run, + "Rome", + id="deepagents-human-in-the-loop", + task_queue="deepagents-human-in-the-loop", + ) + + # Poll the query until the agent surfaces the pending approval. + for _ in range(100): + pending = await handle.query(HumanInTheLoopAgent.pending_approval) + if pending is not None: + print(f"Approval requested: {pending}") + break + await asyncio.sleep(0.5) + else: + raise RuntimeError("workflow never surfaced an approval prompt") + + print("Approving...") + await handle.execute_update(HumanInTheLoopAgent.resume, "approve") + + result = await handle.result() + print(f"Result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/deepagents_plugin/human_in_the_loop/workflow.py b/deepagents_plugin/human_in_the_loop/workflow.py new file mode 100644 index 000000000..fa16f2540 --- /dev/null +++ b/deepagents_plugin/human_in_the_loop/workflow.py @@ -0,0 +1,94 @@ +"""Human-in-the-loop: the native LangGraph interrupt mapped to Query + Update. + +``create_deep_agent(..., interrupt_on=...)`` makes the agent pause before a +guarded tool runs. With an in-workflow ``InMemorySaver`` checkpointer, LangGraph +does *not* raise out of ``ainvoke`` — it returns the current state with an +``__interrupt__`` entry describing the pending approval. Because the agent loop +runs in the workflow, that pause surfaces directly in workflow code. + +The plugin adds no shim here. The recommended Temporal mapping is: + +* expose the pending approval via a ``@workflow.query`` so a client can read it; +* resume via a ``@workflow.update`` that feeds the human's decision back with the + native ``Command(resume={"decisions": [...]})`` protocol; its validator rejects + unsupported decisions before they are accepted into workflow history. + +The ``InMemorySaver`` is replay-safe because its state lives in the workflow's +own memory (rehydrated by deterministic replay); the ``thread_id`` is the stable +workflow id. +""" + +# @@@SNIPSTART python-deepagents-human-in-the-loop-workflow +from datetime import timedelta + +from deepagents import create_deep_agent +from langchain_core.runnables import RunnableConfig +from langgraph.checkpoint.memory import InMemorySaver +from langgraph.types import Command +from temporalio import workflow +from temporalio.contrib.deepagents import tool_as_activity + + +@workflow.defn +class HumanInTheLoopAgent: + def __init__(self) -> None: + self._pending: str | None = None + self._decision: str | None = None + self._resumed = False + + @workflow.run + async def run(self, city: str) -> str: + def book_trip(city: str) -> str: + """Book a trip to a city (requires human approval).""" + return f"Booked a trip to {city}." + + trip_tool = tool_as_activity( + book_trip, start_to_close_timeout=timedelta(seconds=30) + ) + agent = create_deep_agent( + model="anthropic:claude-sonnet-4-5", + tools=[trip_tool], + interrupt_on={"book_trip": True}, + checkpointer=InMemorySaver(), + ) + config = RunnableConfig(configurable={"thread_id": workflow.info().workflow_id}) + + result = await agent.ainvoke( + {"messages": [{"role": "user", "content": f"Book a trip to {city}."}]}, + config=config, + ) + # LangGraph returns (not raises) the pending approval under __interrupt__. + pending = result.get("__interrupt__") + if pending: + self._pending = str(getattr(pending[0], "value", pending[0])) + # Block until a client approves/rejects via the `resume` update. + await workflow.wait_condition(lambda: self._resumed) + # No longer paused: the query goes back to reporting None. + self._pending = None + result = await agent.ainvoke( + Command(resume={"decisions": [{"type": self._decision}]}), + config=config, + ) + return result["messages"][-1].content + + @workflow.query + def pending_approval(self) -> str | None: + """Return the pending approval prompt, or ``None`` if not paused.""" + return self._pending + + @workflow.update + async def resume(self, decision: str) -> None: + """Resume the paused agent with ``"approve"`` or ``"reject"``.""" + self._decision = decision + self._resumed = True + + @resume.validator + def validate_resume(self, decision: str) -> None: + # Runs before the update is accepted, keeping invalid decisions out of + # workflow history entirely. Only the decisions this workflow feeds to + # `Command(resume=...)` are allowed. + if decision not in ("approve", "reject"): + raise ValueError('decision must be "approve" or "reject"') + + +# @@@SNIPEND diff --git a/deepagents_plugin/langsmith_tracing/README.md b/deepagents_plugin/langsmith_tracing/README.md new file mode 100644 index 000000000..f9828b62e --- /dev/null +++ b/deepagents_plugin/langsmith_tracing/README.md @@ -0,0 +1,46 @@ +# LangSmith Tracing + +Run a Deep Agent durably **and** trace its LLM calls to +[LangSmith](https://smith.langchain.com/), by composing `LangSmithPlugin` +alongside `DeepAgentsPlugin`. The plugin carries no tracing context of its own; +the observability plugin captures the model calls that `DeepAgentsPlugin` runs as +activities. Registration order of the two plugins does not matter. + +Following the shipped tracing samples, this scenario bundles the worker and +starter into a single `main.py` and has no automated test (it requires external +API keys). + +## What This Sample Demonstrates + +- Composing `DeepAgentsPlugin` with `temporalio.contrib.langsmith.LangSmithPlugin` +- Order-independent plugin registration + +## Running the Sample + +Prerequisites: Python >= 3.11 with the [suite setup](../README.md#prerequisites) +applied (interim plugin install), a running Temporal dev server +(`temporal server start-dev`), and these environment variables: + +```bash +export ANTHROPIC_API_KEY=... +export LANGSMITH_API_KEY=... # or LANGCHAIN_API_KEY +export LANGSMITH_TRACING=true +``` + +The experimental plugin is not in the `deepagents` group — install it as shown +in the [suite README](../README.md#prerequisites) and run with `--no-sync`, or a +bare `uv run`/`uv sync` re-syncs the environment and uninstalls it. Then run the +single-process driver: + +```bash +uv run --no-sync deepagents_plugin/langsmith_tracing/main.py +``` + +Traces appear in your LangSmith project. + +## Files + +| File | Description | +|------|-------------| +| `workflow.py` | `TracedAgent`: an ordinary Deep Agent | +| `main.py` | Composes `LangSmithPlugin` + `DeepAgentsPlugin`, runs the workflow once | diff --git a/deepagents_plugin/langsmith_tracing/__init__.py b/deepagents_plugin/langsmith_tracing/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/deepagents_plugin/langsmith_tracing/main.py b/deepagents_plugin/langsmith_tracing/main.py new file mode 100644 index 000000000..555ebc0d1 --- /dev/null +++ b/deepagents_plugin/langsmith_tracing/main.py @@ -0,0 +1,48 @@ +"""Run the LangSmith tracing Deep Agents sample. + +Single-process driver: starts a Worker, executes the Workflow once, prints the +result, then shuts down. Composes ``LangSmithPlugin`` with ``DeepAgentsPlugin`` +so the agent runs durably *and* its LLM calls are traced to LangSmith. +Registration order of the two plugins does not matter. + +Requires ``ANTHROPIC_API_KEY`` and ``LANGSMITH_API_KEY`` (or ``LANGCHAIN_API_KEY``) +in the environment. +""" + +import asyncio +import os + +from temporalio.client import Client +from temporalio.contrib.deepagents import DeepAgentsPlugin +from temporalio.contrib.langsmith import LangSmithPlugin +from temporalio.worker import Worker + +from deepagents_plugin.langsmith_tracing.workflow import TracedAgent + + +async def main() -> None: + client = await Client.connect( + os.environ.get("TEMPORAL_ADDRESS", "localhost:7233"), + plugins=[ + # Register observability first, then the Deep Agents plugin. + LangSmithPlugin(), + DeepAgentsPlugin(), + ], + ) + + async with Worker( + client, + task_queue="deepagents-langsmith-tracing", + workflows=[TracedAgent], + ): + result = await client.execute_workflow( + TracedAgent.run, + "What is durable execution, in one sentence?", + id="deepagents-langsmith-tracing", + task_queue="deepagents-langsmith-tracing", + ) + print(f"Result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/deepagents_plugin/langsmith_tracing/workflow.py b/deepagents_plugin/langsmith_tracing/workflow.py new file mode 100644 index 000000000..15f8ad596 --- /dev/null +++ b/deepagents_plugin/langsmith_tracing/workflow.py @@ -0,0 +1,28 @@ +"""A Deep Agent whose durable execution is also traced to LangSmith. + +The workflow itself is an ordinary Deep Agent — the tracing comes entirely from +composing ``LangSmithPlugin`` alongside ``DeepAgentsPlugin`` on the client (see +``main.py``). The plugin carries no tracing context of its own; the observability +plugin captures the LLM calls that ``DeepAgentsPlugin`` runs as activities. +""" + +# @@@SNIPSTART python-deepagents-langsmith-tracing-workflow +from deepagents import create_deep_agent +from temporalio import workflow + + +@workflow.defn +class TracedAgent: + @workflow.run + async def run(self, question: str) -> str: + agent = create_deep_agent( + model="anthropic:claude-sonnet-4-5", + system_prompt="You are a helpful assistant.", + ) + result = await agent.ainvoke( + {"messages": [{"role": "user", "content": question}]} + ) + return result["messages"][-1].content + + +# @@@SNIPEND diff --git a/deepagents_plugin/react_agent/README.md b/deepagents_plugin/react_agent/README.md new file mode 100644 index 000000000..97edbb809 --- /dev/null +++ b/deepagents_plugin/react_agent/README.md @@ -0,0 +1,50 @@ +# React Agent (tool-calling loop) + +A Deep Agent that loops over tool calls until it produces a final answer. This +sample demonstrates the **explicit Workflow-vs-Activity choice per tool** — the +core decision when making an agent durable: + +- **`activity_as_tool`** exposes an existing Temporal activity (`get_weather`) + as a Deep Agents tool. The tool advertises the activity's argument schema to + the model and dispatches to the activity via `workflow.execute_activity`. +- **`tool_as_activity`** wraps a LangChain tool (`web_search`) whose body does + I/O so its execution runs as a `deepagents.invoke_tool` activity instead of + inline in the workflow. + +The agent is built with `create_temporal_deep_agent(..., activity_options=...)` +— the recommended way to scope model-call activity options (timeouts, retry +policy) to one agent instead of relying on the plugin-wide default. Every model +turn and every tool call is a durable activity. + +## What This Sample Demonstrates + +- `create_temporal_deep_agent` with per-agent `activity_options` for model calls +- `activity_as_tool` for an existing `@activity.defn` +- `tool_as_activity` for a LangChain tool that does I/O +- Registering the user activity on the worker alongside the plugin's activities + +## Running the Sample + +Prerequisites: Python >= 3.11 with the [suite setup](../README.md#prerequisites) +applied (interim plugin install), an `ANTHROPIC_API_KEY` in your +environment, and a running Temporal dev server (`temporal server start-dev`). + +> The experimental plugin is not in the `deepagents` group — install it as shown +> in the [suite README](../README.md#prerequisites) and run with `--no-sync`, or +> a bare `uv run`/`uv sync` re-syncs the environment and uninstalls it. + +```bash +# Terminal 1 +uv run --no-sync deepagents_plugin/react_agent/run_worker.py + +# Terminal 2 +uv run --no-sync deepagents_plugin/react_agent/run_workflow.py +``` + +## Files + +| File | Description | +|------|-------------| +| `workflow.py` | `get_weather` activity, `web_search` tool, and the `ReactAgent` workflow | +| `run_worker.py` | Adds `DeepAgentsPlugin`, registers `get_weather`, starts the worker | +| `run_workflow.py` | Executes the workflow and prints the result | diff --git a/deepagents_plugin/react_agent/__init__.py b/deepagents_plugin/react_agent/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/deepagents_plugin/react_agent/run_worker.py b/deepagents_plugin/react_agent/run_worker.py new file mode 100644 index 000000000..531375f76 --- /dev/null +++ b/deepagents_plugin/react_agent/run_worker.py @@ -0,0 +1,37 @@ +"""Worker for the react agent sample. + +The plugin registers the ``deepagents.*`` activities automatically, but the +user's own ``get_weather`` activity (exposed to the agent via +``activity_as_tool``) must be registered on the worker like any other activity. +The ``web_search`` tool wrapped with ``tool_as_activity`` needs no separate +registration — it runs through the plugin's ``deepagents.invoke_tool`` activity. +""" + +import asyncio +import os + +from temporalio.client import Client +from temporalio.contrib.deepagents import DeepAgentsPlugin +from temporalio.worker import Worker + +from deepagents_plugin.react_agent.workflow import ReactAgent, get_weather + + +async def main() -> None: + client = await Client.connect( + os.environ.get("TEMPORAL_ADDRESS", "localhost:7233"), + plugins=[DeepAgentsPlugin()], + ) + + worker = Worker( + client, + task_queue="deepagents-react-agent", + workflows=[ReactAgent], + activities=[get_weather], + ) + print("Worker started. Ctrl+C to exit.") + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/deepagents_plugin/react_agent/run_workflow.py b/deepagents_plugin/react_agent/run_workflow.py new file mode 100644 index 000000000..a7470bc31 --- /dev/null +++ b/deepagents_plugin/react_agent/run_workflow.py @@ -0,0 +1,25 @@ +"""Start the react agent workflow and print the final answer.""" + +import asyncio +import os + +from temporalio.client import Client + +from deepagents_plugin.react_agent.workflow import ReactAgent + + +async def main() -> None: + client = await Client.connect(os.environ.get("TEMPORAL_ADDRESS", "localhost:7233")) + + result = await client.execute_workflow( + ReactAgent.run, + "What's the weather in Seattle, and what is Temporal known for?", + id="deepagents-react-agent", + task_queue="deepagents-react-agent", + ) + + print(f"Result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/deepagents_plugin/react_agent/workflow.py b/deepagents_plugin/react_agent/workflow.py new file mode 100644 index 000000000..dfa3c57c6 --- /dev/null +++ b/deepagents_plugin/react_agent/workflow.py @@ -0,0 +1,82 @@ +"""Tool-calling Deep Agent: the explicit Workflow-vs-Activity choice per tool. + +A Deep Agent holds its tools in-workflow. A tool that only reads/writes agent +state is pure and belongs there; a tool that does real I/O must not run in +workflow code. This sample shows the two explicit ways to move a tool's work to +an activity: + +* ``activity_as_tool`` — surface an existing ``@activity.defn`` (``get_weather``) + as a Deep Agents tool. Temporal adopters already have activities; they should + not have to re-declare them. +* ``tool_as_activity`` — wrap a LangChain tool (``web_search``) whose body does + I/O so its execution runs as a ``deepagents.invoke_tool`` activity. + +The agent itself is built with ``create_temporal_deep_agent`` — the recommended +way to scope ``activity_options`` (timeouts, retry policy) for *this agent's* +model calls, instead of relying on the plugin-wide default. (A vanilla +``create_deep_agent`` with a bare ``model=`` string also works; the plugin wraps +it automatically with the plugin-wide options.) Every model turn and every tool +call in the loop is a durable activity. +""" + +from datetime import timedelta + +from langchain_core.tools import tool +from temporalio import activity, workflow +from temporalio.contrib.deepagents import ( + activity_as_tool, + create_temporal_deep_agent, + tool_as_activity, +) + + +# @@@SNIPSTART python-deepagents-react-agent-activity +@activity.defn +async def get_weather(city: str) -> str: + """Return the current weather for a city.""" + # A real implementation would call a weather API here; this is a stand-in. + return f"It is sunny and 22C in {city}." + + +# @@@SNIPEND + + +# @@@SNIPSTART python-deepagents-react-agent-workflow +@tool +def web_search(query: str) -> str: + """Search the web for a query and return a short result.""" + # Real I/O (an HTTP call) would go here; wrapped with tool_as_activity so it + # runs in an activity, not in workflow code. + return f"Top result for {query!r}: Temporal makes code durable." + + +@workflow.defn +class ReactAgent: + @workflow.run + async def run(self, question: str) -> str: + weather_tool = activity_as_tool( + get_weather, + start_to_close_timeout=timedelta(seconds=30), + ) + search_tool = tool_as_activity( + web_search, + start_to_close_timeout=timedelta(seconds=30), + ) + agent = create_temporal_deep_agent( + model="anthropic:claude-sonnet-4-5", + tools=[weather_tool, search_tool], + system_prompt=( + "You are a research assistant. Use the get_weather and " + "web_search tools when they help answer the question." + ), + # Scopes the model-call activity options to this agent — the + # recommended way to set model timeouts/retries per agent. + activity_options={"start_to_close_timeout": timedelta(minutes=2)}, + ) + result = await agent.ainvoke( + {"messages": [{"role": "user", "content": question}]} + ) + return result["messages"][-1].content + + +# @@@SNIPEND diff --git a/deepagents_plugin/streaming/README.md b/deepagents_plugin/streaming/README.md new file mode 100644 index 000000000..6697df96f --- /dev/null +++ b/deepagents_plugin/streaming/README.md @@ -0,0 +1,51 @@ +# Streaming + +Stream model output from a durable workflow to external subscribers in real +time, while keeping the durable workflow result identical to the non-streaming +path. Streaming is async-only, so the workflow drives the plugin's +`TemporalModel.astream(...)` directly — the same seam a full Deep Agent's model +calls go through. + +Constructing the plugin with `DeepAgentsPlugin(streaming_topic=...)` flips model +dispatch from `deepagents.invoke_model` to `deepagents.invoke_model_streaming`. +The streaming activity coalesces chunk batches and publishes them to a +`temporalio.contrib.workflow_streams` topic; the aggregated final message is +still returned to the workflow. + +Streaming is async-only, so the workflow drives an explicit +`TemporalModel.astream(...)` and hosts a `WorkflowStream` so subscribers can +attach by workflow id. The starter subscribes to the topic and prints chunks as +they arrive. + +## What This Sample Demonstrates + +- `DeepAgentsPlugin(streaming_topic=...)` to enable streaming dispatch +- Hosting a `WorkflowStream` in the workflow and driving `TemporalModel.astream` +- A client subscribing via `WorkflowStreamClient` and decoding `AIMessageChunk` + batches with `langchain_core.load.load` + +## Running the Sample + +Prerequisites: Python >= 3.11 with the [suite setup](../README.md#prerequisites) +applied (interim plugin install), an `ANTHROPIC_API_KEY` in your +environment, and a running Temporal dev server (`temporal server start-dev`). + +> The experimental plugin is not in the `deepagents` group — install it as shown +> in the [suite README](../README.md#prerequisites) and run with `--no-sync`, or +> a bare `uv run`/`uv sync` re-syncs the environment and uninstalls it. + +```bash +# Terminal 1 +uv run --no-sync deepagents_plugin/streaming/run_worker.py + +# Terminal 2 +uv run --no-sync deepagents_plugin/streaming/run_workflow.py +``` + +## Files + +| File | Description | +|------|-------------| +| `workflow.py` | `StreamingWorkflow` hosting a `WorkflowStream`, driving `astream` | +| `run_worker.py` | Adds `DeepAgentsPlugin(streaming_topic=...)`, starts the worker | +| `run_workflow.py` | Starts the workflow and prints streamed chunks live | diff --git a/deepagents_plugin/streaming/__init__.py b/deepagents_plugin/streaming/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/deepagents_plugin/streaming/run_worker.py b/deepagents_plugin/streaming/run_worker.py new file mode 100644 index 000000000..024a9e1c4 --- /dev/null +++ b/deepagents_plugin/streaming/run_worker.py @@ -0,0 +1,34 @@ +"""Worker for the streaming sample. + +``streaming_topic=`` is what turns on streaming: with it set, the plugin routes +model calls through the ``deepagents.invoke_model_streaming`` activity, which +publishes chunk batches to that workflow-streams topic. +""" + +import asyncio +import os + +from temporalio.client import Client +from temporalio.contrib.deepagents import DeepAgentsPlugin +from temporalio.worker import Worker + +from deepagents_plugin.streaming.workflow import STREAMING_TOPIC, StreamingWorkflow + + +async def main() -> None: + client = await Client.connect( + os.environ.get("TEMPORAL_ADDRESS", "localhost:7233"), + plugins=[DeepAgentsPlugin(streaming_topic=STREAMING_TOPIC)], + ) + + worker = Worker( + client, + task_queue="deepagents-streaming", + workflows=[StreamingWorkflow], + ) + print("Worker started. Ctrl+C to exit.") + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/deepagents_plugin/streaming/run_workflow.py b/deepagents_plugin/streaming/run_workflow.py new file mode 100644 index 000000000..fe41261d5 --- /dev/null +++ b/deepagents_plugin/streaming/run_workflow.py @@ -0,0 +1,74 @@ +"""Start the streaming workflow and print model chunks live. + +Subscribes to the workflow-streams topic the streaming activity publishes to and +renders each chunk's text as it arrives. Each published item is an +``AIMessageChunk`` in ``langchain_core.load.dumpd`` form, so it is reconstructed +with ``langchain_core.load.load``. The final aggregated message is also returned +as the workflow result (identical to the non-streaming path). +""" + +import asyncio +import os +from datetime import timedelta + +from langchain_core.load import load +from temporalio.client import Client +from temporalio.contrib.workflow_streams import WorkflowStreamClient + +from deepagents_plugin.streaming.workflow import STREAMING_TOPIC, StreamingWorkflow + + +async def main() -> None: + client = await Client.connect(os.environ.get("TEMPORAL_ADDRESS", "localhost:7233")) + workflow_id = "deepagents-streaming" + + handle = await client.start_workflow( + StreamingWorkflow.run, + "Write a short paragraph about durable execution.", + id=workflow_id, + task_queue="deepagents-streaming", + ) + + printed: list[str] = [] + + async def consume() -> None: + stream = WorkflowStreamClient.create(client, workflow_id) + async for item in stream.subscribe( + [STREAMING_TOPIC], + from_offset=0, + result_type=dict, + poll_cooldown=timedelta(milliseconds=50), + ): + chunk = load(item.data) + text = getattr(chunk, "content", "") + if text: + printed.append(str(text)) + print(text, end="", flush=True) + + consume_task = asyncio.create_task(consume()) + result = await handle.result() + + # The workflow has completed, but the subscriber may still be catching up on + # the tail of the stream. The streamed chunks add up to the durable result, + # so drain until all of it has been printed; the timeout only bounds a + # regression, it never gates the happy path. + async def drained() -> None: + while result not in "".join(printed): + await asyncio.sleep(0.05) + + try: + await asyncio.wait_for(drained(), timeout=10.0) + except asyncio.TimeoutError: + print("\n(timed out waiting for the subscriber to drain the stream)") + consume_task.cancel() + try: + await consume_task + except asyncio.CancelledError: + pass + + print() + print(f"Final result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/deepagents_plugin/streaming/workflow.py b/deepagents_plugin/streaming/workflow.py new file mode 100644 index 000000000..3cb0910d3 --- /dev/null +++ b/deepagents_plugin/streaming/workflow.py @@ -0,0 +1,40 @@ +"""Stream model output to external subscribers while keeping a durable result. + +Constructing the plugin with ``DeepAgentsPlugin(streaming_topic=...)`` flips +model dispatch from ``deepagents.invoke_model`` to +``deepagents.invoke_model_streaming``: the streaming activity coalesces chunk +batches and publishes them to a ``temporalio.contrib.workflow_streams`` topic for +subscribers, while the aggregated final message is still returned to the workflow +(so the durable result is identical to the non-streaming path). + +Streaming is async-only, so the workflow drives an explicit +``TemporalModel.astream(...)``. It hosts a ``WorkflowStream`` so external +subscribers can attach by workflow id (see ``run_workflow.py``). +""" + +# @@@SNIPSTART python-deepagents-streaming-workflow +from langchain_core.messages import HumanMessage +from temporalio import workflow +from temporalio.contrib.deepagents import TemporalModel +from temporalio.contrib.workflow_streams import WorkflowStream + +STREAMING_TOPIC = "model-chunks" + + +@workflow.defn +class StreamingWorkflow: + def __init__(self) -> None: + # Host the stream so the publish-Signal handler is registered before the + # streaming activity (the external publisher) starts publishing. + self.stream = WorkflowStream() + + @workflow.run + async def run(self, prompt: str) -> str: + model = TemporalModel(model="anthropic:claude-sonnet-4-5") + parts: list[str] = [] + async for chunk in model.astream([HumanMessage(content=prompt)]): + parts.append(str(chunk.content)) + return "".join(parts) + + +# @@@SNIPEND diff --git a/deepagents_plugin/subagents/README.md b/deepagents_plugin/subagents/README.md new file mode 100644 index 000000000..c3f8f8a2b --- /dev/null +++ b/deepagents_plugin/subagents/README.md @@ -0,0 +1,45 @@ +# Subagents + +Durability propagates across the entire agent tree with **no per-sub-agent +wiring**. A coordinator agent built with `create_deep_agent(..., subagents=[...])` +delegates to its sub-agents through the built-in `task` tool. Deep Agents builds +each sub-agent as a separate graph, but they inherit the parent's `model` object +by default — so because the plugin makes that one model object durable, every +sub-agent's model call also becomes a `deepagents.invoke_model` activity +automatically. + +In this sample the coordinator delegates deep investigation to a `researcher` +sub-agent and then synthesizes a final answer. Both the coordinator's and the +researcher's LLM calls run as durable activities. + +## What This Sample Demonstrates + +- `create_deep_agent(subagents=[...])` with a delegated `researcher` +- Durability inheritance: sub-agent model calls route through activities with no + extra wiring + +## Running the Sample + +Prerequisites: Python >= 3.11 with the [suite setup](../README.md#prerequisites) +applied (interim plugin install), an `ANTHROPIC_API_KEY` in your +environment, and a running Temporal dev server (`temporal server start-dev`). + +> The experimental plugin is not in the `deepagents` group — install it as shown +> in the [suite README](../README.md#prerequisites) and run with `--no-sync`, or +> a bare `uv run`/`uv sync` re-syncs the environment and uninstalls it. + +```bash +# Terminal 1 +uv run --no-sync deepagents_plugin/subagents/run_worker.py + +# Terminal 2 +uv run --no-sync deepagents_plugin/subagents/run_workflow.py +``` + +## Files + +| File | Description | +|------|-------------| +| `workflow.py` | `SubagentsWorkflow`: a coordinator with a `researcher` sub-agent | +| `run_worker.py` | Adds `DeepAgentsPlugin`, starts the worker | +| `run_workflow.py` | Executes the workflow and prints the result | diff --git a/deepagents_plugin/subagents/__init__.py b/deepagents_plugin/subagents/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/deepagents_plugin/subagents/run_worker.py b/deepagents_plugin/subagents/run_worker.py new file mode 100644 index 000000000..c5abb5636 --- /dev/null +++ b/deepagents_plugin/subagents/run_worker.py @@ -0,0 +1,29 @@ +"""Worker for the subagents sample.""" + +import asyncio +import os + +from temporalio.client import Client +from temporalio.contrib.deepagents import DeepAgentsPlugin +from temporalio.worker import Worker + +from deepagents_plugin.subagents.workflow import SubagentsWorkflow + + +async def main() -> None: + client = await Client.connect( + os.environ.get("TEMPORAL_ADDRESS", "localhost:7233"), + plugins=[DeepAgentsPlugin()], + ) + + worker = Worker( + client, + task_queue="deepagents-subagents", + workflows=[SubagentsWorkflow], + ) + print("Worker started. Ctrl+C to exit.") + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/deepagents_plugin/subagents/run_workflow.py b/deepagents_plugin/subagents/run_workflow.py new file mode 100644 index 000000000..226b52cb1 --- /dev/null +++ b/deepagents_plugin/subagents/run_workflow.py @@ -0,0 +1,25 @@ +"""Start the subagents workflow and print the coordinator's synthesized answer.""" + +import asyncio +import os + +from temporalio.client import Client + +from deepagents_plugin.subagents.workflow import SubagentsWorkflow + + +async def main() -> None: + client = await Client.connect(os.environ.get("TEMPORAL_ADDRESS", "localhost:7233")) + + result = await client.execute_workflow( + SubagentsWorkflow.run, + "Investigate how Temporal handles workflow retries and summarize it.", + id="deepagents-subagents", + task_queue="deepagents-subagents", + ) + + print(f"Result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/deepagents_plugin/subagents/workflow.py b/deepagents_plugin/subagents/workflow.py new file mode 100644 index 000000000..8822ba16d --- /dev/null +++ b/deepagents_plugin/subagents/workflow.py @@ -0,0 +1,46 @@ +"""Durability propagates across the whole agent tree — no per-sub-agent wiring. + +A coordinator built with ``create_deep_agent(..., subagents=[...])`` delegates to +its sub-agents via the built-in ``task`` tool. Deep Agents builds each sub-agent +as a separate graph, but they inherit the parent's ``model`` object by default. +Because the plugin makes that model object durable (each generation is a +``deepagents.invoke_model`` activity), every sub-agent's model call is +automatically durable too — you wire the plugin once and the whole tree is +covered. + +Here the coordinator delegates deep investigation to a ``researcher`` sub-agent +and then synthesizes a final answer; both the coordinator's and the researcher's +model calls run as activities. +""" + +# @@@SNIPSTART python-deepagents-subagents-workflow +from deepagents import create_deep_agent +from temporalio import workflow + + +@workflow.defn +class SubagentsWorkflow: + @workflow.run + async def run(self, question: str) -> str: + agent = create_deep_agent( + model="anthropic:claude-sonnet-4-5", + system_prompt=( + "You are a research coordinator. Delegate deep investigation to " + "the researcher sub-agent via the task tool, then synthesize a " + "final answer." + ), + subagents=[ + { + "name": "researcher", + "description": "Researches a topic in depth and reports findings.", + "system_prompt": "You research topics thoroughly and report back.", + } + ], + ) + result = await agent.ainvoke( + {"messages": [{"role": "user", "content": question}]} + ) + return result["messages"][-1].content + + +# @@@SNIPEND diff --git a/dsl/README.md b/dsl/README.md index 4000b3a3d..5eca4fa44 100644 --- a/dsl/README.md +++ b/dsl/README.md @@ -6,24 +6,24 @@ samples [in TypeScript](https://github.com/temporalio/samples-typescript/tree/ma For this sample, the optional `dsl` dependency group must be included. To include, run: - poetry install --with dsl + uv sync --group dsl -To run, first see [README.md](../README.md) for prerequisites. Then, run the following from this directory to start the +To run, first see [README.md](../README.md) for prerequisites. Then, run the following from the root directory to start the worker: - poetry run python worker.py + uv run dsl/worker.py This will start the worker. Then, in another terminal, run the following to execute a workflow of steps defined in -[workflow1.yaml](workflow1.yaml): +[workflow1.yaml](dsl/workflow1.yaml): - poetry run python starter.py workflow1.yaml + uv run dsl/starter.py dsl/workflow1.yaml This will run the workflow and show the final variables that the workflow returns. Looking in the worker terminal, each step executed will be visible. -Similarly we can do the same for the more advanced [workflow2.yaml](workflow2.yaml) file: +Similarly we can do the same for the more advanced [workflow2.yaml](dsl/workflow2.yaml) file: - poetry run python starter.py workflow2.yaml + uv run dsl/starter.py dsl/workflow2.yaml This sample gives a guide of how one can write a workflow to interpret arbitrary steps from a user-provided DSL. Many DSL models are more advanced and are more specific to conform to business logic needs. \ No newline at end of file diff --git a/dsl/starter.py b/dsl/starter.py index e530b10ee..eb0f328de 100644 --- a/dsl/starter.py +++ b/dsl/starter.py @@ -6,6 +6,7 @@ import dacite import yaml from temporalio.client import Client +from temporalio.envconfig import ClientConfig from dsl.workflow import DSLInput, DSLWorkflow @@ -16,7 +17,9 @@ async def main(dsl_yaml: str) -> None: dsl_input = dacite.from_dict(DSLInput, yaml.safe_load(dsl_yaml)) # Connect client - client = await Client.connect("localhost:7233") + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) # Run workflow result = await client.execute_workflow( diff --git a/dsl/worker.py b/dsl/worker.py index 9945492ed..e52ec8720 100644 --- a/dsl/worker.py +++ b/dsl/worker.py @@ -2,6 +2,7 @@ import logging from temporalio.client import Client +from temporalio.envconfig import ClientConfig from temporalio.worker import Worker from dsl.activities import DSLActivities @@ -12,7 +13,9 @@ async def main(): # Connect client - client = await Client.connect("localhost:7233") + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) # Run a worker for the activities and workflow activities = DSLActivities() diff --git a/eager_wf_start/README.md b/eager_wf_start/README.md new file mode 100644 index 000000000..834c6a55d --- /dev/null +++ b/eager_wf_start/README.md @@ -0,0 +1,16 @@ +# Eager Workflow Start + +This sample shows how to create a workflow that uses Eager Workflow Start. + +The target use case is workflows whose first task needs to execute quickly (ex: payment verification in an online checkout workflow). That work typically can't be done directly in the workflow (ex: using web APIs, databases, etc.), and also needs to avoid the overhead of dispatching another task. Using a Local Activity suffices both needs, which this sample demonstrates. + +You can read more about Eager Workflow Start in our: + +- [Eager Workflow Start blog](https://temporal.io/blog/improving-latency-with-eager-workflow-start) +- [Worker Performance Docs](https://docs.temporal.io/develop/worker-performance#eager-workflow-start) + +To run, first see the main [README.md](../README.md) for prerequisites. + +Then run the sample via: + + uv run eager_wf_start/run.py \ No newline at end of file diff --git a/eager_wf_start/__init__.py b/eager_wf_start/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/eager_wf_start/activities.py b/eager_wf_start/activities.py new file mode 100644 index 000000000..6533140f2 --- /dev/null +++ b/eager_wf_start/activities.py @@ -0,0 +1,6 @@ +from temporalio import activity + + +@activity.defn() +async def greeting(name: str) -> str: + return f"Hello {name}!" diff --git a/eager_wf_start/run.py b/eager_wf_start/run.py new file mode 100644 index 000000000..34d807e0a --- /dev/null +++ b/eager_wf_start/run.py @@ -0,0 +1,45 @@ +import asyncio +import uuid + +from temporalio.client import Client +from temporalio.envconfig import ClientConfig +from temporalio.worker import Worker + +from eager_wf_start.activities import greeting +from eager_wf_start.workflows import EagerWorkflow + +TASK_QUEUE = "eager-wf-start-task-queue" + + +async def main(): + # Note that the worker and client run in the same process and share the same client connection. + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) + + worker = Worker( + client, + task_queue=TASK_QUEUE, + workflows=[EagerWorkflow], + activities=[greeting], + ) + + # Run worker in the background + async with worker: + # Start workflow(s) while worker is running + wf_handle = await client.start_workflow( + EagerWorkflow.run, + "Temporal", + id=f"eager-workflow-id-{uuid.uuid4()}", + task_queue=TASK_QUEUE, + request_eager_start=True, + ) + + # This is an internal flag not intended to be used publicly. + # It is used here purely to display that the workflow was eagerly started. + print(f"Workflow eagerly started: {wf_handle.__temporal_eagerly_started}") + print(await wf_handle.result()) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/eager_wf_start/workflows.py b/eager_wf_start/workflows.py new file mode 100644 index 000000000..9107d4027 --- /dev/null +++ b/eager_wf_start/workflows.py @@ -0,0 +1,15 @@ +from datetime import timedelta + +from temporalio import workflow + +with workflow.unsafe.imports_passed_through(): + from eager_wf_start.activities import greeting + + +@workflow.defn +class EagerWorkflow: + @workflow.run + async def run(self, name: str) -> str: + return await workflow.execute_local_activity( + greeting, name, schedule_to_close_timeout=timedelta(seconds=5) + ) diff --git a/encryption/README.md b/encryption/README.md index b3e1de433..c323e38ba 100644 --- a/encryption/README.md +++ b/encryption/README.md @@ -4,51 +4,48 @@ This sample shows how to make an encryption codec for end-to-end encryption. It samples [in TypeScript](https://github.com/temporalio/samples-typescript/tree/main/encryption) and [in Go](https://github.com/temporalio/samples-go/tree/main/encryption). + For this sample, the optional `encryption` dependency group must be included. To include, run: - poetry install --with encryption + uv sync --group encryption -To run, first see [README.md](../README.md) for prerequisites. Then, run the following from this directory to start the +To run, first see [README.md](../README.md) for prerequisites. Then, run the following from the root directory to start the worker: - poetry run python worker.py + uv run encryption/worker.py This will start the worker. Then, in another terminal, run the following to execute the workflow: - poetry run python starter.py + uv run encryption/starter.py -The workflow should complete with the hello result. To view the workflow, use [tctl](https://docs.temporal.io/tctl-v1/): +The workflow should complete with the hello result. To view the workflow, use [temporal](https://docs.temporal.io/cli): - tctl workflow show --workflow_id encryption-workflow-id + temporal workflow show --workflow-id encryption-workflow-id -Note how the input/result look like (with wrapping removed): +Note how the result looks like (with wrapping removed): ``` - Input:[encoding binary/encrypted: payload encoding is not supported] - ... - Result:[encoding binary/encrypted: payload encoding is not supported] + Output:[encoding binary/encrypted: payload encoding is not supported] ``` -This is because the data is encrypted and not visible. To make data visible to external Temporal tools like `tctl` and +This is because the data is encrypted and not visible. To make data visible to external Temporal tools like `temporal` and the UI, start a codec server in another terminal: - poetry run python codec_server.py + uv run encryption/codec_server.py -Now with that running, run `tctl` again with the codec endpoint: +Now with that running, run `temporal` again with the codec endpoint: - tctl --codec_endpoint http://localhost:8081 workflow show --workflow_id encryption-workflow-id + temporal workflow show --workflow-id encryption-workflow-id --codec-endpoint http://localhost:8081 Notice now the output has the unencrypted values: ``` - Input:["Temporal"] - ... Result:["Hello, Temporal"] ``` This decryption did not leave the local machine here. Same case with the web UI. If you go to the web UI, you'll only see encrypted input/results. But, assuming your web UI -is at `http://localhost:8080`, if you set the "Remote Codec Endpoint" in the web UI to `http://localhost:8081` you can +is at `http://localhost:8233` (this is the default for the local dev server), if you set the "Remote Codec Endpoint" in the web UI to `http://localhost:8081` you can then see the unencrypted results. This is possible because CORS settings in the codec server allow the browser to access the codec server directly over localhost. They can be changed to suit Temporal cloud web UI instead if necessary. \ No newline at end of file diff --git a/encryption/codec.py b/encryption/codec.py index 6aaa58be8..9f79526f8 100644 --- a/encryption/codec.py +++ b/encryption/codec.py @@ -1,4 +1,3 @@ -import base64 import os from typing import Iterable, List diff --git a/encryption/codec_server.py b/encryption/codec_server.py index 4bd04214e..3e3029f2a 100644 --- a/encryption/codec_server.py +++ b/encryption/codec_server.py @@ -12,8 +12,8 @@ def build_codec_server() -> web.Application: # Cors handler async def cors_options(req: web.Request) -> web.Response: resp = web.Response() - if req.headers.get(hdrs.ORIGIN) == "http://localhost:8080": - resp.headers[hdrs.ACCESS_CONTROL_ALLOW_ORIGIN] = "http://localhost:8080" + if req.headers.get(hdrs.ORIGIN) == "http://localhost:8233": + resp.headers[hdrs.ACCESS_CONTROL_ALLOW_ORIGIN] = "http://localhost:8233" resp.headers[hdrs.ACCESS_CONTROL_ALLOW_METHODS] = "POST" resp.headers[hdrs.ACCESS_CONTROL_ALLOW_HEADERS] = "content-type,x-namespace" return resp diff --git a/encryption/starter.py b/encryption/starter.py index 4c39f5536..f25709367 100644 --- a/encryption/starter.py +++ b/encryption/starter.py @@ -3,15 +3,19 @@ import temporalio.converter from temporalio.client import Client +from temporalio.envconfig import ClientConfig from encryption.codec import EncryptionCodec from encryption.worker import GreetingWorkflow async def main(): + # Load configuration + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") # Connect client client = await Client.connect( - "localhost:7233", + **config, # Use the default converter, but change the codec data_converter=dataclasses.replace( temporalio.converter.default(), payload_codec=EncryptionCodec() diff --git a/encryption/worker.py b/encryption/worker.py index b99a2eab7..d3387c70c 100644 --- a/encryption/worker.py +++ b/encryption/worker.py @@ -4,6 +4,7 @@ import temporalio.converter from temporalio import workflow from temporalio.client import Client +from temporalio.envconfig import ClientConfig from temporalio.worker import Worker from encryption.codec import EncryptionCodec @@ -20,9 +21,11 @@ async def run(self, name: str) -> str: async def main(): + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") # Connect client client = await Client.connect( - "localhost:7233", + **config, # Use the default converter, but change the codec data_converter=dataclasses.replace( temporalio.converter.default(), payload_codec=EncryptionCodec() diff --git a/env_config/README.md b/env_config/README.md new file mode 100644 index 000000000..6496900a5 --- /dev/null +++ b/env_config/README.md @@ -0,0 +1,43 @@ +# Temporal External Client Configuration Samples + +This directory contains Python samples that demonstrate how to use the Temporal SDK's external client configuration feature. This feature allows you to configure a `temporalio.client.Client` using a TOML file and/or programmatic overrides, decoupling connection settings from your application code. + +## Prerequisites + +To run, first see [README.md](../README.md) for prerequisites. + +## Configuration File + +The `config.toml` file defines three profiles for different environments: + +- `[profile.default]`: A working configuration for local development. +- `[profile.staging]`: A configuration with an intentionally **incorrect** address (`localhost:9999`) to demonstrate how it can be corrected by an override. +- `[profile.prod]`: A non-runnable, illustrative-only configuration showing a realistic setup for Temporal Cloud with placeholder credentials. This profile is not used by the samples but serves as a reference. + +## Samples + +The following Python scripts demonstrate different ways to load and use these configuration profiles. Each runnable sample highlights a unique feature. + +### `load_from_file.py` + +This sample shows the most common use case: loading the `default` profile from the `config.toml` file. + +**To run this sample:** + +```bash +uv run env_config/load_from_file.py +``` + +### `load_profile.py` + +This sample demonstrates loading the `staging` profile by name (which has an incorrect address) and then correcting the address programmatically. This highlights the recommended approach for overriding configuration values at runtime. + +**To run this sample:** + +```bash +uv run env_config/load_profile.py +``` + +## Running the Samples + +You can run each sample script directly from the root of the `samples-python` repository. Ensure you have the necessary dependencies installed by running `pip install -e .` (or the equivalent for your environment). \ No newline at end of file diff --git a/env_config/__init__.py b/env_config/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/env_config/config.toml b/env_config/config.toml new file mode 100644 index 000000000..81f07f786 --- /dev/null +++ b/env_config/config.toml @@ -0,0 +1,40 @@ +# This is a sample configuration file for demonstrating Temporal's environment +# configuration feature. It defines multiple profiles for different environments, +# such as local development, production, and staging. + +# Default profile for local development +[profile.default] +address = "localhost:7233" +namespace = "default" + +# Optional: Add custom gRPC headers +[profile.default.grpc_meta] +my-custom-header = "development-value" +trace-id = "dev-trace-123" + +# Staging profile with inline certificate data +[profile.staging] +address = "localhost:9999" +namespace = "staging" + +# An example production profile for Temporal Cloud +[profile.prod] +address = "your-namespace.a1b2c.tmprl.cloud:7233" +namespace = "your-namespace" +# Replace with your actual Temporal Cloud API key +api_key = "your-api-key-here" + +# TLS configuration for production +[profile.prod.tls] +# TLS is auto-enabled when an API key is present, but you can configure it +# explicitly. +# disabled = false + +# Use certificate files for mTLS. Replace with actual paths. +client_cert_path = "/etc/temporal/certs/client.pem" +client_key_path = "/etc/temporal/certs/client.key" + +# Custom headers for production +[profile.prod.grpc_meta] +environment = "production" +service-version = "v1.2.3" \ No newline at end of file diff --git a/env_config/load_from_file.py b/env_config/load_from_file.py new file mode 100644 index 000000000..ab3bad145 --- /dev/null +++ b/env_config/load_from_file.py @@ -0,0 +1,46 @@ +""" +This sample demonstrates loading the default environment configuration profile +from a TOML file. +""" + +import asyncio +from pathlib import Path + +from temporalio.client import Client +from temporalio.envconfig import ClientConfig + + +async def main(): + """ + Loads the default profile from the config.toml file in this directory. + """ + print("--- Loading default profile from config.toml ---") + + # For this sample to be self-contained, we explicitly provide the path to + # the config.toml file included in this directory. + # By default though, the config.toml file will be loaded from + # ~/.config/temporalio/temporal.toml (or the equivalent standard config directory on your OS). + config_file = Path(__file__).parent / "config.toml" + + # load_client_connect_config is a helper that loads a profile and prepares + # the config dictionary for Client.connect. By default, it loads the + # "default" profile. + connect_config = ClientConfig.load_client_connect_config( + config_file=str(config_file) + ) + + print(f"Loaded 'default' profile from {config_file}.") + print(f" Address: {connect_config.get('target_host')}") + print(f" Namespace: {connect_config.get('namespace')}") + print(f" gRPC Metadata: {connect_config.get('rpc_metadata')}") + + print("\nAttempting to connect to client...") + try: + await Client.connect(**connect_config) # type: ignore + print("✅ Client connected successfully!") + except Exception as e: + print(f"❌ Failed to connect: {e}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/env_config/load_profile.py b/env_config/load_profile.py new file mode 100644 index 000000000..fe4f51cf9 --- /dev/null +++ b/env_config/load_profile.py @@ -0,0 +1,52 @@ +""" +This sample demonstrates loading a named environment configuration profile and +programmatically overriding its values. +""" + +import asyncio +from pathlib import Path + +from temporalio.client import Client +from temporalio.envconfig import ClientConfig + + +async def main(): + """ + Demonstrates loading a named profile and overriding values programmatically. + """ + print("--- Loading 'staging' profile with programmatic overrides ---") + + config_file = Path(__file__).parent / "config.toml" + profile_name = "staging" + + print( + "The 'staging' profile in config.toml has an incorrect address (localhost:9999)." + ) + print("We'll programmatically override it to the correct address.") + + # Load the 'staging' profile. + connect_config = ClientConfig.load_client_connect_config( + profile=profile_name, + config_file=str(config_file), + ) + + # Override the target host to the correct address. + # This is the recommended way to override configuration values. + connect_config["target_host"] = "localhost:7233" + + print(f"\nLoaded '{profile_name}' profile from {config_file} with overrides.") + print( + f" Address: {connect_config.get('target_host')} (overridden from localhost:9999)" + ) + print(f" Namespace: {connect_config.get('namespace')}") + + print("\nAttempting to connect to client...") + try: + await Client.connect(**connect_config) # type: ignore + print("✅ Client connected successfully!") + except Exception as e: + print(f"❌ Failed to connect: {e}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/external_storage/README.md b/external_storage/README.md new file mode 100644 index 000000000..9da616252 --- /dev/null +++ b/external_storage/README.md @@ -0,0 +1,143 @@ +# External Storage Sample + +This sample demonstrates how to offload large workflow payloads to Amazon S3-compatible +object storage using the Temporal Python SDK's built-in `ExternalStorage` system, +combined with a gzip `PayloadCodec` so the payloads stored inline in Temporal and in +S3 are both compressed. + +**Scenario:** A fulfillment center processes batches of shipping orders. The workflow +receives a small request (a batch ID and order count), then internally calls a +`fetch_orders` activity that returns the full list of orders with customer records, +line items, and handling notes. That list — several hundred kilobytes even after +compression — is passed to a second `process_orders` activity. Finally the workflow +returns a small `BatchSummary` with totals. + +Each payload is first compressed by `CompressionCodec`. The SDK then checks the +compressed size against the default 256 KiB threshold; payloads still above it are +stored in S3 and replaced inline with compact claim-check references. The workflow's +own input (`OrderBatchRequest`) and result (`BatchSummary`) compress to a few hundred +bytes and remain inline. + +A mock S3 service (`s3.py`) is included so you can run the sample locally without +an AWS account or Docker. A `codec_server.py` is included to decompress payloads +on demand for the Temporal Web UI. + +## Prerequisites + +* [uv](https://docs.astral.sh/uv/) +* [Temporal CLI](https://docs.temporal.io/cli) with a local dev server running: + ``` + temporal server start-dev + ``` + +## 1. Sync dependencies + +```bash +uv sync --group external-storage +``` + +## 2. Start the mock S3 service + +In a dedicated terminal: + +```bash +uv run external_storage/s3.py +``` + +This starts a local S3-compatible server on port 5000 and creates the `temporal-payloads` +bucket. Leave it running for the duration of the sample. + +## 3. Run the worker + +In a second terminal: + +```bash +uv run external_storage/worker.py +``` + +## 4. Run the starter + +In a third terminal: + +```bash +uv run external_storage/starter.py +``` + +Example output: + +``` +Starting workflow external-storage-20260501-120000 (batch_id=BATCH-20260501-120000, order_count=200) + +Batch BATCH-20260501-120000: 200 orders processed + Total shipping cost: $28,512.40 + Total weight: 19,684.2 kg + Avg delivery: 4.4 days +``` + +## 5. (Optional) Run the codec server + +Workflow payloads are gzip-compressed; the large ones additionally live in S3 as +external storage references. The codec server serves both transformations on demand +for the Temporal Web UI. Run it in a fourth terminal: + +```bash +uv run external_storage/codec_server.py +``` + +In the Temporal Web UI (http://localhost:8233), open Settings → Data Encoder and set +the Remote Codec Endpoint to `http://localhost:8081`. Reload the workflow page and the +inline compressed payloads will be displayed as readable JSON, and externally-stored +payloads can be downloaded to fetch their actual content from S3. + +The Web UI sends the namespace as the `X-Namespace` header on each request, so +multi-namespace setups can dispatch by reading that header. + +| Endpoint | Behavior | +| --- | --- | +| `POST /encode` | Compress the payload, then offload to S3 if it exceeds the threshold. | +| `POST /decode` | Retrieve any external storage references from S3, then decompress. Pass `?preserveStorageRefs=true` to leave references as-is. | +| `POST /download` | All inputs must be storage references. Retrieves them from S3 and decompresses. | + +## 6. Inspect the workflow + +Run `temporal workflow show` to see how payloads are stored: + +```bash +temporal workflow show --workflow-id external-storage- +``` + +The workflow's input (`OrderBatchRequest`) and result (`BatchSummary`) are gzip-encoded +and stored inline in Temporal — small enough to compress to a few hundred bytes. The +two activity payloads carrying the order list — the output of `fetch_orders` and the +input to `process_orders` — exceed 256 KiB even after compression, so they appear as +external storage references, confirming the SDK offloaded them to S3. + +## How it works + +The `DataConverter` is configured with both a `payload_codec` and an `external_storage`: + +```python +driver = S3StorageDriver( + client=new_aioboto3_client(s3_client), + bucket=S3_BUCKET, +) +data_converter = dataclasses.replace( + temporalio.converter.default(), + payload_codec=CompressionCodec(), + external_storage=ExternalStorage(drivers=[driver]), +) +``` + +On the encode path the SDK: + +1. Serializes the Python value to a `Payload`. +2. Runs `CompressionCodec.encode` to gzip the payload bytes. +3. Checks the compressed size against `payload_size_threshold` (default: 256 KiB). +4. If still above the threshold, stores the compressed bytes in S3 via + `S3StorageDriver` and replaces the inline payload with a claim-check reference. + +On the decode path the SDK reverses these steps, transparently retrieving from S3 and +decompressing as needed. + +Both the worker and the starter must use the **same** `DataConverter` configuration +(codec **and** storage) so each side can read what the other wrote. diff --git a/external_storage/__init__.py b/external_storage/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/external_storage/_sample_data.py b/external_storage/_sample_data.py new file mode 100644 index 000000000..9d684f093 --- /dev/null +++ b/external_storage/_sample_data.py @@ -0,0 +1,72 @@ +"""Order data generation for the external_storage sample. + +Produces payloads large enough to exceed the default 256 KiB ExternalStorage +threshold without hand-crafted catalogs of items, addresses, or notes — each +order is padded with random filler text in its item descriptions and shipping +notes. Calibrated so 100 orders serialize to roughly 300 KiB of JSON. +""" + +import random +import string +from typing import List + +from external_storage.workflows import Address, Customer, Order, OrderItem + +_CITIES = [ + ("Houston", "TX"), + ("Dallas", "TX"), + ("Los Angeles", "CA"), + ("San Francisco", "CA"), + ("Denver", "CO"), + ("Miami", "FL"), + ("Chicago", "IL"), + ("New York", "NY"), + ("Seattle", "WA"), + ("Atlanta", "GA"), +] + +_ITEMS_PER_ORDER = 5 +_ITEM_DESCRIPTION_CHARS = 500 +_SHIPPING_NOTES_CHARS = 200 + + +def _filler(rng: random.Random, n: int) -> str: + return "".join(rng.choices(string.ascii_letters + " ", k=n)) + + +def generate_orders(batch_id: str, count: int) -> List[Order]: + return [_generate_order(batch_id, i) for i in range(1, count + 1)] + + +def _generate_order(batch_id: str, index: int) -> Order: + rng = random.Random(f"{batch_id}-{index}") + city, state = rng.choice(_CITIES) + items = [ + OrderItem( + sku=f"SKU-{rng.randint(10000, 99999)}", + name=f"Product {rng.randint(1, 999)}", + description=_filler(rng, _ITEM_DESCRIPTION_CHARS), + quantity=rng.randint(1, 10), + unit_price_usd=round(rng.uniform(10.0, 1000.0), 2), + weight_kg=round(rng.uniform(0.5, 50.0), 2), + ) + for _ in range(_ITEMS_PER_ORDER) + ] + return Order( + id=f"ORD-{index:06d}", + customer=Customer( + id=f"CUST-{index:06d}", + name=f"Customer {index}", + email=f"customer{index}@example.com", + address=Address( + street=f"{rng.randint(100, 9999)} Main Street", + city=city, + state=state, + zip_code=f"{rng.randint(10000, 99999)}", + country="US", + ), + ), + items=items, + total_weight_kg=round(sum(i.weight_kg * i.quantity for i in items), 2), + shipping_notes=_filler(rng, _SHIPPING_NOTES_CHARS), + ) diff --git a/external_storage/codec.py b/external_storage/codec.py new file mode 100644 index 000000000..cdc7d2d0b --- /dev/null +++ b/external_storage/codec.py @@ -0,0 +1,30 @@ +import gzip +from collections.abc import Iterable +from typing import List + +from temporalio.api.common.v1 import Payload +from temporalio.converter import PayloadCodec + + +class CompressionCodec(PayloadCodec): + """Gzip-based payload codec.""" + + ENCODING = b"binary/gzip" + + async def encode(self, payloads: Iterable[Payload]) -> List[Payload]: + return [ + Payload( + metadata={"encoding": self.ENCODING}, + data=gzip.compress(p.SerializeToString()), + ) + for p in payloads + ] + + async def decode(self, payloads: Iterable[Payload]) -> List[Payload]: + result: List[Payload] = [] + for p in payloads: + if p.metadata.get("encoding") == self.ENCODING: + result.append(Payload.FromString(gzip.decompress(p.data))) + else: + result.append(p) + return result diff --git a/external_storage/codec_server.py b/external_storage/codec_server.py new file mode 100644 index 000000000..88dc0944e --- /dev/null +++ b/external_storage/codec_server.py @@ -0,0 +1,175 @@ +"""Run the codec server for the external_storage sample. + +Sets up S3 access via aioboto3 against the mock service in ``s3.py``, then +serves the payload HTTP handler from :mod:`handler` configured with the same +codec and external storage that the worker and starter use. + +``build_namespace_dispatcher`` routes by the ``X-Namespace`` request header +so a single codec server process can host different codec/storage +configurations per namespace — this sample only configures one ("default"). + +To use this server, set the Web UI's Remote Codec Endpoint (Settings → Data +Encoder) to the URL printed when the server starts. + +Deliberately left out for sample simplicity: authentication (would slot in +as a middleware between CORS and the dispatcher), configurable listen +address/port, and structured logging/tracing. +""" + +import asyncio +import logging +from typing import Awaitable, Callable, Iterable, Mapping + +import aioboto3 +from aiohttp import hdrs, web +from temporalio.contrib.aws.s3driver import S3StorageDriver +from temporalio.contrib.aws.s3driver.aioboto3 import new_aioboto3_client +from temporalio.converter import ExternalStorage + +from external_storage.codec import CompressionCodec +from external_storage.handler import payload_routes +from external_storage.worker import ( + S3_ACCESS_KEY, + S3_BUCKET, + S3_ENDPOINT, + S3_SECRET_KEY, +) + +WEB_UI_ORIGIN = "http://localhost:8233" + +logger = logging.getLogger(__name__) + + +@web.middleware +async def cors_middleware( + request: web.Request, + handler: Callable[[web.Request], Awaitable[web.StreamResponse]], +) -> web.StreamResponse: + """Add CORS support so the Web UI can call the codec server from its origin.""" + allow_origin = request.headers.get(hdrs.ORIGIN) == WEB_UI_ORIGIN + + response: web.StreamResponse + if request.method == "OPTIONS": + response = web.Response() + if allow_origin: + response.headers[hdrs.ACCESS_CONTROL_ALLOW_METHODS] = "POST" + response.headers[hdrs.ACCESS_CONTROL_ALLOW_HEADERS] = ( + "content-type,x-namespace" + ) + else: + # Catch HTTPException so CORS headers also land on error responses + # (e.g. the dispatcher's 404 for an unknown namespace). + try: + response = await handler(request) + except web.HTTPException as exc: + response = exc + + if allow_origin: + response.headers[hdrs.ACCESS_CONTROL_ALLOW_ORIGIN] = WEB_UI_ORIGIN + + if isinstance(response, web.HTTPException): + raise response + return response + + +def build_namespace_dispatcher( + routes_by_namespace: Mapping[str, Iterable[web.RouteDef]], +) -> Callable[[web.Request], Awaitable[web.StreamResponse]]: + """Build an aiohttp handler that dispatches by the X-Namespace header. + + Returns a single handler that the caller registers for every codec server + URL (``/encode``, ``/decode``, ``/download``). The handler inspects the + ``X-Namespace`` header on each request and forwards to the handler + configured for that namespace + method + path combination. + """ + # Pre-build the flat lookup table at construction so the per-request + # dispatch is just two dictionary lookups. + handler_map = { + ns: {(r.method, r.path): r.handler for r in routes} + for ns, routes in routes_by_namespace.items() + } + + async def dispatch_by_namespace(request: web.Request) -> web.StreamResponse: + # The Temporal Web UI passes the namespace it's viewing in the + # X-Namespace header on every codec-server request. A single process + # can therefore host different codec/storage configs per namespace + # without needing per-namespace URL prefixes. + namespace = request.headers.get("X-Namespace", "") + handlers = handler_map.get(namespace) + if handlers is None: + logger.warning("dispatch: unknown namespace %r", namespace) + raise web.HTTPNotFound() + handler = handlers.get((request.method, request.path)) + if handler is None: + logger.warning( + "dispatch: no handler for %s %s in namespace %r", + request.method, + request.path, + namespace, + ) + raise web.HTTPNotFound() + return await handler(request) + + return dispatch_by_namespace + + +async def main() -> None: + logging.basicConfig( + level=logging.INFO, + format="%(asctime)s [%(levelname)s] %(name)s: %(message)s", + ) + + session = aioboto3.Session() + async with session.client( + "s3", + endpoint_url=S3_ENDPOINT, + aws_access_key_id=S3_ACCESS_KEY, + aws_secret_access_key=S3_SECRET_KEY, + region_name="us-east-1", + ) as s3_client: + driver = S3StorageDriver( + client=new_aioboto3_client(s3_client), + bucket=S3_BUCKET, + ) + + # The dispatcher map drives per-namespace configuration. Add more + # entries here to host additional namespaces with their own codec + # chain and/or storage backend. Each value is a list of routes + # produced by ``payload_routes``; the dispatcher reads the + # X-Namespace header to pick the right one per request. + dispatch_by_namespace = build_namespace_dispatcher( + { + "default": payload_routes( + external_storage=ExternalStorage(drivers=[driver]), + prestorage_codec=CompressionCodec(), + ), + } + ) + + app = web.Application(middlewares=[cors_middleware]) + app.add_routes( + [ + web.post("/encode", dispatch_by_namespace), + web.post("/decode", dispatch_by_namespace), + web.post("/download", dispatch_by_namespace), + ] + ) + + runner = web.AppRunner(app) + await runner.setup() + site = web.TCPSite(runner, "127.0.0.1", 8081) + await site.start() + print("Codec server running at http://127.0.0.1:8081, ctrl+c to exit") + try: + await asyncio.Event().wait() + except (KeyboardInterrupt, asyncio.CancelledError): + pass + finally: + await runner.cleanup() + + +if __name__ == "__main__": + try: + asyncio.run(main()) + except KeyboardInterrupt: + pass diff --git a/external_storage/handler.py b/external_storage/handler.py new file mode 100644 index 000000000..1ba8035ab --- /dev/null +++ b/external_storage/handler.py @@ -0,0 +1,154 @@ +"""HTTP routes for a Temporal codec server with external storage support. + +A *codec server* is an HTTP service the Temporal Web UI and CLI can call to +transform payloads on demand — typically to decode (decompress, decrypt, +fetch-from-storage) so a human can read what the workflow saw. The contract +expected by the Web UI is: + +* ``POST /encode`` and ``POST /decode`` accept a JSON-encoded ``Payloads`` + protobuf message in the request body and return a transformed ``Payloads`` + message in the same format. +* ``POST /download`` is a specialized endpoint that only accepts external + storage references and resolves them to their actual contents. Kept + separate from ``/decode`` so the Web UI can defer fetching potentially + large blobs until a user explicitly asks for them. + +The :func:`payload_routes` factory builds these three route definitions +configured with an :class:`ExternalStorage` plus optional codec layers that +match the client's ``DataConverter`` setup.""" + +from typing import List, Optional + +from aiohttp import web +from google.protobuf import json_format +from temporalio.api.common.v1 import Payload, Payloads +from temporalio.api.sdk.v1 import ExternalStorageReference +from temporalio.converter import ExternalStorage, PayloadCodec + +_REFERENCE_MESSAGE_TYPE = ExternalStorageReference.DESCRIPTOR.full_name.encode() + + +def _is_storage_reference(payload: Payload) -> bool: + """A payload is an external-storage reference iff its metadata identifies + it as a serialized :class:`ExternalStorageReference` claim check. + """ + return ( + payload.metadata.get("encoding") == b"json/protobuf" + and payload.metadata.get("messageType") == _REFERENCE_MESSAGE_TYPE + ) + + +def payload_routes( + external_storage: ExternalStorage, + prestorage_codec: Optional[PayloadCodec] = None, + poststorage_codec: Optional[PayloadCodec] = None, +) -> List[web.RouteDef]: + """Build aiohttp routes for the codec server's /encode, /decode, and /download endpoints. + + ``external_storage`` is the external storage configured on the client's + DataConverter and is required — without it the routes have nothing to add + over a plain codec server. + + The codec layers are optional, and run in a defined order relative to + external storage to mirror what the client's DataConverter does: + + * ``prestorage_codec`` — the codec configured on the client's + DataConverter. On encode it runs BEFORE the external-storage offload + decision, so the SDK checks whether the *transformed* bytes + (compressed, encrypted, etc.) exceed the threshold. On decode it runs + AFTER storage retrieval, so the codec sees the original transformed + bytes regardless of where they came from. + * ``poststorage_codec`` — typically only used when payloads pass through + a proxy that wraps them with an additional codec layer outside the + storage envelope (envelope encryption applied at a network edge, for + example). On encode it runs after offload; on decode it runs first + (to strip the proxy envelope before storage retrieval). + + Register the result on any :class:`aiohttp.web.Application` via + :meth:`aiohttp.web.Application.add_routes`. + """ + + async def _decode_non_refs(payloads: List[Payload]) -> List[Payload]: + # Storage references are claim-check protos with their own encoding — + # they aren't bytes the codec produced, so the codec can't decode + # them. Skip refs and only feed real payloads through the codec. + if prestorage_codec is None: + return payloads + result = list(payloads) + non_ref_idx = [ + i for i, p in enumerate(payloads) if not _is_storage_reference(p) + ] + decoded = await prestorage_codec.decode([payloads[i] for i in non_ref_idx]) + for i, p in zip(non_ref_idx, decoded): + result[i] = p + return result + + async def _read_payloads(req: web.Request) -> List[Payload]: + assert req.content_type == "application/json" + proto = json_format.Parse(await req.read(), Payloads()) + return list(proto.payloads) + + def _write_payloads(payloads: List[Payload]) -> web.Response: + return web.Response( + content_type="application/json", + text=json_format.MessageToJson(Payloads(payloads=payloads)), + ) + + async def encode_handler(req: web.Request) -> web.Response: + payloads = await _read_payloads(req) + # Encode pipeline mirrors what a client-side DataConverter does: + # 1. pre-storage codec encodes (compress / encrypt / etc.) + # 2. external storage decides whether the transformed bytes + # exceed the threshold and replaces them with a reference + # 3. post-storage codec encodes whatever ended up inline + if prestorage_codec is not None: + payloads = await prestorage_codec.encode(payloads) + # ``_store_payload_sequence`` is the SDK's internal helper used by + # DataConverter. Calling it directly lets the codec server reuse the + # exact same offload-if-large logic the client uses, instead of + # re-implementing the size-threshold check here. The leading + # underscore signals it's not a stable public API. + payloads = await external_storage._store_payload_sequence(payloads) + if poststorage_codec is not None: + payloads = await poststorage_codec.encode(payloads) + return _write_payloads(payloads) + + async def decode_handler(req: web.Request) -> web.Response: + payloads = await _read_payloads(req) + # Decode pipeline is the encode pipeline in reverse: + # 1. post-storage codec decodes (strip proxy envelope, if any) + # 2. external storage retrieves any references in-band + # 3. pre-storage codec decodes the real bytes + if poststorage_codec is not None: + payloads = await poststorage_codec.decode(payloads) + # preserveStorageRefs=true is for clients that want to inspect the + # raw references themselves (a debug view, an "audit who fetched + # what" log, etc.). When unset, default behavior fetches from + # storage so the user sees the payload as the workflow saw it. + preserve_refs = req.query.get("preserveStorageRefs", "false").lower() == "true" + if not preserve_refs: + payloads = await external_storage._retrieve_payload_sequence(payloads) + payloads = await _decode_non_refs(payloads) + return _write_payloads(payloads) + + async def download_handler(req: web.Request) -> web.Response: + # /download exists as a separate endpoint from /decode because + # resolving an external-storage reference can be expensive (network + # round-trip, potentially large blob). The Web UI uses /download as + # an opt-in "click to fetch" action — keeping it off /decode's + # auto-resolution path avoids accidentally pulling many large blobs + # when a user simply navigates to a workflow page. + payloads = await _read_payloads(req) + if not all(_is_storage_reference(p) for p in payloads): + return web.Response( + status=400, text="all payloads must be storage references" + ) + retrieved = await external_storage._retrieve_payload_sequence(payloads) + decoded = await _decode_non_refs(retrieved) + return _write_payloads(decoded) + + return [ + web.post("/encode", encode_handler), + web.post("/decode", decode_handler), + web.post("/download", download_handler), + ] diff --git a/external_storage/s3.py b/external_storage/s3.py new file mode 100644 index 000000000..144b8346d --- /dev/null +++ b/external_storage/s3.py @@ -0,0 +1,39 @@ +import signal +import threading + +import boto3 +from moto.server import ThreadedMotoServer + +from external_storage.worker import S3_ACCESS_KEY, S3_BUCKET, S3_ENDPOINT, S3_SECRET_KEY + + +def main() -> None: + server = ThreadedMotoServer(port=5000) + server.start() + print(f"Mock S3 server running at {S3_ENDPOINT}") + + s3 = boto3.client( + "s3", + endpoint_url=S3_ENDPOINT, + aws_access_key_id=S3_ACCESS_KEY, + aws_secret_access_key=S3_SECRET_KEY, + region_name="us-east-1", + ) + s3.create_bucket(Bucket=S3_BUCKET) + print(f"Bucket '{S3_BUCKET}' created. Press ctrl+c to stop.") + + try: + # signal.pause() is Unix-only; use Event on Windows + if hasattr(signal, "pause"): + signal.pause() + else: + threading.Event().wait() + except KeyboardInterrupt: + pass + finally: + server.stop() + print("Mock S3 server stopped.") + + +if __name__ == "__main__": + main() diff --git a/external_storage/starter.py b/external_storage/starter.py new file mode 100644 index 000000000..3d7b165f3 --- /dev/null +++ b/external_storage/starter.py @@ -0,0 +1,75 @@ +import asyncio +import dataclasses +from datetime import datetime + +import aioboto3 +import temporalio.converter +from temporalio.client import Client +from temporalio.contrib.aws.s3driver import S3StorageDriver +from temporalio.contrib.aws.s3driver.aioboto3 import new_aioboto3_client +from temporalio.converter import ExternalStorage +from temporalio.envconfig import ClientConfig + +from external_storage.codec import CompressionCodec +from external_storage.worker import ( + S3_ACCESS_KEY, + S3_BUCKET, + S3_ENDPOINT, + S3_SECRET_KEY, + TASK_QUEUE, +) +from external_storage.workflows import OrderBatchRequest, ProcessOrderBatchWorkflow + +ORDER_COUNT = 200 + + +async def main() -> None: + session = aioboto3.Session() + async with session.client( + "s3", + endpoint_url=S3_ENDPOINT, + aws_access_key_id=S3_ACCESS_KEY, + aws_secret_access_key=S3_SECRET_KEY, + region_name="us-east-1", + ) as s3_client: + driver = S3StorageDriver( + client=new_aioboto3_client(s3_client), + bucket=S3_BUCKET, + ) + + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + + client = await Client.connect( + **config, + data_converter=dataclasses.replace( + temporalio.converter.default(), + payload_codec=CompressionCodec(), + external_storage=ExternalStorage(drivers=[driver]), + ), + ) + + run_id = datetime.now().strftime("%Y%m%d-%H%M%S") + workflow_id = f"external-storage-{run_id}" + request = OrderBatchRequest(batch_id=f"BATCH-{run_id}", order_count=ORDER_COUNT) + + print( + f"Starting workflow {workflow_id} (batch_id={request.batch_id}, " + f"order_count={request.order_count})" + ) + + summary = await client.execute_workflow( + ProcessOrderBatchWorkflow.run, + request, + id=workflow_id, + task_queue=TASK_QUEUE, + ) + + print(f"\nBatch {summary.batch_id}: {summary.order_count} orders processed") + print(f" Total shipping cost: ${summary.total_shipping_cost_usd:,.2f}") + print(f" Total weight: {summary.total_weight_kg:,.1f} kg") + print(f" Avg delivery: {summary.avg_delivery_days} days") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/external_storage/worker.py b/external_storage/worker.py new file mode 100644 index 000000000..151761bcd --- /dev/null +++ b/external_storage/worker.py @@ -0,0 +1,72 @@ +import asyncio +import dataclasses + +import aioboto3 +import temporalio.converter +from temporalio.client import Client +from temporalio.contrib.aws.s3driver import S3StorageDriver +from temporalio.contrib.aws.s3driver.aioboto3 import new_aioboto3_client +from temporalio.converter import ExternalStorage +from temporalio.envconfig import ClientConfig +from temporalio.worker import Worker + +from external_storage.codec import CompressionCodec +from external_storage.workflows import ( + ProcessOrderBatchWorkflow, + fetch_orders, + process_orders, +) + +S3_ENDPOINT = "http://localhost:5000" +S3_BUCKET = "temporal-payloads" +S3_ACCESS_KEY = "test" +S3_SECRET_KEY = "test" +TASK_QUEUE = "external-storage-task-queue" + +interrupt_event = asyncio.Event() + + +async def main() -> None: + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + + session = aioboto3.Session() + async with session.client( + "s3", + endpoint_url=S3_ENDPOINT, + aws_access_key_id=S3_ACCESS_KEY, + aws_secret_access_key=S3_SECRET_KEY, + region_name="us-east-1", + ) as s3_client: + driver = S3StorageDriver( + client=new_aioboto3_client(s3_client), + bucket=S3_BUCKET, + ) + + client = await Client.connect( + **config, + data_converter=dataclasses.replace( + temporalio.converter.default(), + payload_codec=CompressionCodec(), + external_storage=ExternalStorage(drivers=[driver]), + ), + ) + + async with Worker( + client, + task_queue=TASK_QUEUE, + workflows=[ProcessOrderBatchWorkflow], + activities=[fetch_orders, process_orders], + ): + print("Worker started, ctrl+c to exit") + await interrupt_event.wait() + print("Shutting down") + + +if __name__ == "__main__": + loop = asyncio.new_event_loop() + try: + loop.run_until_complete(main()) + except KeyboardInterrupt: + interrupt_event.set() + loop.run_until_complete(loop.shutdown_asyncgens()) diff --git a/external_storage/workflows.py b/external_storage/workflows.py new file mode 100644 index 000000000..8d034fa10 --- /dev/null +++ b/external_storage/workflows.py @@ -0,0 +1,128 @@ +from dataclasses import dataclass +from datetime import timedelta +from typing import List + +from temporalio import activity, workflow + +WAREHOUSE_STATE = "TX" # fulfillment center state used to estimate delivery + + +@dataclass +class Address: + street: str + city: str + state: str + zip_code: str + country: str + + +@dataclass +class Customer: + id: str + name: str + email: str + address: Address + + +@dataclass +class OrderItem: + sku: str + name: str + description: str + quantity: int + unit_price_usd: float + weight_kg: float + + +@dataclass +class Order: + id: str + customer: Customer + items: List[OrderItem] + total_weight_kg: float + shipping_notes: str + + +@dataclass +class ProcessedOrder: + id: str + customer_id: str + destination_city: str + destination_state: str + total_weight_kg: float + shipping_cost_usd: float + estimated_delivery_days: int + + +@dataclass +class OrderBatchRequest: + batch_id: str + order_count: int + + +@dataclass +class BatchSummary: + batch_id: str + order_count: int + total_shipping_cost_usd: float + total_weight_kg: float + avg_delivery_days: float + + +@activity.defn +async def fetch_orders(request: OrderBatchRequest) -> List[Order]: + # Lazy import keeps the data-generation code out of the workflow module's + # top-level imports and avoids a circular import with _sample_data. + from external_storage._sample_data import generate_orders + + return generate_orders(request.batch_id, request.order_count) + + +@activity.defn +async def process_orders(orders: List[Order]) -> List[ProcessedOrder]: + results: List[ProcessedOrder] = [] + for order in orders: + cost = round(2.50 + 1.20 * order.total_weight_kg, 2) + days = 2 if order.customer.address.state == WAREHOUSE_STATE else 5 + results.append( + ProcessedOrder( + id=order.id, + customer_id=order.customer.id, + destination_city=order.customer.address.city, + destination_state=order.customer.address.state, + total_weight_kg=order.total_weight_kg, + shipping_cost_usd=cost, + estimated_delivery_days=days, + ) + ) + return results + + +@workflow.defn +class ProcessOrderBatchWorkflow: + @workflow.run + async def run(self, request: OrderBatchRequest) -> BatchSummary: + orders = await workflow.execute_activity( + fetch_orders, + request, + start_to_close_timeout=timedelta(minutes=5), + ) + processed = await workflow.execute_activity( + process_orders, + orders, + start_to_close_timeout=timedelta(minutes=5), + ) + total_cost = sum(p.shipping_cost_usd for p in processed) + total_weight = sum(p.total_weight_kg for p in processed) + avg_days = ( + sum(p.estimated_delivery_days for p in processed) / len(processed) + if processed + else 0.0 + ) + return BatchSummary( + batch_id=request.batch_id, + order_count=len(processed), + total_shipping_cost_usd=round(total_cost, 2), + total_weight_kg=round(total_weight, 2), + avg_delivery_days=round(avg_days, 1), + ) diff --git a/external_storage_redis/README.md b/external_storage_redis/README.md new file mode 100644 index 000000000..662b5ea76 --- /dev/null +++ b/external_storage_redis/README.md @@ -0,0 +1,132 @@ +# Redis External Storage + +This sample packages a Redis-backed `StorageDriver` implementation for Temporal +external storage. + +The code lives in: + +* `external_storage_redis/_driver.py` for the `RedisStorageDriver` +* `external_storage_redis/_client.py` for the storage client abstraction +* `external_storage_redis/redis_asyncio.py` for the `redis.asyncio` adapter +* `tests/external_storage_redis/` for unit and worker integration tests + +Unlike most samples in this repository, this one is primarily reusable driver +code plus tests rather than a standalone `worker.py` / `starter.py` pair. + +## Install Dependencies + +From the repository root: + + uv sync --group external-storage-redis --group dev + +The `external-storage-redis` group installs `redis`, and the `dev` group +installs `fakeredis` for the test suite. + +## Using The Driver + +```python +import dataclasses + +import redis.asyncio as redis +import temporalio.converter +from temporalio.client import Client +from temporalio.converter import ExternalStorage + +from external_storage_redis import RedisStorageDriver +from external_storage_redis.redis_asyncio import new_redis_asyncio_client + +redis_client = redis.Redis.from_url( + "redis://localhost:6379/0", + decode_responses=False, +) +try: + driver = RedisStorageDriver( + client=new_redis_asyncio_client(redis_client), + key_prefix="temporalio:payloads", + ) + + client = await Client.connect( + "localhost:7233", + data_converter=dataclasses.replace( + temporalio.converter.default(), + external_storage=ExternalStorage( + drivers=[driver], + payload_size_threshold=256 * 1024, + ), + ), + ) +finally: + await redis_client.aclose() +``` + +`decode_responses=False` is required because the driver stores serialized +Temporal `Payload` protobuf bytes as Redis values rather than text. + +## Driver Behavior + +`RedisStorageDriver` accepts these constructor options: + +* `driver_name`: defaults to `"redis"` +* `key_prefix`: defaults to `"temporalio:payloads"` +* `ttl`: optional expiration applied only when a key is first inserted +* `max_payload_size`: defaults to 50 MiB + +Stored keys are content-addressed using SHA-256 and include Temporal execution +context when it is available. A typical workflow-scoped key looks like: + + temporalio:payloads:v0:ns:default:wt:MyWorkflow:wi:my-workflow-id:ri:my-run-id:d:sha256: + +Some behavior to be aware of: + +* Any driver used to store payloads must also be configured on the component + that retrieves them. +* The Redis instance must already exist; the driver does not provision it. +* Identical serialized bytes within the same namespace and workflow/activity + scope share the same Redis key. +* Workflow, activity, namespace, and run identifiers are URL-encoded before + being placed into the key. +* Only payloads at or above `ExternalStorage.payload_size_threshold` are + offloaded. +* If `ttl` is set, duplicate stores do not refresh expiration. +* If a payload key is missing at retrieval time, the driver raises a + non-retryable `ApplicationError`. + +## Custom Redis Clients + +To use a Redis library other than `redis.asyncio`, implement +`RedisStorageDriverClient`: + +```python +from datetime import timedelta + +from external_storage_redis import RedisStorageDriverClient + + +class MyRedisClient(RedisStorageDriverClient): + async def get(self, *, key: str) -> bytes | None: ... + + async def set_if_absent( + self, + *, + key: str, + data: bytes, + ttl: timedelta | None = None, + ) -> bool: ... +``` + +## Tests + +Run the full Redis sample test suite with: + + uv run pytest tests/external_storage_redis + +Run only the in-memory unit tests with: + + uv run pytest tests/external_storage_redis/test_redis.py + +The worker integration tests use `WorkflowEnvironment.start_local()` and +`fakeredis`. They do not require a real Redis server, but the first run may +download a Temporal dev-server binary. + +Some Temporal dev-server builds disable standalone activity execution. When +that happens, the two standalone-activity integration tests skip automatically. diff --git a/external_storage_redis/__init__.py b/external_storage_redis/__init__.py new file mode 100644 index 000000000..4f0a066dd --- /dev/null +++ b/external_storage_redis/__init__.py @@ -0,0 +1,9 @@ +"""Redis storage driver sample for Temporal external storage.""" + +from external_storage_redis._client import RedisStorageDriverClient +from external_storage_redis._driver import RedisStorageDriver + +__all__ = [ + "RedisStorageDriverClient", + "RedisStorageDriver", +] diff --git a/external_storage_redis/_client.py b/external_storage_redis/_client.py new file mode 100644 index 000000000..94b493d46 --- /dev/null +++ b/external_storage_redis/_client.py @@ -0,0 +1,34 @@ +"""Redis storage driver client abstraction.""" + +from __future__ import annotations + +from abc import ABC, abstractmethod +from datetime import timedelta + + +class RedisStorageDriverClient(ABC): + """Abstract base class for the Redis operations used by the driver.""" + + @abstractmethod + async def get(self, *, key: str) -> bytes | None: + """Return the raw bytes stored for *key*, or ``None`` if absent.""" + + @abstractmethod + async def set_if_absent( + self, + *, + key: str, + data: bytes, + ttl: timedelta | None = None, + ) -> bool: + """Store *data* under *key* only if the key does not already exist. + + Args: + key: Redis key to store. + data: Serialized payload bytes. + ttl: Optional expiration to apply only when the value is inserted. + + Returns: + ``True`` if the value was inserted, ``False`` if the key already + existed. + """ diff --git a/external_storage_redis/_driver.py b/external_storage_redis/_driver.py new file mode 100644 index 000000000..c13785b5c --- /dev/null +++ b/external_storage_redis/_driver.py @@ -0,0 +1,228 @@ +"""Redis storage driver for Temporal external storage.""" + +from __future__ import annotations + +import asyncio +import hashlib +import urllib.parse +from collections.abc import Coroutine, Sequence +from datetime import timedelta +from typing import Any, TypeVar + +from temporalio.api.common.v1 import Payload +from temporalio.converter import ( + StorageDriver, + StorageDriverActivityInfo, + StorageDriverClaim, + StorageDriverRetrieveContext, + StorageDriverStoreContext, + StorageDriverWorkflowInfo, +) +from temporalio.exceptions import ApplicationError + +from external_storage_redis._client import RedisStorageDriverClient + +_T = TypeVar("_T") + + +async def _gather_with_cancellation( + coros: Sequence[Coroutine[Any, Any, _T]], +) -> list[_T]: + """Run coroutines concurrently, cancelling remaining tasks on failure.""" + if not coros: + return [] + tasks = [asyncio.create_task(coro) for coro in coros] + try: + return list(await asyncio.gather(*tasks)) + except BaseException: + for task in tasks: + task.cancel() + await asyncio.gather(*tasks, return_exceptions=True) + raise + + +class RedisStorageDriver(StorageDriver): + """Driver for storing and retrieving Temporal payloads in Redis. + + Payloads are stored as Redis string values keyed by a SHA-256 digest of the + serialized payload bytes. The key also includes namespace and + workflow/activity identity segments derived from the storage context so + distinct Temporal scopes remain isolated. + """ + + def __init__( + self, + client: RedisStorageDriverClient, + *, + driver_name: str = "redis", + key_prefix: str = "temporalio:payloads", + ttl: timedelta | None = None, + max_payload_size: int = 50 * 1024 * 1024, + ) -> None: + """Construct the Redis driver. + + Args: + client: A :class:`RedisStorageDriverClient` implementation. Use + :func:`external_storage_redis.redis_asyncio.new_redis_asyncio_client` + to wrap a ``redis.asyncio`` client. + driver_name: Name of this driver instance. Defaults to ``"redis"``. + Override this when registering multiple RedisStorageDriver + instances with distinct configurations under the same + ``ExternalStorage.drivers`` list. + key_prefix: Prefix prepended to all Redis keys. Defaults to + ``"temporalio:payloads"``. + ttl: Optional expiration to apply when a key is first written. + Existing keys are not refreshed when the same payload is stored + again. + max_payload_size: Maximum serialized payload size in bytes that the + driver will accept. Defaults to 52428800 (50 MiB). + """ + if max_payload_size <= 0: + raise ValueError("max_payload_size must be greater than zero") + if ttl is not None and ttl <= timedelta(0): + raise ValueError("ttl must be greater than zero") + self._client = client + self._driver_name = driver_name or "redis" + self._key_prefix = key_prefix.rstrip(":") + self._ttl = ttl + self._max_payload_size = max_payload_size + + def name(self) -> str: + """Return the driver instance name.""" + return self._driver_name + + def type(self) -> str: + """Return the driver type identifier.""" + return "redis" + + def _build_key( + self, + context: StorageDriverStoreContext, + hash_digest: str, + ) -> str: + """Construct a context-aware Redis key for a payload digest.""" + + def _quote(value: str | None) -> str | None: + return urllib.parse.quote(value, safe="") if value else None + + segments = ["v0"] + target = context.target + namespace = _quote(target.namespace) if target is not None else None + if namespace: + segments.extend(["ns", namespace]) + + if isinstance(target, StorageDriverWorkflowInfo): + segments.extend( + [ + "wt", + _quote(target.type) or "null", + "wi", + _quote(target.id) or "null", + "ri", + _quote(target.run_id) or "null", + ] + ) + elif isinstance(target, StorageDriverActivityInfo): + segments.extend( + [ + "at", + _quote(target.type) or "null", + "ai", + _quote(target.id) or "null", + "ri", + _quote(target.run_id) or "null", + ] + ) + + segments.extend(["d", "sha256", hash_digest]) + if not self._key_prefix: + return ":".join(segments) + return f"{self._key_prefix}:{':'.join(segments)}" + + async def store( + self, + context: StorageDriverStoreContext, + payloads: Sequence[Payload], + ) -> list[StorageDriverClaim]: + """Store payloads in Redis and return a claim for each payload.""" + + async def _store_payload(payload: Payload) -> StorageDriverClaim: + payload_bytes = payload.SerializeToString() + payload_size = len(payload_bytes) + if payload_size > self._max_payload_size: + raise ValueError( + f"Payload size {payload_size} bytes exceeds the configured " + f"max_payload_size of {self._max_payload_size} bytes" + ) + + hash_digest = hashlib.sha256(payload_bytes).hexdigest().lower() + key = self._build_key(context, hash_digest) + + try: + await self._client.set_if_absent( + key=key, + data=payload_bytes, + ttl=self._ttl, + ) + except Exception as err: + raise RuntimeError( + f"RedisStorageDriver store failed [key={key}]" + ) from err + + return StorageDriverClaim( + claim_data={ + "key": key, + "hash_algorithm": "sha256", + "hash_value": hash_digest, + }, + ) + + return await _gather_with_cancellation([_store_payload(p) for p in payloads]) + + async def retrieve( + self, + context: StorageDriverRetrieveContext, # noqa: ARG002 + claims: Sequence[StorageDriverClaim], + ) -> list[Payload]: + """Retrieve payloads from Redis for the given claims.""" + + async def _retrieve_payload(claim: StorageDriverClaim) -> Payload: + key = claim.claim_data["key"] + + try: + payload_bytes = await self._client.get(key=key) + except Exception as err: + raise RuntimeError( + f"RedisStorageDriver retrieve failed [key={key}]" + ) from err + + if payload_bytes is None: + raise ApplicationError( + f"Payload not found for key '{key}'", + type="PayloadNotFoundError", + non_retryable=True, + ) + + expected_hash = claim.claim_data.get("hash_value") + hash_algorithm = claim.claim_data.get("hash_algorithm") + if expected_hash and hash_algorithm: + if hash_algorithm != "sha256": + raise ValueError( + f"RedisStorageDriver unsupported hash algorithm " + f"[key={key}]: expected sha256, got {hash_algorithm}" + ) + actual_hash = hashlib.sha256(payload_bytes).hexdigest().lower() + if actual_hash != expected_hash: + raise ValueError( + f"RedisStorageDriver integrity check failed " + f"[key={key}]: expected {hash_algorithm}:{expected_hash}, " + f"got {hash_algorithm}:{actual_hash}" + ) + + payload = Payload() + payload.ParseFromString(payload_bytes) + return payload + + return await _gather_with_cancellation( + [_retrieve_payload(claim) for claim in claims] + ) diff --git a/external_storage_redis/redis_asyncio.py b/external_storage_redis/redis_asyncio.py new file mode 100644 index 000000000..917b239c0 --- /dev/null +++ b/external_storage_redis/redis_asyncio.py @@ -0,0 +1,54 @@ +"""redis.asyncio adapter for the Redis storage driver client.""" + +from __future__ import annotations + +import math +from datetime import timedelta +from typing import TYPE_CHECKING + +from external_storage_redis._client import RedisStorageDriverClient + +if TYPE_CHECKING: + from redis.asyncio.client import Redis + + +class _RedisAsyncioStorageDriverClient(RedisStorageDriverClient): + """Adapter that wraps a ``redis.asyncio.Redis`` client. + + The wrapped client must be configured for binary-safe reads and writes, + which means ``decode_responses`` must remain disabled. + """ + + def __init__(self, client: Redis) -> None: + """Wrap a ``redis.asyncio.Redis`` client.""" + self._client = client + + async def get(self, *, key: str) -> bytes | None: + """Fetch raw bytes for *key* from Redis.""" + value = await self._client.get(key) + if value is None: + return None + if not isinstance(value, bytes): + raise TypeError( + "redis.asyncio client must be configured with decode_responses=False" + ) + return value + + async def set_if_absent( + self, + *, + key: str, + data: bytes, + ttl: timedelta | None = None, + ) -> bool: + """Atomically set *key* only when it is absent.""" + ttl_ms = None + if ttl is not None: + ttl_ms = max(1, math.ceil(ttl.total_seconds() * 1000)) + result = await self._client.set(key, data, px=ttl_ms, nx=True) + return bool(result) + + +def new_redis_asyncio_client(client: Redis) -> RedisStorageDriverClient: + """Create a driver client from a ``redis.asyncio.Redis`` instance.""" + return _RedisAsyncioStorageDriverClient(client) diff --git a/gevent_async/README.md b/gevent_async/README.md index 9b9bdff37..0c66a54b2 100644 --- a/gevent_async/README.md +++ b/gevent_async/README.md @@ -12,17 +12,17 @@ workflows. For this sample, the optional `gevent` dependency group must be included. To include, run: - poetry install --with gevent + uv sync --group gevent To run the sample, first see [README.md](../README.md) for prerequisites such as having a localhost Temporal server -running. Then, run the following from this directory to start the worker: +running. Then, run the following from the root directory to start the worker: - poetry run python worker.py + uv run gevent_async/worker.py This will start the worker. The worker has a workflow and two activities, one `asyncio` based and one gevent based. Now -in another terminal, run the following from this directory to execute the workflow: +in another terminal, run the following to execute the workflow: - poetry run python starter.py + uv run gevent_async/starter.py The workflow should run and complete with the hello result. Note on the worker terminal there will be logs of the workflow and activity executions. \ No newline at end of file diff --git a/gevent_async/starter.py b/gevent_async/starter.py index 43e8356b7..010803e5e 100644 --- a/gevent_async/starter.py +++ b/gevent_async/starter.py @@ -7,6 +7,7 @@ import logging from temporalio.client import Client +from temporalio.envconfig import ClientConfig from gevent_async import workflow from gevent_async.executor import GeventExecutor @@ -24,7 +25,9 @@ def main(): async def async_main(): # Connect client - client = await Client.connect("localhost:7233") + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) # Run workflow result = await client.execute_workflow( diff --git a/gevent_async/worker.py b/gevent_async/worker.py index 9b4945cf3..db419399f 100644 --- a/gevent_async/worker.py +++ b/gevent_async/worker.py @@ -9,6 +9,7 @@ import gevent from temporalio.client import Client +from temporalio.envconfig import ClientConfig from temporalio.worker import Worker from gevent_async import activity, workflow @@ -39,13 +40,14 @@ async def async_main(): ) # Connect client - client = await Client.connect("localhost:7233") + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) # Create an executor for use by Temporal. This cannot be the outer one # running this async main. The max_workers here needs to have enough room to # support the max concurrent activities/workflows settings. with GeventExecutor(max_workers=200) as executor: - # Run a worker for the workflow and activities async with Worker( client, @@ -65,7 +67,6 @@ async def async_main(): max_concurrent_activities=100, max_concurrent_workflow_tasks=100, ): - # Wait until interrupted logging.info("Worker started, ctrl+c to exit") await interrupt_event.wait() diff --git a/google_adk_agents/README.md b/google_adk_agents/README.md new file mode 100644 index 000000000..7d71ba64b --- /dev/null +++ b/google_adk_agents/README.md @@ -0,0 +1,57 @@ +# Temporal Google ADK Integration + +⚠️ **Experimental** — This integration is experimental and its interfaces may +change prior to General Availability. + +This directory contains samples demonstrating how to run +[Google ADK](https://google.github.io/adk-docs/) agents durably inside Temporal +workflows using `temporalio.contrib.google_adk_agents`. Each scenario is a +self-contained subdirectory with its own worker, workflow starter, workflow and +activity packages, and README. + +## Overview + +The integration combines: + +- **Temporal workflows** for durable orchestration of agent control flow +- **Google ADK** for agent creation, model calls, tools, and MCP integration + +`GoogleAdkPlugin` configures a Pydantic payload converter, sandbox passthrough +for `google.adk` / `google.genai` / `mcp`, a deterministic ADK runtime, and the +model activities. `TemporalModel` runs each LLM call as an activity, so every +model turn is durable and observable. + +## Prerequisites + +- [Temporal CLI](https://docs.temporal.io/cli) with a local dev server running: + ``` + temporal server start-dev + ``` +- Dependencies installed via `uv sync --group google-adk` +- Google API key set as an environment variable: + `export GOOGLE_API_KEY=your_key_here` + +All scenarios default to the `gemini-2.5-flash` model. ADK also supports other +providers (for example, non-Gemini models via LiteLLM); swap the model name on +`TemporalModel` to use one. + +## Scenarios + +Each directory contains a complete example with its own README: + +| Scenario | What it shows | +| --- | --- | +| [basic](./basic/README.md) | A single ADK agent with `TemporalModel` and one model call — no tools. The minimal end-to-end example. | +| [chatbot](./chatbot/README.md) | A multi-turn conversation over one persisted ADK session, with each turn driven by a workflow Update handler that returns the assistant's reply. | +| [tools](./tools/README.md) | A Temporal activity wrapped as an ADK tool with `activity_tool`, so tool calls run as their own activities. | +| [agent_patterns](./agent_patterns/README.md) | A coordinator `LlmAgent` with `sub_agents`, each a `TemporalModel` with a per-agent activity summary. | +| [mcp](./mcp/README.md) | A local echo MCP toolset via `TemporalMcpToolSet` / `TemporalMcpToolSetProvider`, running MCP tools as activities. Self-contained, no Node required. | +| [streaming](./streaming/README.md) | Token streaming via `TemporalModel(streaming_topic=...)` + `WorkflowStream`, consumed by a starter with `WorkflowStreamClient`. | + +To run any scenario, start its worker in one terminal and its workflow starter +in another: + +```bash +uv run google_adk_agents//run_worker.py +uv run google_adk_agents//run__workflow.py +``` diff --git a/google_adk_agents/__init__.py b/google_adk_agents/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/google_adk_agents/agent_patterns/README.md b/google_adk_agents/agent_patterns/README.md new file mode 100644 index 000000000..d4c7b92e9 --- /dev/null +++ b/google_adk_agents/agent_patterns/README.md @@ -0,0 +1,37 @@ +# Agent Patterns — Multi-Agent Coordinator + +A coordinator `LlmAgent` with `sub_agents=[researcher, writer]`. Each agent uses +its own `TemporalModel` with an `ActivityConfig(summary=...)`, so the agents +show up as named activities in workflow history. This demonstrates ADK's +built-in `transfer_to_agent` handoff running durably, with per-agent activity +summaries. + +Before running, review the [prerequisites in the suite README](../README.md) +(Temporal dev server, `uv sync --group google-adk`, and +`export GOOGLE_API_KEY=...`). + +## Running + +Start the worker in one terminal: + +```bash +uv run google_adk_agents/agent_patterns/run_worker.py +``` + +Then start the workflow in another terminal: + +```bash +uv run google_adk_agents/agent_patterns/run_multi_agent_workflow.py +``` + +## What to expect + +The starter asks for a haiku about the ocean. The coordinator delegates to the +researcher and then the writer; the starter prints the final haiku. + +## In the Temporal UI + +Open the workflow `google-adk-agents-agent-patterns-workflow-id`. The +`invoke_model` activities are labeled with their agent summaries — +"Coordinator Agent", "Researcher Agent", "Writer Agent" — so you can follow the +handoffs between agents directly in the history. diff --git a/google_adk_agents/agent_patterns/__init__.py b/google_adk_agents/agent_patterns/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/google_adk_agents/agent_patterns/run_multi_agent_workflow.py b/google_adk_agents/agent_patterns/run_multi_agent_workflow.py new file mode 100644 index 000000000..0ff13edc1 --- /dev/null +++ b/google_adk_agents/agent_patterns/run_multi_agent_workflow.py @@ -0,0 +1,28 @@ +import asyncio +import os + +from temporalio.client import Client +from temporalio.contrib.google_adk_agents import GoogleAdkPlugin + +from google_adk_agents.agent_patterns.workflows.multi_agent_workflow import ( + MultiAgentWorkflow, +) + + +async def main(): + client = await Client.connect( + os.environ.get("TEMPORAL_ADDRESS", "localhost:7233"), + plugins=[GoogleAdkPlugin()], + ) + + result = await client.execute_workflow( + MultiAgentWorkflow.run, + "the ocean", + id="google-adk-agents-agent-patterns-workflow-id", + task_queue="google-adk-agents-agent-patterns", + ) + print(f"Result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/google_adk_agents/agent_patterns/run_worker.py b/google_adk_agents/agent_patterns/run_worker.py new file mode 100644 index 000000000..48eb6e030 --- /dev/null +++ b/google_adk_agents/agent_patterns/run_worker.py @@ -0,0 +1,32 @@ +from __future__ import annotations + +import asyncio +import os + +from temporalio.client import Client +from temporalio.contrib.google_adk_agents import GoogleAdkPlugin +from temporalio.worker import Worker + +from google_adk_agents.agent_patterns.workflows.multi_agent_workflow import ( + MultiAgentWorkflow, +) + + +async def main(): + plugin = GoogleAdkPlugin() + + client = await Client.connect( + os.environ.get("TEMPORAL_ADDRESS", "localhost:7233"), plugins=[plugin] + ) + + worker = Worker( + client, + task_queue="google-adk-agents-agent-patterns", + workflows=[MultiAgentWorkflow], + plugins=[plugin], + ) + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/google_adk_agents/agent_patterns/workflows/__init__.py b/google_adk_agents/agent_patterns/workflows/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/google_adk_agents/agent_patterns/workflows/multi_agent_workflow.py b/google_adk_agents/agent_patterns/workflows/multi_agent_workflow.py new file mode 100644 index 000000000..749477973 --- /dev/null +++ b/google_adk_agents/agent_patterns/workflows/multi_agent_workflow.py @@ -0,0 +1,80 @@ +from datetime import timedelta + +from google.adk.agents import LlmAgent +from google.adk.runners import Runner +from google.adk.sessions import InMemorySessionService +from google.genai import types +from temporalio import workflow +from temporalio.contrib.google_adk_agents import TemporalModel +from temporalio.workflow import ActivityConfig + + +# @@@SNIPSTART google-adk-agents-agent-patterns-multi-agent-workflow +@workflow.defn +class MultiAgentWorkflow: + @workflow.run + async def run(self, topic: str) -> str: + session_service = InMemorySessionService() + session = await session_service.create_session( + app_name="multi_agent_app", user_id="user" + ) + + # The ActivityConfig summary makes each model turn a named activity in + # history. + researcher = LlmAgent( + name="researcher", + model=TemporalModel( + "gemini-2.5-flash", + activity_config=ActivityConfig(summary="Researcher Agent"), + ), + instruction="You are a researcher. Find information about the topic.", + ) + + writer = LlmAgent( + name="writer", + model=TemporalModel( + "gemini-2.5-flash", + activity_config=ActivityConfig(summary="Writer Agent"), + ), + instruction="You are a poet. Write a haiku based on the research.", + ) + + # ADK's transfer_to_agent handoff runs durably here. + coordinator = LlmAgent( + name="coordinator", + model=TemporalModel( + "gemini-2.5-flash", + activity_config=ActivityConfig( + start_to_close_timeout=timedelta(seconds=30), + summary="Coordinator Agent", + ), + ), + instruction="You are a coordinator. Delegate to researcher then writer.", + sub_agents=[researcher, writer], + ) + + runner = Runner( + agent=coordinator, + app_name="multi_agent_app", + session_service=session_service, + ) + + final_text = "" + user_msg = types.Content( + role="user", + parts=[ + types.Part( + text=f"Write a haiku about {topic}. First research it, then write it." + ) + ], + ) + async for event in runner.run_async( + user_id="user", session_id=session.id, new_message=user_msg + ): + if event.content and event.content.parts and event.content.parts[0].text: + final_text = event.content.parts[0].text + + return final_text + + +# @@@SNIPEND diff --git a/google_adk_agents/basic/README.md b/google_adk_agents/basic/README.md new file mode 100644 index 000000000..f9b6e2707 --- /dev/null +++ b/google_adk_agents/basic/README.md @@ -0,0 +1,39 @@ +# Basic — Single Agent, One Model Call + +The minimal Google ADK + Temporal sample: an ordinary ADK `Agent` whose +`model=TemporalModel("gemini-2.5-flash")`, driven by an `InMemoryRunner` inside +a workflow. No tools, no streaming — just one model turn. + +It demonstrates that `GoogleAdkPlugin`'s deterministic runtime and Pydantic +payload converter let an unmodified ADK agent run durably in a workflow, with +every LLM call surfaced as an `invoke_model` activity. + +Before running, review the [prerequisites in the suite README](../README.md) +(Temporal dev server, `uv sync --group google-adk`, and +`export GOOGLE_API_KEY=...`). + +## Running + +Start the worker in one terminal: + +```bash +uv run google_adk_agents/basic/run_worker.py +``` + +Then start the workflow in another terminal: + +```bash +uv run google_adk_agents/basic/run_hello_world_workflow.py +``` + +## What to expect + +The starter prints the agent's response — a haiku about recursion. The agent is +instructed to respond only in haikus. + +## In the Temporal UI + +Open the workflow `google-adk-agents-basic-workflow-id`. In the history you will +see one `invoke_model` activity for the single model turn. That activity is +where the call to Gemini actually happens — the workflow itself stays +deterministic and replay-safe. diff --git a/google_adk_agents/basic/__init__.py b/google_adk_agents/basic/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/google_adk_agents/basic/run_hello_world_workflow.py b/google_adk_agents/basic/run_hello_world_workflow.py new file mode 100644 index 000000000..c7fa34697 --- /dev/null +++ b/google_adk_agents/basic/run_hello_world_workflow.py @@ -0,0 +1,30 @@ +import asyncio +import os + +from temporalio.client import Client +from temporalio.contrib.google_adk_agents import GoogleAdkPlugin + +from google_adk_agents.basic.workflows.hello_world_workflow import ( + HelloWorldAgentWorkflow, +) + + +async def main(): + # @@@SNIPSTART google-adk-agents-basic-starter + client = await Client.connect( + os.environ.get("TEMPORAL_ADDRESS", "localhost:7233"), + plugins=[GoogleAdkPlugin()], + ) + + result = await client.execute_workflow( + HelloWorldAgentWorkflow.run, + "Tell me about recursion in programming.", + id="google-adk-agents-basic-workflow-id", + task_queue="google-adk-agents-basic", + ) + print(f"Result: {result}") + # @@@SNIPEND + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/google_adk_agents/basic/run_worker.py b/google_adk_agents/basic/run_worker.py new file mode 100644 index 000000000..4044b4baa --- /dev/null +++ b/google_adk_agents/basic/run_worker.py @@ -0,0 +1,34 @@ +from __future__ import annotations + +import asyncio +import os + +from temporalio.client import Client +from temporalio.contrib.google_adk_agents import GoogleAdkPlugin +from temporalio.worker import Worker + +from google_adk_agents.basic.workflows.hello_world_workflow import ( + HelloWorldAgentWorkflow, +) + + +async def main(): + # @@@SNIPSTART google-adk-agents-basic-worker + plugin = GoogleAdkPlugin() + + client = await Client.connect( + os.environ.get("TEMPORAL_ADDRESS", "localhost:7233"), plugins=[plugin] + ) + + worker = Worker( + client, + task_queue="google-adk-agents-basic", + workflows=[HelloWorldAgentWorkflow], + plugins=[plugin], + ) + await worker.run() + # @@@SNIPEND + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/google_adk_agents/basic/workflows/__init__.py b/google_adk_agents/basic/workflows/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/google_adk_agents/basic/workflows/hello_world_workflow.py b/google_adk_agents/basic/workflows/hello_world_workflow.py new file mode 100644 index 000000000..eee43d5d7 --- /dev/null +++ b/google_adk_agents/basic/workflows/hello_world_workflow.py @@ -0,0 +1,45 @@ +from google.adk import Agent +from google.adk.runners import InMemoryRunner +from google.adk.utils.context_utils import Aclosing +from google.genai import types +from temporalio import workflow +from temporalio.contrib.google_adk_agents import TemporalModel + + +# @@@SNIPSTART google-adk-agents-basic-hello-world-agent-workflow +@workflow.defn +class HelloWorldAgentWorkflow: + @workflow.run + async def run(self, prompt: str) -> str: + # TemporalModel runs each model call as an `invoke_model` activity. + agent = Agent( + name="hello_world_agent", + model=TemporalModel("gemini-2.5-flash"), + instruction="You only respond in haikus.", + ) + + # The plugin points ADK's session-id generation at workflow.uuid4(), so + # creating a session here is replay-safe. + runner = InMemoryRunner(agent=agent, app_name="hello_world_app") + session = await runner.session_service.create_session( + app_name="hello_world_app", user_id="user" + ) + + final_text = "" + async with Aclosing( + runner.run_async( + user_id="user", + session_id=session.id, + new_message=types.Content(role="user", parts=[types.Part(text=prompt)]), + ) + ) as agen: + async for event in agen: + if event.content and event.content.parts: + for part in event.content.parts: + if part.text: + final_text = part.text + + return final_text + + +# @@@SNIPEND diff --git a/google_adk_agents/chatbot/README.md b/google_adk_agents/chatbot/README.md new file mode 100644 index 000000000..7f3510e78 --- /dev/null +++ b/google_adk_agents/chatbot/README.md @@ -0,0 +1,43 @@ +# Chatbot — Multi-Turn Conversation via Updates + +A no-frills conversational chatbot: an ADK `Agent` whose +`model=TemporalModel("gemini-2.5-flash")`, driven by an `InMemoryRunner` inside a +workflow that stays alive across turns. Unlike the [basic](../basic/README.md) +single-shot sample, one ADK session persists for the life of the workflow, so +the assistant remembers earlier turns. + +Each conversational turn arrives as a Temporal **Update**: the `message` update +handler feeds the user's text into `runner.run_async` on the persisted session +and returns the assistant's reply as the update result. The handler has a noop +validator that accepts every message. Every model turn still runs as its own +`invoke_model` activity. + +Before running, review the [prerequisites in the suite README](../README.md) +(Temporal dev server, `uv sync --group google-adk`, and +`export GOOGLE_API_KEY=...`). + +## Running + +Start the worker in one terminal: + +```bash +uv run google_adk_agents/chatbot/run_worker.py +``` + +Then start the interactive client in another terminal: + +```bash +uv run google_adk_agents/chatbot/run_chatbot_workflow.py +``` + +## What to expect + +The client starts the workflow, then reads messages from stdin. Each line is +sent as an update and the assistant's reply is printed. Enter an empty line or +`/quit` to end the session, which terminates the workflow. + +## In the Temporal UI + +Open the workflow `google-adk-agents-chatbot-workflow-id`. In the history you +will see the workflow stay running to accept updates, with one `invoke_model` +activity per turn. The workflow itself stays deterministic and replay-safe. diff --git a/google_adk_agents/chatbot/__init__.py b/google_adk_agents/chatbot/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/google_adk_agents/chatbot/run_chatbot_workflow.py b/google_adk_agents/chatbot/run_chatbot_workflow.py new file mode 100644 index 000000000..00d841f3b --- /dev/null +++ b/google_adk_agents/chatbot/run_chatbot_workflow.py @@ -0,0 +1,38 @@ +import asyncio +import os + +from temporalio.client import Client +from temporalio.contrib.google_adk_agents import GoogleAdkPlugin + +from google_adk_agents.chatbot.workflows.chatbot_workflow import ( + ChatbotAgentWorkflow, +) + + +async def main(): + # @@@SNIPSTART google-adk-agents-chatbot-starter + client = await Client.connect( + os.environ.get("TEMPORAL_ADDRESS", "localhost:7233"), + plugins=[GoogleAdkPlugin()], + ) + + handle = await client.start_workflow( + ChatbotAgentWorkflow.run, + id="google-adk-agents-chatbot-workflow-id", + task_queue="google-adk-agents-chatbot", + ) + + print('Chat with the assistant. Enter an empty line or "/quit" to exit.') + while True: + message = input("> ").strip() + if not message or message == "/quit": + break + reply = await handle.execute_update(ChatbotAgentWorkflow.message, message) + print(f"Assistant: {reply}") + + await handle.terminate() + # @@@SNIPEND + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/google_adk_agents/chatbot/run_worker.py b/google_adk_agents/chatbot/run_worker.py new file mode 100644 index 000000000..c199835ff --- /dev/null +++ b/google_adk_agents/chatbot/run_worker.py @@ -0,0 +1,34 @@ +from __future__ import annotations + +import asyncio +import os + +from temporalio.client import Client +from temporalio.contrib.google_adk_agents import GoogleAdkPlugin +from temporalio.worker import Worker + +from google_adk_agents.chatbot.workflows.chatbot_workflow import ( + ChatbotAgentWorkflow, +) + + +async def main(): + # @@@SNIPSTART google-adk-agents-chatbot-worker + plugin = GoogleAdkPlugin() + + client = await Client.connect( + os.environ.get("TEMPORAL_ADDRESS", "localhost:7233"), plugins=[plugin] + ) + + worker = Worker( + client, + task_queue="google-adk-agents-chatbot", + workflows=[ChatbotAgentWorkflow], + plugins=[plugin], + ) + await worker.run() + # @@@SNIPEND + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/google_adk_agents/chatbot/workflows/__init__.py b/google_adk_agents/chatbot/workflows/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/google_adk_agents/chatbot/workflows/chatbot_workflow.py b/google_adk_agents/chatbot/workflows/chatbot_workflow.py new file mode 100644 index 000000000..5019c2e0b --- /dev/null +++ b/google_adk_agents/chatbot/workflows/chatbot_workflow.py @@ -0,0 +1,71 @@ +import asyncio + +from google.adk import Agent +from google.adk.runners import InMemoryRunner +from google.adk.utils.context_utils import Aclosing +from google.genai import types +from temporalio import workflow +from temporalio.contrib.google_adk_agents import TemporalModel + + +# @@@SNIPSTART google-adk-agents-chatbot-agent-workflow +@workflow.defn +class ChatbotAgentWorkflow: + def __init__(self) -> None: + self._ready = False + self._runner: InMemoryRunner | None = None + self._session_id: str | None = None + + @workflow.run + async def run(self) -> str: + agent = Agent( + name="chatbot_agent", + model=TemporalModel("gemini-2.5-flash"), + instruction="You are a helpful assistant.", + ) + + # The plugin points ADK's session-id generation at workflow.uuid4(), so + # creating a session here is replay-safe. + self._runner = InMemoryRunner(agent=agent, app_name="chatbot_app") + session = await self._runner.session_service.create_session( + app_name="chatbot_app", user_id="user" + ) + self._session_id = session.id + self._ready = True + + # Block forever to stay alive serving update turns; the client + # terminates the workflow when the user quits. + return await asyncio.Future() + + @workflow.update + async def message(self, message: str) -> str: + # An update can arrive before run() has created the runner and session, + # so wait until they are ready before using them. + await workflow.wait_condition(lambda: self._ready) + assert self._runner is not None and self._session_id is not None + + final_text = "" + async with Aclosing( + self._runner.run_async( + user_id="user", + session_id=self._session_id, + new_message=types.Content( + role="user", parts=[types.Part(text=message)] + ), + ) + ) as event_stream: + async for event in event_stream: + if event.content and event.content.parts: + for part in event.content.parts: + if part.text: + final_text = part.text + + return final_text + + @message.validator + def validate_message(self, message: str) -> None: + # Verify user messages here + pass + + +# @@@SNIPEND diff --git a/google_adk_agents/mcp/README.md b/google_adk_agents/mcp/README.md new file mode 100644 index 000000000..8dfa58b7b --- /dev/null +++ b/google_adk_agents/mcp/README.md @@ -0,0 +1,53 @@ +# MCP — MCP Tools as Activities + +An agent backed by `TemporalMcpToolSet("echo", ...)`, with the worker's plugin +configured as +`GoogleAdkPlugin(toolset_providers=[TemporalMcpToolSetProvider("echo", echo_toolset)])`. +The shared factory in [`toolsets.py`](./toolsets.py) builds an `McpToolset` over +the in-repo [`echo_mcp_server.py`](./echo_mcp_server.py), a one-tool FastMCP +server that echoes back the message it is given. + +The MCP server's `list-tools` and `call-tool` operations run as Temporal +activities (`echo-list-tools` / `echo-call-tool`), so the server is touched only +from activity context, never inside the workflow sandbox. The +`not_in_workflow_toolset` factory lets the same agent also run locally, outside a +workflow. + +## Self-contained, no Node required + +The echo server is a small Python script launched as a subprocess with the +current interpreter (`sys.executable`), so there is nothing to install and no +network access. Swap `echo_mcp_server.py` for any real MCP server (filesystem, +Postgres, GitHub, etc.) by changing the factory in `toolsets.py`. + +Also review the [prerequisites in the suite README](../README.md) (Temporal dev +server, `uv sync --group google-adk`, and `export GOOGLE_API_KEY=...`). + +## Running + +Start the worker in one terminal: + +```bash +uv run google_adk_agents/mcp/run_worker.py +``` + +Then start the workflow in another terminal: + +```bash +uv run google_adk_agents/mcp/run_echo_workflow.py +``` + +The worker spawns `echo_mcp_server.py` itself; you don't need to start it +separately. + +## What to expect + +The starter asks the agent to echo `hello from MCP`. The agent calls the echo +MCP tool and reports back when it's done. + +## In the Temporal UI + +Open the workflow `google-adk-agents-mcp-workflow-id`. The history shows an +`echo-list-tools` activity (discovering the MCP tools), `invoke_model` +activities for the model turns, and an `echo-call-tool` activity for each tool +the model invokes. The MCP subprocess only ever runs inside those activities. diff --git a/google_adk_agents/mcp/__init__.py b/google_adk_agents/mcp/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/google_adk_agents/mcp/echo_mcp_server.py b/google_adk_agents/mcp/echo_mcp_server.py new file mode 100644 index 000000000..2eb1447f6 --- /dev/null +++ b/google_adk_agents/mcp/echo_mcp_server.py @@ -0,0 +1,19 @@ +"""Tiny FastMCP server: exposes a single ``echo`` tool over stdio. + +The worker launches this script as a subprocess and connects to it via the +MCP stdio transport. Swap it for any real MCP server in production. +""" + +from mcp.server.fastmcp import FastMCP + +mcp = FastMCP("echo-server") + + +@mcp.tool() +def echo(message: str) -> str: + """Return the input message unchanged.""" + return message + + +if __name__ == "__main__": + mcp.run() diff --git a/google_adk_agents/mcp/run_echo_workflow.py b/google_adk_agents/mcp/run_echo_workflow.py new file mode 100644 index 000000000..d6a8c8b5d --- /dev/null +++ b/google_adk_agents/mcp/run_echo_workflow.py @@ -0,0 +1,26 @@ +import asyncio +import os + +from temporalio.client import Client +from temporalio.contrib.google_adk_agents import GoogleAdkPlugin + +from google_adk_agents.mcp.workflows.echo_workflow import EchoMcpWorkflow + + +async def main(): + client = await Client.connect( + os.environ.get("TEMPORAL_ADDRESS", "localhost:7233"), + plugins=[GoogleAdkPlugin()], + ) + + result = await client.execute_workflow( + EchoMcpWorkflow.run, + "Echo 'hello from MCP'.", + id="google-adk-agents-mcp-workflow-id", + task_queue="google-adk-agents-mcp", + ) + print(f"Result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/google_adk_agents/mcp/run_worker.py b/google_adk_agents/mcp/run_worker.py new file mode 100644 index 000000000..8c8f6c2a2 --- /dev/null +++ b/google_adk_agents/mcp/run_worker.py @@ -0,0 +1,38 @@ +from __future__ import annotations + +import asyncio +import os + +from temporalio.client import Client +from temporalio.contrib.google_adk_agents import ( + GoogleAdkPlugin, + TemporalMcpToolSetProvider, +) +from temporalio.worker import Worker + +from google_adk_agents.mcp.toolsets import echo_toolset +from google_adk_agents.mcp.workflows.echo_workflow import EchoMcpWorkflow + + +async def main(): + # @@@SNIPSTART google-adk-agents-mcp-worker + plugin = GoogleAdkPlugin( + toolset_providers=[TemporalMcpToolSetProvider("echo", echo_toolset)] + ) + + client = await Client.connect( + os.environ.get("TEMPORAL_ADDRESS", "localhost:7233"), plugins=[plugin] + ) + + worker = Worker( + client, + task_queue="google-adk-agents-mcp", + workflows=[EchoMcpWorkflow], + plugins=[plugin], + ) + await worker.run() + # @@@SNIPEND + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/google_adk_agents/mcp/toolsets.py b/google_adk_agents/mcp/toolsets.py new file mode 100644 index 000000000..4dfd5c947 --- /dev/null +++ b/google_adk_agents/mcp/toolsets.py @@ -0,0 +1,30 @@ +import sys +from pathlib import Path +from typing import Any + +from google.adk.tools.mcp_tool import McpToolset +from google.adk.tools.mcp_tool.mcp_session_manager import StdioConnectionParams +from mcp import StdioServerParameters + +ECHO_SERVER = Path(__file__).parent / "echo_mcp_server.py" + + +# @@@SNIPSTART google-adk-agents-mcp-echo-toolset-factory +def echo_toolset(_: Any | None) -> McpToolset: + """Build an McpToolset over the local echo MCP server. + + The server is the in-repo echo_mcp_server.py script, launched as a + subprocess with the current Python interpreter. This factory only runs + inside an activity or a local ADK run, never inside workflow code. + """ + return McpToolset( + connection_params=StdioConnectionParams( + server_params=StdioServerParameters( + command=sys.executable, + args=[str(ECHO_SERVER)], + ), + ), + ) + + +# @@@SNIPEND diff --git a/google_adk_agents/mcp/workflows/__init__.py b/google_adk_agents/mcp/workflows/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/google_adk_agents/mcp/workflows/echo_workflow.py b/google_adk_agents/mcp/workflows/echo_workflow.py new file mode 100644 index 000000000..d3104f704 --- /dev/null +++ b/google_adk_agents/mcp/workflows/echo_workflow.py @@ -0,0 +1,54 @@ +from google.adk import Agent +from google.adk.runners import Runner +from google.adk.sessions import InMemorySessionService +from google.adk.utils.context_utils import Aclosing +from google.genai import types +from temporalio import workflow +from temporalio.contrib.google_adk_agents import TemporalMcpToolSet, TemporalModel + +from google_adk_agents.mcp.toolsets import echo_toolset + + +# @@@SNIPSTART google-adk-agents-mcp-echo-mcp-workflow +@workflow.defn +class EchoMcpWorkflow: + @workflow.run + async def run(self, prompt: str) -> str: + # TemporalMcpToolSet runs the MCP server's list-tools and call-tool + # calls as activities. + agent = Agent( + name="echo_agent", + model=TemporalModel("gemini-2.5-flash"), + instruction="Use the echo tool to echo back the user's message.", + tools=[TemporalMcpToolSet("echo", not_in_workflow_toolset=echo_toolset)], + ) + + session_service = InMemorySessionService() + session = await session_service.create_session( + app_name="echo_app", user_id="user" + ) + + runner = Runner( + agent=agent, + app_name="echo_app", + session_service=session_service, + ) + + final_text = "" + async with Aclosing( + runner.run_async( + user_id="user", + session_id=session.id, + new_message=types.Content(role="user", parts=[types.Part(text=prompt)]), + ) + ) as agen: + async for event in agen: + if event.content and event.content.parts: + for part in event.content.parts: + if part.text: + final_text = part.text + + return final_text + + +# @@@SNIPEND diff --git a/google_adk_agents/streaming/README.md b/google_adk_agents/streaming/README.md new file mode 100644 index 000000000..09e75a577 --- /dev/null +++ b/google_adk_agents/streaming/README.md @@ -0,0 +1,40 @@ +# Streaming — Token Streaming to an External Consumer + +A workflow that hosts a `WorkflowStream` and uses +`TemporalModel("gemini-2.5-flash", streaming_topic="responses")` with +`RunConfig(streaming_mode=StreamingMode.SSE)`. The streaming model activity +publishes raw `LlmResponse` chunks to the topic as they arrive; the starter +subscribes via `WorkflowStreamClient` and prints chunks as they come in, then +prints the final result. + +> Streaming support in the plugin is experimental and may change. + +Before running, review the [prerequisites in the suite README](../README.md) +(Temporal dev server, `uv sync --group google-adk`, and +`export GOOGLE_API_KEY=...`). + +## Running + +Start the worker in one terminal: + +```bash +uv run google_adk_agents/streaming/run_worker.py +``` + +Then start the workflow in another terminal: + +```bash +uv run google_adk_agents/streaming/run_streaming_workflow.py +``` + +## What to expect + +The starter asks for a short story and prints the response token-by-token as the +model streams it, then prints the assembled final result. + +## In the Temporal UI + +Open the workflow `google-adk-agents-streaming-workflow-id`. The history shows +an `invoke_model_streaming` activity (instead of `invoke_model`). That activity +calls the model with streaming enabled and publishes each chunk to the +`responses` topic, which is what the starter subscribes to. diff --git a/google_adk_agents/streaming/__init__.py b/google_adk_agents/streaming/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/google_adk_agents/streaming/run_streaming_workflow.py b/google_adk_agents/streaming/run_streaming_workflow.py new file mode 100644 index 000000000..b371e96e6 --- /dev/null +++ b/google_adk_agents/streaming/run_streaming_workflow.py @@ -0,0 +1,58 @@ +import asyncio +import os +from datetime import timedelta + +from google.adk.models.llm_response import LlmResponse +from temporalio.client import Client +from temporalio.contrib.google_adk_agents import GoogleAdkPlugin +from temporalio.contrib.workflow_streams import WorkflowStreamClient + +from google_adk_agents.streaming.workflows.streaming_workflow import ( + StreamingAgentWorkflow, +) + +WORKFLOW_ID = "google-adk-agents-streaming-workflow-id" + + +async def main(): + client = await Client.connect( + os.environ.get("TEMPORAL_ADDRESS", "localhost:7233"), + plugins=[GoogleAdkPlugin()], + ) + + handle = await client.start_workflow( + StreamingAgentWorkflow.run, + "Tell me a short story about a robot learning to paint.", + id=WORKFLOW_ID, + task_queue="google-adk-agents-streaming", + ) + + stream = WorkflowStreamClient.create(client, WORKFLOW_ID) + + async def print_chunks() -> None: + print("Streaming response:") + async for item in stream.subscribe( + ["responses"], + from_offset=0, + result_type=LlmResponse, + poll_cooldown=timedelta(milliseconds=50), + ): + response = item.data + if response.content and response.content.parts: + for part in response.content.parts: + if part.text: + print(part.text, end="", flush=True) + + chunk_task = asyncio.create_task(print_chunks()) + result = await handle.result() + chunk_task.cancel() + try: + await chunk_task + except asyncio.CancelledError: + pass + + print(f"\n\nFinal result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/google_adk_agents/streaming/run_worker.py b/google_adk_agents/streaming/run_worker.py new file mode 100644 index 000000000..cbf8f76af --- /dev/null +++ b/google_adk_agents/streaming/run_worker.py @@ -0,0 +1,32 @@ +from __future__ import annotations + +import asyncio +import os + +from temporalio.client import Client +from temporalio.contrib.google_adk_agents import GoogleAdkPlugin +from temporalio.worker import Worker + +from google_adk_agents.streaming.workflows.streaming_workflow import ( + StreamingAgentWorkflow, +) + + +async def main(): + plugin = GoogleAdkPlugin() + + client = await Client.connect( + os.environ.get("TEMPORAL_ADDRESS", "localhost:7233"), plugins=[plugin] + ) + + worker = Worker( + client, + task_queue="google-adk-agents-streaming", + workflows=[StreamingAgentWorkflow], + plugins=[plugin], + ) + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/google_adk_agents/streaming/workflows/__init__.py b/google_adk_agents/streaming/workflows/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/google_adk_agents/streaming/workflows/streaming_workflow.py b/google_adk_agents/streaming/workflows/streaming_workflow.py new file mode 100644 index 000000000..1a5d76149 --- /dev/null +++ b/google_adk_agents/streaming/workflows/streaming_workflow.py @@ -0,0 +1,50 @@ +from google.adk import Agent +from google.adk.agents.run_config import RunConfig, StreamingMode +from google.adk.runners import InMemoryRunner +from google.genai import types +from temporalio import workflow +from temporalio.contrib.google_adk_agents import TemporalModel +from temporalio.contrib.workflow_streams import WorkflowStream + + +# @@@SNIPSTART google-adk-agents-streaming-streaming-agent-workflow +@workflow.defn +class StreamingAgentWorkflow: + @workflow.init + def __init__(self, prompt: str) -> None: + # The streaming activity publishes LlmResponse chunks to this stream as + # they come back from the model. + self.stream = WorkflowStream() + + @workflow.run + async def run(self, prompt: str) -> str: + # streaming_mode=SSE routes the call through the invoke_model_streaming + # activity. + model = TemporalModel("gemini-2.5-flash", streaming_topic="responses") + agent = Agent( + name="streaming_agent", + model=model, + instruction="You are a helpful assistant.", + ) + + runner = InMemoryRunner(agent=agent, app_name="streaming_app") + session = await runner.session_service.create_session( + app_name="streaming_app", user_id="user" + ) + + final_text = "" + async for event in runner.run_async( + user_id="user", + session_id=session.id, + new_message=types.Content(role="user", parts=[types.Part(text=prompt)]), + run_config=RunConfig(streaming_mode=StreamingMode.SSE), + ): + if event.content and event.content.parts: + for part in event.content.parts: + if part.text: + final_text = part.text + + return final_text + + +# @@@SNIPEND diff --git a/google_adk_agents/tools/README.md b/google_adk_agents/tools/README.md new file mode 100644 index 000000000..006efe108 --- /dev/null +++ b/google_adk_agents/tools/README.md @@ -0,0 +1,37 @@ +# Tools — Temporal Activities as ADK Tools + +A weather agent whose `get_weather` Temporal activity is wrapped as an ADK tool +with `activity_tool(...)`. The model decides to call the tool; the tool runs as +its own Temporal activity — retryable and observable — rather than inline in the +workflow. This demonstrates the activity boundary for tool calls. + +Before running, review the [prerequisites in the suite README](../README.md) +(Temporal dev server, `uv sync --group google-adk`, and +`export GOOGLE_API_KEY=...`). + +## Running + +Start the worker in one terminal: + +```bash +uv run google_adk_agents/tools/run_worker.py +``` + +Then start the workflow in another terminal: + +```bash +uv run google_adk_agents/tools/run_weather_workflow.py +``` + +## What to expect + +The starter asks "What is the weather in New York?" and prints the agent's +answer, which incorporates the (canned) weather the tool returned. + +## In the Temporal UI + +Open the workflow `google-adk-agents-tools-workflow-id`. The history shows the +model deciding to call the tool, then a `get_weather` activity for the tool +call, then a second `invoke_model` activity where the model turns the tool +result into a final answer. Each model turn and each tool call is its own +activity. diff --git a/google_adk_agents/tools/__init__.py b/google_adk_agents/tools/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/google_adk_agents/tools/activities/__init__.py b/google_adk_agents/tools/activities/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/google_adk_agents/tools/activities/weather_activity.py b/google_adk_agents/tools/activities/weather_activity.py new file mode 100644 index 000000000..2a1fd10a7 --- /dev/null +++ b/google_adk_agents/tools/activities/weather_activity.py @@ -0,0 +1,15 @@ +from temporalio import activity + + +# @@@SNIPSTART google-adk-agents-tools-get-weather +@activity.defn +async def get_weather(city: str) -> str: + """Return the weather for a city. + + In a real sample this would call a weather API. Here it returns a fixed + response so the sample runs without external credentials. + """ + return f"The weather in {city} is warm and sunny, 17 degrees." + + +# @@@SNIPEND diff --git a/google_adk_agents/tools/run_weather_workflow.py b/google_adk_agents/tools/run_weather_workflow.py new file mode 100644 index 000000000..e775a0db1 --- /dev/null +++ b/google_adk_agents/tools/run_weather_workflow.py @@ -0,0 +1,26 @@ +import asyncio +import os + +from temporalio.client import Client +from temporalio.contrib.google_adk_agents import GoogleAdkPlugin + +from google_adk_agents.tools.workflows.weather_workflow import WeatherAgentWorkflow + + +async def main(): + client = await Client.connect( + os.environ.get("TEMPORAL_ADDRESS", "localhost:7233"), + plugins=[GoogleAdkPlugin()], + ) + + result = await client.execute_workflow( + WeatherAgentWorkflow.run, + "What is the weather in New York?", + id="google-adk-agents-tools-workflow-id", + task_queue="google-adk-agents-tools", + ) + print(f"Result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/google_adk_agents/tools/run_worker.py b/google_adk_agents/tools/run_worker.py new file mode 100644 index 000000000..71aee072e --- /dev/null +++ b/google_adk_agents/tools/run_worker.py @@ -0,0 +1,34 @@ +from __future__ import annotations + +import asyncio +import os + +from temporalio.client import Client +from temporalio.contrib.google_adk_agents import GoogleAdkPlugin +from temporalio.worker import Worker + +from google_adk_agents.tools.activities.weather_activity import get_weather +from google_adk_agents.tools.workflows.weather_workflow import WeatherAgentWorkflow + + +async def main(): + # @@@SNIPSTART google-adk-agents-tools-worker + plugin = GoogleAdkPlugin() + + client = await Client.connect( + os.environ.get("TEMPORAL_ADDRESS", "localhost:7233"), plugins=[plugin] + ) + + worker = Worker( + client, + task_queue="google-adk-agents-tools", + workflows=[WeatherAgentWorkflow], + activities=[get_weather], + plugins=[plugin], + ) + await worker.run() + # @@@SNIPEND + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/google_adk_agents/tools/workflows/__init__.py b/google_adk_agents/tools/workflows/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/google_adk_agents/tools/workflows/weather_workflow.py b/google_adk_agents/tools/workflows/weather_workflow.py new file mode 100644 index 000000000..6351210c4 --- /dev/null +++ b/google_adk_agents/tools/workflows/weather_workflow.py @@ -0,0 +1,54 @@ +from datetime import timedelta + +import temporalio.contrib.google_adk_agents.workflow +from google.adk import Agent +from google.adk.runners import InMemoryRunner +from google.adk.utils.context_utils import Aclosing +from google.genai import types +from temporalio import workflow +from temporalio.contrib.google_adk_agents import TemporalModel + +from google_adk_agents.tools.activities.weather_activity import get_weather + + +# @@@SNIPSTART google-adk-agents-tools-weather-agent-workflow +@workflow.defn +class WeatherAgentWorkflow: + @workflow.run + async def run(self, prompt: str) -> str: + # activity_tool runs the tool call as a real Temporal activity, so it's + # retryable and shows up in history. + weather_tool = temporalio.contrib.google_adk_agents.workflow.activity_tool( + get_weather, start_to_close_timeout=timedelta(seconds=60) + ) + + agent = Agent( + name="weather_agent", + model=TemporalModel("gemini-2.5-flash"), + instruction="Use the get_weather tool to answer weather questions.", + tools=[weather_tool], + ) + + runner = InMemoryRunner(agent=agent, app_name="weather_app") + session = await runner.session_service.create_session( + app_name="weather_app", user_id="user" + ) + + final_text = "" + async with Aclosing( + runner.run_async( + user_id="user", + session_id=session.id, + new_message=types.Content(role="user", parts=[types.Part(text=prompt)]), + ) + ) as agen: + async for event in agen: + if event.content and event.content.parts: + for part in event.content.parts: + if part.text: + final_text = part.text + + return final_text + + +# @@@SNIPEND diff --git a/google_genai/README.md b/google_genai/README.md new file mode 100644 index 000000000..4b49b0e96 --- /dev/null +++ b/google_genai/README.md @@ -0,0 +1,76 @@ +# Google GenAI Samples + +These samples demonstrate the [Temporal Google GenAI plugin](https://github.com/temporalio/sdk-python/tree/main/temporalio/contrib/google_genai), which runs the [Google Gemini SDK](https://googleapis.github.io/python-genai/) inside Temporal Workflows. Workflows construct a `TemporalAsyncClient`, and every Gemini API call — `generate_content`, tool calls, streaming, files, interactions, agents — runs as a Temporal Activity. You get durable execution, Temporal-managed retries and timeouts, and your credentials never enter the workflow or its event history. + +## Samples + +| Sample | Description | +|--------|-------------| +| [hello_world](hello_world) | Minimal `generate_content` call. Start here. | +| [tools](tools) | Automatic function calling: an `activity_as_tool`-wrapped activity and a plain workflow-method tool on one call. | +| [streaming](streaming) | Forward `generate_content_stream` chunks to an external subscriber via `streaming_topic` + `WorkflowStream`. | +| [chat](chat) | Multi-turn conversation with `client.chats`. | +| [structured_output](structured_output) | Typed JSON output via `response_schema` and a Pydantic model. | +| [mcp](mcp) | Give Gemini an MCP server's tools via `TemporalMcpClientSession`. | +| [files](files) | Upload a file with `client.files` and reference it in a call. *(needs a live API key)* | +| [interactions](interactions) | Stateful server-side conversations via `client.interactions`. *(needs a live API key)* | +| [agents](agents) | Managed-agent CRUD via `client.agents`. *(needs a live API key)* | +| [vertex_ai](vertex_ai) | The hello-world flow against Vertex AI (`vertexai=True`). *(needs GCP credentials)* | + +## Prerequisites + +1. Install dependencies: + + ```bash + uv sync --group google-genai + ``` + +2. Configure credentials. Most samples use the Gemini Developer API and read an API key from the environment: + + ```bash + export GOOGLE_API_KEY=... + ``` + + The [vertex_ai](vertex_ai) sample instead uses Vertex AI with Google Cloud Application Default Credentials — see its README. You can authenticate with `gcloud auth application-default login` and set `GOOGLE_CLOUD_PROJECT` (and optionally `GOOGLE_CLOUD_LOCATION`). + +3. Start a [Temporal dev server](https://docs.temporal.io/cli#start-dev-server): + + ```bash + temporal server start-dev + ``` + +## Running a Sample + +Each sample has two scripts. Start the Worker first, then the Workflow starter in a separate terminal: + +```bash +# Terminal 1: start the Worker +uv run google_genai//run_worker.py + +# Terminal 2: start the Workflow +uv run google_genai//run_workflow.py +``` + +For example, to run the tools sample: + +```bash +# Terminal 1 +uv run google_genai/tools/run_worker.py + +# Terminal 2 +uv run google_genai/tools/run_workflow.py +``` + +## Key Features Demonstrated + +- **Durable API calls** — every Gemini call runs as an activity with configurable timeouts and retries; no credentials enter workflow history. +- **Automatic function calling** — the SDK's AFC loop runs in-workflow; tools can be durable activities (`activity_as_tool`) or plain workflow methods. +- **Streaming** — forward model chunks live to external subscribers via `WorkflowStream`. +- **Structured output** — Pydantic-typed results through the plugin's Pydantic data converter. +- **MCP integration** — register MCP servers on the worker; tool calls dispatched through per-server activities. +- **Full API surface** — chat, the Files API, the Interactions API, managed agents, and Vertex AI. + +## Related + +- [Temporal Google GenAI plugin docs](https://github.com/temporalio/sdk-python/tree/main/temporalio/contrib/google_genai) +- [Google Gemini SDK (`google-genai`)](https://googleapis.github.io/python-genai/) diff --git a/google_genai/__init__.py b/google_genai/__init__.py new file mode 100644 index 000000000..4cd479869 --- /dev/null +++ b/google_genai/__init__.py @@ -0,0 +1 @@ +"""Temporal Google GenAI plugin samples.""" diff --git a/google_genai/agents/README.md b/google_genai/agents/README.md new file mode 100644 index 000000000..3e09ab842 --- /dev/null +++ b/google_genai/agents/README.md @@ -0,0 +1,51 @@ +# Managed Agents + +Managed agents (`client.agents`) are server-side resources you can create, fetch, +list, and delete. This sample runs the full CRUD cycle, each operation as a +Temporal activity. + +> **Requires a live Gemini API key.** The Agents API talks to a real backend that +> the plugin's test server does not mock, so this sample has no automated test — +> run it against a real `GOOGLE_API_KEY`. + +## What This Sample Demonstrates + +- `client.agents.create(id=..., system_instruction=...)` +- `client.agents.get(id)`, `client.agents.list(page_size=...)`, `client.agents.delete(id)` +- Cleaning up the agent in a `finally` block so a failure mid-cycle doesn't leak it + +## Creating Server-Side Resources Durably + +Every call here runs as an activity, so each one can be retried — including +after it already succeeded on the backend but its completion was lost. Two +habits worth carrying into real code: + +- **Make creates idempotent.** `client.agents.create(id=...)` with a + caller-chosen id fails with "already exists" on such a retry. Derive the id + deterministically from workflow state (`workflow.uuid4()`, or the workflow id) + so every attempt targets the same resource, and treat "already exists" as + success — for example by falling back to `client.agents.get(id)`. +- **Clean up in a `finally`.** Without it, a failing `get`/`list` skips the + `delete` and the agent lingers on Google's backend even though the workflow + ended. + +## Running the Sample + +Prerequisites: install dependencies, set `GOOGLE_API_KEY`, and start a Temporal +dev server. See the [suite README](../README.md). + +```bash +# Terminal 1 +uv run google_genai/agents/run_worker.py + +# Terminal 2 +uv run google_genai/agents/run_workflow.py +``` + +## Files + +| File | Description | +|------|-------------| +| `workflow.py` | `AgentsWorkflow` — create, get, list, delete a managed agent | +| `run_worker.py` | Registers `GoogleGenAIPlugin`, starts the worker | +| `run_workflow.py` | Executes the workflow and prints the result | diff --git a/google_genai/agents/__init__.py b/google_genai/agents/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/google_genai/agents/run_worker.py b/google_genai/agents/run_worker.py new file mode 100644 index 000000000..1bd294ba1 --- /dev/null +++ b/google_genai/agents/run_worker.py @@ -0,0 +1,35 @@ +"""Worker for the agents sample.""" + +# @@@SNIPSTART python-google-genai-agents-worker +import asyncio +import os + +from google import genai +from temporalio.client import Client +from temporalio.contrib.google_genai import GoogleGenAIPlugin +from temporalio.worker import Worker + +from google_genai.agents.workflow import AgentsWorkflow + + +async def main() -> None: + genai_client = genai.Client(api_key=os.environ["GOOGLE_API_KEY"]) + plugin = GoogleGenAIPlugin(genai_client) + + client = await Client.connect( + os.environ.get("TEMPORAL_ADDRESS", "localhost:7233"), + plugins=[plugin], + ) + + worker = Worker( + client, + task_queue="google-genai-agents", + workflows=[AgentsWorkflow], + ) + print("Worker started. Ctrl+C to exit.") + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) +# @@@SNIPEND diff --git a/google_genai/agents/run_workflow.py b/google_genai/agents/run_workflow.py new file mode 100644 index 000000000..63307b9c1 --- /dev/null +++ b/google_genai/agents/run_workflow.py @@ -0,0 +1,27 @@ +"""Start the agents workflow.""" + +# @@@SNIPSTART python-google-genai-agents-run-workflow +import asyncio +import os + +from temporalio.client import Client + +from google_genai.agents.workflow import AgentsWorkflow + + +async def main() -> None: + client = await Client.connect(os.environ.get("TEMPORAL_ADDRESS", "localhost:7233")) + + result = await client.execute_workflow( + AgentsWorkflow.run, + "samples-demo-agent", + id="google-genai-agents", + task_queue="google-genai-agents", + ) + + print(f"Result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) +# @@@SNIPEND diff --git a/google_genai/agents/workflow.py b/google_genai/agents/workflow.py new file mode 100644 index 000000000..59277e97a --- /dev/null +++ b/google_genai/agents/workflow.py @@ -0,0 +1,43 @@ +"""Managed agents CRUD via client.agents. + +Managed agents are server-side resources you create, fetch, list, and delete. +Each operation runs as a Temporal activity. +""" + +from typing import Any + +from temporalio import workflow +from temporalio.contrib.google_genai import TemporalAsyncClient + + +# @@@SNIPSTART python-google-genai-agents-workflow +@workflow.defn +class AgentsWorkflow: + @workflow.run + async def run(self, agent_id: str) -> dict[str, Any]: + client = TemporalAsyncClient() + + # Creating with a caller-chosen id is not idempotent: if the activity + # succeeds but its completion is lost, the retry sees "already exists". + # Real code should generate the id from workflow state and treat that + # error as success (see this sample's README). + created = await client.agents.create( + id=agent_id, + system_instruction="You are a helpful assistant.", + ) + try: + fetched = await client.agents.get(agent_id) + listing = await client.agents.list(page_size=10) + finally: + # Delete in a finally block so a failed get/list doesn't leak the + # agent on Google's backend. + await client.agents.delete(agent_id) + + return { + "created_id": created.id, + "fetched_id": fetched.id, + "listed_ids": [a.id for a in (listing.agents or [])], + } + + +# @@@SNIPEND diff --git a/google_genai/chat/README.md b/google_genai/chat/README.md new file mode 100644 index 000000000..2275f51e4 --- /dev/null +++ b/google_genai/chat/README.md @@ -0,0 +1,31 @@ +# Chat + +A multi-turn conversation using `client.chats`. The chat session carries history +across turns, and each `send_message` call runs as a durable Temporal activity. + +## What This Sample Demonstrates + +- Creating a chat session with `client.chats.create(...)` +- Sending multiple turns with `await chat.send_message(...)` +- Conversation state persisting across durable activity calls + +## Running the Sample + +Prerequisites: install dependencies, set `GOOGLE_API_KEY`, and start a Temporal +dev server. See the [suite README](../README.md). + +```bash +# Terminal 1 +uv run google_genai/chat/run_worker.py + +# Terminal 2 +uv run google_genai/chat/run_workflow.py +``` + +## Files + +| File | Description | +|------|-------------| +| `workflow.py` | `ChatWorkflow` — sends a list of prompts over one chat session | +| `run_worker.py` | Registers `GoogleGenAIPlugin`, starts the worker | +| `run_workflow.py` | Executes the workflow and prints each turn's reply | diff --git a/google_genai/chat/__init__.py b/google_genai/chat/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/google_genai/chat/run_worker.py b/google_genai/chat/run_worker.py new file mode 100644 index 000000000..3bd56f679 --- /dev/null +++ b/google_genai/chat/run_worker.py @@ -0,0 +1,35 @@ +"""Worker for the chat sample.""" + +# @@@SNIPSTART python-google-genai-chat-worker +import asyncio +import os + +from google import genai +from temporalio.client import Client +from temporalio.contrib.google_genai import GoogleGenAIPlugin +from temporalio.worker import Worker + +from google_genai.chat.workflow import ChatWorkflow + + +async def main() -> None: + genai_client = genai.Client(api_key=os.environ["GOOGLE_API_KEY"]) + plugin = GoogleGenAIPlugin(genai_client) + + client = await Client.connect( + os.environ.get("TEMPORAL_ADDRESS", "localhost:7233"), + plugins=[plugin], + ) + + worker = Worker( + client, + task_queue="google-genai-chat", + workflows=[ChatWorkflow], + ) + print("Worker started. Ctrl+C to exit.") + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) +# @@@SNIPEND diff --git a/google_genai/chat/run_workflow.py b/google_genai/chat/run_workflow.py new file mode 100644 index 000000000..15ac4281a --- /dev/null +++ b/google_genai/chat/run_workflow.py @@ -0,0 +1,31 @@ +"""Start the chat workflow with a multi-turn conversation.""" + +# @@@SNIPSTART python-google-genai-chat-run-workflow +import asyncio +import os + +from temporalio.client import Client + +from google_genai.chat.workflow import ChatWorkflow + + +async def main() -> None: + client = await Client.connect(os.environ.get("TEMPORAL_ADDRESS", "localhost:7233")) + + result = await client.execute_workflow( + ChatWorkflow.run, + [ + "My favorite color is teal. Remember that.", + "What is my favorite color?", + ], + id="google-genai-chat", + task_queue="google-genai-chat", + ) + + for turn, reply in enumerate(result, start=1): + print(f"Turn {turn}: {reply}") + + +if __name__ == "__main__": + asyncio.run(main()) +# @@@SNIPEND diff --git a/google_genai/chat/workflow.py b/google_genai/chat/workflow.py new file mode 100644 index 000000000..e1558f233 --- /dev/null +++ b/google_genai/chat/workflow.py @@ -0,0 +1,26 @@ +"""Multi-turn chat using client.chats. + +A chat session keeps conversation history across turns. Each ``send_message`` +call runs as a durable Temporal activity, and the SDK threads prior turns into +each request automatically. +""" + +from temporalio import workflow +from temporalio.contrib.google_genai import TemporalAsyncClient + + +# @@@SNIPSTART python-google-genai-chat-workflow +@workflow.defn +class ChatWorkflow: + @workflow.run + async def run(self, prompts: list[str]) -> list[str]: + client = TemporalAsyncClient() + chat = client.chats.create(model="gemini-2.5-flash") + replies: list[str] = [] + for prompt in prompts: + response = await chat.send_message(prompt) + replies.append(response.text or "") + return replies + + +# @@@SNIPEND diff --git a/google_genai/files/README.md b/google_genai/files/README.md new file mode 100644 index 000000000..64cffeae3 --- /dev/null +++ b/google_genai/files/README.md @@ -0,0 +1,38 @@ +# Files + +Upload a file with the Gemini Files API, then ask the model about it. +`client.files.upload` runs as a Temporal activity on the worker (the file is +read there), and the returned file handle is referenced in a `generate_content` +call. + +> **Requires a live Gemini API key.** The Files API talks to a real backend that +> the plugin's test server does not mock, so this sample has no automated test — +> run it against a real `GOOGLE_API_KEY`. The file path is resolved on the +> worker, so `sample.txt` must be reachable by the worker process. + +## What This Sample Demonstrates + +- `client.files.upload(file=..., config=UploadFileConfig(...))` as a durable activity +- Referencing the uploaded file handle in `generate_content` `contents` + +## Running the Sample + +Prerequisites: install dependencies, set `GOOGLE_API_KEY`, and start a Temporal +dev server. See the [suite README](../README.md). + +```bash +# Terminal 1 +uv run google_genai/files/run_worker.py + +# Terminal 2 +uv run google_genai/files/run_workflow.py +``` + +## Files + +| File | Description | +|------|-------------| +| `sample.txt` | The document uploaded and summarized | +| `workflow.py` | `FilesWorkflow` — uploads a file, then summarizes it | +| `run_worker.py` | Registers `GoogleGenAIPlugin`, starts the worker | +| `run_workflow.py` | Executes the workflow with the sample file path | diff --git a/google_genai/files/__init__.py b/google_genai/files/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/google_genai/files/run_worker.py b/google_genai/files/run_worker.py new file mode 100644 index 000000000..5e843ed4b --- /dev/null +++ b/google_genai/files/run_worker.py @@ -0,0 +1,35 @@ +"""Worker for the files sample.""" + +# @@@SNIPSTART python-google-genai-files-worker +import asyncio +import os + +from google import genai +from temporalio.client import Client +from temporalio.contrib.google_genai import GoogleGenAIPlugin +from temporalio.worker import Worker + +from google_genai.files.workflow import FilesWorkflow + + +async def main() -> None: + genai_client = genai.Client(api_key=os.environ["GOOGLE_API_KEY"]) + plugin = GoogleGenAIPlugin(genai_client) + + client = await Client.connect( + os.environ.get("TEMPORAL_ADDRESS", "localhost:7233"), + plugins=[plugin], + ) + + worker = Worker( + client, + task_queue="google-genai-files", + workflows=[FilesWorkflow], + ) + print("Worker started. Ctrl+C to exit.") + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) +# @@@SNIPEND diff --git a/google_genai/files/run_workflow.py b/google_genai/files/run_workflow.py new file mode 100644 index 000000000..c66129964 --- /dev/null +++ b/google_genai/files/run_workflow.py @@ -0,0 +1,34 @@ +"""Start the files workflow. + +The file is read on the worker, so ``sample.txt`` must be on a path the worker +process can access (here it ships alongside this sample). +""" + +# @@@SNIPSTART python-google-genai-files-run-workflow +import asyncio +import os +from pathlib import Path + +from temporalio.client import Client + +from google_genai.files.workflow import FilesWorkflow + +SAMPLE_FILE = str(Path(__file__).parent / "sample.txt") + + +async def main() -> None: + client = await Client.connect(os.environ.get("TEMPORAL_ADDRESS", "localhost:7233")) + + result = await client.execute_workflow( + FilesWorkflow.run, + args=[SAMPLE_FILE, "Summarize this document in one sentence."], + id="google-genai-files", + task_queue="google-genai-files", + ) + + print(f"Result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) +# @@@SNIPEND diff --git a/google_genai/files/sample.txt b/google_genai/files/sample.txt new file mode 100644 index 000000000..d192614a1 --- /dev/null +++ b/google_genai/files/sample.txt @@ -0,0 +1,6 @@ +Temporal is a durable execution platform. Workflows written with the Temporal +SDK run as ordinary code, but their state is persisted by the Temporal service, +so they survive process crashes, machine restarts, and deploys. Activities +encapsulate side effects like network calls; Temporal handles their retries and +timeouts. The Google GenAI plugin builds on this by running every Gemini API +call as a durable activity. diff --git a/google_genai/files/workflow.py b/google_genai/files/workflow.py new file mode 100644 index 000000000..ab4f9a71e --- /dev/null +++ b/google_genai/files/workflow.py @@ -0,0 +1,33 @@ +"""Upload a file with the Files API, then ask Gemini about it. + +``client.files.upload`` runs as an activity on the worker — the file is read +there, not in the workflow — and the returned file handle is then referenced in +a ``generate_content`` call. +""" + +from typing import cast + +from google.genai import types +from temporalio import workflow +from temporalio.contrib.google_genai import TemporalAsyncClient + + +# @@@SNIPSTART python-google-genai-files-workflow +@workflow.defn +class FilesWorkflow: + @workflow.run + async def run(self, file_path: str, prompt: str) -> str: + client = TemporalAsyncClient() + uploaded = await client.files.upload( + file=file_path, + config=types.UploadFileConfig(mime_type="text/plain"), + ) + contents = cast(types.ContentListUnion, [prompt, uploaded]) + response = await client.models.generate_content( + model="gemini-2.5-flash", + contents=contents, + ) + return response.text or "" + + +# @@@SNIPEND diff --git a/google_genai/hello_world/README.md b/google_genai/hello_world/README.md new file mode 100644 index 000000000..5f6dd8b66 --- /dev/null +++ b/google_genai/hello_world/README.md @@ -0,0 +1,33 @@ +# Hello World + +The simplest Google GenAI + Temporal sample: one `generate_content` call. The +call runs as a Temporal activity, so it gets durable retries, timeouts, and +crash recovery, and the Gemini credentials never enter the workflow. + +## What This Sample Demonstrates + +- Wiring `GoogleGenAIPlugin` onto the worker with a real `genai.Client` +- Constructing a `TemporalAsyncClient` inside a `@workflow.defn` +- Calling `client.models.generate_content(...)` durably + +## Running the Sample + +Prerequisites: install dependencies, set `GOOGLE_API_KEY`, and start a Temporal +dev server (`temporal server start-dev`). See the +[suite README](../README.md) for details. + +```bash +# Terminal 1 +uv run google_genai/hello_world/run_worker.py + +# Terminal 2 +uv run google_genai/hello_world/run_workflow.py +``` + +## Files + +| File | Description | +|------|-------------| +| `workflow.py` | `HelloWorldWorkflow` with a single `generate_content` call | +| `run_worker.py` | Registers `GoogleGenAIPlugin`, starts the worker | +| `run_workflow.py` | Executes the workflow and prints the result | diff --git a/google_genai/hello_world/__init__.py b/google_genai/hello_world/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/google_genai/hello_world/run_worker.py b/google_genai/hello_world/run_worker.py new file mode 100644 index 000000000..422a1b541 --- /dev/null +++ b/google_genai/hello_world/run_worker.py @@ -0,0 +1,36 @@ +"""Worker for the hello world sample.""" + +# @@@SNIPSTART python-google-genai-hello-world-worker +import asyncio +import os + +from google import genai +from temporalio.client import Client +from temporalio.contrib.google_genai import GoogleGenAIPlugin +from temporalio.worker import Worker + +from google_genai.hello_world.workflow import HelloWorldWorkflow + + +async def main() -> None: + # The real genai.Client (with credentials) lives only on the worker. + genai_client = genai.Client(api_key=os.environ["GOOGLE_API_KEY"]) + plugin = GoogleGenAIPlugin(genai_client) + + client = await Client.connect( + os.environ.get("TEMPORAL_ADDRESS", "localhost:7233"), + plugins=[plugin], + ) + + worker = Worker( + client, + task_queue="google-genai-hello-world", + workflows=[HelloWorldWorkflow], + ) + print("Worker started. Ctrl+C to exit.") + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) +# @@@SNIPEND diff --git a/google_genai/hello_world/run_workflow.py b/google_genai/hello_world/run_workflow.py new file mode 100644 index 000000000..89d93e45b --- /dev/null +++ b/google_genai/hello_world/run_workflow.py @@ -0,0 +1,27 @@ +"""Start the hello world workflow.""" + +# @@@SNIPSTART python-google-genai-hello-world-run-workflow +import asyncio +import os + +from temporalio.client import Client + +from google_genai.hello_world.workflow import HelloWorldWorkflow + + +async def main() -> None: + client = await Client.connect(os.environ.get("TEMPORAL_ADDRESS", "localhost:7233")) + + result = await client.execute_workflow( + HelloWorldWorkflow.run, + "Write a haiku about durable execution.", + id="google-genai-hello-world", + task_queue="google-genai-hello-world", + ) + + print(f"Result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) +# @@@SNIPEND diff --git a/google_genai/hello_world/workflow.py b/google_genai/hello_world/workflow.py new file mode 100644 index 000000000..c9664580f --- /dev/null +++ b/google_genai/hello_world/workflow.py @@ -0,0 +1,25 @@ +"""Minimal Temporal + Google GenAI workflow: one prompt, one response. + +Every Gemini API call made through ``TemporalAsyncClient`` runs as a durable +Temporal activity, so it gets retries, timeouts, and crash recovery for free — +and no credentials ever enter the workflow. +""" + +from temporalio import workflow +from temporalio.contrib.google_genai import TemporalAsyncClient + + +# @@@SNIPSTART python-google-genai-hello-world-workflow +@workflow.defn +class HelloWorldWorkflow: + @workflow.run + async def run(self, prompt: str) -> str: + client = TemporalAsyncClient() + response = await client.models.generate_content( + model="gemini-2.5-flash", + contents=prompt, + ) + return response.text or "" + + +# @@@SNIPEND diff --git a/google_genai/interactions/README.md b/google_genai/interactions/README.md new file mode 100644 index 000000000..34d28b024 --- /dev/null +++ b/google_genai/interactions/README.md @@ -0,0 +1,40 @@ +# Interactions + +The Interactions API (`client.interactions`) is a server-managed, stateful +conversation API: state lives on Google's backend, addressed by an interaction +id. This sample creates an interaction, fetches it, and deletes it — each +operation running as a Temporal activity. + +> **Requires a live Gemini API key.** The Interactions API talks to a real +> backend that the plugin's test server does not mock, so this sample has no +> automated test — run it against a real `GOOGLE_API_KEY`. + +Note: unlike `client.models`, the Interactions API has no automatic function +calling. To use tools, declare them as `{"type": "function", ...}` dicts and +drive the tool loop yourself. + +## What This Sample Demonstrates + +- `client.interactions.create(model=..., input=...)` as a durable activity +- `client.interactions.get(id)` and `client.interactions.delete(id)` + +## Running the Sample + +Prerequisites: install dependencies, set `GOOGLE_API_KEY`, and start a Temporal +dev server. See the [suite README](../README.md). + +```bash +# Terminal 1 +uv run google_genai/interactions/run_worker.py + +# Terminal 2 +uv run google_genai/interactions/run_workflow.py +``` + +## Files + +| File | Description | +|------|-------------| +| `workflow.py` | `InteractionsWorkflow` — create, get, delete an interaction | +| `run_worker.py` | Registers `GoogleGenAIPlugin`, starts the worker | +| `run_workflow.py` | Executes the workflow and prints the result | diff --git a/google_genai/interactions/__init__.py b/google_genai/interactions/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/google_genai/interactions/run_worker.py b/google_genai/interactions/run_worker.py new file mode 100644 index 000000000..36539f29f --- /dev/null +++ b/google_genai/interactions/run_worker.py @@ -0,0 +1,35 @@ +"""Worker for the interactions sample.""" + +# @@@SNIPSTART python-google-genai-interactions-worker +import asyncio +import os + +from google import genai +from temporalio.client import Client +from temporalio.contrib.google_genai import GoogleGenAIPlugin +from temporalio.worker import Worker + +from google_genai.interactions.workflow import InteractionsWorkflow + + +async def main() -> None: + genai_client = genai.Client(api_key=os.environ["GOOGLE_API_KEY"]) + plugin = GoogleGenAIPlugin(genai_client) + + client = await Client.connect( + os.environ.get("TEMPORAL_ADDRESS", "localhost:7233"), + plugins=[plugin], + ) + + worker = Worker( + client, + task_queue="google-genai-interactions", + workflows=[InteractionsWorkflow], + ) + print("Worker started. Ctrl+C to exit.") + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) +# @@@SNIPEND diff --git a/google_genai/interactions/run_workflow.py b/google_genai/interactions/run_workflow.py new file mode 100644 index 000000000..b0413fe3d --- /dev/null +++ b/google_genai/interactions/run_workflow.py @@ -0,0 +1,27 @@ +"""Start the interactions workflow.""" + +# @@@SNIPSTART python-google-genai-interactions-run-workflow +import asyncio +import os + +from temporalio.client import Client + +from google_genai.interactions.workflow import InteractionsWorkflow + + +async def main() -> None: + client = await Client.connect(os.environ.get("TEMPORAL_ADDRESS", "localhost:7233")) + + result = await client.execute_workflow( + InteractionsWorkflow.run, + "What is durable execution?", + id="google-genai-interactions", + task_queue="google-genai-interactions", + ) + + print(f"Result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) +# @@@SNIPEND diff --git a/google_genai/interactions/workflow.py b/google_genai/interactions/workflow.py new file mode 100644 index 000000000..5c2270946 --- /dev/null +++ b/google_genai/interactions/workflow.py @@ -0,0 +1,34 @@ +"""Stateful server-side conversations via the Interactions API. + +``client.interactions`` is a server-managed API: the conversation state lives on +Google's backend, addressed by an interaction id. Each operation — create, get, +delete — runs as a Temporal activity. Unlike ``client.models``, this API has no +automatic function calling. +""" + +from typing import Any + +from temporalio import workflow +from temporalio.contrib.google_genai import TemporalAsyncClient + + +# @@@SNIPSTART python-google-genai-interactions-workflow +@workflow.defn +class InteractionsWorkflow: + @workflow.run + async def run(self, prompt: str) -> dict[str, Any]: + client = TemporalAsyncClient() + + # create/get return either an Interaction or a streaming response; without + # stream=True the result is always an Interaction. + interaction: Any = await client.interactions.create( + model="gemini-2.5-flash", + input=prompt, + ) + fetched: Any = await client.interactions.get(interaction.id) + await client.interactions.delete(interaction.id) + + return {"id": interaction.id, "status": str(fetched.status)} + + +# @@@SNIPEND diff --git a/google_genai/mcp/README.md b/google_genai/mcp/README.md new file mode 100644 index 000000000..da4a10b55 --- /dev/null +++ b/google_genai/mcp/README.md @@ -0,0 +1,37 @@ +# MCP + +Give Gemini access to an [MCP](https://modelcontextprotocol.io/) server's tools. +The worker launches the `echo_mcp_server.py` stdio server and registers it with +the plugin under the name `echo`. Inside the workflow, a +`TemporalMcpClientSession("echo")` is passed as a tool — Gemini's AFC loop +discovers and calls its tools, and `list_tools` / `call_tool` run as Temporal +activities against a pooled worker-side connection. + +## What This Sample Demonstrates + +- Registering an MCP server with `GoogleGenAIPlugin(mcp_servers={...})` +- Passing `TemporalMcpClientSession(name)` as a `generate_content` tool +- `cache_tools=True` to discover the tool list once and reuse it (replay-safe) +- MCP tool calls dispatched through per-server Temporal activities + +## Running the Sample + +Prerequisites: install dependencies, set `GOOGLE_API_KEY`, and start a Temporal +dev server. See the [suite README](../README.md). + +```bash +# Terminal 1 +uv run google_genai/mcp/run_worker.py + +# Terminal 2 +uv run google_genai/mcp/run_workflow.py +``` + +## Files + +| File | Description | +|------|-------------| +| `echo_mcp_server.py` | A minimal `FastMCP` stdio server exposing an `echo` tool | +| `workflow.py` | `McpWorkflow` — passes `TemporalMcpClientSession("echo")` as a tool | +| `run_worker.py` | Registers the `echo` MCP server with the plugin, starts the worker | +| `run_workflow.py` | Executes the workflow and prints the result | diff --git a/google_genai/mcp/__init__.py b/google_genai/mcp/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/google_genai/mcp/echo_mcp_server.py b/google_genai/mcp/echo_mcp_server.py new file mode 100644 index 000000000..d75f82cd7 --- /dev/null +++ b/google_genai/mcp/echo_mcp_server.py @@ -0,0 +1,15 @@ +"""A minimal stdio MCP server used by the MCP sample.""" + +from mcp.server.fastmcp import FastMCP + +mcp = FastMCP("echo-server") + + +@mcp.tool() +def echo(message: str) -> str: + """Return the input message unchanged.""" + return message + + +if __name__ == "__main__": + mcp.run() diff --git a/google_genai/mcp/run_worker.py b/google_genai/mcp/run_worker.py new file mode 100644 index 000000000..821c3555b --- /dev/null +++ b/google_genai/mcp/run_worker.py @@ -0,0 +1,58 @@ +"""Worker for the MCP sample. + +Registers an ``echo`` MCP server (the ``echo_mcp_server.py`` stdio script) with +the plugin. The plugin opens a pooled MCP connection on the worker and runs +``list_tools`` / ``call_tool`` as activities. +""" + +import asyncio +import os +import sys +from collections.abc import AsyncIterator +from contextlib import asynccontextmanager +from pathlib import Path + +from google import genai +from mcp import ClientSession, StdioServerParameters +from mcp.client.stdio import stdio_client +from temporalio.client import Client +from temporalio.contrib.google_genai import GoogleGenAIPlugin +from temporalio.worker import Worker + +from google_genai.mcp.workflow import McpWorkflow + +ECHO_SERVER = str(Path(__file__).parent / "echo_mcp_server.py") + + +# @@@SNIPSTART python-google-genai-mcp-worker +@asynccontextmanager +async def echo_session() -> AsyncIterator[ClientSession]: + """Yield a connected, initialized session to the stdio echo MCP server.""" + params = StdioServerParameters(command=sys.executable, args=[ECHO_SERVER]) + async with stdio_client(params) as (read, write): + async with ClientSession(read, write) as session: + await session.initialize() + yield session + + +async def main() -> None: + genai_client = genai.Client(api_key=os.environ["GOOGLE_API_KEY"]) + plugin = GoogleGenAIPlugin(genai_client, mcp_servers={"echo": echo_session}) + # @@@SNIPEND + + client = await Client.connect( + os.environ.get("TEMPORAL_ADDRESS", "localhost:7233"), + plugins=[plugin], + ) + + worker = Worker( + client, + task_queue="google-genai-mcp", + workflows=[McpWorkflow], + ) + print("Worker started. Ctrl+C to exit.") + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/google_genai/mcp/run_workflow.py b/google_genai/mcp/run_workflow.py new file mode 100644 index 000000000..b817c77ea --- /dev/null +++ b/google_genai/mcp/run_workflow.py @@ -0,0 +1,27 @@ +"""Start the MCP workflow.""" + +# @@@SNIPSTART python-google-genai-mcp-run-workflow +import asyncio +import os + +from temporalio.client import Client + +from google_genai.mcp.workflow import McpWorkflow + + +async def main() -> None: + client = await Client.connect(os.environ.get("TEMPORAL_ADDRESS", "localhost:7233")) + + result = await client.execute_workflow( + McpWorkflow.run, + "Use the echo tool to echo back the phrase: durable execution.", + id="google-genai-mcp", + task_queue="google-genai-mcp", + ) + + print(f"Result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) +# @@@SNIPEND diff --git a/google_genai/mcp/workflow.py b/google_genai/mcp/workflow.py new file mode 100644 index 000000000..207e0a061 --- /dev/null +++ b/google_genai/mcp/workflow.py @@ -0,0 +1,41 @@ +"""Use an MCP server's tools from a Gemini call via TemporalMcpClientSession. + +The worker registers an ``echo`` MCP server with the plugin. Inside the +workflow, ``TemporalMcpClientSession("echo")`` is passed as a tool; Gemini's AFC +loop discovers and calls the MCP tools, with ``list_tools`` / ``call_tool`` +running as Temporal activities against a pooled worker-side connection. +""" + +from datetime import timedelta + +from google.genai import types +from temporalio import workflow +from temporalio.contrib.google_genai import ( + TemporalAsyncClient, + TemporalMcpClientSession, +) +from temporalio.workflow import ActivityConfig + + +# @@@SNIPSTART python-google-genai-mcp-workflow +@workflow.defn +class McpWorkflow: + @workflow.run + async def run(self, prompt: str) -> str: + client = TemporalAsyncClient() + session = TemporalMcpClientSession( + "echo", + cache_tools=True, + activity_config=ActivityConfig( + start_to_close_timeout=timedelta(seconds=30), + ), + ) + response = await client.models.generate_content( + model="gemini-2.5-flash", + contents=prompt, + config=types.GenerateContentConfig(tools=[session]), + ) + return response.text or "" + + +# @@@SNIPEND diff --git a/google_genai/streaming/README.md b/google_genai/streaming/README.md new file mode 100644 index 000000000..f6acf7a48 --- /dev/null +++ b/google_genai/streaming/README.md @@ -0,0 +1,48 @@ +# Streaming + +Forward Gemini model output to an external subscriber in real time. +`TemporalAsyncClient(streaming_topic="gemini")` publishes each +`generate_content_stream` chunk onto a workflow-hosted `WorkflowStream` as it +arrives. A subscriber connects with `WorkflowStreamClient` and reads the topic +live while the workflow runs durably. + +## What This Sample Demonstrates + +- `TemporalAsyncClient(streaming_topic=...)` publishing chunks to a topic +- Hosting a `WorkflowStream` in `@workflow.init` (required for streaming) +- Consuming the stream externally via `WorkflowStreamClient.subscribe(...)` +- Holding the workflow open on a signal so the subscriber can drain the stream +- Timing out both sides of the handshake so neither waits forever + +## Timeouts + +The rendezvous between workflow and subscriber has two failure modes, and both +sides are bounded here rather than waiting forever: + +- The subscriber stops reading only when a chunk carries `finish_reason`. If + generation fails mid-stream that chunk never arrives, so the consume loop is + wrapped in `asyncio.wait_for`. +- The workflow stops waiting only on the `finish` signal. If the subscriber + crashes before signaling, nothing releases it — so `workflow.wait_condition` + takes a timeout and the workflow completes without the signal. + +## Running the Sample + +Prerequisites: install dependencies, set `GOOGLE_API_KEY`, and start a Temporal +dev server. See the [suite README](../README.md). + +```bash +# Terminal 1 +uv run google_genai/streaming/run_worker.py + +# Terminal 2 +uv run google_genai/streaming/run_workflow.py +``` + +## Files + +| File | Description | +|------|-------------| +| `workflow.py` | `StreamingWorkflow` — streams chunks to the `gemini` topic | +| `run_worker.py` | Registers `GoogleGenAIPlugin`, starts the worker | +| `run_workflow.py` | Starts the workflow and consumes the stream live | diff --git a/google_genai/streaming/__init__.py b/google_genai/streaming/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/google_genai/streaming/run_worker.py b/google_genai/streaming/run_worker.py new file mode 100644 index 000000000..399242c14 --- /dev/null +++ b/google_genai/streaming/run_worker.py @@ -0,0 +1,35 @@ +"""Worker for the streaming sample.""" + +# @@@SNIPSTART python-google-genai-streaming-worker +import asyncio +import os + +from google import genai +from temporalio.client import Client +from temporalio.contrib.google_genai import GoogleGenAIPlugin +from temporalio.worker import Worker + +from google_genai.streaming.workflow import StreamingWorkflow + + +async def main() -> None: + genai_client = genai.Client(api_key=os.environ["GOOGLE_API_KEY"]) + plugin = GoogleGenAIPlugin(genai_client) + + client = await Client.connect( + os.environ.get("TEMPORAL_ADDRESS", "localhost:7233"), + plugins=[plugin], + ) + + worker = Worker( + client, + task_queue="google-genai-streaming", + workflows=[StreamingWorkflow], + ) + print("Worker started. Ctrl+C to exit.") + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) +# @@@SNIPEND diff --git a/google_genai/streaming/run_workflow.py b/google_genai/streaming/run_workflow.py new file mode 100644 index 000000000..0cded9186 --- /dev/null +++ b/google_genai/streaming/run_workflow.py @@ -0,0 +1,66 @@ +"""Start the streaming workflow and consume model chunks live.""" + +import asyncio +import os +from datetime import timedelta + +from google.genai import types +from temporalio.client import Client +from temporalio.contrib.pydantic import pydantic_data_converter +from temporalio.contrib.workflow_streams import WorkflowStreamClient + +from google_genai.streaming.workflow import StreamingWorkflow + +# Only a chunk carrying finish_reason ends the subscribe loop, so bound the +# wait: if generation fails mid-stream, no such chunk ever arrives. +STREAM_TIMEOUT = 60.0 + + +# @@@SNIPSTART python-google-genai-streaming-run-workflow +async def consume(client: Client, workflow_id: str) -> None: + """Subscribe to the "gemini" topic and print chunks as the model produces them.""" + stream = WorkflowStreamClient.create(client, workflow_id) + async for item in stream.subscribe( + ["gemini"], + from_offset=0, + result_type=types.GenerateContentResponse, + poll_cooldown=timedelta(milliseconds=50), + ): + chunk: types.GenerateContentResponse = item.data + if chunk.text: + print(chunk.text, end="", flush=True) + if chunk.candidates and chunk.candidates[0].finish_reason: + print() + return + + +async def main() -> None: + # The stream publishes Pydantic GenerateContentResponse chunks, so the + # consumer needs the Pydantic data converter to decode them. + client = await Client.connect( + os.environ.get("TEMPORAL_ADDRESS", "localhost:7233"), + data_converter=pydantic_data_converter, + ) + # @@@SNIPEND + workflow_id = "google-genai-streaming" + + handle = await client.start_workflow( + StreamingWorkflow.run, + "Count from 1 to 5, one number per sentence.", + id=workflow_id, + task_queue="google-genai-streaming", + ) + + try: + await asyncio.wait_for(consume(client, workflow_id), timeout=STREAM_TIMEOUT) + except asyncio.TimeoutError: + print(f"\nNo end-of-stream chunk after {STREAM_TIMEOUT}s; giving up.") + + # Release the workflow now that we've consumed the stream. + await handle.signal(StreamingWorkflow.finish) + result = await handle.result() + print(f"Final result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/google_genai/streaming/workflow.py b/google_genai/streaming/workflow.py new file mode 100644 index 000000000..ebf657012 --- /dev/null +++ b/google_genai/streaming/workflow.py @@ -0,0 +1,55 @@ +"""Stream Gemini output to an external subscriber via WorkflowStream. + +``TemporalAsyncClient(streaming_topic="gemini")`` publishes each +``generate_content_stream`` chunk onto a workflow-hosted ``WorkflowStream`` as +it arrives, so external consumers can watch the model produce text in real time +while the workflow runs durably. The workflow holds itself open on a ``finish`` +signal so a subscriber can reliably read the stream before the run completes. +""" + +import asyncio +from datetime import timedelta + +from temporalio import workflow +from temporalio.contrib.google_genai import TemporalAsyncClient +from temporalio.contrib.workflow_streams import WorkflowStream + +# A subscriber that crashes before signaling should not pin the workflow open. +FINISH_TIMEOUT = timedelta(minutes=5) + + +# @@@SNIPSTART python-google-genai-streaming-workflow +@workflow.defn +class StreamingWorkflow: + @workflow.init + def __init__(self, prompt: str) -> None: + # Hosting a WorkflowStream is required when streaming_topic is set. + self.stream = WorkflowStream() + self._done = False + + @workflow.run + async def run(self, prompt: str) -> str: + client = TemporalAsyncClient(streaming_topic="gemini") + chunks: list[str] = [] + async for chunk in await client.models.generate_content_stream( + model="gemini-2.5-flash", + contents=prompt, + ): + chunks.append(chunk.text or "") + # Bound the wait: if the subscriber dies without signaling, complete + # anyway instead of waiting forever. + try: + await workflow.wait_condition(lambda: self._done, timeout=FINISH_TIMEOUT) + except asyncio.TimeoutError: + workflow.logger.warning( + "No finish signal after %s; completing without a subscriber.", + FINISH_TIMEOUT, + ) + return "".join(chunks) + + @workflow.signal + def finish(self) -> None: + self._done = True + + +# @@@SNIPEND diff --git a/google_genai/structured_output/README.md b/google_genai/structured_output/README.md new file mode 100644 index 000000000..5b19ed945 --- /dev/null +++ b/google_genai/structured_output/README.md @@ -0,0 +1,32 @@ +# Structured Output + +Get typed JSON back from Gemini by passing a Pydantic model as `response_schema`. +The plugin installs a `PydanticPayloadConverter`, so the model serializes cleanly +through Temporal payloads — the workflow returns a real `Recipe` instance. + +## What This Sample Demonstrates + +- `GenerateContentConfig(response_mime_type="application/json", response_schema=Recipe)` +- Reading the parsed model from `response.parsed` +- Returning a Pydantic model as a workflow result via the plugin's Pydantic converter + +## Running the Sample + +Prerequisites: install dependencies, set `GOOGLE_API_KEY`, and start a Temporal +dev server. See the [suite README](../README.md). + +```bash +# Terminal 1 +uv run google_genai/structured_output/run_worker.py + +# Terminal 2 +uv run google_genai/structured_output/run_workflow.py +``` + +## Files + +| File | Description | +|------|-------------| +| `workflow.py` | The `Recipe` model and `StructuredOutputWorkflow` | +| `run_worker.py` | Registers `GoogleGenAIPlugin`, starts the worker | +| `run_workflow.py` | Executes the workflow and prints the typed recipe | diff --git a/google_genai/structured_output/__init__.py b/google_genai/structured_output/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/google_genai/structured_output/run_worker.py b/google_genai/structured_output/run_worker.py new file mode 100644 index 000000000..3b0b2f2fb --- /dev/null +++ b/google_genai/structured_output/run_worker.py @@ -0,0 +1,35 @@ +"""Worker for the structured output sample.""" + +# @@@SNIPSTART python-google-genai-structured-output-worker +import asyncio +import os + +from google import genai +from temporalio.client import Client +from temporalio.contrib.google_genai import GoogleGenAIPlugin +from temporalio.worker import Worker + +from google_genai.structured_output.workflow import StructuredOutputWorkflow + + +async def main() -> None: + genai_client = genai.Client(api_key=os.environ["GOOGLE_API_KEY"]) + plugin = GoogleGenAIPlugin(genai_client) + + client = await Client.connect( + os.environ.get("TEMPORAL_ADDRESS", "localhost:7233"), + plugins=[plugin], + ) + + worker = Worker( + client, + task_queue="google-genai-structured-output", + workflows=[StructuredOutputWorkflow], + ) + print("Worker started. Ctrl+C to exit.") + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) +# @@@SNIPEND diff --git a/google_genai/structured_output/run_workflow.py b/google_genai/structured_output/run_workflow.py new file mode 100644 index 000000000..e661ab78a --- /dev/null +++ b/google_genai/structured_output/run_workflow.py @@ -0,0 +1,31 @@ +"""Start the structured output workflow.""" + +# @@@SNIPSTART python-google-genai-structured-output-run-workflow +import asyncio +import os + +from temporalio.client import Client + +from google_genai.structured_output.workflow import StructuredOutputWorkflow + + +async def main() -> None: + client = await Client.connect(os.environ.get("TEMPORAL_ADDRESS", "localhost:7233")) + + recipe = await client.execute_workflow( + StructuredOutputWorkflow.run, + "Give me a simple recipe for avocado toast.", + id="google-genai-structured-output", + task_queue="google-genai-structured-output", + ) + + print(f"Recipe: {recipe.name}") + print(f"Ingredients: {', '.join(recipe.ingredients)}") + print("Steps:") + for i, step in enumerate(recipe.steps, start=1): + print(f" {i}. {step}") + + +if __name__ == "__main__": + asyncio.run(main()) +# @@@SNIPEND diff --git a/google_genai/structured_output/workflow.py b/google_genai/structured_output/workflow.py new file mode 100644 index 000000000..b80a75598 --- /dev/null +++ b/google_genai/structured_output/workflow.py @@ -0,0 +1,49 @@ +"""Typed JSON output via response_schema + a Pydantic model. + +The plugin installs a ``PydanticPayloadConverter``, so a Pydantic model flows +through Temporal payloads cleanly. Passing the model as ``response_schema`` +makes Gemini return matching JSON, which the SDK parses into the model on +``response.parsed``. +""" + +from google.genai import types +from pydantic import BaseModel +from temporalio import workflow +from temporalio.contrib.google_genai import TemporalAsyncClient +from temporalio.exceptions import ApplicationError + + +# @@@SNIPSTART python-google-genai-structured-output-workflow +class Recipe(BaseModel): + name: str + ingredients: list[str] + steps: list[str] + + +@workflow.defn +class StructuredOutputWorkflow: + @workflow.run + async def run(self, prompt: str) -> Recipe: + client = TemporalAsyncClient() + response = await client.models.generate_content( + model="gemini-2.5-flash", + contents=prompt, + config=types.GenerateContentConfig( + response_mime_type="application/json", + response_schema=Recipe, + ), + ) + recipe = response.parsed + if not isinstance(recipe, Recipe): + # ``parsed`` is None when the model returns malformed JSON. Fail the + # workflow with an ApplicationError rather than asserting: an + # assertion is a workflow task failure, which Temporal retries + # forever, so the run would hang instead of failing visibly. + raise ApplicationError( + f"Gemini did not return a valid Recipe: {response.text!r}", + non_retryable=True, + ) + return recipe + + +# @@@SNIPEND diff --git a/google_genai/tools/README.md b/google_genai/tools/README.md new file mode 100644 index 000000000..5c3fd7ac9 --- /dev/null +++ b/google_genai/tools/README.md @@ -0,0 +1,39 @@ +# Tools + +Two tool surfaces wired into one Gemini `generate_content` call, both driven by +the SDK's automatic function-calling (AFC) loop: + +| Pattern | When to use it | +|---------|----------------| +| `@activity.defn` wrapped via `activity_as_tool` | Anything with I/O or non-determinism — runs as a durable activity. | +| Plain workflow method | Pure, deterministic logic — runs in-workflow with no activity dispatch. | + +A single prompt exercises both: the model calls `get_weather` (an activity), +then `recommend_thing_to_do` (a workflow method). + +## What This Sample Demonstrates + +- `activity_as_tool` carrying per-tool `ActivityConfig` (timeouts, retries) +- Passing a plain workflow method as a tool alongside an activity-backed one +- The AFC loop running inside the workflow, dispatching tool calls durably + +## Running the Sample + +Prerequisites: install dependencies, set `GOOGLE_API_KEY`, and start a Temporal +dev server. See the [suite README](../README.md). + +```bash +# Terminal 1 +uv run google_genai/tools/run_worker.py + +# Terminal 2 +uv run google_genai/tools/run_workflow.py +``` + +## Files + +| File | Description | +|------|-------------| +| `workflow.py` | `get_weather` activity, `recommend_thing_to_do` method, and `ToolsWorkflow` | +| `run_worker.py` | Registers `GoogleGenAIPlugin` + the `get_weather` activity | +| `run_workflow.py` | Executes the workflow and prints the result | diff --git a/google_genai/tools/__init__.py b/google_genai/tools/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/google_genai/tools/run_worker.py b/google_genai/tools/run_worker.py new file mode 100644 index 000000000..05951fa34 --- /dev/null +++ b/google_genai/tools/run_worker.py @@ -0,0 +1,36 @@ +"""Worker for the tools sample.""" + +import asyncio +import os + +from google import genai +from temporalio.client import Client +from temporalio.contrib.google_genai import GoogleGenAIPlugin +from temporalio.worker import Worker + +from google_genai.tools.workflow import ToolsWorkflow, get_weather + + +async def main() -> None: + genai_client = genai.Client(api_key=os.environ["GOOGLE_API_KEY"]) + plugin = GoogleGenAIPlugin(genai_client) + + client = await Client.connect( + os.environ.get("TEMPORAL_ADDRESS", "localhost:7233"), + plugins=[plugin], + ) + + # @@@SNIPSTART python-google-genai-tools-worker + worker = Worker( + client, + task_queue="google-genai-tools", + workflows=[ToolsWorkflow], + activities=[get_weather], + ) + # @@@SNIPEND + print("Worker started. Ctrl+C to exit.") + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/google_genai/tools/run_workflow.py b/google_genai/tools/run_workflow.py new file mode 100644 index 000000000..649ce0ffe --- /dev/null +++ b/google_genai/tools/run_workflow.py @@ -0,0 +1,27 @@ +"""Start the tools workflow.""" + +# @@@SNIPSTART python-google-genai-tools-run-workflow +import asyncio +import os + +from temporalio.client import Client + +from google_genai.tools.workflow import ToolsWorkflow + + +async def main() -> None: + client = await Client.connect(os.environ.get("TEMPORAL_ADDRESS", "localhost:7233")) + + result = await client.execute_workflow( + ToolsWorkflow.run, + "What's the weather in Tokyo, and what should I do there?", + id="google-genai-tools", + task_queue="google-genai-tools", + ) + + print(f"Result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) +# @@@SNIPEND diff --git a/google_genai/tools/workflow.py b/google_genai/tools/workflow.py new file mode 100644 index 000000000..7c7e73e23 --- /dev/null +++ b/google_genai/tools/workflow.py @@ -0,0 +1,61 @@ +"""Two tool surfaces on one Gemini call, both driven by automatic function calling. + +1. ``@activity.defn get_weather`` wrapped via ``activity_as_tool`` — runs as a + durable Temporal activity. Use this for I/O or non-deterministic work. +2. ``recommend_thing_to_do`` — a plain workflow method passed directly as a tool. + It runs deterministically in-workflow with no activity dispatch. + +Gemini's automatic function-calling (AFC) loop runs inside the workflow and +invokes both as needed. +""" + +from datetime import timedelta + +from google.genai import types +from temporalio import activity, workflow +from temporalio.contrib.google_genai import TemporalAsyncClient, activity_as_tool +from temporalio.workflow import ActivityConfig + + +# @@@SNIPSTART python-google-genai-tools-activity +@activity.defn +async def get_weather(city: str) -> str: + """Look up the current weather for a city.""" + # Stub — replace with a real HTTP call in production. + return f"It's 72F and sunny in {city}." + + +# @@@SNIPEND + + +# @@@SNIPSTART python-google-genai-tools-workflow +@workflow.defn +class ToolsWorkflow: + @workflow.run + async def run(self, prompt: str) -> str: + client = TemporalAsyncClient() + response = await client.models.generate_content( + model="gemini-2.5-flash", + contents=prompt, + config=types.GenerateContentConfig( + tools=[ + activity_as_tool( + get_weather, + activity_config=ActivityConfig( + start_to_close_timeout=timedelta(seconds=30), + ), + ), + self.recommend_thing_to_do, + ], + ), + ) + return response.text or "" + + async def recommend_thing_to_do(self, weather: str) -> str: + """Recommend something to do given a weather description.""" + if "sunny" in weather.lower(): + return "Go for a hike." + return "Visit a museum." + + +# @@@SNIPEND diff --git a/google_genai/vertex_ai/README.md b/google_genai/vertex_ai/README.md new file mode 100644 index 000000000..45bee97a4 --- /dev/null +++ b/google_genai/vertex_ai/README.md @@ -0,0 +1,47 @@ +# Vertex AI + +The same hello-world flow as [`hello_world`](../hello_world), but pointed at +**Vertex AI** instead of the Gemini Developer API. The only difference is +configuration: both the worker's `genai.Client` and the workflow's +`TemporalAsyncClient` set `vertexai=True` with a Google Cloud project and +location. The `vertexai` setting must match on both sides. + +> **Requires Google Cloud credentials**, not a Gemini API key. Authenticate with +> Application Default Credentials (`gcloud auth application-default login`) or a +> service-account key (`GOOGLE_APPLICATION_CREDENTIALS`). This sample has no +> automated test. + +## Configuration + +| Variable | Description | +|----------|-------------| +| `GOOGLE_CLOUD_PROJECT` | Your Google Cloud project ID (required) | +| `GOOGLE_CLOUD_LOCATION` | Region, e.g. `us-central1` (defaults to `us-central1`) | + +## What This Sample Demonstrates + +- `genai.Client(vertexai=True, project=..., location=...)` on the worker +- `TemporalAsyncClient(vertexai=True, project=..., location=...)` in the workflow +- Passing project/location as workflow arguments to keep the workflow deterministic + +## Running the Sample + +Prerequisites: install dependencies, configure GCP credentials, set +`GOOGLE_CLOUD_PROJECT`, and start a Temporal dev server. See the +[suite README](../README.md). + +```bash +# Terminal 1 +uv run google_genai/vertex_ai/run_worker.py + +# Terminal 2 +uv run google_genai/vertex_ai/run_workflow.py +``` + +## Files + +| File | Description | +|------|-------------| +| `workflow.py` | `VertexAIWorkflow` — generate_content via Vertex AI | +| `run_worker.py` | Registers a Vertex-configured `GoogleGenAIPlugin` | +| `run_workflow.py` | Reads project/location from env and executes the workflow | diff --git a/google_genai/vertex_ai/__init__.py b/google_genai/vertex_ai/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/google_genai/vertex_ai/run_worker.py b/google_genai/vertex_ai/run_worker.py new file mode 100644 index 000000000..1952a1a4d --- /dev/null +++ b/google_genai/vertex_ai/run_worker.py @@ -0,0 +1,44 @@ +"""Worker for the Vertex AI sample. + +Uses ``genai.Client(vertexai=True, ...)`` with Application Default Credentials +(no API key). Run ``gcloud auth application-default login`` first, or set +``GOOGLE_APPLICATION_CREDENTIALS`` to a service-account key file. +""" + +import asyncio +import os + +from google import genai +from temporalio.client import Client +from temporalio.contrib.google_genai import GoogleGenAIPlugin +from temporalio.worker import Worker + +from google_genai.vertex_ai.workflow import VertexAIWorkflow + + +async def main() -> None: + # @@@SNIPSTART python-google-genai-vertex-ai-worker + genai_client = genai.Client( + vertexai=True, + project=os.environ["GOOGLE_CLOUD_PROJECT"], + location=os.environ.get("GOOGLE_CLOUD_LOCATION", "us-central1"), + ) + plugin = GoogleGenAIPlugin(genai_client) + # @@@SNIPEND + + client = await Client.connect( + os.environ.get("TEMPORAL_ADDRESS", "localhost:7233"), + plugins=[plugin], + ) + + worker = Worker( + client, + task_queue="google-genai-vertex-ai", + workflows=[VertexAIWorkflow], + ) + print("Worker started. Ctrl+C to exit.") + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/google_genai/vertex_ai/run_workflow.py b/google_genai/vertex_ai/run_workflow.py new file mode 100644 index 000000000..d06276833 --- /dev/null +++ b/google_genai/vertex_ai/run_workflow.py @@ -0,0 +1,30 @@ +"""Start the Vertex AI workflow.""" + +# @@@SNIPSTART python-google-genai-vertex-ai-run-workflow +import asyncio +import os + +from temporalio.client import Client + +from google_genai.vertex_ai.workflow import VertexAIWorkflow + + +async def main() -> None: + client = await Client.connect(os.environ.get("TEMPORAL_ADDRESS", "localhost:7233")) + + project = os.environ["GOOGLE_CLOUD_PROJECT"] + location = os.environ.get("GOOGLE_CLOUD_LOCATION", "us-central1") + + result = await client.execute_workflow( + VertexAIWorkflow.run, + args=["Write a haiku about durable execution.", project, location], + id="google-genai-vertex-ai", + task_queue="google-genai-vertex-ai", + ) + + print(f"Result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) +# @@@SNIPEND diff --git a/google_genai/vertex_ai/workflow.py b/google_genai/vertex_ai/workflow.py new file mode 100644 index 000000000..31e35aff5 --- /dev/null +++ b/google_genai/vertex_ai/workflow.py @@ -0,0 +1,31 @@ +"""Hello world against Vertex AI instead of the Gemini Developer API. + +The only difference from the basic sample is configuration: both the workflow's +``TemporalAsyncClient`` and the worker's ``genai.Client`` use ``vertexai=True`` +with a Google Cloud project and location. The project and location are passed in +as workflow arguments (read from the environment by the starter) to keep the +workflow deterministic. +""" + +from temporalio import workflow +from temporalio.contrib.google_genai import TemporalAsyncClient + + +# @@@SNIPSTART python-google-genai-vertex-ai-workflow +@workflow.defn +class VertexAIWorkflow: + @workflow.run + async def run(self, prompt: str, project: str, location: str) -> str: + client = TemporalAsyncClient( + vertexai=True, + project=project, + location=location, + ) + response = await client.models.generate_content( + model="gemini-2.5-flash", + contents=prompt, + ) + return response.text or "" + + +# @@@SNIPEND diff --git a/hello/README.md b/hello/README.md index 9e13f5448..7b5ca6ebb 100644 --- a/hello/README.md +++ b/hello/README.md @@ -2,32 +2,34 @@ These samples show basic workflow and activity features. -To run, first see [README.md](../README.md) for prerequisites. Then, run the following from this directory to run the +To run, first see [README.md](../README.md) for prerequisites. Then, run the following from the root directory to run the `hello_activity.py` sample: - poetry run python hello_activity.py + uv run hello/hello_activity.py The result will be: Result: Hello, World! -Replace `hello_activity.py` in the command with any other example filename to run it instead. +Replace `hello/hello_activity.py` in the command with any other example filename (with the `hello/` prefix) to run it instead. ## Samples * [hello_activity](hello_activity.py) - Execute an activity from a workflow. +* [hello_activity_async](hello_activity_async.py) - Execute an async activity from a workflow. * [hello_activity_choice](hello_activity_choice.py) - Execute certain activities inside a workflow based on dynamic input. * [hello_activity_method](hello_activity_method.py) - Demonstrate an activity that is an instance method on a class and can access class state. +* [hello_activity_heartbeat](hello_activity_heartbeat.py) - Demonstrate usage of heartbeat timeouts. * [hello_activity_multiprocess](hello_activity_multiprocess.py) - Execute a synchronous activity on a process pool. * [hello_activity_retry](hello_activity_retry.py) - Demonstrate activity retry by failing until a certain number of attempts. -* [hello_activity_threaded](hello_activity_threaded.py) - Execute a synchronous activity on a thread pool. * [hello_async_activity_completion](hello_async_activity_completion.py) - Complete an activity outside of the function that was called. * [hello_cancellation](hello_cancellation.py) - Manually react to cancellation inside workflows and activities. +* [hello_change_log_level](hello_change_log_level.py) - Change the level of workflow task failure from WARN to ERROR. * [hello_child_workflow](hello_child_workflow.py) - Execute a child workflow from a workflow. * [hello_continue_as_new](hello_continue_as_new.py) - Use continue as new to restart a workflow. * [hello_cron](hello_cron.py) - Execute a workflow once a minute. @@ -37,12 +39,13 @@ Replace `hello_activity.py` in the command with any other example filename to ru * [hello_mtls](hello_mtls.py) - Accept URL, namespace, and certificate info as CLI args and use mTLS for connecting to server. * [hello_parallel_activity](hello_parallel_activity.py) - Execute multiple activities at once. +* [hello_patch](hello_patch.py) - Demonstrates how to patch executions. * [hello_query](hello_query.py) - Invoke queries on a workflow. * [hello_search_attributes](hello_search_attributes.py) - Start workflow with search attributes then change while running. * [hello_signal](hello_signal.py) - Send signals to a workflow. -* [hello_update](hello_update.py) - Send a request to and a response from a client to a workflow execution. +* [hello_update](hello_update.py) - **Send a request to and a response from a client to a workflow execution.** Note: To enable the workflow update, set the `frontend.enableUpdateWorkflowExecution` dynamic config value to true. - temporal server start-dev --dynamic-config-value frontend.enableUpdateWorkflowExecution=true \ No newline at end of file + temporal server start-dev --dynamic-config-value frontend.enableUpdateWorkflowExecution=true diff --git a/hello/hello_activity.py b/hello/hello_activity.py index 6615b55dd..c7972c2e3 100644 --- a/hello/hello_activity.py +++ b/hello/hello_activity.py @@ -1,10 +1,11 @@ import asyncio -import logging +from concurrent.futures import ThreadPoolExecutor from dataclasses import dataclass from datetime import timedelta from temporalio import activity, workflow from temporalio.client import Client +from temporalio.envconfig import ClientConfig from temporalio.worker import Worker @@ -19,7 +20,7 @@ class ComposeGreetingInput: # Basic activity that logs and does string concatenation @activity.defn -async def compose_greeting(input: ComposeGreetingInput) -> str: +def compose_greeting(input: ComposeGreetingInput) -> str: activity.logger.info("Running activity with parameter %s" % input) return f"{input.greeting}, {input.name}!" @@ -38,11 +39,16 @@ async def run(self, name: str) -> str: async def main(): - # Uncomment the line below to see logging + # Uncomment the lines below to see logging output + # import logging # logging.basicConfig(level=logging.INFO) + # Load configuration + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + # Start client - client = await Client.connect("localhost:7233") + client = await Client.connect(**config) # Run a worker for the workflow async with Worker( @@ -50,8 +56,11 @@ async def main(): task_queue="hello-activity-task-queue", workflows=[GreetingWorkflow], activities=[compose_greeting], + # Non-async activities require an executor; + # a thread pool executor is recommended. + # This same thread pool could be passed to multiple workers if desired. + activity_executor=ThreadPoolExecutor(5), ): - # While the worker is running, use the client to run the workflow and # print out its result. Note, in many production setups, the client # would be in a completely separate process from the worker. diff --git a/hello/hello_activity_async.py b/hello/hello_activity_async.py new file mode 100644 index 000000000..47b5bd79a --- /dev/null +++ b/hello/hello_activity_async.py @@ -0,0 +1,73 @@ +import asyncio +from dataclasses import dataclass +from datetime import timedelta + +from temporalio import activity, workflow +from temporalio.client import Client +from temporalio.envconfig import ClientConfig +from temporalio.worker import Worker + + +# While we could use multiple parameters in the activity, Temporal strongly +# encourages using a single dataclass instead which can have fields added to it +# in a backwards-compatible way. +@dataclass +class ComposeGreetingInput: + greeting: str + name: str + + +# Basic activity that logs and does string concatenation +@activity.defn +async def compose_greeting(input: ComposeGreetingInput) -> str: + activity.logger.info("Running activity with parameter %s" % input) + return f"{input.greeting}, {input.name}!" + + +# Basic workflow that logs and invokes an activity +@workflow.defn +class GreetingWorkflow: + @workflow.run + async def run(self, name: str) -> str: + workflow.logger.info("Running workflow with parameter %s" % name) + return await workflow.execute_activity( + compose_greeting, + ComposeGreetingInput("Hello", name), + start_to_close_timeout=timedelta(seconds=10), + ) + + +async def main(): + # Uncomment the lines below to see logging output + # import logging + # logging.basicConfig(level=logging.INFO) + + # Start client + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) + + # Run a worker for the workflow + async with Worker( + client, + task_queue="hello-activity-task-queue", + workflows=[GreetingWorkflow], + activities=[compose_greeting], + # If the worker is only running async activities, you don't need + # to supply an activity executor because they run in + # the worker's event loop. + ): + # While the worker is running, use the client to run the workflow and + # print out its result. Note, in many production setups, the client + # would be in a completely separate process from the worker. + result = await client.execute_workflow( + GreetingWorkflow.run, + "World", + id="hello-activity-workflow-id", + task_queue="hello-activity-task-queue", + ) + print(f"Result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/hello/hello_activity_choice.py b/hello/hello_activity_choice.py index 56b7b7e6c..6da9fe69a 100644 --- a/hello/hello_activity_choice.py +++ b/hello/hello_activity_choice.py @@ -1,4 +1,5 @@ import asyncio +from concurrent.futures import ThreadPoolExecutor from dataclasses import dataclass from datetime import timedelta from enum import IntEnum @@ -6,28 +7,29 @@ from temporalio import activity, workflow from temporalio.client import Client +from temporalio.envconfig import ClientConfig from temporalio.worker import Worker # Activities that will be called by the workflow @activity.defn -async def order_apples(amount: int) -> str: +def order_apples(amount: int) -> str: return f"Ordered {amount} Apples..." @activity.defn -async def order_bananas(amount: int) -> str: +def order_bananas(amount: int) -> str: return f"Ordered {amount} Bananas..." @activity.defn -async def order_cherries(amount: int) -> str: +def order_cherries(amount: int) -> str: return f"Ordered {amount} Cherries..." @activity.defn -async def order_oranges(amount: int) -> str: +def order_oranges(amount: int) -> str: return f"Ordered {amount} Oranges..." @@ -54,50 +56,36 @@ class ShoppingList: @workflow.defn class PurchaseFruitsWorkflow: @workflow.run - async def run(self, list: ShoppingList) -> str: + async def run(self, shopping_list: ShoppingList) -> str: # Order each thing on the list ordered: List[str] = [] - for item in list.items: + for item in shopping_list.items: if item.fruit is Fruit.APPLE: - ordered.append( - await workflow.execute_activity( - order_apples, - item.amount, - start_to_close_timeout=timedelta(seconds=5), - ) - ) + order_function = order_apples elif item.fruit is Fruit.BANANA: - ordered.append( - await workflow.execute_activity( - order_bananas, - item.amount, - start_to_close_timeout=timedelta(seconds=5), - ) - ) + order_function = order_bananas elif item.fruit is Fruit.CHERRY: - ordered.append( - await workflow.execute_activity( - order_cherries, - item.amount, - start_to_close_timeout=timedelta(seconds=5), - ) - ) + order_function = order_cherries elif item.fruit is Fruit.ORANGE: - ordered.append( - await workflow.execute_activity( - order_oranges, - item.amount, - start_to_close_timeout=timedelta(seconds=5), - ) - ) + order_function = order_oranges else: raise ValueError(f"Unrecognized fruit: {item.fruit}") + ordered.append( + await workflow.execute_activity( + order_function, + item.amount, + start_to_close_timeout=timedelta(seconds=5), + ) + ) return "".join(ordered) async def main(): + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + # Start client - client = await Client.connect("localhost:7233") + client = await Client.connect(**config) # Run a worker for the workflow async with Worker( @@ -105,8 +93,8 @@ async def main(): task_queue="hello-activity-choice-task-queue", workflows=[PurchaseFruitsWorkflow], activities=[order_apples, order_bananas, order_cherries, order_oranges], + activity_executor=ThreadPoolExecutor(5), ): - # While the worker is running, use the client to run the workflow and # print out its result. Note, in many production setups, the client # would be in a completely separate process from the worker. diff --git a/hello/hello_activity_threaded.py b/hello/hello_activity_heartbeat.py similarity index 75% rename from hello/hello_activity_threaded.py rename to hello/hello_activity_heartbeat.py index e7bf8344a..2b80f9a6f 100644 --- a/hello/hello_activity_threaded.py +++ b/hello/hello_activity_heartbeat.py @@ -1,5 +1,4 @@ import asyncio -import threading import time from concurrent.futures import ThreadPoolExecutor from dataclasses import dataclass @@ -7,6 +6,7 @@ from temporalio import activity, workflow from temporalio.client import Client +from temporalio.envconfig import ClientConfig from temporalio.worker import Worker @@ -21,7 +21,7 @@ def compose_greeting(input: ComposeGreetingInput) -> str: # We'll wait for 3 seconds, heartbeating in between (like all long-running # activities should do), then return the greeting for _ in range(0, 3): - print(f"Heartbeating activity on thread {threading.get_ident()}") + print(f"Heartbeating activity") activity.heartbeat() time.sleep(1) return f"{input.greeting}, {input.name}!" @@ -42,30 +42,28 @@ async def run(self, name: str) -> str: async def main(): # Start client - client = await Client.connect("localhost:7233") + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) # Run a worker for the workflow async with Worker( client, - task_queue="hello-activity-threaded-task-queue", + task_queue="hello-activity-heartbeating-task-queue", workflows=[GreetingWorkflow], activities=[compose_greeting], - # Synchronous activities are not allowed unless we provide some kind of - # executor. This same thread pool could be passed to multiple workers if - # desired. activity_executor=ThreadPoolExecutor(5), ): - # While the worker is running, use the client to run the workflow and # print out its result. Note, in many production setups, the client # would be in a completely separate process from the worker. result = await client.execute_workflow( GreetingWorkflow.run, "World", - id="hello-activity-threaded-workflow-id", - task_queue="hello-activity-threaded-task-queue", + id="hello-activity-heartbeating-workflow-id", + task_queue="hello-activity-heartbeating-task-queue", ) - print(f"Result on thread {threading.get_ident()}: {result}") + print(f"Result: {result}") if __name__ == "__main__": diff --git a/hello/hello_activity_method.py b/hello/hello_activity_method.py index db5272632..d87bc8e70 100644 --- a/hello/hello_activity_method.py +++ b/hello/hello_activity_method.py @@ -3,6 +3,7 @@ from temporalio import activity, workflow from temporalio.client import Client +from temporalio.envconfig import ClientConfig from temporalio.worker import Worker @@ -32,7 +33,9 @@ async def run(self) -> None: async def main(): # Start client - client = await Client.connect("localhost:7233") + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) # Create our database client that can then be used in the activity db_client = MyDatabaseClient() @@ -47,7 +50,6 @@ async def main(): workflows=[MyWorkflow], activities=[my_activities.do_database_thing], ): - # While the worker is running, use the client to run the workflow and # print out its result. Note, in many production setups, the client # would be in a completely separate process from the worker. diff --git a/hello/hello_activity_multiprocess.py b/hello/hello_activity_multiprocess.py index 6630234dd..c7793c2c1 100644 --- a/hello/hello_activity_multiprocess.py +++ b/hello/hello_activity_multiprocess.py @@ -8,6 +8,7 @@ from temporalio import activity, workflow from temporalio.client import Client +from temporalio.envconfig import ClientConfig from temporalio.worker import SharedStateManager, Worker @@ -43,7 +44,9 @@ async def run(self, name: str) -> str: async def main(): # Start client - client = await Client.connect("localhost:7233") + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) # Run a worker for the workflow async with Worker( @@ -65,7 +68,6 @@ async def main(): multiprocessing.Manager() ), ): - # While the worker is running, use the client to run the workflow and # print out its result. Note, in many production setups, the client # would be in a completely separate process from the worker. diff --git a/hello/hello_activity_retry.py b/hello/hello_activity_retry.py index 233f9613f..105aa847b 100644 --- a/hello/hello_activity_retry.py +++ b/hello/hello_activity_retry.py @@ -1,10 +1,12 @@ import asyncio +from concurrent.futures import ThreadPoolExecutor from dataclasses import dataclass from datetime import timedelta from temporalio import activity, workflow from temporalio.client import Client from temporalio.common import RetryPolicy +from temporalio.envconfig import ClientConfig from temporalio.worker import Worker @@ -15,7 +17,7 @@ class ComposeGreetingInput: @activity.defn -async def compose_greeting(input: ComposeGreetingInput) -> str: +def compose_greeting(input: ComposeGreetingInput) -> str: print(f"Invoking activity, attempt number {activity.info().attempt}") # Fail the first 3 attempts, succeed the 4th if activity.info().attempt < 4: @@ -44,7 +46,9 @@ async def run(self, name: str) -> str: async def main(): # Start client - client = await Client.connect("localhost:7233") + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) # Run a worker for the workflow async with Worker( @@ -52,8 +56,8 @@ async def main(): task_queue="hello-activity-retry-task-queue", workflows=[GreetingWorkflow], activities=[compose_greeting], + activity_executor=ThreadPoolExecutor(5), ): - # While the worker is running, use the client to run the workflow and # print out its result. Note, in many production setups, the client # would be in a completely separate process from the worker. diff --git a/hello/hello_async_activity_completion.py b/hello/hello_async_activity_completion.py index 10aa89df7..e777c49ff 100644 --- a/hello/hello_async_activity_completion.py +++ b/hello/hello_async_activity_completion.py @@ -4,6 +4,7 @@ from temporalio import activity, workflow from temporalio.client import Client +from temporalio.envconfig import ClientConfig from temporalio.worker import Worker @@ -68,7 +69,9 @@ async def run(self, name: str) -> str: async def main(): # Start client - client = await Client.connect("localhost:7233") + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) # Run a worker for the workflow composer = GreetingComposer(client) @@ -78,7 +81,6 @@ async def main(): workflows=[GreetingWorkflow], activities=[composer.compose_greeting], ): - # While the worker is running, use the client to run the workflow and # print out its result. Note, in many production setups, the client # would be in a completely separate process from the worker. diff --git a/hello/hello_cancellation.py b/hello/hello_cancellation.py index 3467893c5..a74bc2b47 100644 --- a/hello/hello_cancellation.py +++ b/hello/hello_cancellation.py @@ -1,29 +1,33 @@ import asyncio +import time import traceback +from concurrent.futures import ThreadPoolExecutor from datetime import timedelta from typing import NoReturn from temporalio import activity, workflow from temporalio.client import Client, WorkflowFailureError +from temporalio.envconfig import ClientConfig +from temporalio.exceptions import CancelledError from temporalio.worker import Worker @activity.defn -async def never_complete_activity() -> NoReturn: +def never_complete_activity() -> NoReturn: # All long-running activities should heartbeat. Heartbeat is how # cancellation is delivered from the server. try: while True: print("Heartbeating activity") activity.heartbeat() - await asyncio.sleep(1) - except asyncio.CancelledError: + time.sleep(1) + except CancelledError: print("Activity cancelled") raise @activity.defn -async def cleanup_activity() -> None: +def cleanup_activity() -> None: print("Executing cleanup activity") @@ -47,8 +51,11 @@ async def run(self) -> None: async def main(): + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + # Start client - client = await Client.connect("localhost:7233") + client = await Client.connect(**config) # Run a worker for the workflow async with Worker( @@ -56,8 +63,8 @@ async def main(): task_queue="hello-cancellation-task-queue", workflows=[CancellationWorkflow], activities=[never_complete_activity, cleanup_activity], + activity_executor=ThreadPoolExecutor(5), ): - # While the worker is running, use the client to start the workflow. # Note, in many production setups, the client would be in a completely # separate process from the worker. diff --git a/hello/hello_change_log_level.py b/hello/hello_change_log_level.py new file mode 100644 index 000000000..4b7697f41 --- /dev/null +++ b/hello/hello_change_log_level.py @@ -0,0 +1,71 @@ +""" +Changes the log level of workflow task failures from WARN to ERROR. + +Note that the __temporal_error_identifier attribute was added in +version 1.13.0 of the Python SDK. +""" + +import asyncio +import logging +import sys + +from temporalio import workflow +from temporalio.client import Client +from temporalio.envconfig import ClientConfig +from temporalio.worker import Worker + +# --- Begin logging set‑up ---------------------------------------------------------- +logging.basicConfig( + stream=sys.stdout, + level=logging.INFO, + format="%(asctime)s %(levelname)-8s %(name)s %(message)s", +) + + +class CustomLogFilter(logging.Filter): + def filter(self, record: logging.LogRecord) -> bool: + # Note that the __temporal_error_identifier attribute was added in + # version 1.13.0 of the Python SDK. + if ( + hasattr(record, "__temporal_error_identifier") + and getattr(record, "__temporal_error_identifier") == "WorkflowTaskFailure" + ): + record.levelno = logging.ERROR + record.levelname = logging.getLevelName(logging.ERROR) + return True + + +for h in logging.getLogger().handlers: + h.addFilter(CustomLogFilter()) +# --- End logging set‑up ---------------------------------------------------------- + + +LOG_MESSAGE = "This error is an experiment to check the log level" + + +@workflow.defn +class GreetingWorkflow: + @workflow.run + async def run(self): + raise RuntimeError(LOG_MESSAGE) + + +async def main(): + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) + + async with Worker( + client, + task_queue="hello-change-log-level-task-queue", + workflows=[GreetingWorkflow], + ): + await client.execute_workflow( + GreetingWorkflow.run, + id="hello-change-log-level-workflow-id", + task_queue="hello-change-log-level-task-queue", + ) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/hello/hello_child_workflow.py b/hello/hello_child_workflow.py index 2be0bc1b6..136b788f3 100644 --- a/hello/hello_child_workflow.py +++ b/hello/hello_child_workflow.py @@ -3,6 +3,7 @@ from temporalio import workflow from temporalio.client import Client +from temporalio.envconfig import ClientConfig from temporalio.worker import Worker @@ -31,8 +32,11 @@ async def run(self, name: str) -> str: async def main(): + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + # Start client - client = await Client.connect("localhost:7233") + client = await Client.connect(**config) # Run a worker for the workflow async with Worker( @@ -40,7 +44,6 @@ async def main(): task_queue="hello-child-workflow-task-queue", workflows=[GreetingWorkflow, ComposeGreetingWorkflow], ): - # While the worker is running, use the client to run the workflow and # print out its result. Note, in many production setups, the client # would be in a completely separate process from the worker. diff --git a/hello/hello_continue_as_new.py b/hello/hello_continue_as_new.py index 586aac1d4..a899c7fc7 100644 --- a/hello/hello_continue_as_new.py +++ b/hello/hello_continue_as_new.py @@ -3,6 +3,7 @@ from temporalio import workflow from temporalio.client import Client +from temporalio.envconfig import ClientConfig from temporalio.worker import Worker @@ -22,7 +23,9 @@ async def main(): logging.basicConfig(level=logging.INFO) # Start client - client = await Client.connect("localhost:7233") + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) # Run a worker for the workflow async with Worker( @@ -30,7 +33,6 @@ async def main(): task_queue="hello-continue-as-new-task-queue", workflows=[LoopingWorkflow], ): - # While the worker is running, use the client to run the workflow. Note, # in many production setups, the client would be in a completely # separate process from the worker. diff --git a/hello/hello_cron.py b/hello/hello_cron.py index 68b26099d..1ca29ea13 100644 --- a/hello/hello_cron.py +++ b/hello/hello_cron.py @@ -1,9 +1,11 @@ import asyncio +from concurrent.futures import ThreadPoolExecutor from dataclasses import dataclass from datetime import timedelta from temporalio import activity, workflow from temporalio.client import Client +from temporalio.envconfig import ClientConfig from temporalio.worker import Worker @@ -14,7 +16,7 @@ class ComposeGreetingInput: @activity.defn -async def compose_greeting(input: ComposeGreetingInput) -> str: +def compose_greeting(input: ComposeGreetingInput) -> str: return f"{input.greeting}, {input.name}!" @@ -32,7 +34,9 @@ async def run(self, name: str) -> None: async def main(): # Start client - client = await Client.connect("localhost:7233") + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) # Run a worker for the workflow async with Worker( @@ -40,8 +44,8 @@ async def main(): task_queue="hello-cron-task-queue", workflows=[GreetingWorkflow], activities=[compose_greeting], + activity_executor=ThreadPoolExecutor(5), ): - print("Running workflow once a minute") # While the worker is running, use the client to start the workflow. diff --git a/hello/hello_exception.py b/hello/hello_exception.py index bfb198d50..6e94bf7c0 100644 --- a/hello/hello_exception.py +++ b/hello/hello_exception.py @@ -1,5 +1,6 @@ import asyncio import logging +from concurrent.futures import ThreadPoolExecutor from dataclasses import dataclass from datetime import timedelta from typing import NoReturn, Optional @@ -7,6 +8,7 @@ from temporalio import activity, workflow from temporalio.client import Client, WorkflowFailureError from temporalio.common import RetryPolicy +from temporalio.envconfig import ClientConfig from temporalio.exceptions import FailureError from temporalio.worker import Worker @@ -18,7 +20,7 @@ class ComposeGreetingInput: @activity.defn -async def compose_greeting(input: ComposeGreetingInput) -> NoReturn: +def compose_greeting(input: ComposeGreetingInput) -> NoReturn: # Always raise exception raise RuntimeError(f"Greeting exception: {input.greeting}, {input.name}!") @@ -38,7 +40,9 @@ async def run(self, name: str) -> str: async def main(): # Start client - client = await Client.connect("localhost:7233") + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) # Run a worker for the workflow async with Worker( @@ -46,8 +50,8 @@ async def main(): task_queue="hello-exception-task-queue", workflows=[GreetingWorkflow], activities=[compose_greeting], + activity_executor=ThreadPoolExecutor(5), ): - # While the worker is running, use the client to run the workflow and # print out its result. Note, in many production setups, the client # would be in a completely separate process from the worker. diff --git a/hello/hello_local_activity.py b/hello/hello_local_activity.py index 08c1d9f2c..8954ea426 100644 --- a/hello/hello_local_activity.py +++ b/hello/hello_local_activity.py @@ -1,9 +1,11 @@ import asyncio +from concurrent.futures import ThreadPoolExecutor from dataclasses import dataclass from datetime import timedelta from temporalio import activity, workflow from temporalio.client import Client +from temporalio.envconfig import ClientConfig from temporalio.worker import Worker @@ -14,7 +16,7 @@ class ComposeGreetingInput: @activity.defn -async def compose_greeting(input: ComposeGreetingInput) -> str: +def compose_greeting(input: ComposeGreetingInput) -> str: return f"{input.greeting}, {input.name}!" @@ -31,7 +33,9 @@ async def run(self, name: str) -> str: async def main(): # Start client - client = await Client.connect("localhost:7233") + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) # Run a worker for the workflow async with Worker( @@ -39,8 +43,8 @@ async def main(): task_queue="hello-local-activity-task-queue", workflows=[GreetingWorkflow], activities=[compose_greeting], + activity_executor=ThreadPoolExecutor(5), ): - # While the worker is running, use the client to run the workflow and # print out its result. Note, in many production setups, the client # would be in a completely separate process from the worker. diff --git a/hello/hello_mtls.py b/hello/hello_mtls.py index f613fba44..b88c5f852 100644 --- a/hello/hello_mtls.py +++ b/hello/hello_mtls.py @@ -1,11 +1,13 @@ import argparse import asyncio +from concurrent.futures import ThreadPoolExecutor from dataclasses import dataclass from datetime import timedelta from typing import Optional from temporalio import activity, workflow -from temporalio.client import Client, TLSConfig +from temporalio.client import Client +from temporalio.service import TLSConfig from temporalio.worker import Worker @@ -17,7 +19,7 @@ class ComposeGreetingInput: # Basic activity that logs and does string concatenation @activity.defn -async def compose_greeting(input: ComposeGreetingInput) -> str: +def compose_greeting(input: ComposeGreetingInput) -> str: return f"{input.greeting}, {input.name}!" @@ -78,8 +80,8 @@ async def main(): task_queue="hello-mtls-task-queue", workflows=[GreetingWorkflow], activities=[compose_greeting], + activity_executor=ThreadPoolExecutor(5), ): - # While the worker is running, use the client to run the workflow and # print out its result. Note, in many production setups, the client # would be in a completely separate process from the worker. diff --git a/hello/hello_parallel_activity.py b/hello/hello_parallel_activity.py index 9de098468..42931a707 100644 --- a/hello/hello_parallel_activity.py +++ b/hello/hello_parallel_activity.py @@ -1,14 +1,16 @@ import asyncio +from concurrent.futures import ThreadPoolExecutor from datetime import timedelta from typing import List from temporalio import activity, workflow from temporalio.client import Client +from temporalio.envconfig import ClientConfig from temporalio.worker import Worker @activity.defn -async def say_hello_activity(name: str) -> str: +def say_hello_activity(name: str) -> str: return f"Hello, {name}!" @@ -40,7 +42,9 @@ async def run(self) -> List[str]: async def main(): # Start client - client = await Client.connect("localhost:7233") + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) # Run a worker for the workflow async with Worker( @@ -48,8 +52,8 @@ async def main(): task_queue="hello-parallel-activity-task-queue", workflows=[SayHelloWorkflow], activities=[say_hello_activity], + activity_executor=ThreadPoolExecutor(10), ): - # While the worker is running, use the client to run the workflow and # print out its result. Note, in many production setups, the client # would be in a completely separate process from the worker. diff --git a/hello/hello_patch.py b/hello/hello_patch.py index 43371d4fb..a26e5474b 100644 --- a/hello/hello_patch.py +++ b/hello/hello_patch.py @@ -1,11 +1,12 @@ import asyncio -import logging import sys +from concurrent.futures import ThreadPoolExecutor from dataclasses import dataclass from datetime import timedelta from temporalio import activity, exceptions, workflow from temporalio.client import Client +from temporalio.envconfig import ClientConfig from temporalio.worker import Worker @@ -20,7 +21,7 @@ class ComposeGreetingInput: # Basic activity that logs and does string concatenation @activity.defn -async def compose_greeting(input: ComposeGreetingInput) -> str: +def compose_greeting(input: ComposeGreetingInput) -> str: activity.logger.info("Running activity with parameter %s" % input) return f"{input.greeting}, {input.name}!" @@ -96,11 +97,14 @@ async def main(): version = sys.argv[1] - # Uncomment the line below to see logging + # Uncomment the lines below to see logging output + # import logging # logging.basicConfig(level=logging.INFO) # Start client - client = await Client.connect("localhost:7233") + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) # Set workflow_class to the proper class based on version workflow_class = "" @@ -123,6 +127,7 @@ async def main(): task_queue="hello-patch-task-queue", workflows=[workflow_class], # type: ignore activities=[compose_greeting], + activity_executor=ThreadPoolExecutor(5), ): try: result = await client.execute_workflow( diff --git a/hello/hello_query.py b/hello/hello_query.py index 8deb30ba1..d1bc54aa0 100644 --- a/hello/hello_query.py +++ b/hello/hello_query.py @@ -2,6 +2,7 @@ from temporalio import workflow from temporalio.client import Client +from temporalio.envconfig import ClientConfig from temporalio.worker import Worker @@ -26,8 +27,11 @@ def greeting(self) -> str: async def main(): + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + # Start client - client = await Client.connect("localhost:7233") + client = await Client.connect(**config) # Run a worker for the workflow async with Worker( @@ -35,7 +39,6 @@ async def main(): task_queue="hello-query-task-queue", workflows=[GreetingWorkflow], ): - # While the worker is running, use the client to start the workflow. # Note, in many production setups, the client would be in a completely # separate process from the worker. diff --git a/hello/hello_search_attributes.py b/hello/hello_search_attributes.py index 6892fb718..d6d1a2053 100644 --- a/hello/hello_search_attributes.py +++ b/hello/hello_search_attributes.py @@ -1,10 +1,8 @@ import asyncio -from typing import List from temporalio import workflow -from temporalio.client import Client, WorkflowExecutionDescription -from temporalio.common import SearchAttributeValues -from temporalio.converter import default as default_converter +from temporalio.client import Client +from temporalio.envconfig import ClientConfig from temporalio.worker import Worker @@ -19,7 +17,9 @@ async def run(self) -> None: async def main(): # Start client - client = await Client.connect("localhost:7233") + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) # Run a worker for the workflow async with Worker( @@ -27,7 +27,6 @@ async def main(): task_queue="hello-search-attributes-task-queue", workflows=[GreetingWorkflow], ): - # While the worker is running, use the client to start the workflow. # Note, in many production setups, the client would be in a completely # separate process from the worker. diff --git a/hello/hello_signal.py b/hello/hello_signal.py index a4f9b5547..02b9d3cef 100644 --- a/hello/hello_signal.py +++ b/hello/hello_signal.py @@ -3,6 +3,7 @@ from temporalio import workflow from temporalio.client import Client +from temporalio.envconfig import ClientConfig from temporalio.worker import Worker @@ -40,8 +41,11 @@ def exit(self) -> None: async def main(): + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + # Start client - client = await Client.connect("localhost:7233") + client = await Client.connect(**config) # Run a worker for the workflow async with Worker( @@ -49,7 +53,6 @@ async def main(): task_queue="hello-signal-task-queue", workflows=[GreetingWorkflow], ): - # While the worker is running, use the client to start the workflow. # Note, in many production setups, the client would be in a completely # separate process from the worker. diff --git a/hello/hello_update.py b/hello/hello_update.py index 1b1236d3a..4daa250af 100644 --- a/hello/hello_update.py +++ b/hello/hello_update.py @@ -2,12 +2,14 @@ from temporalio import workflow from temporalio.client import Client +from temporalio.envconfig import ClientConfig from temporalio.worker import Worker @workflow.defn class GreetingWorkflow: - is_complete = False + def __init__(self): + self.is_complete = False @workflow.run async def run(self) -> str: @@ -21,7 +23,9 @@ async def update_workflow_status(self) -> str: async def main(): - client = await Client.connect("localhost:7233") + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) # Run a worker for the workflow async with Worker( diff --git a/hello_nexus/README.md b/hello_nexus/README.md new file mode 100644 index 000000000..e8067ec3e --- /dev/null +++ b/hello_nexus/README.md @@ -0,0 +1,37 @@ +This sample shows how to define a Nexus service, implement the operation handlers, and +call the operations from a workflow. + +### Sample directory structure + +- [service.py](./service.py) - shared Nexus service definition +- [caller](./caller) - a caller workflow that executes Nexus operations, together with a worker and starter code +- [handler](./handler) - Nexus operation handlers, together with a workflow used by one of the Nexus operations, and a worker that polls for both workflow and Nexus tasks. + + +### Instructions + +Start a Temporal server. (See the main samples repo [README](../README.md)). + +Run the following to create the caller and handler namespaces, and the Nexus endpoint: + +``` +temporal operator namespace create --namespace hello-nexus-basic-handler-namespace +temporal operator namespace create --namespace hello-nexus-basic-caller-namespace + +temporal operator nexus endpoint create \ + --name hello-nexus-basic-nexus-endpoint \ + --target-namespace hello-nexus-basic-handler-namespace \ + --target-task-queue my-handler-task-queue \ + --description-file hello_nexus/endpoint_description.md +``` + +In one terminal, run the Temporal worker in the handler namespace: +``` +uv run hello_nexus/handler/worker.py +``` + +In another terminal, run the Temporal worker in the caller namespace and start the caller +workflow: +``` +uv run hello_nexus/caller/app.py +``` diff --git a/hello_nexus/__init__.py b/hello_nexus/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/hello_nexus/caller/__init__.py b/hello_nexus/caller/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/hello_nexus/caller/app.py b/hello_nexus/caller/app.py new file mode 100644 index 000000000..639456fa7 --- /dev/null +++ b/hello_nexus/caller/app.py @@ -0,0 +1,46 @@ +import asyncio +import uuid +from typing import Optional + +from temporalio.client import Client +from temporalio.envconfig import ClientConfig +from temporalio.worker import Worker + +from hello_nexus.caller.workflows import CallerWorkflow +from hello_nexus.service import MyOutput + +NAMESPACE = "hello-nexus-basic-caller-namespace" +TASK_QUEUE = "hello-nexus-basic-caller-task-queue" + + +async def execute_caller_workflow( + client: Optional[Client] = None, +) -> tuple[MyOutput, MyOutput]: + if not client: + config = ClientConfig.load_client_connect_config() + # Override the namespace from config file. + config.setdefault("target_host", "localhost:7233") + config.setdefault("namespace", NAMESPACE) + client = await Client.connect(**config) + + async with Worker( + client, + task_queue=TASK_QUEUE, + workflows=[CallerWorkflow], + ): + return await client.execute_workflow( + CallerWorkflow.run, + arg="world", + id=str(uuid.uuid4()), + task_queue=TASK_QUEUE, + ) + + +if __name__ == "__main__": + loop = asyncio.new_event_loop() + try: + results = loop.run_until_complete(execute_caller_workflow()) + for output in results: + print(output.message) + except KeyboardInterrupt: + loop.run_until_complete(loop.shutdown_asyncgens()) diff --git a/hello_nexus/caller/workflows.py b/hello_nexus/caller/workflows.py new file mode 100644 index 000000000..2016f99fe --- /dev/null +++ b/hello_nexus/caller/workflows.py @@ -0,0 +1,35 @@ +from temporalio import workflow + +with workflow.unsafe.imports_passed_through(): + from hello_nexus.service import MyInput, MyNexusService, MyOutput + +NEXUS_ENDPOINT = "hello-nexus-basic-nexus-endpoint" + + +# This is a workflow that calls two nexus operations. +@workflow.defn +class CallerWorkflow: + # An __init__ method is always optional on a workflow class. Here we use it to set the + # nexus client, but that could alternatively be done in the run method. + def __init__(self): + self.nexus_client = workflow.create_nexus_client( + service=MyNexusService, + endpoint=NEXUS_ENDPOINT, + ) + + # The workflow run method invokes two nexus operations. + @workflow.run + async def run(self, name: str) -> tuple[MyOutput, MyOutput]: + # Start the nexus operation and wait for the result in one go, using execute_operation. + op_1_result = await self.nexus_client.execute_operation( + MyNexusService.my_sync_operation, + MyInput(name), + ) + # Alternatively, you can use start_operation to obtain the operation handle and + # then `await` the handle to obtain the result. + op_2_handle = await self.nexus_client.start_operation( + MyNexusService.my_workflow_run_operation, + MyInput(name), + ) + op_2_result = await op_2_handle + return op_1_result, op_2_result diff --git a/hello_nexus/endpoint_description.md b/hello_nexus/endpoint_description.md new file mode 100644 index 000000000..9a381cd03 --- /dev/null +++ b/hello_nexus/endpoint_description.md @@ -0,0 +1,3 @@ +## Service: [MyNexusService](https://github.com/temporalio/samples-python/blob/main/hello_nexus/basic/service.py) + - operation: `my_sync_operation` + - operation: `my_workflow_run_operation` diff --git a/hello_nexus/handler/__init__.py b/hello_nexus/handler/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/hello_nexus/handler/service_handler.py b/hello_nexus/handler/service_handler.py new file mode 100644 index 000000000..812a5c5d7 --- /dev/null +++ b/hello_nexus/handler/service_handler.py @@ -0,0 +1,47 @@ +""" +This file demonstrates how to implement a Nexus service. +""" + +from __future__ import annotations + +import nexusrpc +from temporalio import nexus + +from hello_nexus.handler.workflows import WorkflowStartedByNexusOperation +from hello_nexus.service import MyInput, MyNexusService, MyOutput + + +@nexusrpc.handler.service_handler(service=MyNexusService) +class MyNexusServiceHandler: + # You can create an __init__ method accepting what is needed by your operation + # handlers to handle requests. You typically instantiate your service handler class + # when starting your worker. See hello_nexus/basic/handler/worker.py. + + # This is a nexus operation that is backed by a Temporal workflow. The start method + # starts a workflow, and returns a nexus operation token. Meanwhile, the workflow + # executes in the background; Temporal server takes care of delivering the eventual + # workflow result (success or failure) to the calling workflow. + # + # The token will be used by the caller if it subsequently wants to cancel the Nexus + # operation. + @nexus.workflow_run_operation + async def my_workflow_run_operation( + self, ctx: nexus.WorkflowRunOperationContext, input: MyInput + ) -> nexus.WorkflowHandle[MyOutput]: + return await ctx.start_workflow( + WorkflowStartedByNexusOperation.run, + input, + id=f"hello-nexus-workflow-{input.name}", + ) + + # This is a Nexus operation that responds synchronously to all requests. That means + # that unlike the workflow run operation above, in this case the `start` method + # returns the final operation result. + # + # Sync operations are free to make arbitrary network calls, or perform CPU-bound + # computations. Total execution duration must not exceed 10s. + @nexusrpc.handler.sync_operation + async def my_sync_operation( + self, ctx: nexusrpc.handler.StartOperationContext, input: MyInput + ) -> MyOutput: + return MyOutput(message=f"Hello {input.name} from sync operation!") diff --git a/hello_nexus/handler/worker.py b/hello_nexus/handler/worker.py new file mode 100644 index 000000000..ded9c5ab4 --- /dev/null +++ b/hello_nexus/handler/worker.py @@ -0,0 +1,49 @@ +import asyncio +import logging +from typing import Optional + +from temporalio.client import Client +from temporalio.envconfig import ClientConfig +from temporalio.worker import Worker + +from hello_nexus.handler.service_handler import MyNexusServiceHandler +from hello_nexus.handler.workflows import WorkflowStartedByNexusOperation + +interrupt_event = asyncio.Event() + +NAMESPACE = "hello-nexus-basic-handler-namespace" +TASK_QUEUE = "my-handler-task-queue" + + +async def main(client: Optional[Client] = None): + logging.basicConfig(level=logging.INFO) + + if not client: + config = ClientConfig.load_client_connect_config() + # Override the address and namespace from the config file. + config.setdefault("target_host", "localhost:7233") + config.setdefault("namespace", NAMESPACE) + client = await Client.connect(**config) + + # Start the worker, passing the Nexus service handler instance, in addition to the + # workflow classes that are started by your nexus operations, and any activities + # needed. This Worker will poll for both workflow tasks and Nexus tasks (this example + # doesn't use any activities). + async with Worker( + client, + task_queue=TASK_QUEUE, + workflows=[WorkflowStartedByNexusOperation], + nexus_service_handlers=[MyNexusServiceHandler()], + ): + logging.info("Worker started, ctrl+c to exit") + await interrupt_event.wait() + logging.info("Shutting down") + + +if __name__ == "__main__": + loop = asyncio.new_event_loop() + try: + loop.run_until_complete(main()) + except KeyboardInterrupt: + interrupt_event.set() + loop.run_until_complete(loop.shutdown_asyncgens()) diff --git a/hello_nexus/handler/workflows.py b/hello_nexus/handler/workflows.py new file mode 100644 index 000000000..a41b29efe --- /dev/null +++ b/hello_nexus/handler/workflows.py @@ -0,0 +1,12 @@ +from temporalio import workflow + +with workflow.unsafe.imports_passed_through(): + from hello_nexus.service import MyInput, MyOutput + + +# This is the workflow that is started by the `my_workflow_run_operation` nexus operation. +@workflow.defn +class WorkflowStartedByNexusOperation: + @workflow.run + async def run(self, input: MyInput) -> MyOutput: + return MyOutput(message=f"Hello {input.name} from workflow run operation!") diff --git a/hello_nexus/service.py b/hello_nexus/service.py new file mode 100644 index 000000000..352375ca7 --- /dev/null +++ b/hello_nexus/service.py @@ -0,0 +1,34 @@ +""" +This is a Nexus service definition. + +A service definition defines a Nexus service as a named collection of operations, each +with input and output types. It does not implement operation handling: see the service +handler and operation handlers in hello_nexus.handler.nexus_service for that. + +A Nexus service definition is used by Nexus callers (e.g. a Temporal workflow) to create +type-safe clients, and it is used by Nexus handlers to validate that they implement +correctly-named operation handlers with the correct input and output types. + +The service defined in this file exposes two operations: my_sync_operation and +my_workflow_run_operation. +""" + +from dataclasses import dataclass + +import nexusrpc + + +@dataclass +class MyInput: + name: str + + +@dataclass +class MyOutput: + message: str + + +@nexusrpc.service +class MyNexusService: + my_sync_operation: nexusrpc.Operation[MyInput, MyOutput] + my_workflow_run_operation: nexusrpc.Operation[MyInput, MyOutput] diff --git a/hello_standalone_activity/README.md b/hello_standalone_activity/README.md new file mode 100644 index 000000000..ebd639317 --- /dev/null +++ b/hello_standalone_activity/README.md @@ -0,0 +1,115 @@ +# Standalone Activity + +This sample shows how to execute Activities directly from a Temporal Client, without a Workflow. + +For full documentation, see [Standalone Activities - Python SDK](https://docs.temporal.io/develop/python/standalone-activities). + +### Sample directory structure + +- [my_activity.py](./my_activity.py) - Activity definition with `@activity.defn` +- [worker.py](./worker.py) - Worker that registers and runs the Activity +- [execute_activity.py](./execute_activity.py) - Execute a Standalone Activity and wait for the result +- [start_activity.py](./start_activity.py) - Start a Standalone Activity, get a handle, then wait for the result +- [list_activities.py](./list_activities.py) - List Standalone Activity Executions +- [count_activities.py](./count_activities.py) - Count Standalone Activity Executions + +### Quickstart + +**1. Start the Temporal dev server** + +```bash +temporal server start-dev +``` + +**2. Run the Worker** (in a separate terminal) + +```bash +uv run hello_standalone_activity/worker.py +``` + +**3. Execute a Standalone Activity** (in a separate terminal) + +Execute and wait for the result: + +```bash +uv run hello_standalone_activity/execute_activity.py +``` + +Or use the Temporal CLI: + +```bash +temporal activity execute \ + --type compose_greeting \ + --activity-id my-standalone-activity-id \ + --task-queue my-standalone-activity-task-queue \ + --start-to-close-timeout 10s \ + --input '{"greeting": "Hello", "name": "World"}' +``` + +**4. Start a Standalone Activity (without waiting)** + +Start, get a handle, then wait for the result: + +```bash +uv run hello_standalone_activity/start_activity.py +``` + +Or use the Temporal CLI: + +```bash +temporal activity start \ + --type compose_greeting \ + --activity-id my-standalone-activity-id \ + --task-queue my-standalone-activity-task-queue \ + --start-to-close-timeout 10s \ + --input '{"greeting": "Hello", "name": "World"}' +``` + +**5. List Standalone Activities** + +```bash +uv run hello_standalone_activity/list_activities.py +``` + +Or use the Temporal CLI: + +```bash +temporal activity list --query "TaskQueue = 'my-standalone-activity-task-queue'" +``` + +Note: `list` and `count` are only available in the [Standalone Activity prerelease CLI](https://github.com/temporalio/cli/releases/tag/v1.6.2-standalone-activity). + +**6. Count Standalone Activities** + +```bash +uv run hello_standalone_activity/count_activities.py +``` + +Or use the Temporal CLI: + +```bash +temporal activity count --query "TaskQueue = 'my-standalone-activity-task-queue'" +``` + +### Temporal Cloud + +The same code works against Temporal Cloud - just set environment variables. No code changes needed. + +**Connect with mTLS:** + +```bash +export TEMPORAL_ADDRESS=..tmprl.cloud:7233 +export TEMPORAL_NAMESPACE=. +export TEMPORAL_TLS_CLIENT_CERT_PATH='path/to/your/client.pem' +export TEMPORAL_TLS_CLIENT_KEY_PATH='path/to/your/client.key' +``` + +**Connect with an API key:** + +```bash +export TEMPORAL_ADDRESS=..api.temporal.io:7233 +export TEMPORAL_NAMESPACE=. +export TEMPORAL_API_KEY= +``` + +Then run the worker and starter as shown above. diff --git a/hello_standalone_activity/__init__.py b/hello_standalone_activity/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/hello_standalone_activity/count_activities.py b/hello_standalone_activity/count_activities.py new file mode 100644 index 000000000..14a89e7e2 --- /dev/null +++ b/hello_standalone_activity/count_activities.py @@ -0,0 +1,23 @@ +import asyncio + +from temporalio.client import Client +from temporalio.envconfig import ClientConfig + + +async def my_application(): + connect_config = ClientConfig.load_client_connect_config() + connect_config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**connect_config) + + resp = await client.count_activities( + query="TaskQueue = 'my-standalone-activity-task-queue'", + ) + + print("Total activities:", resp.count) + + for group in resp.groups: + print(f"Group {group.group_values}: {group.count}") + + +if __name__ == "__main__": + asyncio.run(my_application()) diff --git a/hello_standalone_activity/execute_activity.py b/hello_standalone_activity/execute_activity.py new file mode 100644 index 000000000..529a1feb8 --- /dev/null +++ b/hello_standalone_activity/execute_activity.py @@ -0,0 +1,26 @@ +import asyncio +from datetime import timedelta + +from temporalio.client import Client +from temporalio.envconfig import ClientConfig + +from hello_standalone_activity.my_activity import ComposeGreetingInput, compose_greeting + + +async def my_application(): + connect_config = ClientConfig.load_client_connect_config() + connect_config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**connect_config) + + activity_result = await client.execute_activity( + compose_greeting, + args=[ComposeGreetingInput("Hello", "World")], + id="my-standalone-activity-id", + task_queue="my-standalone-activity-task-queue", + start_to_close_timeout=timedelta(seconds=10), + ) + print(f"Activity result: {activity_result}") + + +if __name__ == "__main__": + asyncio.run(my_application()) diff --git a/hello_standalone_activity/list_activities.py b/hello_standalone_activity/list_activities.py new file mode 100644 index 000000000..ef7af9699 --- /dev/null +++ b/hello_standalone_activity/list_activities.py @@ -0,0 +1,23 @@ +import asyncio + +from temporalio.client import Client +from temporalio.envconfig import ClientConfig + + +async def my_application(): + connect_config = ClientConfig.load_client_connect_config() + connect_config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**connect_config) + + activities = client.list_activities( + query="TaskQueue = 'my-standalone-activity-task-queue'", + ) + + async for info in activities: + print( + f"ActivityID: {info.activity_id}, Type: {info.activity_type}, Status: {info.status}" + ) + + +if __name__ == "__main__": + asyncio.run(my_application()) diff --git a/hello_standalone_activity/my_activity.py b/hello_standalone_activity/my_activity.py new file mode 100644 index 000000000..086b08a02 --- /dev/null +++ b/hello_standalone_activity/my_activity.py @@ -0,0 +1,15 @@ +from dataclasses import dataclass + +from temporalio import activity + + +@dataclass +class ComposeGreetingInput: + greeting: str + name: str + + +@activity.defn +def compose_greeting(input: ComposeGreetingInput) -> str: + activity.logger.info("Running activity with parameter %s" % input) + return f"{input.greeting}, {input.name}!" diff --git a/hello_standalone_activity/start_activity.py b/hello_standalone_activity/start_activity.py new file mode 100644 index 000000000..8aa9fa549 --- /dev/null +++ b/hello_standalone_activity/start_activity.py @@ -0,0 +1,31 @@ +import asyncio +from datetime import timedelta + +from temporalio.client import Client +from temporalio.envconfig import ClientConfig + +from hello_standalone_activity.my_activity import ComposeGreetingInput, compose_greeting + + +async def my_application(): + connect_config = ClientConfig.load_client_connect_config() + connect_config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**connect_config) + + # Start the activity without waiting for the result + activity_handle = await client.start_activity( + compose_greeting, + args=[ComposeGreetingInput("Hello", "World")], + id="my-standalone-activity-id", + task_queue="my-standalone-activity-task-queue", + start_to_close_timeout=timedelta(seconds=10), + ) + print(f"Started activity: {activity_handle.id}") + + # Wait for the result + activity_result = await activity_handle.result() + print(f"Activity result: {activity_result}") + + +if __name__ == "__main__": + asyncio.run(my_application()) diff --git a/hello_standalone_activity/worker.py b/hello_standalone_activity/worker.py new file mode 100644 index 000000000..d093cc036 --- /dev/null +++ b/hello_standalone_activity/worker.py @@ -0,0 +1,26 @@ +import asyncio +from concurrent.futures import ThreadPoolExecutor + +from temporalio.client import Client +from temporalio.envconfig import ClientConfig +from temporalio.worker import Worker + +from hello_standalone_activity.my_activity import compose_greeting + + +async def main(): + connect_config = ClientConfig.load_client_connect_config() + connect_config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**connect_config) + worker = Worker( + client, + task_queue="my-standalone-activity-task-queue", + activities=[compose_greeting], + activity_executor=ThreadPoolExecutor(5), + ) + print("worker running...", end="", flush=True) + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/lambda_worker/.gitignore b/lambda_worker/.gitignore new file mode 100644 index 000000000..82d6f6377 --- /dev/null +++ b/lambda_worker/.gitignore @@ -0,0 +1,2 @@ +package/ +temporal-sdk.toml diff --git a/lambda_worker/README.md b/lambda_worker/README.md new file mode 100644 index 000000000..353241f0b --- /dev/null +++ b/lambda_worker/README.md @@ -0,0 +1,109 @@ +# Lambda Worker + +This sample demonstrates how to run a Temporal Worker inside an AWS Lambda function using +the [`lambda_worker`](https://python.temporal.io/temporalio.contrib.aws.lambda_worker.html) +contrib package. It includes optional OpenTelemetry instrumentation that exports traces +and metrics through AWS Distro for OpenTelemetry (ADOT). + +The sample registers a simple greeting Workflow and Activity, but the pattern applies to +any Workflow/Activity definitions. + +## Prerequisites + +- A [Temporal Cloud](https://temporal.io/cloud) namespace (or a self-hosted Temporal + cluster accessible from your Lambda) +- AWS CLI configured with permissions to create Lambda functions, IAM roles, and + CloudFormation stacks +- mTLS client certificate and key for your Temporal namespace (place as `client.pem` and + `client.key` in this directory) +- Python 3.10+ + +## Files + +| File | Description | +|------|-------------| +| `lambda_function.py` | Lambda entry point -- configures the worker, registers Workflows/Activities, and exports the handler | +| `workflows.py` | Sample Workflow that executes a greeting Activity | +| `activities.py` | Sample Activity that returns a greeting string | +| `starter.py` | Helper program to start a Workflow execution from a local machine | +| `temporal.toml` | Temporal client connection configuration (update with your namespace) | +| `otel-collector-config.yaml` | OpenTelemetry Collector sidecar configuration for ADOT | +| `deploy-lambda.sh` | Packages and deploys the Lambda function | +| `mk-iam-role.sh` | Creates the IAM role that allows Temporal Cloud to invoke the Lambda | +| `iam-role-for-temporal-lambda-invoke-test.yaml` | CloudFormation template for the IAM role | +| `extra-setup-steps` | Additional IAM and Lambda configuration for OpenTelemetry support | + +## Setup + +The instructions here are a slimmed down version of the more complete getting started guide which +you can find [here](https://docs.temporal.io/production-deployment/worker-deployments/serverless-workers/aws-lambda). + +### 1. Create a lambda function for your Python worker + +Use either the AWS web UI or CLI to create a Python runtime Lambda function. Ex: + +```bash +aws lambda create-function \ + --function-name my-temporal-worker \ + --runtime python3.13 \ + --handler lambda_function.lambda_handler \ + --role arn:aws:iam:::role/my-temporal-worker-execution \ + --timeout 600 \ + --memory-size 256 +``` + +### 2. Configure Temporal connection + +Edit `temporal.toml` with your Temporal Cloud namespace address and credentials. In production, +we'd recommend reading your credentials from a secret store, but to keep this example simple +the toml file defaults to reading them from keys bundled along with the Lambda code. + +### 3. Create the IAM role + +This creates the IAM role that Temporal Cloud assumes to invoke your Lambda function: + +```bash +./mk-iam-role.sh +``` + +The External ID is provided by Temporal Cloud in your namespace's serverless worker +configuration. + +### 4. (Optional) Enable OpenTelemetry + +If you want traces, metrics, and logs, you'll have to attach the ADOT layet to your Lambda function. +You will need to add the appropriate layer for your runtime and region. See [this page +](https://aws-otel.github.io/docs/getting-started/lambda#getting-started-with-aws-lambda-layers) +for more info. + +Then run the extra setup to grant the Lambda role the necessary permissions: + +```bash +./extra-setup-steps +``` + +Update `otel-collector-config.yaml` with your function name and region as needed. + +### 5. Deploy the Lambda function + +```bash +./deploy-lambda.sh +``` + +This installs Python dependencies, bundles them with your code and configuration files, +and uploads to AWS Lambda. + +### 6. Configure Temporal to be able to invoke your lambda function + +Refer to the docs [here](https://docs.temporal.io/production-deployment/worker-deployments/serverless-workers/aws-lambda#create-worker-deployment-version). + +### 7. Start a Workflow + +Use the starter program to execute a Workflow on the Lambda worker, using +the same config file the Lambda uses for connecting to the server: + +From inside this directory: + +```bash +TEMPORAL_CONFIG_FILE=temporal.toml uv run python starter.py +``` diff --git a/lambda_worker/activities.py b/lambda_worker/activities.py new file mode 100644 index 000000000..a6a9c8e22 --- /dev/null +++ b/lambda_worker/activities.py @@ -0,0 +1,7 @@ +from temporalio import activity + + +@activity.defn +async def hello_activity(name: str) -> str: + activity.logger.info("HelloActivity started with name: %s", name) + return f"Hello, {name}!" diff --git a/lambda_worker/deploy-lambda.sh b/lambda_worker/deploy-lambda.sh new file mode 100755 index 000000000..ce331a80e --- /dev/null +++ b/lambda_worker/deploy-lambda.sh @@ -0,0 +1,24 @@ +#!/bin/bash +set -euo pipefail + +FUNCTION_NAME="${1:?Usage: deploy-lambda.sh }" +SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)" + +# Install the published temporalio package (Linux wheels) with the OTel extras +# needed by the lambda_worker contrib module +uv pip install --target "$SCRIPT_DIR/package" --python-platform x86_64-unknown-linux-gnu \ + --only-binary=:all: "temporalio[lambda-worker-otel]" + +# Copy application code into the package directory (all at zip root) +cp "$SCRIPT_DIR/lambda_function.py" "$SCRIPT_DIR/workflows.py" \ + "$SCRIPT_DIR/activities.py" "$SCRIPT_DIR/package/" + +# Bundle with configuration files +cd "$SCRIPT_DIR/package" +zip -r "$SCRIPT_DIR/function.zip" . +cd "$SCRIPT_DIR" +zip function.zip client.pem client.key temporal.toml otel-collector-config.yaml + +aws lambda update-function-code --function-name "$FUNCTION_NAME" --zip-file fileb://function.zip + +rm -rf "$SCRIPT_DIR/package" "$SCRIPT_DIR/function.zip" diff --git a/lambda_worker/extra-setup-steps b/lambda_worker/extra-setup-steps new file mode 100755 index 000000000..11493a0f8 --- /dev/null +++ b/lambda_worker/extra-setup-steps @@ -0,0 +1,48 @@ +#!/bin/bash +set -euo pipefail + +# Additional setup steps for OpenTelemetry support. +# These are needed if you want metrics, logs, and traces from your Lambda worker. + +ROLE_NAME="${1:?Usage: extra-setup-steps }" +FUNCTION_NAME="${2:?Usage: extra-setup-steps }" +REGION="${3:?Usage: extra-setup-steps }" +ACCOUNT_ID="${4:?Usage: extra-setup-steps }" + +# Needed to allow metrics/logs/traces to be published +aws iam put-role-policy \ + --role-name "$ROLE_NAME" \ + --policy-name ADOT-Telemetry-Permissions \ + --policy-document "{ + \"Version\": \"2012-10-17\", + \"Statement\": [ + { + \"Effect\": \"Allow\", + \"Action\": [ + \"logs:CreateLogGroup\", + \"logs:CreateLogStream\", + \"logs:PutLogEvents\" + ], + \"Resource\": \"arn:aws:logs:${REGION}:${ACCOUNT_ID}:log-group:/aws/lambda/${FUNCTION_NAME}:*\" + }, + { + \"Effect\": \"Allow\", + \"Action\": [ + \"xray:PutTraceSegments\", + \"xray:PutTelemetryRecords\" + ], + \"Resource\": \"*\" + }, + { + \"Effect\": \"Allow\", + \"Action\": [ + \"cloudwatch:PutMetricData\" + ], + \"Resource\": \"*\" + } + ] + }" + +# Needed to make traces show up with type: `"AWS::Lambda::Function"` filter +aws lambda update-function-configuration \ + --function-name "$FUNCTION_NAME" --tracing-config Mode=Active diff --git a/lambda_worker/iam-role-for-temporal-lambda-invoke-test.yaml b/lambda_worker/iam-role-for-temporal-lambda-invoke-test.yaml new file mode 100644 index 000000000..82dd16aa9 --- /dev/null +++ b/lambda_worker/iam-role-for-temporal-lambda-invoke-test.yaml @@ -0,0 +1,97 @@ +# CloudFormation template for creating an IAM role that Temporal Cloud can assume to invoke Lambda functions. +AWSTemplateFormatVersion: "2010-09-09" +Description: Creates an IAM role that Temporal Cloud can assume to invoke multiple Lambda functions for Serverless Workers. + +Parameters: + AssumeRoleExternalId: + Type: String + Description: The External ID provided by Temporal Cloud + AllowedPattern: "[a-zA-Z0-9_+=,.@-]*" + MinLength: 5 + MaxLength: 45 + + LambdaFunctionARNs: + Type: CommaDelimitedList + Description: >- + Comma-separated list of Lambda function ARNs to invoke (e.g., + arn:aws:lambda:us-west-2:123456789012:function:worker-1,arn:aws:lambda:us-west-2:123456789012:function:worker-2) + + RoleName: + Type: String + Default: "Temporal-Cloud-Serverless-Worker" + +Metadata: + AWS::CloudFormation::Interface: + ParameterGroups: + - Label: + default: "Temporal Cloud Configuration" + Parameters: + - AssumeRoleExternalId + - Label: + default: "Lambda Configuration" + Parameters: + - LambdaFunctionARNs + - RoleName + ParameterLabels: + AssumeRoleExternalId: + default: "External ID (provided by Temporal Cloud)" + LambdaFunctionARNs: + default: "Lambda Function ARNs (comma-separated list)" + RoleName: + default: "IAM Role Name" + +Resources: + TemporalCloudServerlessWorker: + Type: AWS::IAM::Role + Properties: + RoleName: !Sub "${RoleName}-${AWS::StackName}" + AssumeRolePolicyDocument: + Version: "2012-10-17" + Statement: + - Effect: Allow + Principal: + AWS: + [ + arn:aws:iam::902542641901:role/wci-lambda-invoke, + arn:aws:iam::160190466495:role/wci-lambda-invoke, + arn:aws:iam::819232936619:role/wci-lambda-invoke, + arn:aws:iam::829909441867:role/wci-lambda-invoke, + arn:aws:iam::354116250941:role/wci-lambda-invoke, + ] + Action: sts:AssumeRole + Condition: + StringEquals: + "sts:ExternalId": [!Ref AssumeRoleExternalId] + Description: "The role Temporal Cloud uses to invoke Lambda functions for Serverless Workers" + MaxSessionDuration: 3600 # 1 hour + + TemporalCloudLambdaInvokePermissions: + Type: AWS::IAM::Policy + DependsOn: TemporalCloudServerlessWorker + Properties: + PolicyName: "Temporal-Cloud-Lambda-Invoke-Permissions" + PolicyDocument: + Version: "2012-10-17" + Statement: + - Effect: Allow + Action: + - lambda:InvokeFunction + - lambda:GetFunction + Resource: !Ref LambdaFunctionARNs + Roles: + - !Sub "${RoleName}-${AWS::StackName}" + +Outputs: + RoleARN: + Description: The ARN of the IAM role created for Temporal Cloud + Value: !GetAtt TemporalCloudServerlessWorker.Arn + Export: + Name: !Sub "${AWS::StackName}-RoleARN" + + RoleName: + Description: The name of the IAM role + Value: !Ref RoleName + + LambdaFunctionARNs: + Description: The Lambda function ARNs that can be invoked + Value: !Join [", ", !Ref LambdaFunctionARNs] diff --git a/lambda_worker/lambda_function.py b/lambda_worker/lambda_function.py new file mode 100644 index 000000000..4c312097f --- /dev/null +++ b/lambda_worker/lambda_function.py @@ -0,0 +1,18 @@ +from activities import hello_activity +from temporalio.common import WorkerDeploymentVersion +from temporalio.contrib.aws.lambda_worker import LambdaWorkerConfig, run_worker +from temporalio.contrib.aws.lambda_worker.otel import apply_defaults +from workflows import TASK_QUEUE, SampleWorkflow + + +def configure(config: LambdaWorkerConfig) -> None: + config.worker_config["task_queue"] = TASK_QUEUE + config.worker_config["workflows"] = [SampleWorkflow] + config.worker_config["activities"] = [hello_activity] + apply_defaults(config) + + +lambda_handler = run_worker( + WorkerDeploymentVersion(deployment_name="my-app", build_id="build-1"), + configure, +) diff --git a/lambda_worker/mk-iam-role.sh b/lambda_worker/mk-iam-role.sh new file mode 100755 index 000000000..506dd52e6 --- /dev/null +++ b/lambda_worker/mk-iam-role.sh @@ -0,0 +1,17 @@ +#!/bin/bash +set -euo pipefail + +# Creates the IAM role that allows Temporal Cloud to invoke your Lambda function. +# You can find the External ID in your Temporal Cloud namespace settings. + +STACK_NAME="${1:?Usage: mk-iam-role.sh }" +EXTERNAL_ID="${2:?Usage: mk-iam-role.sh }" +LAMBDA_ARN="${3:?Usage: mk-iam-role.sh }" + +aws cloudformation create-stack \ + --stack-name "$STACK_NAME" \ + --template-body file://iam-role-for-temporal-lambda-invoke-test.yaml \ + --parameters \ + ParameterKey=AssumeRoleExternalId,ParameterValue="$EXTERNAL_ID" \ + ParameterKey=LambdaFunctionARNs,ParameterValue="$LAMBDA_ARN" \ + --capabilities CAPABILITY_NAMED_IAM diff --git a/lambda_worker/otel-collector-config.yaml b/lambda_worker/otel-collector-config.yaml new file mode 100644 index 000000000..bd54c4a78 --- /dev/null +++ b/lambda_worker/otel-collector-config.yaml @@ -0,0 +1,35 @@ +receivers: + otlp: + protocols: + grpc: + endpoint: "localhost:4317" + http: + endpoint: "localhost:4318" + +exporters: + debug: + awsxray: + region: us-west-2 + awsemf: + # AWS EMF exporter for metrics + # These are example configurations + namespace: TemporalWorkerMetrics + log_group_name: /aws/lambda/ + region: us-west-2 + dimension_rollup_option: NoDimensionRollup + resource_to_telemetry_conversion: + enabled: true + +service: + pipelines: + traces: + receivers: [otlp] + exporters: [awsxray, debug] + metrics: + receivers: [otlp] + exporters: [awsemf] + telemetry: + logs: + level: debug + metrics: + address: localhost:8888 diff --git a/lambda_worker/pyproject.toml b/lambda_worker/pyproject.toml new file mode 100644 index 000000000..77a65440c --- /dev/null +++ b/lambda_worker/pyproject.toml @@ -0,0 +1,41 @@ +[project] +name = "temporalio-samples-lambda-worker" +version = "0.1a1" +description = "Temporal.io Python SDK Lambda worker sample" +authors = [{ name = "Temporal Technologies Inc", email = "sdk@temporal.io" }] +requires-python = ">=3.10" +readme = "README.md" +license = "MIT" +dependencies = ["temporalio[lambda-worker-otel]>=1.26.0,<2"] + +[dependency-groups] +dev = [ + "ruff>=0.5.0,<0.6", + "mypy>=1.4.1,<2", + "poethepoet>=0.36.0", +] + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[tool.hatch.metadata] +allow-direct-references = true + +[tool.hatch.build.targets.wheel] +packages = ["."] + +[tool.poe.tasks] +format = [ + { cmd = "uv run ruff check --select I --fix" }, + { cmd = "uv run ruff format" }, +] +lint = [ + { cmd = "uv run ruff check --select I" }, + { cmd = "uv run ruff format --check" }, + { ref = "lint-types" }, +] +lint-types = "uv run --all-groups mypy --check-untyped-defs --namespace-packages ." + +[tool.ruff] +target-version = "py310" diff --git a/lambda_worker/starter.py b/lambda_worker/starter.py new file mode 100644 index 000000000..4e485c006 --- /dev/null +++ b/lambda_worker/starter.py @@ -0,0 +1,24 @@ +import asyncio + +from temporalio.client import Client +from temporalio.envconfig import ClientConfig + +from workflows import TASK_QUEUE, SampleWorkflow + + +async def main() -> None: + config = ClientConfig.load_client_connect_config() + client = await Client.connect(**config) + print("Connected to Temporal Service") + + result = await client.execute_workflow( + SampleWorkflow.run, + "Serverless Lambda Worker!", + id="serverless-workflow-id-1", + task_queue=TASK_QUEUE, + ) + print(f"Workflow result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/lambda_worker/temporal.toml b/lambda_worker/temporal.toml new file mode 100644 index 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@@ +from datetime import timedelta + +from temporalio import common, workflow + +TASK_QUEUE = "serverless-task-queue-python" + +with workflow.unsafe.imports_passed_through(): + from activities import hello_activity + + +@workflow.defn(versioning_behavior=common.VersioningBehavior.PINNED) +class SampleWorkflow: + @workflow.run + async def run(self, name: str) -> str: + workflow.logger.info("SampleWorkflow started with name: %s", name) + result = await workflow.execute_activity( + hello_activity, + name, + start_to_close_timeout=timedelta(seconds=10), + ) + workflow.logger.info("SampleWorkflow completed with result: %s", result) + return result diff --git a/langfuse_tracing/.env.example b/langfuse_tracing/.env.example new file mode 100644 index 000000000..2c3152cb2 --- /dev/null +++ b/langfuse_tracing/.env.example @@ -0,0 +1,29 @@ +# Copy to .env and adjust. Load with: set -a; source langfuse_tracing/.env; set +a + +# Langfuse — these defaults match the headless-init values baked into +# langfuse_tracing/langfuse/docker-compose.yml (local demo stack). +LANGFUSE_HOST=http://localhost:3000 +LANGFUSE_PUBLIC_KEY=pk-lf-temporal-demo-0000 +LANGFUSE_SECRET_KEY=sk-lf-temporal-demo-0000 +# Used only for the trace link the starter prints; set to your project ID +# when pointing at your own Langfuse instance or Langfuse Cloud. +LANGFUSE_PROJECT_ID=langfuse-tracing-demo +# Reported as the Langfuse user ID on each trace by the starter. +LANGFUSE_DEMO_USER=demo-user + +# LLM — any OpenAI-compatible endpoint works. +OPENAI_API_KEY=sk-... +MODEL_CLASSIFY=gpt-4o-mini +MODEL_DRAFT=gpt-4o-mini +# To use a local OpenAI-compatible gateway (e.g. a LiteLLM proxy) instead: +# OPENAI_BASE_URL=http://localhost:4000/v1 +# OPENAI_API_KEY= +# MODEL_CLASSIFY= +# MODEL_DRAFT= + +# LLM span instrumentation flavor: openinference (default) or openai-v2. +# See README for the trade-offs. +LLM_INSTRUMENTATION=openinference +# Required only for LLM_INSTRUMENTATION=openai-v2 content capture: +# OTEL_SEMCONV_STABILITY_OPT_IN=gen_ai_latest_experimental +# OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT=span_only diff --git a/langfuse_tracing/README.md b/langfuse_tracing/README.md new file mode 100644 index 000000000..28048ca57 --- /dev/null +++ b/langfuse_tracing/README.md @@ -0,0 +1,157 @@ +# Langfuse Tracing + +This sample shows the recommended way to get Temporal workflow traces into +[Langfuse](https://langfuse.com/): Temporal's +[`OpenTelemetryPlugin`](https://python.temporal.io/temporalio.contrib.opentelemetry.html) +plus a standard OTLP/HTTP exporter pointed at Langfuse's native OpenTelemetry +endpoint. No Langfuse SDK or Langfuse-specific plugin is involved, workflow +code stays deterministic and sandboxed, and traces are correctly nested, +correctly typed, and duplicate-free across replay and worker restarts. + +Contents: + +- **[ticket_triage/](ticket_triage/)** — the recommended pattern: an LLM + ticket-triage workflow (two LLM activities, one plain activity, one human + approval delivered as a workflow update). +- **[verify_trace.py](verify_trace.py)** — checks a trace via the Langfuse + public API: whole-tree equality, observation types, token usage, and + no-duplicates. +- **[langfuse/docker-compose.yml](langfuse/docker-compose.yml)** — pinned + self-hosted Langfuse with org/project/API keys provisioned headlessly. +- **[telemetry.py](telemetry.py)** — the OpenTelemetry wiring (replay-safe + tracer provider, OTLP exporter with Langfuse auth, LLM instrumentation). + +## Prerequisites + +- Docker (for Langfuse), a local Temporal server + (`temporal server start-dev`), and `uv`. +- An OpenAI-compatible LLM endpoint: either a real `OPENAI_API_KEY`, or any + OpenAI-compatible gateway via `OPENAI_BASE_URL`. + +## Run it + +```bash +# 1. Start Langfuse (first pull takes a few minutes) +cd langfuse_tracing/langfuse +docker compose up -d +curl -sf http://localhost:3000/api/public/health # repeat until {"status":"OK",...} +# UI: http://localhost:3000 — login demo@temporal.io / langfuse-demo-pw-1 + +# 2. Install dependencies and set environment (repo root) +cd ../.. +uv sync --group langfuse-tracing +cp langfuse_tracing/.env.example langfuse_tracing/.env # edit the LLM settings +set -a; source langfuse_tracing/.env; set +a + +# 3. Run the sample (two terminals, same environment) +uv run python -m langfuse_tracing.ticket_triage.worker +uv run python -m langfuse_tracing.ticket_triage.starter + +# 4. Verify the trace through the Langfuse API (uses the printed trace ID) +uv run python -m langfuse_tracing.verify_trace --trace-id +``` + +The starter prints a direct link to the trace in the Langfuse UI. You should +see one trace shaped like this (types as Langfuse derives them): + +``` +ticket-triage SPAN (root; session/user/tags) +├─ StartWorkflow:TicketTriageWorkflow SPAN +│ └─ RunWorkflow:TicketTriageWorkflow SPAN +│ ├─ triage SPAN (custom span from workflow code) +│ │ ├─ StartActivity:classify_ticket → RunActivity:classify_ticket +│ │ │ └─ ChatCompletion GENERATION (model, tokens, cost) +│ │ └─ StartActivity:lookup_account → RunActivity:lookup_account +│ └─ StartActivity:draft_reply → RunActivity:draft_reply +│ └─ ChatCompletion GENERATION +└─ StartWorkflowUpdate:approve SPAN + ├─ ValidateUpdate:approve SPAN + └─ HandleUpdate:approve SPAN +``` + +## Prove the replay-safety claims + +Durable execution means workflow code re-executes (replays) on worker +restarts and cache evictions. These two experiments show tracing is +unaffected — each run still verifies cleanly with the same tree shape and no +duplicate observations: + +```bash +# Replay stress: disable the workflow cache so EVERY workflow task replays +# the workflow from the start of history. +uv run python -m langfuse_tracing.ticket_triage.worker --replay-stress +uv run python -m langfuse_tracing.ticket_triage.starter +uv run python -m langfuse_tracing.verify_trace --trace-id + +# Worker restart mid-workflow: the starter waits 20s before sending the +# approval. Give the triage activities a few seconds to finish, then kill the +# worker while the workflow durably awaits approval; start a new worker and +# watch the workflow (and its trace) complete cleanly. +uv run python -m langfuse_tracing.ticket_triage.starter --pause-before-approval 20 +# ... after ~5s, ctrl+c the worker, then start it again in another terminal +uv run python -m langfuse_tracing.verify_trace --trace-id +``` + +## Where spans come from + +| Span | Emitted by | Where it runs | +|---|---|---| +| `ticket-triage` (root) + `langfuse.*` trace attributes | starter code | starter | +| `StartWorkflow:*`, `StartWorkflowUpdate:*` | `OpenTelemetryPlugin` | starter (client side) | +| `RunWorkflow:*`, `StartActivity:*`, `ValidateUpdate:*`, `HandleUpdate:*` | `OpenTelemetryPlugin` | worker (workflow) | +| `triage` | plain OpenTelemetry API in workflow code | worker (workflow) | +| `RunActivity:*` | `OpenTelemetryPlugin` | worker (activity) | +| `ChatCompletion` / `chat ` GENERATIONs | OpenAI auto-instrumentation | worker (activity) | + +## Where tracing works + +| Location | Works? | Notes | +|---|---|---| +| Activity bodies | ✅ | Plain OpenTelemetry + any auto-instrumentation, no restrictions. This is where LLM calls (and their GENERATION spans) belong. | +| Workflow bodies | ✅ | Plain OpenTelemetry APIs are replay-safe under the plugin: deterministic span IDs, no re-export on replay. Spans export when they end; the `RunWorkflow` span exports when the run completes. | +| Signal/query/update handlers | ✅ | Handled by the plugin automatically (`HandleUpdate:*` etc.). | +| Client / starter code | ✅ | Standard OpenTelemetry; put Langfuse trace-level attributes on your root span. | + +## LLM instrumentation flavors + +`LLM_INSTRUMENTATION` selects how OpenAI calls are instrumented (both are +verified against Langfuse by this sample): + +| | `openinference` (default) | `openai-v2` | +|---|---|---| +| Package | `openinference-instrumentation-openai` | `opentelemetry-instrumentation-openai-v2` | +| Semantic conventions | OpenInference | OpenTelemetry GenAI (`gen_ai.*`) | +| GENERATION type, model, token usage, cost | ✅ | ✅ | +| Prompt/completion content | ✅ by default | Requires `OTEL_SEMCONV_STABILITY_OPT_IN=gen_ai_latest_experimental` and `OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT=span_only` | +| GENERATION span name | `ChatCompletion` | `chat ` | + +## Operational notes + +- Langfuse's OTLP endpoint is HTTP-only — this sample uses + `opentelemetry-exporter-otlp-proto-http` (the gRPC exporter will not work). +- Short-lived processes must flush: the starter and worker call + `force_flush()` on exit (see `telemetry.py`). +- Use a fresh workflow ID per run: the starter reports it as the Langfuse + session ID, so each run groups cleanly in the Sessions view. (The trace + itself is keyed by the starter's root span, which is new on every run.) +- `OTEL_SDK_DISABLED=true` turns off export without code changes. +- Ingestion is asynchronous; `verify_trace.py` polls until the trace is + stable. + +## Tests + +`tests/langfuse_tracing/` runs without Langfuse, Docker, or an LLM: mocked +activities, an in-memory span exporter, a worker with the workflow cache +disabled, whole-tree span assertions, and a `Replayer` pass asserting that +replaying the finished workflow's history emits zero new spans. + +```bash +uv run --group langfuse-tracing pytest tests/langfuse_tracing -v +``` + +## Using this outside samples-python + +The sample is self-contained: copy the `langfuse_tracing/` directory, change +the absolute imports (`langfuse_tracing.ticket_triage.activities` → +`ticket_triage.activities` or similar), and install the dependencies listed +under `langfuse-tracing` in this repo's `pyproject.toml`. diff --git a/langfuse_tracing/__init__.py b/langfuse_tracing/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/langfuse_tracing/langfuse/docker-compose.yml b/langfuse_tracing/langfuse/docker-compose.yml new file mode 100644 index 000000000..d7295ec90 --- /dev/null +++ b/langfuse_tracing/langfuse/docker-compose.yml @@ -0,0 +1,188 @@ +# Self-hosted Langfuse for the langfuse_tracing sample. +# +# Based on the upstream https://github.com/langfuse/langfuse/blob/v3.224.1/docker-compose.yml +# with these changes for a local demo: +# - Langfuse images pinned to a specific release instead of the floating `:3` tag. +# - Only two ports are published on the host: the Langfuse UI/API (3000) and MinIO's +# S3 endpoint (127.0.0.1:9090, used by Langfuse's presigned media/export URLs). +# Postgres, ClickHouse, Redis, and the Langfuse worker stay on the compose network — +# upstream publishes several of these on the host (e.g. Postgres on 127.0.0.1:5432, +# MinIO on all interfaces), which can collide with other local stacks. +# - Headless initialization (LANGFUSE_INIT_*) provisions the org, project, API keys, and +# login user on first start; provisioning is skipped when the resources already +# exist, so restarts are safe. +# - All secrets below are throwaway demo values. Do not reuse them outside local demos. +# +# Usage: +# docker compose up -d +# curl -sf http://localhost:3000/api/public/health # poll until 200 +# UI login: demo@temporal.io / langfuse-demo-pw-1 +name: langfuse-demo +services: + langfuse-worker: + image: docker.io/langfuse/langfuse-worker:3.224.1 + restart: always + depends_on: &langfuse-depends-on + postgres: + condition: service_healthy + minio: + condition: service_healthy + redis: + condition: service_healthy + clickhouse: + condition: service_healthy + environment: &langfuse-worker-env + NEXTAUTH_URL: ${NEXTAUTH_URL:-http://localhost:3000} + DATABASE_URL: ${DATABASE_URL:-postgresql://postgres:postgres@postgres:5432/postgres} + SALT: ${SALT:-langfuse-demo-salt} + ENCRYPTION_KEY: ${ENCRYPTION_KEY:-0000000000000000000000000000000000000000000000000000000000000000} + TELEMETRY_ENABLED: ${TELEMETRY_ENABLED:-false} + LANGFUSE_ENABLE_EXPERIMENTAL_FEATURES: ${LANGFUSE_ENABLE_EXPERIMENTAL_FEATURES:-false} + CLICKHOUSE_MIGRATION_URL: ${CLICKHOUSE_MIGRATION_URL:-clickhouse://clickhouse:9000} + CLICKHOUSE_URL: ${CLICKHOUSE_URL:-http://clickhouse:8123} + CLICKHOUSE_USER: ${CLICKHOUSE_USER:-clickhouse} + CLICKHOUSE_PASSWORD: ${CLICKHOUSE_PASSWORD:-clickhouse} + CLICKHOUSE_CLUSTER_ENABLED: ${CLICKHOUSE_CLUSTER_ENABLED:-false} + LANGFUSE_USE_AZURE_BLOB: ${LANGFUSE_USE_AZURE_BLOB:-false} + LANGFUSE_USE_OCI_NATIVE_OBJECT_STORAGE: ${LANGFUSE_USE_OCI_NATIVE_OBJECT_STORAGE:-false} + LANGFUSE_OCI_AUTH_TYPE: ${LANGFUSE_OCI_AUTH_TYPE:-workload_identity} + LANGFUSE_S3_EVENT_UPLOAD_BUCKET: ${LANGFUSE_S3_EVENT_UPLOAD_BUCKET:-langfuse} + LANGFUSE_S3_EVENT_UPLOAD_REGION: ${LANGFUSE_S3_EVENT_UPLOAD_REGION:-auto} + LANGFUSE_S3_EVENT_UPLOAD_ACCESS_KEY_ID: ${LANGFUSE_S3_EVENT_UPLOAD_ACCESS_KEY_ID:-minio} + LANGFUSE_S3_EVENT_UPLOAD_SECRET_ACCESS_KEY: ${LANGFUSE_S3_EVENT_UPLOAD_SECRET_ACCESS_KEY:-miniosecret} + LANGFUSE_S3_EVENT_UPLOAD_ENDPOINT: ${LANGFUSE_S3_EVENT_UPLOAD_ENDPOINT:-http://minio:9000} + LANGFUSE_S3_EVENT_UPLOAD_FORCE_PATH_STYLE: ${LANGFUSE_S3_EVENT_UPLOAD_FORCE_PATH_STYLE:-true} + LANGFUSE_S3_EVENT_UPLOAD_PREFIX: ${LANGFUSE_S3_EVENT_UPLOAD_PREFIX:-events/} + LANGFUSE_S3_MEDIA_UPLOAD_BUCKET: ${LANGFUSE_S3_MEDIA_UPLOAD_BUCKET:-langfuse} + LANGFUSE_S3_MEDIA_UPLOAD_REGION: ${LANGFUSE_S3_MEDIA_UPLOAD_REGION:-auto} + LANGFUSE_S3_MEDIA_UPLOAD_ACCESS_KEY_ID: ${LANGFUSE_S3_MEDIA_UPLOAD_ACCESS_KEY_ID:-minio} + LANGFUSE_S3_MEDIA_UPLOAD_SECRET_ACCESS_KEY: ${LANGFUSE_S3_MEDIA_UPLOAD_SECRET_ACCESS_KEY:-miniosecret} + LANGFUSE_S3_MEDIA_UPLOAD_ENDPOINT: ${LANGFUSE_S3_MEDIA_UPLOAD_ENDPOINT:-http://localhost:9090} + LANGFUSE_S3_MEDIA_UPLOAD_FORCE_PATH_STYLE: ${LANGFUSE_S3_MEDIA_UPLOAD_FORCE_PATH_STYLE:-true} + LANGFUSE_S3_MEDIA_UPLOAD_PREFIX: ${LANGFUSE_S3_MEDIA_UPLOAD_PREFIX:-media/} + LANGFUSE_S3_BATCH_EXPORT_ENABLED: ${LANGFUSE_S3_BATCH_EXPORT_ENABLED:-false} + LANGFUSE_S3_BATCH_EXPORT_BUCKET: ${LANGFUSE_S3_BATCH_EXPORT_BUCKET:-langfuse} + LANGFUSE_S3_BATCH_EXPORT_PREFIX: ${LANGFUSE_S3_BATCH_EXPORT_PREFIX:-exports/} + LANGFUSE_S3_BATCH_EXPORT_REGION: ${LANGFUSE_S3_BATCH_EXPORT_REGION:-auto} + LANGFUSE_S3_BATCH_EXPORT_ENDPOINT: ${LANGFUSE_S3_BATCH_EXPORT_ENDPOINT:-http://minio:9000} + LANGFUSE_S3_BATCH_EXPORT_EXTERNAL_ENDPOINT: ${LANGFUSE_S3_BATCH_EXPORT_EXTERNAL_ENDPOINT:-http://localhost:9090} + LANGFUSE_S3_BATCH_EXPORT_ACCESS_KEY_ID: ${LANGFUSE_S3_BATCH_EXPORT_ACCESS_KEY_ID:-minio} + LANGFUSE_S3_BATCH_EXPORT_SECRET_ACCESS_KEY: ${LANGFUSE_S3_BATCH_EXPORT_SECRET_ACCESS_KEY:-miniosecret} + LANGFUSE_S3_BATCH_EXPORT_FORCE_PATH_STYLE: ${LANGFUSE_S3_BATCH_EXPORT_FORCE_PATH_STYLE:-true} + LANGFUSE_INGESTION_QUEUE_DELAY_MS: ${LANGFUSE_INGESTION_QUEUE_DELAY_MS:-} + LANGFUSE_INGESTION_CLICKHOUSE_WRITE_INTERVAL_MS: ${LANGFUSE_INGESTION_CLICKHOUSE_WRITE_INTERVAL_MS:-} + REDIS_HOST: ${REDIS_HOST:-redis} + REDIS_PORT: ${REDIS_PORT:-6379} + REDIS_AUTH: ${REDIS_AUTH:-myredissecret} + LANGFUSE_BULLMQ_SKIP_REDIS_VERSION_CHECK: ${LANGFUSE_BULLMQ_SKIP_REDIS_VERSION_CHECK:-false} + REDIS_TLS_ENABLED: ${REDIS_TLS_ENABLED:-false} + REDIS_TLS_CA: ${REDIS_TLS_CA:-/certs/ca.crt} + REDIS_TLS_CERT: ${REDIS_TLS_CERT:-/certs/redis.crt} + REDIS_TLS_KEY: ${REDIS_TLS_KEY:-/certs/redis.key} + EMAIL_FROM_ADDRESS: ${EMAIL_FROM_ADDRESS:-} + SMTP_CONNECTION_URL: ${SMTP_CONNECTION_URL:-} + + langfuse-web: + image: docker.io/langfuse/langfuse:3.224.1 + restart: always + depends_on: *langfuse-depends-on + ports: + - 3000:3000 + environment: + <<: *langfuse-worker-env + NEXTAUTH_SECRET: ${NEXTAUTH_SECRET:-langfuse-demo-nextauth-secret} + # Headless initialization: org, project, API keys, and login user are created on + # first startup. These keys must match langfuse_tracing/.env.example. + LANGFUSE_INIT_ORG_ID: ${LANGFUSE_INIT_ORG_ID:-temporal-demo} + LANGFUSE_INIT_ORG_NAME: ${LANGFUSE_INIT_ORG_NAME:-Temporal Demo} + LANGFUSE_INIT_PROJECT_ID: ${LANGFUSE_INIT_PROJECT_ID:-langfuse-tracing-demo} + LANGFUSE_INIT_PROJECT_NAME: ${LANGFUSE_INIT_PROJECT_NAME:-langfuse-tracing-demo} + LANGFUSE_INIT_PROJECT_PUBLIC_KEY: ${LANGFUSE_INIT_PROJECT_PUBLIC_KEY:-pk-lf-temporal-demo-0000} + LANGFUSE_INIT_PROJECT_SECRET_KEY: ${LANGFUSE_INIT_PROJECT_SECRET_KEY:-sk-lf-temporal-demo-0000} + LANGFUSE_INIT_USER_EMAIL: ${LANGFUSE_INIT_USER_EMAIL:-demo@temporal.io} + LANGFUSE_INIT_USER_NAME: ${LANGFUSE_INIT_USER_NAME:-Demo User} + LANGFUSE_INIT_USER_PASSWORD: ${LANGFUSE_INIT_USER_PASSWORD:-langfuse-demo-pw-1} + + clickhouse: + image: docker.io/clickhouse/clickhouse-server + restart: always + user: "101:101" + environment: + CLICKHOUSE_DB: default + CLICKHOUSE_USER: ${CLICKHOUSE_USER:-clickhouse} + CLICKHOUSE_PASSWORD: ${CLICKHOUSE_PASSWORD:-clickhouse} + volumes: + - langfuse_clickhouse_data:/var/lib/clickhouse + - langfuse_clickhouse_logs:/var/log/clickhouse-server + healthcheck: + test: wget --no-verbose --tries=1 --spider http://localhost:8123/ping || exit 1 + interval: 5s + timeout: 5s + retries: 10 + start_period: 1s + + minio: + image: cgr.dev/chainguard/minio + restart: always + entrypoint: sh + # create the 'langfuse' bucket before starting the service + command: -c 'mkdir -p /data/langfuse && minio server --address ":9000" --console-address ":9001" /data' + environment: + MINIO_ROOT_USER: ${MINIO_ROOT_USER:-minio} + MINIO_ROOT_PASSWORD: ${MINIO_ROOT_PASSWORD:-miniosecret} + ports: + # Loopback-only S3 endpoint; matches the LANGFUSE_S3_MEDIA_UPLOAD_ENDPOINT + # and LANGFUSE_S3_BATCH_EXPORT_EXTERNAL_ENDPOINT defaults (localhost:9090) + # so presigned media/export URLs resolve from the browser. + - 127.0.0.1:9090:9000 + volumes: + - langfuse_minio_data:/data + healthcheck: + test: ["CMD", "mc", "ready", "local"] + interval: 1s + timeout: 5s + retries: 5 + start_period: 1s + + redis: + image: docker.io/redis:7 + restart: always + command: > + --requirepass ${REDIS_AUTH:-myredissecret} + --maxmemory-policy noeviction + volumes: + - langfuse_redis_data:/data + healthcheck: + test: ["CMD", "redis-cli", "ping"] + interval: 3s + timeout: 10s + retries: 10 + + postgres: + image: docker.io/postgres:${POSTGRES_VERSION:-17} + restart: always + healthcheck: + test: ["CMD-SHELL", "pg_isready -U postgres"] + interval: 3s + timeout: 3s + retries: 10 + environment: + POSTGRES_USER: ${POSTGRES_USER:-postgres} + POSTGRES_PASSWORD: ${POSTGRES_PASSWORD:-postgres} + POSTGRES_DB: ${POSTGRES_DB:-postgres} + TZ: UTC + PGTZ: UTC + volumes: + - langfuse_postgres_data:/var/lib/postgresql/data + +volumes: + langfuse_postgres_data: + driver: local + langfuse_clickhouse_data: + driver: local + langfuse_clickhouse_logs: + driver: local + langfuse_minio_data: + driver: local + langfuse_redis_data: + driver: local diff --git a/langfuse_tracing/telemetry.py b/langfuse_tracing/telemetry.py new file mode 100644 index 000000000..b17a7d7df --- /dev/null +++ b/langfuse_tracing/telemetry.py @@ -0,0 +1,119 @@ +"""Shared OpenTelemetry-to-Langfuse wiring for the langfuse_tracing samples. + +Langfuse natively ingests OpenTelemetry traces, so no Langfuse SDK is needed: +spans are exported over OTLP/HTTP to Langfuse's ``/api/public/otel`` endpoint, +authenticated with a project's public/secret API key pair. + +The tracer provider comes from ``temporalio.contrib.opentelemetry +.create_tracer_provider()``, which is safe to use inside workflow code: span +IDs are generated deterministically from workflow state and span export is +suppressed during replay, so a workflow that replays (worker restart, cache +eviction, host failover) never produces duplicate spans in Langfuse. +""" + +import base64 +import logging +import os + +from opentelemetry import trace +from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter +from opentelemetry.sdk.resources import SERVICE_NAME, Resource +from opentelemetry.sdk.trace.export import BatchSpanProcessor +from temporalio.contrib.opentelemetry import create_tracer_provider + +logger = logging.getLogger(__name__) + + +def _langfuse_exporter() -> OTLPSpanExporter: + host = os.environ.get("LANGFUSE_HOST", "http://localhost:3000").rstrip("/") + public_key = os.environ.get("LANGFUSE_PUBLIC_KEY") + secret_key = os.environ.get("LANGFUSE_SECRET_KEY") + if not public_key or not secret_key: + raise SystemExit( + "LANGFUSE_PUBLIC_KEY and LANGFUSE_SECRET_KEY must be set. Copy " + "langfuse_tracing/.env.example to langfuse_tracing/.env and load it " + "in this terminal: set -a; source langfuse_tracing/.env; set +a" + ) + auth = base64.b64encode(f"{public_key}:{secret_key}".encode()).decode() + return OTLPSpanExporter( + # Langfuse's OTLP endpoint is HTTP-only (protobuf or JSON); it has no gRPC + # listener, so this must be the http exporter, not the grpc one. + endpoint=f"{host}/api/public/otel/v1/traces", + headers={ + "Authorization": f"Basic {auth}", + # Documented by Langfuse: opts into real-time ingestion. Without it, + # ingested traces can take several minutes to appear in the UI. + "x-langfuse-ingestion-version": "4", + }, + timeout=10, + ) + + +def setup_tracing(service_name: str) -> None: + """Install a replay-safe tracer provider that exports spans to Langfuse. + + Must be called once at process start, before connecting the Temporal + client, in every process that traces (worker and starter alike). + + Honors ``OTEL_SDK_DISABLED=true`` as a kill-switch: the provider is still + installed (the Temporal worker requires it) but no exporter is attached. + """ + provider = create_tracer_provider( + resource=Resource.create({SERVICE_NAME: service_name}) + ) + if os.environ.get("OTEL_SDK_DISABLED", "").lower() != "true": + # A short schedule delay so demo spans show up in Langfuse quickly. + # Buffered spans are also flushed at process exit (the provider + # registers a shutdown hook), but call force_flush() before reading + # traces back to avoid racing the batch. + provider.add_span_processor( + BatchSpanProcessor(_langfuse_exporter(), schedule_delay_millis=500) + ) + else: + logger.info("OTEL_SDK_DISABLED=true - spans will not be exported") + trace.set_tracer_provider(provider) + + +def force_flush() -> None: + """Flush any buffered spans to Langfuse immediately.""" + # The replay-safe provider implements force_flush but the base + # opentelemetry TracerProvider type does not declare it, hence getattr. + flush = getattr(trace.get_tracer_provider(), "force_flush", None) + if callable(flush): + flush() + + +def instrument_openai() -> str: + """Instrument the OpenAI client library once, in the worker process. + + Every OpenAI API call made from an activity then emits a span that nests + under that activity's span and that Langfuse renders as a GENERATION + observation with model, token usage, and cost. + + Two OpenTelemetry instrumentation flavors are supported via the + ``LLM_INSTRUMENTATION`` env var: + + - ``openinference`` (default): OpenInference semantic conventions. Prompt + and completion content are recorded on span attributes, which Langfuse + maps to the observation's input/output. + - ``openai-v2``: the OpenTelemetry GenAI semantic conventions + (``gen_ai.*``). Content capture additionally requires + ``OTEL_SEMCONV_STABILITY_OPT_IN=gen_ai_latest_experimental`` and + ``OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT=span_only``. + """ + flavor = os.environ.get("LLM_INSTRUMENTATION", "openinference") + if flavor == "openinference": + from openinference.instrumentation.openai import OpenAIInstrumentor + + OpenAIInstrumentor().instrument() + elif flavor == "openai-v2": + from opentelemetry.instrumentation.openai_v2 import ( + OpenAIInstrumentor as OpenAIV2Instrumentor, + ) + + OpenAIV2Instrumentor().instrument() + else: + raise ValueError( + f"Unknown LLM_INSTRUMENTATION {flavor!r}; use 'openinference' or 'openai-v2'" + ) + return flavor diff --git a/langfuse_tracing/ticket_triage/README.md b/langfuse_tracing/ticket_triage/README.md new file mode 100644 index 000000000..eef7d7cc2 --- /dev/null +++ b/langfuse_tracing/ticket_triage/README.md @@ -0,0 +1,62 @@ +# Ticket Triage + +An LLM support-ticket triage workflow demonstrating the recommended +Temporal → Langfuse tracing setup (see [../README.md](../README.md) for the +full runbook). + +Flow: `classify_ticket` (LLM) and `lookup_account` (plain activity) run under +a custom `triage` span, the workflow then waits for a human decision delivered +as a workflow **update** (`approve`, with a validator), and on approval +`draft_reply` (LLM) produces the customer reply. + +| File | Purpose | +|---|---| +| `workflows.py` | `TicketTriageWorkflow` — deterministic, sandboxed; uses plain OpenTelemetry APIs for the `triage` span; `approve` update handler + validator | +| `activities.py` | The two LLM activities and the plain lookup activity; all I/O lives here | +| `worker.py` | Worker with `OpenTelemetryPlugin(add_temporal_spans=True)`; `--replay-stress` disables the workflow cache | +| `starter.py` | Opens the root span with `langfuse.*` trace attributes, starts the workflow, sends the approval update, prints the Langfuse trace link; `--decline`, `--pause-before-approval N` | + +## Run + +With Langfuse up, dependencies synced, and the environment loaded (see +[../README.md](../README.md)): + +```bash +uv run python -m langfuse_tracing.ticket_triage.worker +uv run python -m langfuse_tracing.ticket_triage.starter +uv run python -m langfuse_tracing.verify_trace --trace-id +``` + +Variants: + +```bash +uv run python -m langfuse_tracing.ticket_triage.starter --decline +uv run python -m langfuse_tracing.verify_trace --trace-id --expect declined + +# Replay stress: every workflow task replays the workflow from history — +# the Langfuse trace must come out identical. +uv run python -m langfuse_tracing.ticket_triage.worker --replay-stress + +# Durability demo: park the workflow awaiting approval for 20s, kill and +# restart the worker meanwhile — one clean trace regardless. +uv run python -m langfuse_tracing.ticket_triage.starter --pause-before-approval 20 +``` + +## Expected trace + +``` +ticket-triage SPAN (root; session=workflow id, user, tags) +├─ StartWorkflow:TicketTriageWorkflow SPAN +│ └─ RunWorkflow:TicketTriageWorkflow SPAN +│ ├─ triage SPAN +│ │ ├─ StartActivity:classify_ticket → RunActivity:classify_ticket +│ │ │ └─ ChatCompletion GENERATION +│ │ └─ StartActivity:lookup_account → RunActivity:lookup_account +│ └─ StartActivity:draft_reply → RunActivity:draft_reply +│ └─ ChatCompletion GENERATION +└─ StartWorkflowUpdate:approve SPAN + ├─ ValidateUpdate:approve SPAN + └─ HandleUpdate:approve SPAN +``` + +With `--decline`, the `draft_reply` subtree is absent. diff --git a/langfuse_tracing/ticket_triage/__init__.py b/langfuse_tracing/ticket_triage/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/langfuse_tracing/ticket_triage/activities.py b/langfuse_tracing/ticket_triage/activities.py new file mode 100644 index 000000000..77c30b547 --- /dev/null +++ b/langfuse_tracing/ticket_triage/activities.py @@ -0,0 +1,140 @@ +"""Activities for the ticket triage sample. + +All LLM and I/O work happens here, in activities — never in workflow code. +The OpenAI client is instrumented process-wide (see ``telemetry.instrument_openai``), +so each API call below automatically emits a child span of the activity span, +which Langfuse displays as a GENERATION observation with model and token usage. +""" + +import json +import os +from dataclasses import dataclass +from typing import Optional + +from openai import AsyncOpenAI +from temporalio import activity + + +@dataclass +class Ticket: + ticket_id: str + customer_email: str + subject: str + body: str + + +@dataclass +class Classification: + category: str + priority: str + + +@dataclass +class AccountInfo: + customer_email: str + account_name: str + plan: str + + +@dataclass +class DraftReplyInput: + ticket: Ticket + classification: Classification + account: AccountInfo + + +@dataclass +class ApprovalDecision: + approved: bool + reviewer: str + + +@dataclass +class TriageResult: + status: str + classification: Classification + reply: Optional[str] = None + + +CLASSIFY_PROMPT = ( + "You are a support ticket triage assistant. Classify the ticket and respond " + 'with ONLY a JSON object like {"category": "billing|bug|how-to|other", ' + '"priority": "low|normal|high"}.' +) + +DRAFT_PROMPT = ( + "You are a support agent. Draft a short (under 120 words), friendly reply to " + "the customer's ticket. Use the provided classification and account details." +) + + +def _openai_client() -> AsyncOpenAI: + # Configuration comes from the environment, never from activity inputs + # (activity inputs are recorded in workflow history and shown in the UI). + # max_retries=0 disables the OpenAI client's built-in retries — Temporal's + # activity retry policy owns retries, with full visibility in the UI. + return AsyncOpenAI( + base_url=os.environ.get("OPENAI_BASE_URL"), + api_key=os.environ.get("OPENAI_API_KEY"), + max_retries=0, + ) + + +def _parse_classification(text: str) -> Classification: + try: + data = json.loads(text[text.index("{") : text.rindex("}") + 1]) + return Classification( + category=str(data.get("category", "other")).lower(), + priority=str(data.get("priority", "normal")).lower(), + ) + except ValueError: + return Classification(category="other", priority="normal") + + +@activity.defn +async def classify_ticket(ticket: Ticket) -> Classification: + response = await _openai_client().chat.completions.create( + model=os.environ.get("MODEL_CLASSIFY", "gpt-4o-mini"), + messages=[ + {"role": "system", "content": CLASSIFY_PROMPT}, + {"role": "user", "content": f"{ticket.subject}\n\n{ticket.body}"}, + ], + timeout=30, + ) + return _parse_classification(response.choices[0].message.content or "") + + +@activity.defn +async def lookup_account(customer_email: str) -> AccountInfo: + # A deterministic, non-LLM activity: appears in Langfuse as a plain SPAN + # observation alongside the GENERATION observations from the LLM activities. + known_accounts = { + "ada@acme.example": AccountInfo( + customer_email="ada@acme.example", + account_name="Acme Corp", + plan="enterprise", + ), + } + return known_accounts.get( + customer_email, + AccountInfo(customer_email=customer_email, account_name="Unknown", plan="free"), + ) + + +@activity.defn +async def draft_reply(input: DraftReplyInput) -> str: + context = ( + f"Ticket: {input.ticket.subject}\n{input.ticket.body}\n\n" + f"Category: {input.classification.category}, " + f"priority: {input.classification.priority}\n" + f"Account: {input.account.account_name} ({input.account.plan} plan)" + ) + response = await _openai_client().chat.completions.create( + model=os.environ.get("MODEL_DRAFT", "gpt-4o-mini"), + messages=[ + {"role": "system", "content": DRAFT_PROMPT}, + {"role": "user", "content": context}, + ], + timeout=30, + ) + return response.choices[0].message.content or "" diff --git a/langfuse_tracing/ticket_triage/starter.py b/langfuse_tracing/ticket_triage/starter.py new file mode 100644 index 000000000..e77e8e8f8 --- /dev/null +++ b/langfuse_tracing/ticket_triage/starter.py @@ -0,0 +1,120 @@ +"""Starter for the ticket triage sample. + +Opens one root span around the whole interaction (start workflow, send the +approval update, await the result) so that everything — including the +workflow, activity, and LLM spans produced on the worker — lands in a single +Langfuse trace. Langfuse trace-level attributes (name, session, user, tags) +are set on this root span. +""" + +import argparse +import asyncio +import os +import uuid + +from opentelemetry import trace +from temporalio.client import Client +from temporalio.contrib.opentelemetry import OpenTelemetryPlugin +from temporalio.envconfig import ClientConfig + +from langfuse_tracing.telemetry import force_flush, setup_tracing +from langfuse_tracing.ticket_triage.activities import ApprovalDecision, Ticket +from langfuse_tracing.ticket_triage.workflows import TicketTriageWorkflow + +TASK_QUEUE = "langfuse-ticket-triage-task-queue" + + +async def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--decline", action="store_true", help="Decline the ticket") + parser.add_argument( + "--pause-before-approval", + type=int, + default=0, + metavar="SECONDS", + help="Wait before sending the approval update. While the workflow durably " + "awaits approval you can kill and restart the worker to see that the " + "Langfuse trace still comes out as a single clean tree.", + ) + args = parser.parse_args() + approved = not args.decline + + setup_tracing("ticket-triage-starter") + + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect( + **config, + plugins=[OpenTelemetryPlugin(add_temporal_spans=True)], + ) + + # A fresh workflow ID per run avoids Temporal workflow ID conflicts and + # gives each run its own Langfuse session (langfuse.session.id on the root + # span below). The trace itself is keyed by the root span's trace ID, + # which is new on every run. + workflow_id = f"ticket-triage-{uuid.uuid4().hex[:8]}" + + ticket = Ticket( + ticket_id="T-1001", + customer_email="ada@acme.example", + subject="Charged twice for the July invoice", + body=( + "Hi, my card statement shows two identical charges for our July " + "invoice. Can you check what happened and refund the duplicate?" + ), + ) + + tracer = trace.get_tracer(__name__) + try: + with tracer.start_as_current_span( + "ticket-triage", + attributes={ + # langfuse.* attributes on the trace's root span set Langfuse + # trace-level fields, enabling filtering by session/user/tags. + "langfuse.trace.name": "ticket-triage", + "langfuse.session.id": workflow_id, + "langfuse.user.id": os.environ.get("LANGFUSE_DEMO_USER", "demo-user"), + "langfuse.trace.tags": ["temporal", "ticket-triage"], + "langfuse.trace.metadata.temporal_workflow_id": workflow_id, + }, + ) as root: + trace_id = format(root.get_span_context().trace_id, "032x") + handle = await client.start_workflow( + TicketTriageWorkflow.run, + ticket, + id=workflow_id, + task_queue=TASK_QUEUE, + ) + print(f"Started workflow: {workflow_id}") + + if args.pause_before_approval: + print(f"Pausing {args.pause_before_approval}s before approving ...") + await asyncio.sleep(args.pause_before_approval) + + update_result = await handle.execute_update( + TicketTriageWorkflow.approve, + ApprovalDecision(approved=approved, reviewer="demo-reviewer"), + ) + print(f"Approval update: {update_result}") + + result = await handle.result() + + print(f"Workflow status: {result.status}") + if result.reply: + print(f"Drafted reply:\n{result.reply}") + + host = os.environ.get("LANGFUSE_HOST", "http://localhost:3000").rstrip("/") + # The UI link needs the Langfuse project ID; the default matches the + # project provisioned by langfuse_tracing/langfuse/docker-compose.yml. + project = os.environ.get("LANGFUSE_PROJECT_ID", "langfuse-tracing-demo") + print(f"Trace ID: {trace_id}") + print(f"Langfuse trace: {host}/project/{project}/traces/{trace_id}") + finally: + # The starter is short-lived; flush so its spans (the trace root and + # the client-side StartWorkflow/StartWorkflowUpdate spans) are not + # dropped at process exit. + force_flush() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/langfuse_tracing/ticket_triage/worker.py b/langfuse_tracing/ticket_triage/worker.py new file mode 100644 index 000000000..db33e1e8d --- /dev/null +++ b/langfuse_tracing/ticket_triage/worker.py @@ -0,0 +1,70 @@ +"""Worker for the ticket triage sample.""" + +import argparse +import asyncio +import logging + +from temporalio.client import Client +from temporalio.contrib.opentelemetry import OpenTelemetryPlugin +from temporalio.envconfig import ClientConfig +from temporalio.worker import Worker + +from langfuse_tracing.telemetry import force_flush, instrument_openai, setup_tracing +from langfuse_tracing.ticket_triage.activities import ( + classify_ticket, + draft_reply, + lookup_account, +) +from langfuse_tracing.ticket_triage.workflows import TicketTriageWorkflow + +TASK_QUEUE = "langfuse-ticket-triage-task-queue" + + +async def main() -> None: + logging.basicConfig(level=logging.INFO) + + parser = argparse.ArgumentParser() + parser.add_argument( + "--replay-stress", + action="store_true", + help="Disable the workflow cache so every workflow task replays the " + "workflow from the start of history — the harshest test that tracing " + "emits each span exactly once. Traces in Langfuse must look identical " + "with or without this flag.", + ) + args = parser.parse_args() + replay_stress = args.replay_stress + + setup_tracing("ticket-triage-worker") + flavor = instrument_openai() + + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + + # add_temporal_spans=True emits spans for Temporal operations (StartWorkflow, + # RunWorkflow, RunActivity, HandleUpdate, ...) in addition to propagating + # trace context across the client/workflow/activity boundaries. + client = await Client.connect( + **config, + plugins=[OpenTelemetryPlugin(add_temporal_spans=True)], + ) + + worker = Worker( + client, + task_queue=TASK_QUEUE, + workflows=[TicketTriageWorkflow], + activities=[classify_ticket, lookup_account, draft_reply], + max_cached_workflows=0 if replay_stress else 1000, + # No plugins here: workers inherit them from the client. + ) + + mode = "replay-stress (workflow cache disabled)" if replay_stress else "normal" + print(f"Worker started (mode={mode}, llm_instrumentation={flavor}), ctrl+c to exit") + try: + await worker.run() + finally: + force_flush() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/langfuse_tracing/ticket_triage/workflows.py b/langfuse_tracing/ticket_triage/workflows.py new file mode 100644 index 000000000..7ec9b897a --- /dev/null +++ b/langfuse_tracing/ticket_triage/workflows.py @@ -0,0 +1,88 @@ +"""Ticket triage workflow with Langfuse tracing via OpenTelemetry. + +The workflow is fully deterministic and runs inside Temporal's standard +workflow sandbox. With ``OpenTelemetryPlugin`` registered on the client, +plain OpenTelemetry APIs work in workflow code: the ``triage`` span below is +created with the regular tracer, gets a deterministic span ID, and is never +re-exported on replay. +""" + +from datetime import timedelta +from typing import Optional + +from temporalio import workflow +from temporalio.common import RetryPolicy + +# Bounded retries for the LLM activities so that a misconfigured endpoint or +# API key fails fast instead of retrying forever. Note that each retry +# attempt records its own RunActivity span in the trace. +LLM_RETRY_POLICY = RetryPolicy(maximum_attempts=3) + +with workflow.unsafe.imports_passed_through(): + from opentelemetry import trace + + from langfuse_tracing.ticket_triage.activities import ( + ApprovalDecision, + Classification, + DraftReplyInput, + Ticket, + TriageResult, + classify_ticket, + draft_reply, + lookup_account, + ) + + +@workflow.defn +class TicketTriageWorkflow: + def __init__(self) -> None: + self._approval: Optional[ApprovalDecision] = None + + @workflow.run + async def run(self, ticket: Ticket) -> TriageResult: + # A custom span grouping the two triage activities. Under the + # OpenTelemetryPlugin this is replay-safe; the activity spans (and the + # LLM generation spans inside them) nest underneath it. + with trace.get_tracer(__name__).start_as_current_span("triage") as span: + classification: Classification = await workflow.execute_activity( + classify_ticket, + ticket, + start_to_close_timeout=timedelta(seconds=60), + retry_policy=LLM_RETRY_POLICY, + ) + account = await workflow.execute_activity( + lookup_account, + ticket.customer_email, + start_to_close_timeout=timedelta(seconds=10), + ) + span.set_attribute("triage.category", classification.category) + span.set_attribute("triage.priority", classification.priority) + + # Wait for a human approval, delivered as a workflow update. + await workflow.wait_condition(lambda: self._approval is not None) + approval = self._approval + assert approval is not None + if not approval.approved: + return TriageResult(status="declined", classification=classification) + + reply = await workflow.execute_activity( + draft_reply, + DraftReplyInput( + ticket=ticket, classification=classification, account=account + ), + start_to_close_timeout=timedelta(seconds=60), + retry_policy=LLM_RETRY_POLICY, + ) + return TriageResult( + status="replied", classification=classification, reply=reply + ) + + @workflow.update + async def approve(self, decision: ApprovalDecision) -> str: + self._approval = decision + return "approved" if decision.approved else "declined" + + @approve.validator + def approve_validator(self, decision: ApprovalDecision) -> None: + if decision.approved and not decision.reviewer: + raise ValueError("approval requires a reviewer") diff --git a/langfuse_tracing/verify_trace.py b/langfuse_tracing/verify_trace.py new file mode 100644 index 000000000..b9d47bceb --- /dev/null +++ b/langfuse_tracing/verify_trace.py @@ -0,0 +1,246 @@ +"""Verify a ticket-triage trace in Langfuse via the public API. + +Fetches the trace, reconstructs the observation tree, and deep-compares it +against the expected shape — including observation types — then checks that +every GENERATION carries a model and token usage, and that no observation was +duplicated (running the worker with --replay-stress surfaces replay-caused +duplicates here, if there were any). + +Usage: + python -m langfuse_tracing.verify_trace --trace-id + python -m langfuse_tracing.verify_trace --workflow-id + python -m langfuse_tracing.verify_trace --trace-id --expect declined + +Stdlib-only on purpose so it is trivially copy-out-able. +""" + +import argparse +import base64 +import json +import os +import sys +import time +import urllib.error +import urllib.parse +import urllib.request +from typing import Any, Optional + +# Expected observation trees as (depth, name, type) rows, children sorted by +# name under each parent. LLM spans are normalized to "" because +# their name depends on the instrumentation flavor ("ChatCompletion" for +# openinference, "chat " for openai-v2); their type must always be +# GENERATION. +EXPECTED_APPROVED = [ + (0, "ticket-triage", "SPAN"), + (1, "StartWorkflow:TicketTriageWorkflow", "SPAN"), + (2, "RunWorkflow:TicketTriageWorkflow", "SPAN"), + (3, "StartActivity:draft_reply", "SPAN"), + (4, "RunActivity:draft_reply", "SPAN"), + (5, "", "GENERATION"), + (3, "triage", "SPAN"), + (4, "StartActivity:classify_ticket", "SPAN"), + (5, "RunActivity:classify_ticket", "SPAN"), + (6, "", "GENERATION"), + (4, "StartActivity:lookup_account", "SPAN"), + (5, "RunActivity:lookup_account", "SPAN"), + (1, "StartWorkflowUpdate:approve", "SPAN"), + (2, "HandleUpdate:approve", "SPAN"), + (2, "ValidateUpdate:approve", "SPAN"), +] +EXPECTED_DECLINED = [ + (0, "ticket-triage", "SPAN"), + (1, "StartWorkflow:TicketTriageWorkflow", "SPAN"), + (2, "RunWorkflow:TicketTriageWorkflow", "SPAN"), + (3, "triage", "SPAN"), + (4, "StartActivity:classify_ticket", "SPAN"), + (5, "RunActivity:classify_ticket", "SPAN"), + (6, "", "GENERATION"), + (4, "StartActivity:lookup_account", "SPAN"), + (5, "RunActivity:lookup_account", "SPAN"), + (1, "StartWorkflowUpdate:approve", "SPAN"), + (2, "HandleUpdate:approve", "SPAN"), + (2, "ValidateUpdate:approve", "SPAN"), +] + + +def _api_get(path: str, params: Optional[dict[str, str]] = None) -> Any: + host = os.environ.get("LANGFUSE_HOST", "http://localhost:3000").rstrip("/") + public_key = os.environ.get("LANGFUSE_PUBLIC_KEY") + secret_key = os.environ.get("LANGFUSE_SECRET_KEY") + if not public_key or not secret_key: + raise SystemExit( + "LANGFUSE_PUBLIC_KEY and LANGFUSE_SECRET_KEY must be set. Copy " + "langfuse_tracing/.env.example to langfuse_tracing/.env and load it " + "in this terminal: set -a; source langfuse_tracing/.env; set +a" + ) + auth = base64.b64encode(f"{public_key}:{secret_key}".encode()).decode() + url = f"{host}{path}" + if params: + url += "?" + urllib.parse.urlencode(params) + request = urllib.request.Request(url, headers={"Authorization": f"Basic {auth}"}) + with urllib.request.urlopen(request, timeout=15) as response: + return json.loads(response.read()) + + +def _fetch_trace(trace_id: str) -> Optional[dict[str, Any]]: + try: + return _api_get(f"/api/public/traces/{trace_id}") + except urllib.error.HTTPError as err: + if err.code == 404: + return None + raise + + +def _resolve_trace_id_by_workflow_id(workflow_id: str) -> Optional[str]: + # The starter sets langfuse.session.id to the workflow ID on the root span. + result = _api_get("/api/public/traces", {"sessionId": workflow_id, "limit": "10"}) + data = result.get("data") or [] + return data[0]["id"] if data else None + + +def _normalized_name(observation: dict[str, Any]) -> str: + if observation.get("type") == "GENERATION": + return "" + return str(observation["name"]) + + +def _build_tree(observations: list[dict[str, Any]]) -> list[tuple[int, str, str]]: + children: dict[Optional[str], list[dict[str, Any]]] = {} + for observation in observations: + children.setdefault(observation.get("parentObservationId"), []).append( + observation + ) + rows: list[tuple[int, str, str]] = [] + + def walk(observation: dict[str, Any], depth: int) -> None: + rows.append((depth, _normalized_name(observation), str(observation["type"]))) + for child in sorted( + children.get(observation["id"], []), key=lambda o: str(o["name"]) + ): + walk(child, depth + 1) + + for root in sorted(children.get(None, []), key=lambda o: str(o["name"])): + walk(root, 0) + return rows + + +def _print_tree(rows: list[tuple[int, str, str]]) -> None: + for depth, name, type_ in rows: + print(f" {' ' * depth}{name} [{type_}]") + + +def _poll_stable_trace(trace_id: str, timeout_seconds: int) -> dict[str, Any]: + """Poll until the trace exists and its observation count is stable. + + Langfuse ingestion is asynchronous, so a freshly finished run may land + over a few seconds even though export already succeeded. + """ + deadline = time.monotonic() + timeout_seconds + previous_count = -1 + while time.monotonic() < deadline: + trace = _fetch_trace(trace_id) + if trace is not None: + count = len(trace.get("observations") or []) + if count > 0 and count == previous_count: + return trace + previous_count = count + time.sleep(2) + raise SystemExit( + f"FAIL: trace {trace_id} not fully ingested within {timeout_seconds}s" + ) + + +def _verify_trace(args: argparse.Namespace) -> int: + trace_id = args.trace_id + if not trace_id: + trace_id = _resolve_trace_id_by_workflow_id(args.workflow_id) + if not trace_id: + print(f"FAIL: no trace found for workflow id {args.workflow_id}") + return 1 + + trace = _poll_stable_trace(trace_id, args.timeout) + observations = trace.get("observations") or [] + failures: list[str] = [] + + # 1. No duplicate observations (replay must not re-emit spans). + ids = [o["id"] for o in observations] + if len(set(ids)) != len(ids): + failures.append("duplicate observation ids present") + actual = _build_tree(observations) + if len(set(actual)) != len(actual): + failures.append( + "duplicate (depth, name, type) rows — extra spans present (workflow " + "replay must never re-emit spans; activity retries also add a " + "RunActivity span per attempt)" + ) + + # 2. Whole-tree deep equality, types included. + expected = EXPECTED_DECLINED if args.expect == "declined" else EXPECTED_APPROVED + print(f"Trace {trace_id}: {len(observations)} observations") + _print_tree(actual) + if actual != expected: + failures.append("tree mismatch") + print(" Expected:") + _print_tree(expected) + + # 3. Every GENERATION has a model and token usage. + for observation in observations: + if observation["type"] != "GENERATION": + continue + usage = observation.get("usage") or {} + if not observation.get("model"): + failures.append(f"GENERATION {observation['id']} missing model") + if not usage.get("input") or not usage.get("output"): + failures.append(f"GENERATION {observation['id']} missing token usage") + if args.require_content and ( + observation.get("input") is None or observation.get("output") is None + ): + failures.append( + f"GENERATION {observation['id']} missing input/output content" + ) + + # 4. Trace-level enrichment from the starter's root span. + if trace.get("name") != "ticket-triage": + failures.append( + f"trace name is {trace.get('name')!r}, expected 'ticket-triage'" + ) + if not trace.get("sessionId"): + failures.append("trace has no session id (expected the workflow id)") + if not trace.get("userId"): + failures.append("trace has no user id") + if "temporal" not in (trace.get("tags") or []): + failures.append("trace missing 'temporal' tag") + + if failures: + for failure in failures: + print(f"FAIL: {failure}") + return 1 + print("PASS: tree shape, observation types, generations, and enrichment all match") + return 0 + + +def main() -> int: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--trace-id", help="Trace ID printed by the starter") + parser.add_argument("--workflow-id", help="Workflow ID (resolved via session id)") + parser.add_argument( + "--expect", choices=["approved", "declined"], default="approved" + ) + parser.add_argument("--timeout", type=int, default=60) + parser.add_argument( + "--require-content", + action="store_true", + default=os.environ.get("LLM_INSTRUMENTATION", "openinference") + == "openinference", + help="Assert GENERATIONs carry input/output content " + "(default true for openinference)", + ) + args = parser.parse_args() + + if not args.trace_id and not args.workflow_id: + parser.error("one of --trace-id or --workflow-id is required") + return _verify_trace(args) + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/langgraph_plugin/README.md b/langgraph_plugin/README.md new file mode 100644 index 000000000..8bb8d2c51 --- /dev/null +++ b/langgraph_plugin/README.md @@ -0,0 +1,75 @@ +# LangGraph Plugin Samples + +These samples demonstrate the [Temporal LangGraph plugin](https://github.com/temporalio/sdk-python/tree/main/temporalio/contrib/langgraph), which runs LangGraph workflows as durable Temporal workflows. Each LangGraph graph node (Graph API) or `@task` (Functional API) executes directly inside a workflow or as a Temporal activity with automatic retries, timeouts, and crash recovery. + +Samples are organized by API style: + +- **Graph API** (`graph_api/`) -- Define workflows as `StateGraph` with nodes and edges. +- **Functional API** (`functional_api/`) -- Define workflows with `@task` and `@entrypoint` decorators for an imperative programming style. + +## Samples + +| Sample | Graph API | Functional API | Description | +|--------|:---------:|:--------------:|-------------| +| **Hello World** | [graph_api/hello_world](graph_api/hello_world) | [functional_api/hello_world](functional_api/hello_world) | Minimal sample -- single node/task that processes a query string. Start here. | +| **Human-in-the-loop** | [graph_api/human_in_the_loop](graph_api/human_in_the_loop) | [functional_api/human_in_the_loop](functional_api/human_in_the_loop) | Chatbot that uses `interrupt()` to pause for human approval, Temporal signals to receive feedback, and queries to expose the pending draft. | +| **Continue-as-new** | [graph_api/continue_as_new](graph_api/continue_as_new) | [functional_api/continue_as_new](functional_api/continue_as_new) | Multi-stage data pipeline that uses `continue-as-new` with task result caching so previously-completed stages are not re-executed. | +| **ReAct Agent** | [graph_api/react_agent](graph_api/react_agent) | [functional_api/react_agent](functional_api/react_agent) | Tool-calling agent loop. Graph API uses conditional edges; Functional API uses a `while` loop. | +| **Control Flow** | -- | [functional_api/control_flow](functional_api/control_flow) | Demonstrates parallel task execution, `for` loops, and `if/else` branching -- patterns that are natural in the Functional API. | +| **Streaming** | [graph_api/streaming](graph_api/streaming) | -- | Streams live output from a running workflow via [Workflow Streams](https://github.com/temporalio/sdk-python/tree/main/temporalio/contrib/workflow_streams): node tokens via `get_stream_writer()` + `streaming_topic`, plus workflow-side `astream` progress published with `WorkflowStream.topic().publish()`. | +| **LangSmith Tracing** | [graph_api/langsmith_tracing](graph_api/langsmith_tracing) | [functional_api/langsmith_tracing](functional_api/langsmith_tracing) | Combines `LangGraphPlugin` with Temporal's `LangSmithPlugin` for durable execution + full observability of LLM calls. Requires API keys. | + +## Prerequisites + +1. Install dependencies: + + ```bash + uv sync --group langgraph + ``` + +2. Start a local dev server with the [Temporal CLI](https://docs.temporal.io/cli): + + ```bash + temporal server start-dev + ``` + +## Running a Sample + +Most samples have two scripts -- start the Worker first, then the Workflow starter in a separate terminal. + +```bash +# Terminal 1: start the Worker +uv run langgraph_plugin///run_worker.py + +# Terminal 2: start the Workflow +uv run langgraph_plugin///run_workflow.py +``` + +For example, to run the Graph API human-in-the-loop chatbot: + +```bash +# Terminal 1 +uv run langgraph_plugin/graph_api/human_in_the_loop/run_worker.py + +# Terminal 2 +uv run langgraph_plugin/graph_api/human_in_the_loop/run_workflow.py +``` + +The LangSmith Tracing samples bundle the Worker and Workflow execution into a single `main.py`: + +```bash +uv run langgraph_plugin//langsmith_tracing/main.py +``` + +## Key Features Demonstrated + +- **Durable execution** -- Every graph node / `@task` runs in a Workflow or as a Temporal activity with configurable timeouts and retry policies. +- **Human-in-the-loop** -- LangGraph's `interrupt()` pauses the graph; Temporal signals deliver human input; queries expose pending state to UIs. +- **Continue-as-new with caching** -- `cache()` captures completed task results; passing the cache to the next execution avoids re-running them. +- **Conditional routing** -- Graph API's `add_conditional_edges` and Functional API's native `if/else`/`while` for agent loops. +- **Parallel execution** -- Functional API launches multiple tasks concurrently by creating futures before awaiting them. +- **Streaming** -- Workflow Streams expose a durable, offset-addressed event channel that external clients subscribe to while the workflow is still running. Nodes emit fine-grained tokens via `get_stream_writer()` (routed by the plugin's `streaming_topic`), and the workflow can publish coarse `astream` progress to its own topic. + +## Related + +- [Temporal LangGraph plugin docs](https://github.com/temporalio/sdk-python/tree/main/temporalio/contrib/langgraph) diff --git a/langgraph_plugin/__init__.py b/langgraph_plugin/__init__.py new file mode 100644 index 000000000..a85231853 --- /dev/null +++ b/langgraph_plugin/__init__.py @@ -0,0 +1 @@ +"""Temporal LangGraph plugin samples.""" diff --git a/langgraph_plugin/functional_api/__init__.py b/langgraph_plugin/functional_api/__init__.py new file mode 100644 index 000000000..6caae83ec --- /dev/null +++ b/langgraph_plugin/functional_api/__init__.py @@ -0,0 +1 @@ +"""LangGraph Functional API samples using @task and @entrypoint.""" diff --git a/langgraph_plugin/functional_api/continue_as_new/README.md b/langgraph_plugin/functional_api/continue_as_new/README.md new file mode 100644 index 000000000..384aa89c9 --- /dev/null +++ b/langgraph_plugin/functional_api/continue_as_new/README.md @@ -0,0 +1,36 @@ +# Continue-as-New with Caching (Functional API) + +Demonstrates combining Temporal's continue-as-new with the LangGraph plugin's task result cache to avoid re-executing completed `@task` functions across workflow boundaries. + +## What This Sample Demonstrates + +- Task result caching across continue-as-new boundaries with `cache()` +- Restoring cached results with `entrypoint(name, cache=...)` +- Each `@task` executes exactly once despite multiple workflow invocations + +## How It Works + +1. Three tasks run sequentially: `double` (x2) -> `add_50` (+50) -> `triple` (x3). +2. After the first invocation, the workflow continues-as-new with the cache. +3. On subsequent invocations, all tasks return cached results instantly. +4. Input 10 -> 20 -> 70 -> 210. + +## Running the Sample + +Prerequisites: `uv sync --group langgraph` and a running Temporal dev server (`temporal server start-dev`). + +```bash +# Terminal 1 +uv run langgraph_plugin/functional_api/continue_as_new/run_worker.py + +# Terminal 2 +uv run langgraph_plugin/functional_api/continue_as_new/run_workflow.py +``` + +## Files + +| File | Description | +|------|-------------| +| `workflow.py` | `@task` functions, `@entrypoint`, `PipelineInput`, and `PipelineFunctionalWorkflow` | +| `run_worker.py` | Registers tasks and entrypoint with `LangGraphPlugin`, starts Worker | +| `run_workflow.py` | Executes the pipeline Workflow and prints the result | diff --git a/langgraph_plugin/functional_api/continue_as_new/__init__.py b/langgraph_plugin/functional_api/continue_as_new/__init__.py new file mode 100644 index 000000000..5b90703e9 --- /dev/null +++ b/langgraph_plugin/functional_api/continue_as_new/__init__.py @@ -0,0 +1 @@ +"""Continue-as-new pipeline with task result caching.""" diff --git a/langgraph_plugin/functional_api/continue_as_new/run_worker.py b/langgraph_plugin/functional_api/continue_as_new/run_worker.py new file mode 100644 index 000000000..a6b6a9d76 --- /dev/null +++ b/langgraph_plugin/functional_api/continue_as_new/run_worker.py @@ -0,0 +1,37 @@ +"""Worker for the continue-as-new pipeline (Functional API).""" + +import asyncio +import os + +from temporalio.client import Client +from temporalio.contrib.langgraph import LangGraphPlugin +from temporalio.worker import Worker + +from langgraph_plugin.functional_api.continue_as_new.workflow import ( + PipelineFunctionalWorkflow, + activity_options, + all_tasks, + pipeline_entrypoint, +) + + +async def main() -> None: + client = await Client.connect(os.environ.get("TEMPORAL_ADDRESS", "localhost:7233")) + plugin = LangGraphPlugin( + entrypoints={"pipeline": pipeline_entrypoint}, + tasks=all_tasks, + activity_options=activity_options, + ) + + worker = Worker( + client, + task_queue="langgraph-pipeline-functional", + workflows=[PipelineFunctionalWorkflow], + plugins=[plugin], + ) + print("Worker started. Ctrl+C to exit.") + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/langgraph_plugin/functional_api/continue_as_new/run_workflow.py b/langgraph_plugin/functional_api/continue_as_new/run_workflow.py new file mode 100644 index 000000000..563aaba6b --- /dev/null +++ b/langgraph_plugin/functional_api/continue_as_new/run_workflow.py @@ -0,0 +1,31 @@ +"""Start the continue-as-new pipeline workflow (Functional API).""" + +import asyncio +import os +from datetime import timedelta + +from temporalio.client import Client + +from langgraph_plugin.functional_api.continue_as_new.workflow import ( + PipelineFunctionalWorkflow, + PipelineInput, +) + + +async def main() -> None: + client = await Client.connect(os.environ.get("TEMPORAL_ADDRESS", "localhost:7233")) + + result = await client.execute_workflow( + PipelineFunctionalWorkflow.run, + PipelineInput(data=10), + id="pipeline-functional-workflow", + task_queue="langgraph-pipeline-functional", + execution_timeout=timedelta(seconds=60), + ) + + # 10*2=20 -> 20+50=70 -> 70*3=210 + print(f"Pipeline result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/langgraph_plugin/functional_api/continue_as_new/workflow.py b/langgraph_plugin/functional_api/continue_as_new/workflow.py new file mode 100644 index 000000000..9810c05ed --- /dev/null +++ b/langgraph_plugin/functional_api/continue_as_new/workflow.py @@ -0,0 +1,92 @@ +"""Continue-as-new with caching using the LangGraph Functional API with Temporal. + +Demonstrates Temporal's continue-as-new with the LangGraph plugin's task +result cache to avoid re-executing completed @task functions across +workflow boundaries. +""" + +from dataclasses import dataclass +from datetime import timedelta +from typing import Any + +from langgraph.func import entrypoint, task +from temporalio import workflow +from temporalio.contrib.langgraph import cache +from temporalio.contrib.langgraph import entrypoint as temporal_entrypoint + + +@task +def double(data: int) -> int: + """Stage 1: double the input.""" + return data * 2 + + +@task +def add_50(data: int) -> int: + """Stage 2: add 50.""" + return data + 50 + + +@task +def triple(data: int) -> int: + """Stage 3: triple the result.""" + return data * 3 + + +@entrypoint() +async def pipeline_entrypoint(data: int) -> dict: + """Run the 3-stage pipeline: double -> add_50 -> triple.""" + doubled = await double(data) + plus_50 = await add_50(doubled) + tripled = await triple(plus_50) + return {"result": tripled} + + +all_tasks = [double, add_50, triple] + +activity_options = { + "double": { + "execute_in": "activity", + "start_to_close_timeout": timedelta(seconds=30), + }, + "add_50": { + "execute_in": "activity", + "start_to_close_timeout": timedelta(seconds=30), + }, + "triple": { + "execute_in": "activity", + "start_to_close_timeout": timedelta(seconds=30), + }, +} + + +@dataclass +class PipelineInput: + data: int + cache: dict[str, Any] | None = None + phase: int = 1 + + +@workflow.defn +class PipelineFunctionalWorkflow: + """Runs the pipeline, continuing-as-new after each phase. + + Input 10: 10*2=20 -> 20+50=70 -> 70*3=210 + Each task executes once; phases 2 and 3 use cached results. + """ + + @workflow.run + async def run(self, input_data: PipelineInput) -> dict[str, Any]: + app = temporal_entrypoint("pipeline", cache=input_data.cache) + result = await app.ainvoke(input_data.data) + + if input_data.phase < 3: + workflow.continue_as_new( + PipelineInput( + data=input_data.data, + cache=cache(), + phase=input_data.phase + 1, + ) + ) + + return result diff --git a/langgraph_plugin/functional_api/control_flow/README.md b/langgraph_plugin/functional_api/control_flow/README.md new file mode 100644 index 000000000..e6e1ec493 --- /dev/null +++ b/langgraph_plugin/functional_api/control_flow/README.md @@ -0,0 +1,37 @@ +# Control Flow (Functional API) + +Demonstrates the Functional API's strength for complex control flow: parallel execution, sequential loops, and conditional branching — all as natural Python code. + +## What This Sample Demonstrates + +- **Parallel execution**: launching multiple tasks concurrently by creating futures before awaiting +- **For loops**: processing items sequentially with `for item in items` +- **If/else branching**: routing items based on classification results +- Why the Functional API is ideal for programmatic composition patterns + +## How It Works + +1. A batch of items is validated **in parallel** — all `validate_item` tasks launch concurrently. +2. Valid items are processed **sequentially** in a for loop. +3. Each item is classified, then routed via **if/else** to `process_urgent` or `process_normal`. +4. Results are aggregated with a `summarize` task. + +## Running the Sample + +Prerequisites: `uv sync --group langgraph` and a running Temporal dev server (`temporal server start-dev`). + +```bash +# Terminal 1 +uv run langgraph_plugin/functional_api/control_flow/run_worker.py + +# Terminal 2 +uv run langgraph_plugin/functional_api/control_flow/run_workflow.py +``` + +## Files + +| File | Description | +|------|-------------| +| `workflow.py` | `@task` functions (validate, classify, process, summarize), `@entrypoint`, and `ControlFlowWorkflow` | +| `run_worker.py` | Registers tasks and entrypoint with `LangGraphPlugin`, starts worker | +| `run_workflow.py` | Sends a batch of items and prints processing results | diff --git a/langgraph_plugin/functional_api/control_flow/__init__.py b/langgraph_plugin/functional_api/control_flow/__init__.py new file mode 100644 index 000000000..e78d3ea4a --- /dev/null +++ b/langgraph_plugin/functional_api/control_flow/__init__.py @@ -0,0 +1 @@ +"""Control flow: parallel execution, for loops, and if/else branching.""" diff --git a/langgraph_plugin/functional_api/control_flow/run_worker.py b/langgraph_plugin/functional_api/control_flow/run_worker.py new file mode 100644 index 000000000..0a73c19f3 --- /dev/null +++ b/langgraph_plugin/functional_api/control_flow/run_worker.py @@ -0,0 +1,37 @@ +"""Worker for the control flow pipeline (Functional API).""" + +import asyncio +import os + +from temporalio.client import Client +from temporalio.contrib.langgraph import LangGraphPlugin +from temporalio.worker import Worker + +from langgraph_plugin.functional_api.control_flow.workflow import ( + ControlFlowWorkflow, + activity_options, + all_tasks, + control_flow_pipeline, +) + + +async def main() -> None: + client = await Client.connect(os.environ.get("TEMPORAL_ADDRESS", "localhost:7233")) + plugin = LangGraphPlugin( + entrypoints={"control-flow": control_flow_pipeline}, + tasks=all_tasks, + activity_options=activity_options, + ) + + worker = Worker( + client, + task_queue="langgraph-control-flow", + workflows=[ControlFlowWorkflow], + plugins=[plugin], + ) + print("Worker started. Ctrl+C to exit.") + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/langgraph_plugin/functional_api/control_flow/run_workflow.py b/langgraph_plugin/functional_api/control_flow/run_workflow.py new file mode 100644 index 000000000..2784415b9 --- /dev/null +++ b/langgraph_plugin/functional_api/control_flow/run_workflow.py @@ -0,0 +1,38 @@ +"""Start the control flow pipeline workflow (Functional API).""" + +import asyncio +import os + +from temporalio.client import Client + +from langgraph_plugin.functional_api.control_flow.workflow import ( + ControlFlowWorkflow, +) + + +async def main() -> None: + client = await Client.connect(os.environ.get("TEMPORAL_ADDRESS", "localhost:7233")) + + items = [ + "Fix login bug", + "URGENT: Production outage in payments", + "Update README", + "INVALID:", + "Urgent: Security patch needed", + "Refactor test suite", + ] + + result = await client.execute_workflow( + ControlFlowWorkflow.run, + items, + id="control-flow-workflow", + task_queue="langgraph-control-flow", + ) + + print(f"Summary: {result['summary']}") + for r in result["results"]: + print(f" {r}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/langgraph_plugin/functional_api/control_flow/workflow.py b/langgraph_plugin/functional_api/control_flow/workflow.py new file mode 100644 index 000000000..383af0076 --- /dev/null +++ b/langgraph_plugin/functional_api/control_flow/workflow.py @@ -0,0 +1,113 @@ +"""Control flow sample using the LangGraph Functional API with Temporal. + +Demonstrates the Functional API's advantage for complex control flow: + - Parallel task execution (launch multiple tasks concurrently) + - Sequential for-loop processing + - Conditional if/else branching based on intermediate results +""" + +from datetime import timedelta + +from langgraph.func import entrypoint, task +from temporalio import workflow +from temporalio.contrib.langgraph import entrypoint as temporal_entrypoint + + +@task +def validate_item(item: str) -> bool: + """Validate an item. Returns True if the item is non-empty and well-formed.""" + return len(item.strip()) > 0 and not item.startswith("INVALID:") + + +@task +def classify_item(item: str) -> str: + """Classify an item as 'urgent' or 'normal' based on its content.""" + return "urgent" if "urgent" in item.lower() else "normal" + + +@task +def process_urgent(item: str) -> str: + """Process an urgent item with priority handling.""" + return f"[PRIORITY] Processed: {item}" + + +@task +def process_normal(item: str) -> str: + """Process a normal item with standard handling.""" + return f"[STANDARD] Processed: {item}" + + +@task +def summarize(results: list[str]) -> str: + """Produce a summary of all processed results.""" + urgent_count = sum(1 for r in results if r.startswith("[PRIORITY]")) + normal_count = sum(1 for r in results if r.startswith("[STANDARD]")) + return ( + f"Processed {len(results)} items ({urgent_count} urgent, {normal_count} normal)" + ) + + +@entrypoint() +async def control_flow_pipeline(items: list[str]) -> dict: + """Process a batch of items with parallel validation, sequential + classification, and conditional routing. + """ + # PARALLEL: Validate all items concurrently. + # Creating task futures without awaiting launches them in parallel. + validation_futures = [validate_item(item) for item in items] + valid_flags = [await f for f in validation_futures] + valid_items = [item for item, is_valid in zip(items, valid_flags) if is_valid] + + # SEQUENTIAL + CONDITIONAL: Process each valid item + results = [] + for item in valid_items: + category = await classify_item(item) + if category == "urgent": + result = await process_urgent(item) + else: + result = await process_normal(item) + results.append(result) + + # Aggregate all results + summary_text = await summarize(results) + + return {"results": results, "summary": summary_text, "total": len(results)} + + +all_tasks = [ + validate_item, + classify_item, + process_urgent, + process_normal, + summarize, +] + +activity_options = { + "validate_item": { + "execute_in": "activity", + "start_to_close_timeout": timedelta(seconds=30), + }, + "classify_item": { + "execute_in": "activity", + "start_to_close_timeout": timedelta(seconds=30), + }, + "process_urgent": { + "execute_in": "activity", + "start_to_close_timeout": timedelta(seconds=30), + }, + "process_normal": { + "execute_in": "activity", + "start_to_close_timeout": timedelta(seconds=30), + }, + "summarize": { + "execute_in": "activity", + "start_to_close_timeout": timedelta(seconds=30), + }, +} + + +@workflow.defn +class ControlFlowWorkflow: + @workflow.run + async def run(self, items: list[str]) -> dict: + return await temporal_entrypoint("control-flow").ainvoke(items) diff --git a/langgraph_plugin/functional_api/hello_world/README.md b/langgraph_plugin/functional_api/hello_world/README.md new file mode 100644 index 000000000..4f66cf89c --- /dev/null +++ b/langgraph_plugin/functional_api/hello_world/README.md @@ -0,0 +1,29 @@ +# Hello World (Functional API) + +The simplest possible LangGraph Functional API + Temporal sample: a single `@task` called from an `@entrypoint`. + +## What This Sample Demonstrates + +- Defining a `@task` and `@entrypoint` +- Wrapping them with `LangGraphPlugin` so the task runs as a Temporal activity +- Invoking the entrypoint from a Temporal workflow + +## Running the Sample + +Prerequisites: `uv sync --group langgraph` and a running Temporal dev server (`temporal server start-dev`). + +```bash +# Terminal 1 +uv run langgraph_plugin/functional_api/hello_world/run_worker.py + +# Terminal 2 +uv run langgraph_plugin/functional_api/hello_world/run_workflow.py +``` + +## Files + +| File | Description | +|------|-------------| +| `workflow.py` | `process_query` task, `hello_entrypoint`, and `HelloWorldFunctionalWorkflow` | +| `run_worker.py` | Registers task and entrypoint with `LangGraphPlugin`, starts worker | +| `run_workflow.py` | Executes the workflow and prints the result | diff --git a/langgraph_plugin/functional_api/hello_world/__init__.py b/langgraph_plugin/functional_api/hello_world/__init__.py new file mode 100644 index 000000000..977660112 --- /dev/null +++ b/langgraph_plugin/functional_api/hello_world/__init__.py @@ -0,0 +1 @@ +"""Minimal hello world — @task and @entrypoint.""" diff --git a/langgraph_plugin/functional_api/hello_world/run_worker.py b/langgraph_plugin/functional_api/hello_world/run_worker.py new file mode 100644 index 000000000..7039ee027 --- /dev/null +++ b/langgraph_plugin/functional_api/hello_world/run_worker.py @@ -0,0 +1,37 @@ +"""Worker for the hello world sample (Functional API).""" + +import asyncio +import os + +from temporalio.client import Client +from temporalio.contrib.langgraph import LangGraphPlugin +from temporalio.worker import Worker + +from langgraph_plugin.functional_api.hello_world.workflow import ( + HelloWorldFunctionalWorkflow, + activity_options, + all_tasks, + hello_entrypoint, +) + + +async def main() -> None: + client = await Client.connect(os.environ.get("TEMPORAL_ADDRESS", "localhost:7233")) + plugin = LangGraphPlugin( + entrypoints={"hello-world": hello_entrypoint}, + tasks=all_tasks, + activity_options=activity_options, + ) + + worker = Worker( + client, + task_queue="langgraph-hello-world-functional", + workflows=[HelloWorldFunctionalWorkflow], + plugins=[plugin], + ) + print("Worker started. Ctrl+C to exit.") + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/langgraph_plugin/functional_api/hello_world/run_workflow.py b/langgraph_plugin/functional_api/hello_world/run_workflow.py new file mode 100644 index 000000000..77cc92fb4 --- /dev/null +++ b/langgraph_plugin/functional_api/hello_world/run_workflow.py @@ -0,0 +1,27 @@ +"""Start the hello world workflow (Functional API).""" + +import asyncio +import os + +from temporalio.client import Client + +from langgraph_plugin.functional_api.hello_world.workflow import ( + HelloWorldFunctionalWorkflow, +) + + +async def main() -> None: + client = await Client.connect(os.environ.get("TEMPORAL_ADDRESS", "localhost:7233")) + + result = await client.execute_workflow( + HelloWorldFunctionalWorkflow.run, + "Hello, Temporal + LangGraph!", + id="hello-world-functional-workflow", + task_queue="langgraph-hello-world-functional", + ) + + print(f"Result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/langgraph_plugin/functional_api/hello_world/workflow.py b/langgraph_plugin/functional_api/hello_world/workflow.py new file mode 100644 index 000000000..36552806d --- /dev/null +++ b/langgraph_plugin/functional_api/hello_world/workflow.py @@ -0,0 +1,40 @@ +"""Hello world using the LangGraph Functional API with Temporal. + +The simplest possible sample: a single task called from an entrypoint. +""" + +from datetime import timedelta + +from langgraph.func import entrypoint, task +from temporalio import workflow +from temporalio.contrib.langgraph import entrypoint as temporal_entrypoint + + +@task +def process_query(query: str) -> str: + """Process a query and return a response.""" + return f"Processed: {query}" + + +@entrypoint() +async def hello_entrypoint(query: str) -> dict: + """Process the query and return it in a result dict.""" + result = await process_query(query) + return {"result": result} + + +all_tasks = [process_query] + +activity_options = { + "process_query": { + "execute_in": "activity", + "start_to_close_timeout": timedelta(seconds=10), + }, +} + + +@workflow.defn +class HelloWorldFunctionalWorkflow: + @workflow.run + async def run(self, query: str) -> dict: + return await temporal_entrypoint("hello-world").ainvoke(query) diff --git a/langgraph_plugin/functional_api/human_in_the_loop/README.md b/langgraph_plugin/functional_api/human_in_the_loop/README.md new file mode 100644 index 000000000..9df7ba652 --- /dev/null +++ b/langgraph_plugin/functional_api/human_in_the_loop/README.md @@ -0,0 +1,38 @@ +# Human-in-the-Loop Chatbot (Functional API) + +Demonstrates pausing an entrypoint with LangGraph's `interrupt()` and waiting indefinitely for human review with Temporal's `workflow.wait_condition()`. A Temporal signal delivers the human's feedback; a Temporal query exposes the pending draft to UIs. + +## What This Sample Demonstrates + +- `workflow.wait_condition()` to block the Workflow until human input arrives — for as long as it takes, with no polling and no timeout +- `interrupt()` inside a `@task` to pause the entrypoint at the review point +- Temporal **signals** to deliver human feedback and **queries** to expose the pending draft +- Resuming with `Command(resume=...)` via the v2 API +- Setting a checkpointer on the entrypoint for interrupt/resume support + +## How It Works + +1. The `generate_draft` task produces a draft response. +2. The `request_human_review` task calls `interrupt(draft)`, pausing the entrypoint. +3. The Workflow stores the draft (visible via the query) and calls `workflow.wait_condition()` — blocking durably until the signal sets `_human_input`. This can wait indefinitely; Temporal persists the state. +4. After the signal arrives, the entrypoint resumes with `Command(resume=...)` and returns the final response. + +## Running the Sample + +Prerequisites: `uv sync --group langgraph` and a running Temporal dev server (`temporal server start-dev`). + +```bash +# Terminal 1 +uv run langgraph_plugin/functional_api/human_in_the_loop/run_worker.py + +# Terminal 2 +uv run langgraph_plugin/functional_api/human_in_the_loop/run_workflow.py +``` + +## Files + +| File | Description | +|------|-------------| +| `workflow.py` | `@task` functions, `@entrypoint`, and `ChatbotFunctionalWorkflow` | +| `run_worker.py` | Registers tasks and entrypoint with `LangGraphPlugin`, starts worker | +| `run_workflow.py` | Starts workflow, polls draft via query, sends approval via signal | diff --git a/langgraph_plugin/functional_api/human_in_the_loop/__init__.py b/langgraph_plugin/functional_api/human_in_the_loop/__init__.py new file mode 100644 index 000000000..72da02f88 --- /dev/null +++ b/langgraph_plugin/functional_api/human_in_the_loop/__init__.py @@ -0,0 +1 @@ +"""Human-in-the-loop chatbot using interrupt() and Temporal signals.""" diff --git a/langgraph_plugin/functional_api/human_in_the_loop/run_worker.py b/langgraph_plugin/functional_api/human_in_the_loop/run_worker.py new file mode 100644 index 000000000..c8f84b492 --- /dev/null +++ b/langgraph_plugin/functional_api/human_in_the_loop/run_worker.py @@ -0,0 +1,37 @@ +"""Worker for the human-in-the-loop chatbot (Functional API).""" + +import asyncio +import os + +from temporalio.client import Client +from temporalio.contrib.langgraph import LangGraphPlugin +from temporalio.worker import Worker + +from langgraph_plugin.functional_api.human_in_the_loop.workflow import ( + ChatbotFunctionalWorkflow, + activity_options, + all_tasks, + chatbot_entrypoint, +) + + +async def main() -> None: + client = await Client.connect(os.environ.get("TEMPORAL_ADDRESS", "localhost:7233")) + plugin = LangGraphPlugin( + entrypoints={"chatbot": chatbot_entrypoint}, + tasks=all_tasks, + activity_options=activity_options, + ) + + worker = Worker( + client, + task_queue="langgraph-chatbot-functional", + workflows=[ChatbotFunctionalWorkflow], + plugins=[plugin], + ) + print("Worker started. Ctrl+C to exit.") + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/langgraph_plugin/functional_api/human_in_the_loop/run_workflow.py b/langgraph_plugin/functional_api/human_in_the_loop/run_workflow.py new file mode 100644 index 000000000..5074b9370 --- /dev/null +++ b/langgraph_plugin/functional_api/human_in_the_loop/run_workflow.py @@ -0,0 +1,39 @@ +"""Start the human-in-the-loop chatbot workflow (Functional API).""" + +import asyncio +import os + +from temporalio.client import Client + +from langgraph_plugin.functional_api.human_in_the_loop.workflow import ( + ChatbotFunctionalWorkflow, +) + + +async def main() -> None: + client = await Client.connect(os.environ.get("TEMPORAL_ADDRESS", "localhost:7233")) + + handle = await client.start_workflow( + ChatbotFunctionalWorkflow.run, + "What is the meaning of life?", + id="chatbot-functional-workflow", + task_queue="langgraph-chatbot-functional", + ) + + # Poll until the draft is ready for review + draft = None + while draft is None: + await asyncio.sleep(0.5) + draft = await handle.query(ChatbotFunctionalWorkflow.get_draft) + + print(f"Draft for review: {draft}") + + # Send approval via signal + await handle.signal(ChatbotFunctionalWorkflow.provide_feedback, "approve") + + result = await handle.result() + print(f"Final response: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/langgraph_plugin/functional_api/human_in_the_loop/workflow.py b/langgraph_plugin/functional_api/human_in_the_loop/workflow.py new file mode 100644 index 000000000..857ef37f3 --- /dev/null +++ b/langgraph_plugin/functional_api/human_in_the_loop/workflow.py @@ -0,0 +1,95 @@ +"""Human-in-the-loop chatbot using the LangGraph Functional API with Temporal. + +The @entrypoint pauses at interrupt(); the Workflow waits indefinitely with +workflow.wait_condition() until a Temporal signal delivers human feedback, +then resumes the entrypoint with Command(resume=...). +""" + +from datetime import timedelta +from typing import Any + +from langchain_core.runnables import RunnableConfig +from langgraph.checkpoint.memory import InMemorySaver +from langgraph.func import entrypoint, task +from langgraph.types import Command, interrupt +from temporalio import workflow +from temporalio.contrib.langgraph import entrypoint as temporal_entrypoint + + +@task +def generate_draft(message: str) -> str: + """Generate a draft response. Replace with an LLM call in production.""" + return ( + f"Here's my response to '{message}': " + "The answer is 42. Let me know if this helps!" + ) + + +@task +def request_human_review(draft: str) -> str: + """Pause execution to request human review of the draft.""" + feedback = interrupt(draft) + if feedback == "approve": + return draft + return f"[Revised] {draft} (incorporating feedback: {feedback})" + + +@entrypoint() +async def chatbot_entrypoint(user_message: str) -> dict: + """Chatbot entrypoint: generate a draft, get human review, return result.""" + draft = await generate_draft(user_message) + final_response = await request_human_review(draft) + return {"response": final_response} + + +all_tasks = [generate_draft, request_human_review] + +activity_options = { + "generate_draft": { + "execute_in": "activity", + "start_to_close_timeout": timedelta(seconds=30), + }, + "request_human_review": { + "execute_in": "activity", + "start_to_close_timeout": timedelta(seconds=30), + }, +} + + +@workflow.defn +class ChatbotFunctionalWorkflow: + def __init__(self) -> None: + self._human_input: str | None = None + self._draft: str | None = None + + @workflow.signal + async def provide_feedback(self, feedback: str) -> None: + """Signal handler: receives human feedback.""" + self._human_input = feedback + + @workflow.query + def get_draft(self) -> str | None: + """Query handler: returns the pending draft for review, or None.""" + return self._draft + + @workflow.run + async def run(self, user_message: str) -> dict[str, Any]: + app = temporal_entrypoint("chatbot") + app.checkpointer = InMemorySaver() + config = RunnableConfig( + {"configurable": {"thread_id": workflow.info().workflow_id}} + ) + + # First invocation: runs until interrupt() pauses for human review + result = await app.ainvoke(user_message, config, version="v2") + + self._draft = result.interrupts[0].value + + # Wait for human feedback via Temporal signal + await workflow.wait_condition(lambda: self._human_input is not None) + + # Resume with the human's feedback + resumed = await app.ainvoke( + Command(resume=self._human_input), config, version="v2" + ) + return resumed.value diff --git a/langgraph_plugin/functional_api/langsmith_tracing/README.md b/langgraph_plugin/functional_api/langsmith_tracing/README.md new file mode 100644 index 000000000..b072cd4d6 --- /dev/null +++ b/langgraph_plugin/functional_api/langsmith_tracing/README.md @@ -0,0 +1,37 @@ +# LangSmith Tracing (Functional API) + +Demonstrates combining `LangGraphPlugin` (durable task execution) with Temporal's `LangSmithPlugin` for full tracing of LLM calls through Temporal workflows, using LangGraph's `@task` and `@entrypoint` decorators. + +## What This Sample Demonstrates + +- Using `LangSmithPlugin` on the Temporal client for automatic trace propagation +- Using `LangGraphPlugin` on the Worker for durable LangGraph execution +- `@traceable` in three places: on the `@task` (Activity) itself, on a helper called from inside the `@task`, and on a helper called from inside the `@entrypoint` (Workflow) +- Both plugins working together: durability + observability + +## How It Works + +1. The Temporal client is created with `LangSmithPlugin(add_temporal_runs=True)`. +2. A Worker registers the `chat` task with `LangGraphPlugin`. +3. When the Workflow runs, the `chat` task executes as a Temporal Activity. +4. `@traceable` decorators emit trace data to LangSmith for the task, an in-task helper, and an in-entrypoint helper. + +## Running the Sample + +Prerequisites: `uv sync --group langgraph` and a running Temporal dev server (`temporal server start-dev`). + +```bash +export ANTHROPIC_API_KEY='your-key' +export LANGCHAIN_API_KEY='your-key' + +uv run langgraph_plugin/functional_api/langsmith_tracing/main.py +``` + +Traces will appear in your [LangSmith](https://smith.langchain.com/) dashboard. + +## Files + +| File | Description | +|------|-------------| +| `workflow.py` | `@traceable` chat task + helpers, `@entrypoint`, and `ChatFunctionalWorkflow` | +| `main.py` | Starts a Worker and executes the Workflow in a single process | diff --git a/langgraph_plugin/functional_api/langsmith_tracing/__init__.py b/langgraph_plugin/functional_api/langsmith_tracing/__init__.py new file mode 100644 index 000000000..4cb57b73a --- /dev/null +++ b/langgraph_plugin/functional_api/langsmith_tracing/__init__.py @@ -0,0 +1 @@ +"""LangSmith tracing with LangGraph Functional API and Temporal.""" diff --git a/langgraph_plugin/functional_api/langsmith_tracing/main.py b/langgraph_plugin/functional_api/langsmith_tracing/main.py new file mode 100644 index 000000000..0d29afb76 --- /dev/null +++ b/langgraph_plugin/functional_api/langsmith_tracing/main.py @@ -0,0 +1,51 @@ +"""Run the LangSmith tracing chat sample (Functional API). + +Single-process driver: starts a Worker, executes the Workflow once, prints +the result, then shuts down. Requires ANTHROPIC_API_KEY and LANGCHAIN_API_KEY. +""" + +import asyncio +import os + +from temporalio.client import Client +from temporalio.contrib.langgraph import LangGraphPlugin +from temporalio.contrib.langsmith import LangSmithPlugin +from temporalio.worker import Worker + +from langgraph_plugin.functional_api.langsmith_tracing.workflow import ( + ChatFunctionalWorkflow, + activity_options, + all_tasks, + chat_entrypoint, +) + + +async def main() -> None: + client = await Client.connect( + os.environ.get("TEMPORAL_ADDRESS", "localhost:7233"), + plugins=[LangSmithPlugin(add_temporal_runs=True)], + ) + + async with Worker( + client, + task_queue="langgraph-langsmith-functional", + workflows=[ChatFunctionalWorkflow], + plugins=[ + LangGraphPlugin( + entrypoints={"chat": chat_entrypoint}, + tasks=all_tasks, + activity_options=activity_options, + ) + ], + ): + result = await client.execute_workflow( + ChatFunctionalWorkflow.run, + "What is the meaning of life?", + id="langsmith-chat-functional-workflow", + task_queue="langgraph-langsmith-functional", + ) + print(f"Response: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/langgraph_plugin/functional_api/langsmith_tracing/workflow.py b/langgraph_plugin/functional_api/langsmith_tracing/workflow.py new file mode 100644 index 000000000..ddea44bcb --- /dev/null +++ b/langgraph_plugin/functional_api/langsmith_tracing/workflow.py @@ -0,0 +1,66 @@ +"""LangSmith tracing with LangGraph Functional API and Temporal. + +Demonstrates combining LangGraphPlugin (durable task execution) with +LangSmithPlugin (observability) for full tracing of LLM calls through +Temporal workflows. + +Three @traceable use cases are demonstrated: +1. The @task (Activity) function itself: `chat`. +2. A helper called from inside the @task: `format_prompt`. +3. A helper called from inside the @entrypoint (Workflow): `summarize_for_log`. + +Requires ANTHROPIC_API_KEY and LANGCHAIN_API_KEY environment variables. +""" + +from datetime import timedelta + +from langchain.chat_models import init_chat_model +from langgraph.func import entrypoint, task +from langsmith import traceable +from temporalio import workflow +from temporalio.contrib.langgraph import entrypoint as temporal_entrypoint + + +@traceable(name="format_prompt", run_type="prompt") +def format_prompt(message: str) -> str: + """Helper called from inside the @task. Traced by LangSmith.""" + return f"Please respond concisely to: {message}" + + +@task +@traceable(name="chat_task", run_type="chain") +def chat(message: str) -> str: + """Call an LLM to respond to the message. Traced by LangSmith.""" + prompt = format_prompt(message) + response = init_chat_model("claude-sonnet-4-6").invoke(prompt) + return str(response.content) + + +@traceable(name="summarize_for_log", run_type="chain") +def summarize_for_log(response: str) -> str: + """Helper called from inside the @entrypoint. Traced by LangSmith.""" + return f"Got {len(response)}-char response: {response[:60]}..." + + +@entrypoint() +async def chat_entrypoint(message: str) -> dict: + """Chat entrypoint: call the LLM and return the response.""" + response = await chat(message) + return {"response": response, "summary": summarize_for_log(response)} + + +all_tasks = [chat] + +activity_options = { + "chat": { + "execute_in": "activity", + "start_to_close_timeout": timedelta(seconds=30), + }, +} + + +@workflow.defn +class ChatFunctionalWorkflow: + @workflow.run + async def run(self, message: str) -> dict: + return await temporal_entrypoint("chat").ainvoke(message) diff --git a/langgraph_plugin/functional_api/react_agent/README.md b/langgraph_plugin/functional_api/react_agent/README.md new file mode 100644 index 000000000..79d1b96e5 --- /dev/null +++ b/langgraph_plugin/functional_api/react_agent/README.md @@ -0,0 +1,44 @@ +# ReAct Agent (Functional API) + +Demonstrates the ReAct agent pattern (think -> act -> observe -> repeat) as a natural `while` loop using LangGraph's `@task` and `@entrypoint` decorators. + +## What This Sample Demonstrates + +- The ReAct loop as a natural `while True` loop in Python +- `@task` functions for agent thinking and tool execution +- How the Functional API makes agent loops readable and extensible + +## How It Works + +1. The `agent_think` task examines the query and tool history, deciding the next action. +2. If a tool is needed, `execute_tool` runs it and the result is appended to history. +3. The loop continues until `agent_think` returns a final answer. + +```python +while True: + decision = await agent_think(query, history) + if decision["action"] == "final": + return decision["answer"] + result = await execute_tool(decision["tool_name"], decision["tool_input"]) + history.append(result) +``` + +## Running the Sample + +Prerequisites: `uv sync --group langgraph` and a running Temporal dev server (`temporal server start-dev`). + +```bash +# Terminal 1 +uv run langgraph_plugin/functional_api/react_agent/run_worker.py + +# Terminal 2 +uv run langgraph_plugin/functional_api/react_agent/run_workflow.py +``` + +## Files + +| File | Description | +|------|-------------| +| `workflow.py` | `@task` functions (agent_think, execute_tool), `@entrypoint`, and `ReactAgentFunctionalWorkflow` | +| `run_worker.py` | Registers tasks and entrypoint with `LangGraphPlugin`, starts worker | +| `run_workflow.py` | Executes the agent workflow and prints the answer | diff --git a/langgraph_plugin/functional_api/react_agent/__init__.py b/langgraph_plugin/functional_api/react_agent/__init__.py new file mode 100644 index 000000000..bd34ac2e0 --- /dev/null +++ b/langgraph_plugin/functional_api/react_agent/__init__.py @@ -0,0 +1 @@ +"""ReAct agent with while-loop tool dispatch.""" diff --git a/langgraph_plugin/functional_api/react_agent/run_worker.py b/langgraph_plugin/functional_api/react_agent/run_worker.py new file mode 100644 index 000000000..465e90ad4 --- /dev/null +++ b/langgraph_plugin/functional_api/react_agent/run_worker.py @@ -0,0 +1,37 @@ +"""Worker for the ReAct agent (Functional API).""" + +import asyncio +import os + +from temporalio.client import Client +from temporalio.contrib.langgraph import LangGraphPlugin +from temporalio.worker import Worker + +from langgraph_plugin.functional_api.react_agent.workflow import ( + ReactAgentFunctionalWorkflow, + activity_options, + all_tasks, + react_agent_entrypoint, +) + + +async def main() -> None: + client = await Client.connect(os.environ.get("TEMPORAL_ADDRESS", "localhost:7233")) + plugin = LangGraphPlugin( + entrypoints={"react-agent": react_agent_entrypoint}, + tasks=all_tasks, + activity_options=activity_options, + ) + + worker = Worker( + client, + task_queue="langgraph-react-agent-functional", + workflows=[ReactAgentFunctionalWorkflow], + plugins=[plugin], + ) + print("Worker started. Ctrl+C to exit.") + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/langgraph_plugin/functional_api/react_agent/run_workflow.py b/langgraph_plugin/functional_api/react_agent/run_workflow.py new file mode 100644 index 000000000..8888cbd17 --- /dev/null +++ b/langgraph_plugin/functional_api/react_agent/run_workflow.py @@ -0,0 +1,28 @@ +"""Start the ReAct agent workflow (Functional API).""" + +import asyncio +import os + +from temporalio.client import Client + +from langgraph_plugin.functional_api.react_agent.workflow import ( + ReactAgentFunctionalWorkflow, +) + + +async def main() -> None: + client = await Client.connect(os.environ.get("TEMPORAL_ADDRESS", "localhost:7233")) + + result = await client.execute_workflow( + ReactAgentFunctionalWorkflow.run, + "Tell me about San Francisco", + id="react-agent-functional-workflow", + task_queue="langgraph-react-agent-functional", + ) + + print(f"Agent answer: {result['answer']}") + print(f"Tool calls made: {result['steps']}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/langgraph_plugin/functional_api/react_agent/workflow.py b/langgraph_plugin/functional_api/react_agent/workflow.py new file mode 100644 index 000000000..c61048671 --- /dev/null +++ b/langgraph_plugin/functional_api/react_agent/workflow.py @@ -0,0 +1,88 @@ +"""ReAct agent using the LangGraph Functional API with Temporal. + +The Functional API expresses the ReAct loop as a plain `while` loop, making +the control flow explicit and easy to extend. +""" + +from datetime import timedelta + +from langgraph.func import entrypoint, task +from temporalio import workflow +from temporalio.contrib.langgraph import entrypoint as temporal_entrypoint + + +@task +def agent_think(query: str, history: list[str]) -> dict: + """The agent decides the next action based on query and tool history. + + In production, replace this with an LLM call (e.g., Claude with tools). + """ + tool_results = [h for h in history if h.startswith("[Tool]")] + + if len(tool_results) == 0: + return { + "action": "tool", + "tool_name": "get_weather", + "tool_input": "San Francisco", + } + elif len(tool_results) == 1: + return { + "action": "tool", + "tool_name": "get_population", + "tool_input": "San Francisco", + } + else: + facts = "; ".join(tool_results) + return { + "action": "final", + "answer": (f"Here's what I found about San Francisco: {facts}"), + } + + +@task +def execute_tool(tool_name: str, tool_input: str) -> str: + """Execute a tool by name. In production, dispatch to real implementations.""" + tool_registry = { + "get_weather": lambda inp: f"[Tool] Weather in {inp}: 72°F and sunny.", + "get_population": lambda inp: f"[Tool] {inp} population: ~870,000 residents.", + } + handler = tool_registry.get(tool_name) + if handler: + return handler(tool_input) + return f"[Tool] Unknown tool: {tool_name}" + + +@entrypoint() +async def react_agent_entrypoint(query: str) -> dict: + """ReAct agent loop: think -> act -> observe -> repeat.""" + history: list[str] = [] + + while True: + decision = await agent_think(query, history) + + if decision["action"] == "final": + return {"answer": decision["answer"], "steps": len(history)} + + result = await execute_tool(decision["tool_name"], decision["tool_input"]) + history.append(result) + + +all_tasks = [agent_think, execute_tool] + +activity_options = { + "agent_think": { + "execute_in": "activity", + "start_to_close_timeout": timedelta(seconds=30), + }, + "execute_tool": { + "execute_in": "activity", + "start_to_close_timeout": timedelta(seconds=30), + }, +} + + +@workflow.defn +class ReactAgentFunctionalWorkflow: + @workflow.run + async def run(self, query: str) -> dict: + return await temporal_entrypoint("react-agent").ainvoke(query) diff --git a/langgraph_plugin/graph_api/__init__.py b/langgraph_plugin/graph_api/__init__.py new file mode 100644 index 000000000..579887b60 --- /dev/null +++ b/langgraph_plugin/graph_api/__init__.py @@ -0,0 +1 @@ +"""LangGraph Graph API samples using StateGraph.""" diff --git a/langgraph_plugin/graph_api/continue_as_new/README.md b/langgraph_plugin/graph_api/continue_as_new/README.md new file mode 100644 index 000000000..a5cfbb1c2 --- /dev/null +++ b/langgraph_plugin/graph_api/continue_as_new/README.md @@ -0,0 +1,37 @@ +# Continue-as-New with Caching (Graph API) + +Demonstrates combining Temporal's continue-as-new with the LangGraph plugin's task result cache to avoid re-executing completed graph nodes across workflow boundaries. + +## What This Sample Demonstrates + +- Using `workflow.continue_as_new()` to reset event history for long-running pipelines +- Capturing node results with `cache()` before continuing +- Restoring cached results with `temporal_graph(name, cache=...)` so completed nodes are skipped +- Each node executes exactly once despite multiple workflow invocations + +## How It Works + +1. A 3-stage pipeline runs: `double` (x2) -> `add_50` (+50) -> `triple` (x3). +2. After the first invocation, the workflow continues-as-new with the cached results. +3. On the second and third invocations, all three nodes return cached results instantly. +4. The final result is returned: input 10 -> 20 -> 70 -> 210. + +## Running the Sample + +Prerequisites: `uv sync --group langgraph` and a running Temporal dev server (`temporal server start-dev`). + +```bash +# Terminal 1 +uv run langgraph_plugin/graph_api/continue_as_new/run_worker.py + +# Terminal 2 +uv run langgraph_plugin/graph_api/continue_as_new/run_workflow.py +``` + +## Files + +| File | Description | +|------|-------------| +| `workflow.py` | Pipeline node functions, graph definition, `PipelineInput`, and `PipelineWorkflow` | +| `run_worker.py` | Builds graph, registers with `LangGraphPlugin`, starts Worker | +| `run_workflow.py` | Executes the pipeline Workflow and prints the result | diff --git a/langgraph_plugin/graph_api/continue_as_new/__init__.py b/langgraph_plugin/graph_api/continue_as_new/__init__.py new file mode 100644 index 000000000..5b90703e9 --- /dev/null +++ b/langgraph_plugin/graph_api/continue_as_new/__init__.py @@ -0,0 +1 @@ +"""Continue-as-new pipeline with task result caching.""" diff --git a/langgraph_plugin/graph_api/continue_as_new/run_worker.py b/langgraph_plugin/graph_api/continue_as_new/run_worker.py new file mode 100644 index 000000000..653874a6b --- /dev/null +++ b/langgraph_plugin/graph_api/continue_as_new/run_worker.py @@ -0,0 +1,31 @@ +"""Worker for the continue-as-new pipeline (Graph API).""" + +import asyncio +import os + +from temporalio.client import Client +from temporalio.contrib.langgraph import LangGraphPlugin +from temporalio.worker import Worker + +from langgraph_plugin.graph_api.continue_as_new.workflow import ( + PipelineWorkflow, + make_pipeline_graph, +) + + +async def main() -> None: + client = await Client.connect(os.environ.get("TEMPORAL_ADDRESS", "localhost:7233")) + plugin = LangGraphPlugin(graphs={"pipeline": make_pipeline_graph()}) + + worker = Worker( + client, + task_queue="langgraph-pipeline", + workflows=[PipelineWorkflow], + plugins=[plugin], + ) + print("Worker started. Ctrl+C to exit.") + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/langgraph_plugin/graph_api/continue_as_new/run_workflow.py b/langgraph_plugin/graph_api/continue_as_new/run_workflow.py new file mode 100644 index 000000000..a52d1716c --- /dev/null +++ b/langgraph_plugin/graph_api/continue_as_new/run_workflow.py @@ -0,0 +1,31 @@ +"""Start the continue-as-new pipeline workflow (Graph API).""" + +import asyncio +import os +from datetime import timedelta + +from temporalio.client import Client + +from langgraph_plugin.graph_api.continue_as_new.workflow import ( + PipelineInput, + PipelineWorkflow, +) + + +async def main() -> None: + client = await Client.connect(os.environ.get("TEMPORAL_ADDRESS", "localhost:7233")) + + result = await client.execute_workflow( + PipelineWorkflow.run, + PipelineInput(data=10), + id="pipeline-workflow", + task_queue="langgraph-pipeline", + execution_timeout=timedelta(seconds=60), + ) + + # 10*2=20 -> 20+50=70 -> 70*3=210 + print(f"Pipeline result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/langgraph_plugin/graph_api/continue_as_new/workflow.py b/langgraph_plugin/graph_api/continue_as_new/workflow.py new file mode 100644 index 000000000..1475e63f7 --- /dev/null +++ b/langgraph_plugin/graph_api/continue_as_new/workflow.py @@ -0,0 +1,85 @@ +"""Continue-as-new with caching using the LangGraph Graph API with Temporal. + +Demonstrates how to use Temporal's continue-as-new with LangGraph's task result +caching to avoid re-executing already-completed graph nodes across workflow +boundaries. +""" + +from dataclasses import dataclass +from datetime import timedelta +from typing import Any + +from langgraph.graph import START, StateGraph +from temporalio import workflow +from temporalio.contrib.langgraph import cache +from temporalio.contrib.langgraph import graph as temporal_graph +from typing_extensions import TypedDict + + +class State(TypedDict): + value: int + + +async def double(state: State) -> dict[str, int]: + """Stage 1: double the input.""" + return {"value": state["value"] * 2} + + +async def add_50(state: State) -> dict[str, int]: + """Stage 2: add 50.""" + return {"value": state["value"] + 50} + + +async def triple(state: State) -> dict[str, int]: + """Stage 3: triple the result.""" + return {"value": state["value"] * 3} + + +def make_pipeline_graph() -> StateGraph: + node_metadata = { + "execute_in": "activity", + "start_to_close_timeout": timedelta(seconds=30), + } + g = StateGraph(State) + g.add_node("double", double, metadata=node_metadata) + g.add_node("add_50", add_50, metadata=node_metadata) + g.add_node("triple", triple, metadata=node_metadata) + g.add_edge(START, "double") + g.add_edge("double", "add_50") + g.add_edge("add_50", "triple") + return g + + +@dataclass +class PipelineInput: + data: int + cache: dict[str, Any] | None = None + phase: int = 1 # continues-as-new after phases 1 and 2 + + +@workflow.defn +class PipelineWorkflow: + """Runs a 3-stage pipeline, continuing-as-new after each phase. + + Phase 1: all 3 stages execute, continues-as-new with cache. + Phase 2: all 3 stages cached (instant), continues-as-new. + Phase 3: all 3 stages cached (instant), returns final result. + + Input 10: 10*2=20 -> 20+50=70 -> 70*3=210 + """ + + @workflow.run + async def run(self, input_data: PipelineInput) -> int: + app = temporal_graph("pipeline", cache=input_data.cache).compile() + result = await app.ainvoke({"value": input_data.data}) + + if input_data.phase < 3: + workflow.continue_as_new( + PipelineInput( + data=input_data.data, + cache=cache(), + phase=input_data.phase + 1, + ) + ) + + return result["value"] diff --git a/langgraph_plugin/graph_api/hello_world/README.md b/langgraph_plugin/graph_api/hello_world/README.md new file mode 100644 index 000000000..8d0ebdf1b --- /dev/null +++ b/langgraph_plugin/graph_api/hello_world/README.md @@ -0,0 +1,29 @@ +# Hello World (Graph API) + +The simplest possible LangGraph + Temporal sample: a single-node graph that processes a query string. + +## What This Sample Demonstrates + +- Defining a `StateGraph` with a single node +- Wrapping it with `LangGraphPlugin` so the node runs as a Temporal activity +- Invoking the graph from a Temporal workflow + +## Running the Sample + +Prerequisites: `uv sync --group langgraph` and a running Temporal dev server (`temporal server start-dev`). + +```bash +# Terminal 1 +uv run langgraph_plugin/graph_api/hello_world/run_worker.py + +# Terminal 2 +uv run langgraph_plugin/graph_api/hello_world/run_workflow.py +``` + +## Files + +| File | Description | +|------|-------------| +| `workflow.py` | `process_query` node, graph definition and `HelloWorldWorkflow` | +| `run_worker.py` | Registers graph with `LangGraphPlugin`, starts worker | +| `run_workflow.py` | Executes the workflow and prints the result | diff --git a/langgraph_plugin/graph_api/hello_world/__init__.py b/langgraph_plugin/graph_api/hello_world/__init__.py new file mode 100644 index 000000000..166b2c696 --- /dev/null +++ b/langgraph_plugin/graph_api/hello_world/__init__.py @@ -0,0 +1 @@ +"""Minimal hello world — single-node StateGraph.""" diff --git a/langgraph_plugin/graph_api/hello_world/run_worker.py b/langgraph_plugin/graph_api/hello_world/run_worker.py new file mode 100644 index 000000000..bf2ece96f --- /dev/null +++ b/langgraph_plugin/graph_api/hello_world/run_worker.py @@ -0,0 +1,31 @@ +"""Worker for the hello world sample (Graph API).""" + +import asyncio +import os + +from temporalio.client import Client +from temporalio.contrib.langgraph import LangGraphPlugin +from temporalio.worker import Worker + +from langgraph_plugin.graph_api.hello_world.workflow import ( + HelloWorldWorkflow, + make_hello_graph, +) + + +async def main() -> None: + client = await Client.connect(os.environ.get("TEMPORAL_ADDRESS", "localhost:7233")) + plugin = LangGraphPlugin(graphs={"hello-world": make_hello_graph()}) + + worker = Worker( + client, + task_queue="langgraph-hello-world", + workflows=[HelloWorldWorkflow], + plugins=[plugin], + ) + print("Worker started. Ctrl+C to exit.") + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/langgraph_plugin/graph_api/hello_world/run_workflow.py b/langgraph_plugin/graph_api/hello_world/run_workflow.py new file mode 100644 index 000000000..e1a02a43c --- /dev/null +++ b/langgraph_plugin/graph_api/hello_world/run_workflow.py @@ -0,0 +1,25 @@ +"""Start the hello world workflow (Graph API).""" + +import asyncio +import os + +from temporalio.client import Client + +from langgraph_plugin.graph_api.hello_world.workflow import HelloWorldWorkflow + + +async def main() -> None: + client = await Client.connect(os.environ.get("TEMPORAL_ADDRESS", "localhost:7233")) + + result = await client.execute_workflow( + HelloWorldWorkflow.run, + "Hello, Temporal + LangGraph!", + id="hello-world-workflow", + task_queue="langgraph-hello-world", + ) + + print(f"Result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/langgraph_plugin/graph_api/hello_world/workflow.py b/langgraph_plugin/graph_api/hello_world/workflow.py new file mode 100644 index 000000000..84eafe679 --- /dev/null +++ b/langgraph_plugin/graph_api/hello_world/workflow.py @@ -0,0 +1,42 @@ +"""Hello world using the LangGraph Graph API with Temporal. + +The simplest possible sample: a single-node graph that processes a query string. +""" + +from datetime import timedelta + +from langgraph.graph import START, StateGraph +from temporalio import workflow +from temporalio.contrib.langgraph import graph as temporal_graph +from typing_extensions import TypedDict + + +class State(TypedDict): + value: str + + +async def process_query(state: State) -> dict[str, str]: + """Process a query and return a response.""" + return {"value": f"Processed: {state['value']}"} + + +def make_hello_graph() -> StateGraph: + g = StateGraph(State) + g.add_node( + "process_query", + process_query, + metadata={ + "execute_in": "activity", + "start_to_close_timeout": timedelta(seconds=10), + }, + ) + g.add_edge(START, "process_query") + return g + + +@workflow.defn +class HelloWorldWorkflow: + @workflow.run + async def run(self, query: str) -> str: + result = await temporal_graph("hello-world").compile().ainvoke({"value": query}) + return result["value"] diff --git a/langgraph_plugin/graph_api/human_in_the_loop/README.md b/langgraph_plugin/graph_api/human_in_the_loop/README.md new file mode 100644 index 000000000..d8ff0b48c --- /dev/null +++ b/langgraph_plugin/graph_api/human_in_the_loop/README.md @@ -0,0 +1,40 @@ +# Human-in-the-Loop Chatbot (Graph API) + +Demonstrates pausing a graph with LangGraph's `interrupt()` and waiting indefinitely for human review with Temporal's `workflow.wait_condition()`. A Temporal signal delivers the human's feedback; a Temporal query exposes the pending draft to UIs. + +## What This Sample Demonstrates + +- `workflow.wait_condition()` to block the Workflow until human input arrives — for as long as it takes, with no polling and no timeout +- Pausing a graph mid-execution with `interrupt()` at the review point +- Temporal **signals** to deliver human feedback and **queries** to expose the pending draft +- Resuming the graph with `Command(resume=...)` after the signal arrives + +## How It Works + +1. The Workflow starts and the `generate_draft` node produces a response. +2. The `human_review` node calls `interrupt(draft)`, pausing execution. +3. The Workflow stores the draft (visible via the query) and calls `workflow.wait_condition()` — blocking durably until the signal sets `_human_input`. This can wait indefinitely; Temporal persists the state. +4. An external process (UI, CLI, etc.) queries the draft and sends the human's feedback via signal. +5. The graph resumes — `interrupt()` returns the signal value and the node completes. + +## Running the Sample + +Prerequisites: `uv sync --group langgraph` and a running Temporal dev server (`temporal server start-dev`). + +```bash +# Terminal 1: start the worker +uv run langgraph_plugin/graph_api/human_in_the_loop/run_worker.py + +# Terminal 2: start the workflow (polls for the draft, then prompts you for feedback) +uv run langgraph_plugin/graph_api/human_in_the_loop/run_workflow.py +``` + +When the draft is ready, you'll be prompted at the terminal. Type `approve` to accept it as-is, or type revision feedback and the draft will be regenerated by an LLM incorporating your notes. + +## Files + +| File | Description | +|------|-------------| +| `workflow.py` | Graph node functions, graph definition, and `ChatbotWorkflow` definition | +| `run_worker.py` | Builds graph, registers with `LangGraphPlugin`, starts worker | +| `run_workflow.py` | Starts workflow, polls draft via query, prompts for human feedback, sends it via signal | diff --git a/langgraph_plugin/graph_api/human_in_the_loop/__init__.py b/langgraph_plugin/graph_api/human_in_the_loop/__init__.py new file mode 100644 index 000000000..72da02f88 --- /dev/null +++ b/langgraph_plugin/graph_api/human_in_the_loop/__init__.py @@ -0,0 +1 @@ +"""Human-in-the-loop chatbot using interrupt() and Temporal signals.""" diff --git a/langgraph_plugin/graph_api/human_in_the_loop/run_worker.py b/langgraph_plugin/graph_api/human_in_the_loop/run_worker.py new file mode 100644 index 000000000..2090366c3 --- /dev/null +++ b/langgraph_plugin/graph_api/human_in_the_loop/run_worker.py @@ -0,0 +1,31 @@ +"""Worker for the human-in-the-loop chatbot (Graph API).""" + +import asyncio +import os + +from temporalio.client import Client +from temporalio.contrib.langgraph import LangGraphPlugin +from temporalio.worker import Worker + +from langgraph_plugin.graph_api.human_in_the_loop.workflow import ( + ChatbotWorkflow, + make_chatbot_graph, +) + + +async def main() -> None: + client = await Client.connect(os.environ.get("TEMPORAL_ADDRESS", "localhost:7233")) + plugin = LangGraphPlugin(graphs={"chatbot": make_chatbot_graph()}) + + worker = Worker( + client, + task_queue="langgraph-chatbot", + workflows=[ChatbotWorkflow], + plugins=[plugin], + ) + print("Worker started. Ctrl+C to exit.") + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/langgraph_plugin/graph_api/human_in_the_loop/run_workflow.py b/langgraph_plugin/graph_api/human_in_the_loop/run_workflow.py new file mode 100644 index 000000000..c45e2b72a --- /dev/null +++ b/langgraph_plugin/graph_api/human_in_the_loop/run_workflow.py @@ -0,0 +1,41 @@ +"""Start the human-in-the-loop chatbot workflow (Graph API).""" + +import asyncio +import os + +from temporalio.client import Client + +from langgraph_plugin.graph_api.human_in_the_loop.workflow import ChatbotWorkflow + + +async def main() -> None: + client = await Client.connect(os.environ.get("TEMPORAL_ADDRESS", "localhost:7233")) + + handle = await client.start_workflow( + ChatbotWorkflow.run, + "What is the meaning of life?", + id="chatbot-workflow", + task_queue="langgraph-chatbot", + ) + + # Poll until the draft is ready for review. + # In a real app, a UI would call this query endpoint. + draft = None + while draft is None: + await asyncio.sleep(0.5) + draft = await handle.query(ChatbotWorkflow.get_draft) + + print(f"Draft for review: {draft}") + + # Prompt for human feedback instead of auto-approving. + feedback = await asyncio.to_thread( + input, "Enter 'approve' to accept, or type revision feedback: " + ) + await handle.signal(ChatbotWorkflow.provide_feedback, feedback) + + result = await handle.result() + print(f"Final response: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/langgraph_plugin/graph_api/human_in_the_loop/workflow.py b/langgraph_plugin/graph_api/human_in_the_loop/workflow.py new file mode 100644 index 000000000..d9e39911a --- /dev/null +++ b/langgraph_plugin/graph_api/human_in_the_loop/workflow.py @@ -0,0 +1,94 @@ +"""Human-in-the-loop chatbot using the LangGraph Graph API with Temporal. + +Demonstrates using LangGraph's interrupt() to pause a workflow for human input, +combined with Temporal signals to receive the input asynchronously. +""" + +from datetime import timedelta + +from langchain.chat_models import init_chat_model +from langchain_core.runnables import RunnableConfig +from langgraph.checkpoint.memory import InMemorySaver +from langgraph.graph import START, StateGraph +from langgraph.types import Command, interrupt +from temporalio import workflow +from temporalio.contrib.langgraph import graph as temporal_graph +from typing_extensions import TypedDict + + +class State(TypedDict): + value: str + + +async def generate_draft(state: State) -> dict[str, str]: + """Generate a draft response with an LLM.""" + response = await init_chat_model("claude-sonnet-4-6").ainvoke( + f"Please respond concisely to: {state['value']}" + ) + return {"value": str(response.content)} + + +async def human_review(state: State) -> dict[str, str]: + """Present draft to human for review via interrupt; revise with LLM on feedback.""" + feedback = interrupt(state["value"]) + if feedback == "approve": + return {"value": state["value"]} + response = await init_chat_model("claude-sonnet-4-6").ainvoke( + "Revise the following draft according to the reviewer's feedback. " + "Output only the revised draft, with no preamble.\n\n" + f"Draft:\n{state['value']}\n\n" + f"Feedback:\n{feedback}" + ) + return {"value": str(response.content)} + + +def make_chatbot_graph() -> StateGraph: + node_metadata = { + "execute_in": "activity", + "start_to_close_timeout": timedelta(seconds=30), + } + g = StateGraph(State) + g.add_node("generate_draft", generate_draft, metadata=node_metadata) + g.add_node("human_review", human_review, metadata=node_metadata) + g.add_edge(START, "generate_draft") + g.add_edge("generate_draft", "human_review") + return g + + +@workflow.defn +class ChatbotWorkflow: + def __init__(self) -> None: + self._human_input: str | None = None + self._draft: str | None = None + + @workflow.signal + async def provide_feedback(self, feedback: str) -> None: + """Signal handler: receives human feedback (approval or revision).""" + self._human_input = feedback + + @workflow.query + def get_draft(self) -> str | None: + """Query handler: returns the pending draft for review, or None.""" + return self._draft + + @workflow.run + async def run(self, user_message: str) -> str: + app = temporal_graph("chatbot").compile(checkpointer=InMemorySaver()) + config = RunnableConfig( + {"configurable": {"thread_id": workflow.info().workflow_id}} + ) + + # First invocation: runs generate_draft, then pauses at interrupt() + result = await app.ainvoke({"value": user_message}, config, version="v2") + + # Store the draft from the interrupt for the query handler + self._draft = result.interrupts[0].value + + # Wait for human feedback via Temporal signal + await workflow.wait_condition(lambda: self._human_input is not None) + + # Resume the graph with the human's feedback + resumed = await app.ainvoke( + Command(resume=self._human_input), config, version="v2" + ) + return resumed.value["value"] diff --git a/langgraph_plugin/graph_api/langsmith_tracing/README.md b/langgraph_plugin/graph_api/langsmith_tracing/README.md new file mode 100644 index 000000000..47165ee8f --- /dev/null +++ b/langgraph_plugin/graph_api/langsmith_tracing/README.md @@ -0,0 +1,38 @@ +# LangSmith Tracing (Graph API) + +Demonstrates combining the LangGraph plugin (durable execution) with Temporal's LangSmith plugin (observability) for full tracing of LLM calls through Temporal workflows. + +## What This Sample Demonstrates + +- Using `LangSmithPlugin` on the Temporal client for automatic trace propagation +- Using `LangGraphPlugin` on the Worker for durable LangGraph execution +- `@traceable` in three places: on the Activity itself, on a helper called from inside the Activity, and on a helper called from inside the Workflow +- Both plugins working together: durability + observability + +## How It Works + +1. The Temporal client is created with `LangSmithPlugin(add_temporal_runs=True)`. +2. A Worker is created with `LangGraphPlugin` wrapping the chat graph. +3. When the Workflow runs, the `chat` node executes as a Temporal Activity. +4. `@traceable` decorators emit trace data to LangSmith for the Activity, an in-Activity helper, and an in-Workflow helper. +5. The `LangSmithPlugin` adds Temporal-specific metadata to the traces. + +## Running the Sample + +Prerequisites: `uv sync --group langgraph` and a running Temporal dev server (`temporal server start-dev`). + +```bash +export ANTHROPIC_API_KEY='your-key' +export LANGCHAIN_API_KEY='your-key' + +uv run langgraph_plugin/graph_api/langsmith_tracing/main.py +``` + +Traces will appear in your [LangSmith](https://smith.langchain.com/) dashboard. + +## Files + +| File | Description | +|------|-------------| +| `workflow.py` | `@traceable` chat node + helpers, graph definition, and `ChatWorkflow` | +| `main.py` | Starts a Worker and executes the Workflow in a single process | diff --git a/langgraph_plugin/graph_api/langsmith_tracing/__init__.py b/langgraph_plugin/graph_api/langsmith_tracing/__init__.py new file mode 100644 index 000000000..dab692ec7 --- /dev/null +++ b/langgraph_plugin/graph_api/langsmith_tracing/__init__.py @@ -0,0 +1 @@ +"""LangSmith tracing with LangGraph Graph API and Temporal.""" diff --git a/langgraph_plugin/graph_api/langsmith_tracing/main.py b/langgraph_plugin/graph_api/langsmith_tracing/main.py new file mode 100644 index 000000000..71294f4ad --- /dev/null +++ b/langgraph_plugin/graph_api/langsmith_tracing/main.py @@ -0,0 +1,43 @@ +"""Run the LangSmith tracing chat sample (Graph API). + +Single-process driver: starts a Worker, executes the Workflow once, prints +the result, then shuts down. Requires ANTHROPIC_API_KEY and LANGCHAIN_API_KEY. +""" + +import asyncio +import os + +from temporalio.client import Client +from temporalio.contrib.langgraph import LangGraphPlugin +from temporalio.contrib.langsmith import LangSmithPlugin +from temporalio.worker import Worker + +from langgraph_plugin.graph_api.langsmith_tracing.workflow import ( + ChatWorkflow, + make_chat_graph, +) + + +async def main() -> None: + client = await Client.connect( + os.environ.get("TEMPORAL_ADDRESS", "localhost:7233"), + plugins=[LangSmithPlugin(add_temporal_runs=True)], + ) + + async with Worker( + client, + task_queue="langgraph-langsmith", + workflows=[ChatWorkflow], + plugins=[LangGraphPlugin(graphs={"chat": make_chat_graph()})], + ): + result = await client.execute_workflow( + ChatWorkflow.run, + "What is the meaning of life?", + id="langsmith-chat-workflow", + task_queue="langgraph-langsmith", + ) + print(f"Response: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/langgraph_plugin/graph_api/langsmith_tracing/workflow.py b/langgraph_plugin/graph_api/langsmith_tracing/workflow.py new file mode 100644 index 000000000..50649f314 --- /dev/null +++ b/langgraph_plugin/graph_api/langsmith_tracing/workflow.py @@ -0,0 +1,70 @@ +"""LangSmith tracing with LangGraph Graph API and Temporal. + +Demonstrates combining LangGraphPlugin (durable graph execution) with +LangSmithPlugin (observability) for full tracing of LLM calls through +Temporal workflows. + +Three @traceable use cases are demonstrated: +1. The Activity (graph node) function itself: `chat`. +2. A helper called from inside the Activity: `format_prompt`. +3. A helper called from inside the Workflow: `summarize_for_log`. + +Requires ANTHROPIC_API_KEY and LANGCHAIN_API_KEY environment variables. +""" + +from datetime import timedelta + +from langchain.chat_models import init_chat_model +from langgraph.graph import START, StateGraph +from langsmith import traceable +from temporalio import workflow +from temporalio.contrib.langgraph import graph as temporal_graph +from typing_extensions import TypedDict + + +class State(TypedDict): + value: str + + +@traceable(name="format_prompt", run_type="prompt") +def format_prompt(message: str) -> str: + """Helper called from inside the Activity. Traced by LangSmith.""" + return f"Please respond concisely to: {message}" + + +@traceable(name="chat_activity", run_type="chain") +async def chat(state: State) -> dict[str, str]: + """Call an LLM to respond to the message. Traced by LangSmith.""" + prompt = format_prompt(state["value"]) + response = await init_chat_model("claude-sonnet-4-6").ainvoke(prompt) + return {"value": str(response.content)} + + +@traceable(name="summarize_for_log", run_type="chain") +def summarize_for_log(response: str) -> str: + """Helper called from inside the Workflow. Traced by LangSmith.""" + return f"Got {len(response)}-char response: {response[:60]}..." + + +def make_chat_graph() -> StateGraph: + g = StateGraph(State) + g.add_node( + "chat", + chat, + metadata={ + "execute_in": "activity", + "start_to_close_timeout": timedelta(seconds=30), + }, + ) + g.add_edge(START, "chat") + return g + + +@workflow.defn +class ChatWorkflow: + @workflow.run + async def run(self, message: str) -> str: + result = await temporal_graph("chat").compile().ainvoke({"value": message}) + response = result["value"] + workflow.logger.info(summarize_for_log(response)) + return response diff --git a/langgraph_plugin/graph_api/react_agent/README.md b/langgraph_plugin/graph_api/react_agent/README.md new file mode 100644 index 000000000..de5149ad4 --- /dev/null +++ b/langgraph_plugin/graph_api/react_agent/README.md @@ -0,0 +1,42 @@ +# ReAct Agent (Graph API) + +Demonstrates the most common LangGraph pattern: a tool-calling agent that loops between deciding and acting, using conditional edges for routing. + +## What This Sample Demonstrates + +- Defining a `StateGraph` with an agent->tools loop +- Using `add_conditional_edges` for conditional routing (call tool or finish) +- Accumulating conversation history with `Annotated[list, operator.add]` +- The full ReAct cycle: think -> act -> observe -> repeat + +## How It Works + +1. The `agent` node examines the conversation history and decides the next action. +2. If a tool is needed, `should_continue` routes to the `tools` node. +3. The `tools` node executes the tool and appends the result to history. +4. Control returns to `agent`, which decides again — loop or finish. +5. When the agent has enough information, `should_continue` routes to `END`. + +``` +START -> agent -> tools -> agent -> tools -> agent -> END +``` + +## Running the Sample + +Prerequisites: `uv sync --group langgraph` and a running Temporal dev server (`temporal server start-dev`). + +```bash +# Terminal 1 +uv run langgraph_plugin/graph_api/react_agent/run_worker.py + +# Terminal 2 +uv run langgraph_plugin/graph_api/react_agent/run_workflow.py +``` + +## Files + +| File | Description | +|------|-------------| +| `workflow.py` | `AgentState`, node functions, `should_continue` router, graph definition, and `ReactAgentWorkflow` | +| `run_worker.py` | Builds graph, registers with `LangGraphPlugin`, starts worker | +| `run_workflow.py` | Executes the agent workflow and prints the answer | diff --git a/langgraph_plugin/graph_api/react_agent/__init__.py b/langgraph_plugin/graph_api/react_agent/__init__.py new file mode 100644 index 000000000..64c24c36e --- /dev/null +++ b/langgraph_plugin/graph_api/react_agent/__init__.py @@ -0,0 +1 @@ +"""ReAct agent with conditional edges and tool-calling loop.""" diff --git a/langgraph_plugin/graph_api/react_agent/run_worker.py b/langgraph_plugin/graph_api/react_agent/run_worker.py new file mode 100644 index 000000000..04532a325 --- /dev/null +++ b/langgraph_plugin/graph_api/react_agent/run_worker.py @@ -0,0 +1,31 @@ +"""Worker for the ReAct agent (Graph API).""" + +import asyncio +import os + +from temporalio.client import Client +from temporalio.contrib.langgraph import LangGraphPlugin +from temporalio.worker import Worker + +from langgraph_plugin.graph_api.react_agent.workflow import ( + ReactAgentWorkflow, + make_agent_graph, +) + + +async def main() -> None: + client = await Client.connect(os.environ.get("TEMPORAL_ADDRESS", "localhost:7233")) + plugin = LangGraphPlugin(graphs={"react-agent": make_agent_graph()}) + + worker = Worker( + client, + task_queue="langgraph-react-agent", + workflows=[ReactAgentWorkflow], + plugins=[plugin], + ) + print("Worker started. Ctrl+C to exit.") + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/langgraph_plugin/graph_api/react_agent/run_workflow.py b/langgraph_plugin/graph_api/react_agent/run_workflow.py new file mode 100644 index 000000000..d4649f1e4 --- /dev/null +++ b/langgraph_plugin/graph_api/react_agent/run_workflow.py @@ -0,0 +1,25 @@ +"""Start the ReAct agent workflow (Graph API).""" + +import asyncio +import os + +from temporalio.client import Client + +from langgraph_plugin.graph_api.react_agent.workflow import ReactAgentWorkflow + + +async def main() -> None: + client = await Client.connect(os.environ.get("TEMPORAL_ADDRESS", "localhost:7233")) + + result = await client.execute_workflow( + ReactAgentWorkflow.run, + "Tell me about San Francisco", + id="react-agent-workflow", + task_queue="langgraph-react-agent", + ) + + print(f"Agent answer: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/langgraph_plugin/graph_api/react_agent/workflow.py b/langgraph_plugin/graph_api/react_agent/workflow.py new file mode 100644 index 000000000..1bcae10d7 --- /dev/null +++ b/langgraph_plugin/graph_api/react_agent/workflow.py @@ -0,0 +1,109 @@ +"""ReAct agent using the LangGraph Graph API with Temporal. + +Demonstrates the most common LangGraph pattern: a tool-calling agent that loops +between "thinking" (deciding the next action) and "acting" (executing a tool), +using conditional edges to control the loop. + +Graph topology: + START -> agent -> (tools -> agent)* -> END +""" + +import operator +from datetime import timedelta +from typing import Annotated, Any, TypedDict + +from langgraph.graph import END, START, StateGraph +from temporalio import workflow +from temporalio.contrib.langgraph import graph as temporal_graph + + +class AgentState(TypedDict): + """State for the ReAct agent. + + 'messages' uses operator.add so each node appends to the list rather + than replacing it, accumulating the full conversation history. + """ + + input: str + messages: Annotated[list[str], operator.add] + final_answer: str + + +async def agent(state: AgentState) -> dict[str, Any]: + """The agent decides what to do next based on the conversation history. + + In production, replace this with an LLM call (e.g., Claude with tools). + This stub simulates a 2-step research process. + """ + messages = state.get("messages", []) + tool_results = [m for m in messages if m.startswith("[Tool]")] + + if len(tool_results) == 0: + return { + "messages": [ + "[Agent] I need weather data. Calling get_weather for San Francisco." + ] + } + elif len(tool_results) == 1: + return { + "messages": [ + "[Agent] Now I need population data. " + "Calling get_population for San Francisco." + ] + } + else: + facts = "; ".join(tool_results) + return { + "messages": ["[Agent] I have all the information I need."], + "final_answer": (f"Here's what I found about San Francisco: {facts}"), + } + + +async def tools(state: AgentState) -> dict[str, Any]: + """Execute the tool requested by the agent.""" + last_msg = state["messages"][-1] + + if "get_weather" in last_msg: + return {"messages": ["[Tool] Weather in San Francisco: 72°F and sunny."]} + elif "get_population" in last_msg: + return {"messages": ["[Tool] San Francisco population: ~870,000 residents."]} + else: + return {"messages": ["[Tool] Unknown tool requested."]} + + +async def should_continue(state: AgentState) -> str: + """Route: if the agent requested a tool, go to 'tools'. Otherwise, end. + + Must be async to avoid run_in_executor inside Temporal's workflow sandbox. + """ + last_msg = state["messages"][-1] + if last_msg.startswith("[Agent]") and "Calling" in last_msg: + return "tools" + return END + + +def make_agent_graph() -> StateGraph: + node_metadata = { + "execute_in": "activity", + "start_to_close_timeout": timedelta(seconds=30), + } + g = StateGraph(AgentState) + g.add_node("agent", agent, metadata=node_metadata) + g.add_node("tools", tools, metadata=node_metadata) + g.add_edge(START, "agent") + g.add_conditional_edges("agent", should_continue) + g.add_edge("tools", "agent") + return g + + +@workflow.defn +class ReactAgentWorkflow: + @workflow.run + async def run(self, query: str) -> str: + initial_state: AgentState = { + "input": query, + "messages": [], + "final_answer": "", + } + result = await temporal_graph("react-agent").compile().ainvoke(initial_state) + return result["final_answer"] diff --git a/langgraph_plugin/graph_api/streaming/README.md b/langgraph_plugin/graph_api/streaming/README.md new file mode 100644 index 000000000..d34f3e240 --- /dev/null +++ b/langgraph_plugin/graph_api/streaming/README.md @@ -0,0 +1,31 @@ +# Streaming (Graph API) + +Streams a LangGraph run to an external client while the workflow is still running, using Temporal's durable, offset-addressed [`WorkflowStream`](https://github.com/temporalio/sdk-python/tree/main/temporalio/contrib/workflow_streams). The graph writes a short story about a topic and emits both fine-grained tokens and node-completion progress on separate topics. + +## What This Sample Demonstrates + +- **Node token streaming** — the `write_story` node calls LangGraph's `get_stream_writer()` to emit tokens. The plugin's `streaming_topic="tokens"` routes those writes onto the `"tokens"` topic. +- **Workflow-side `astream` publish** — the workflow drives the graph with `app.astream(...)` and publishes each node-completion chunk onto a `"progress"` topic it owns. +- A single client subscribing to all topics and demultiplexing on `item.topic`. +- Waiting for the client to acknowledge (via signal) before completing, since the stream disappears when the workflow ends. +- **Idempotent consumption** — each token chunk carries a monotonic sequence id so the client can dedupe, because streaming is at-least-once per activity attempt (a retried node re-runs and re-publishes its writes). + +## Running the Sample + +Prerequisites: `uv sync --group langgraph` and a running Temporal dev server (`temporal server start-dev`). + +```bash +# Terminal 1 +uv run langgraph_plugin/graph_api/streaming/run_worker.py + +# Terminal 2 +uv run langgraph_plugin/graph_api/streaming/run_workflow.py +``` + +## Files + +| File | Description | +|------|-------------| +| `workflow.py` | Graph node functions, graph definition, and `StreamingWorkflow` that publishes to the stream | +| `run_worker.py` | Registers graph with `LangGraphPlugin` (`streaming_topic="tokens"`), starts worker | +| `run_workflow.py` | Starts the workflow, subscribes to the stream, prints tokens and progress, then acks | diff --git a/langgraph_plugin/graph_api/streaming/__init__.py b/langgraph_plugin/graph_api/streaming/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/langgraph_plugin/graph_api/streaming/run_worker.py b/langgraph_plugin/graph_api/streaming/run_worker.py new file mode 100644 index 000000000..46e0090ee --- /dev/null +++ b/langgraph_plugin/graph_api/streaming/run_worker.py @@ -0,0 +1,35 @@ +"""Worker for the streaming sample (Graph API).""" + +import asyncio +import os + +from temporalio.client import Client +from temporalio.contrib.langgraph import LangGraphPlugin +from temporalio.worker import Worker + +from langgraph_plugin.graph_api.streaming.workflow import ( + StreamingWorkflow, + make_streaming_graph, +) + + +async def main() -> None: + client = await Client.connect(os.environ.get("TEMPORAL_ADDRESS", "localhost:7233")) + # streaming_topic routes node get_stream_writer() output onto the "tokens" topic. + plugin = LangGraphPlugin( + graphs={"streaming": make_streaming_graph()}, + streaming_topic="tokens", + ) + + worker = Worker( + client, + task_queue="langgraph-streaming", + workflows=[StreamingWorkflow], + plugins=[plugin], + ) + print("Worker started. Ctrl+C to exit.") + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/langgraph_plugin/graph_api/streaming/run_workflow.py b/langgraph_plugin/graph_api/streaming/run_workflow.py new file mode 100644 index 000000000..9d42322e1 --- /dev/null +++ b/langgraph_plugin/graph_api/streaming/run_workflow.py @@ -0,0 +1,51 @@ +"""Start the streaming workflow and subscribe to its Workflow Stream (Graph API).""" + +import asyncio +import os +from datetime import timedelta + +from temporalio.client import Client +from temporalio.contrib.workflow_streams import WorkflowStreamClient + +from langgraph_plugin.graph_api.streaming.workflow import StreamingWorkflow + + +async def main() -> None: + client = await Client.connect(os.environ.get("TEMPORAL_ADDRESS", "localhost:7233")) + + handle = await client.start_workflow( + StreamingWorkflow.run, + "a brave robot", + id="streaming-workflow", + task_queue="langgraph-streaming", + ) + + # Subscribe to all topics on the workflow's stream and demultiplex on topic. + ws = WorkflowStreamClient.create(client, handle.id) + # Streaming is at-least-once per activity attempt, so a retried node may + # re-publish tokens. Dedupe on the chunk's seq to consume idempotently. + seen_tokens: set[int] = set() + async for item in ws.subscribe( + from_offset=0, + result_type=dict, + poll_cooldown=timedelta(milliseconds=50), + ): + if item.topic == "tokens": + seq = item.data["seq"] + if seq in seen_tokens: + continue # duplicate from a node retry; already consumed. + seen_tokens.add(seq) + print(item.data["token"], end="", flush=True) + elif item.topic == "progress": + if item.data.get("done"): + # Let the workflow know we are done consuming so it can complete. + await handle.signal(StreamingWorkflow.ack_stream) + break + print(f"\n[progress] {item.data}") + + result = await handle.result() + print(f"\n\nFinal result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/langgraph_plugin/graph_api/streaming/workflow.py b/langgraph_plugin/graph_api/streaming/workflow.py new file mode 100644 index 000000000..6eeea2025 --- /dev/null +++ b/langgraph_plugin/graph_api/streaming/workflow.py @@ -0,0 +1,97 @@ +"""Streaming with the LangGraph Graph API and Temporal Workflow Streams. + +A workflow's :class:`WorkflowStream` is a durable, offset-addressed event channel +external clients can subscribe to while the workflow is still running. This sample +demonstrates both ways the LangGraph plugin produces stream items: + +- **Node token streaming** -- the ``write_story`` node calls LangGraph's + ``get_stream_writer()`` to emit fine-grained tokens. The plugin is configured with + ``streaming_topic="tokens"`` (see ``run_worker.py``), which routes those writes onto + the ``"tokens"`` topic. +- **Workflow-side ``astream`` publish** -- the workflow drives the graph with + ``app.astream(...)`` and publishes each node-completion chunk onto a ``"progress"`` + topic it owns. + +A single client subscribes to all topics and demultiplexes on ``item.topic``. +""" + +from datetime import timedelta + +from langgraph.config import get_stream_writer +from langgraph.graph import START, StateGraph +from temporalio import workflow +from temporalio.contrib.langgraph import graph as temporal_graph +from temporalio.contrib.workflow_streams import WorkflowStream +from typing_extensions import TypedDict + + +class State(TypedDict): + topic: str + story: str + + +async def outline(state: State) -> dict[str, str]: + """Produce a short opening line. Runs first so ``astream`` emits an early chunk.""" + return {"story": f"A story about {state['topic']}:"} + + +async def write_story(state: State) -> dict[str, str]: + """Write the story, emitting each word as a token via the stream writer. + + Streaming is at-least-once per activity attempt: if this node retries + (transient failure, worker restart) it re-runs from scratch and re-publishes + its writes, so subscribers may see the same token twice. Each chunk therefore + carries a monotonic ``seq`` so consumers can dedupe idempotently. A retry + re-emits the same ``seq`` values, letting the client drop the duplicates. + """ + writer = get_stream_writer() + words = f"{state['story']} Once upon a time, there was {state['topic']}.".split() + for seq, word in enumerate(words): + writer({"seq": seq, "token": word + " "}) + return {"story": " ".join(words)} + + +def make_streaming_graph() -> StateGraph: + g = StateGraph(State) + activity_metadata = { + "execute_in": "activity", + "start_to_close_timeout": timedelta(seconds=10), + } + g.add_node("outline", outline, metadata=activity_metadata) + g.add_node("write_story", write_story, metadata=activity_metadata) + g.add_edge(START, "outline") + g.add_edge("outline", "write_story") + return g + + +@workflow.defn +class StreamingWorkflow: + def __init__(self) -> None: + # WorkflowStream must be constructed during workflow initialization. + self.stream = WorkflowStream() + self._stream_acked = False + + @workflow.signal + def ack_stream(self) -> None: + """Signalled by the client once it has finished consuming the stream.""" + self._stream_acked = True + + @workflow.run + async def run(self, topic: str) -> str: + app = temporal_graph("streaming").compile() + progress = self.stream.topic("progress") + + story = "" + async for chunk in app.astream({"topic": topic, "story": ""}): + # Each chunk is {node_name: {state updates}}. Forward it as progress. + progress.publish(chunk) + for node_update in chunk.values(): + if "story" in node_update: + story = node_update["story"] + + progress.publish({"done": True}) + + # The stream disappears when the workflow completes, so wait until the + # client acknowledges it has finished consuming before returning. + await workflow.wait_condition(lambda: self._stream_acked) + return story diff --git a/langsmith_tracing/README.md b/langsmith_tracing/README.md new file mode 100644 index 000000000..127a80593 --- /dev/null +++ b/langsmith_tracing/README.md @@ -0,0 +1,49 @@ +# LangSmith Tracing + +This sample demonstrates [LangSmith](https://smith.langchain.com/) tracing integration with Temporal workflows using the [`LangSmithPlugin`](https://python.temporal.io/temporalio.contrib.langsmith.html). + +Two examples are included: + +- **[basic/](basic/)** — A one-shot LLM workflow that sends a prompt to OpenAI and returns the response. +- **[chatbot/](chatbot/)** — A long-running conversational workflow with tool calls (save/read notes) and update handlers. + +## Prerequisites + +Install dependencies: + +```bash +uv sync --group langsmith-tracing +``` + +Set environment variables: + +```bash +export OPENAI_API_KEY="sk-..." +export LANGSMITH_API_KEY="lsv2_..." +export LANGCHAIN_TRACING_V2=true +``` + +A local Temporal server must be running (`temporal server start-dev`). + +## Three Layers of Tracing + +This sample shows three complementary ways LangSmith captures trace data: + +1. **Automatic (`wrap_openai`)** — Wrapping the OpenAI client with `wrap_openai()` automatically creates a child span for every LLM call, capturing model parameters, token usage, and latency. No extra code needed. + +2. **Explicit (`@traceable`)** — Decorating functions with `@traceable` creates named spans for your business logic. You control the name, tags, metadata, and `run_type` (chain, llm, tool, retriever). This is how you structure traces to tell a story about what your application is doing. + +3. **Temporal (`add_temporal_runs=True`)** — The `LangSmithPlugin` can optionally create LangSmith runs for each Temporal workflow execution and activity execution, giving visibility into the orchestration layer alongside your LLM calls. + +## `add_temporal_runs` + +By default, `LangSmithPlugin(add_temporal_runs=False)` only propagates LangSmith context so that `@traceable` and `wrap_openai` calls nest correctly. + +Set `add_temporal_runs=True` to also create LangSmith runs for Temporal operations (workflow executions, activity executions, signals, etc.), giving full visibility into the orchestration layer. Both examples support a `--add-temporal-runs` CLI flag to toggle this. + +## Further Reading + +- [LangSmith documentation](https://docs.smith.langchain.com/) +- [Temporal Python SDK LangSmith plugin](https://python.temporal.io/temporalio.contrib.langsmith.html) +- [LangSmith `@traceable` guide](https://docs.smith.langchain.com/observability/how-to/annotate-code) +- [LangSmith `wrap_openai` guide](https://docs.smith.langchain.com/observability/how-to/trace-with-openai) diff --git a/langsmith_tracing/__init__.py b/langsmith_tracing/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/langsmith_tracing/basic/README.md b/langsmith_tracing/basic/README.md new file mode 100644 index 000000000..839312e82 --- /dev/null +++ b/langsmith_tracing/basic/README.md @@ -0,0 +1,50 @@ +# Basic LLM Workflow + +A one-shot workflow that sends a prompt to OpenAI and returns the response. A very simple example of LangSmith tracing with Temporal. + +See the [parent README](../README.md) for prerequisites. + +## Running + +```bash +# Terminal 1 — start the worker +uv run --group langsmith-tracing python -m langsmith_tracing.basic.worker + +# Terminal 2 — run the workflow +uv run --group langsmith-tracing python -m langsmith_tracing.basic.starter +``` + +## Trace Structure + +### `add_temporal_runs=False` (default) + +Only `@traceable` and `wrap_openai` spans appear. The client-side `@traceable` is the root, and workflow/activity traces nest under it via context propagation. + +``` +Basic LLM Request (@traceable, client-side) +└── Ask: What is Temporal? (@traceable, workflow) + └── Call OpenAI (@traceable, activity) + └── ChatOpenAI (automatic via wrap_openai) +``` + +### `add_temporal_runs=True` + +Pass `--add-temporal-runs` to both the worker and starter: + +```bash +uv run --group langsmith-tracing python -m langsmith_tracing.basic.worker --add-temporal-runs +uv run --group langsmith-tracing python -m langsmith_tracing.basic.starter --add-temporal-runs +``` + +Temporal operation spans are added. `StartWorkflow`/`RunWorkflow` and `StartActivity`/`RunActivity` appear as sibling pairs: + +``` +Basic LLM Request (@traceable, client-side) +├── StartWorkflow:BasicLLMWorkflow (automatic, Temporal plugin) +└── RunWorkflow:BasicLLMWorkflow (automatic, Temporal plugin) + └── Ask: What is Temporal? (@traceable, workflow) + ├── StartActivity:call_openai (automatic, Temporal plugin) + └── RunActivity:call_openai (automatic, Temporal plugin) + └── Call OpenAI (@traceable, activity) + └── ChatOpenAI (automatic via wrap_openai) +``` diff --git a/langsmith_tracing/basic/__init__.py b/langsmith_tracing/basic/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/langsmith_tracing/basic/activities.py b/langsmith_tracing/basic/activities.py new file mode 100644 index 000000000..e04e17119 --- /dev/null +++ b/langsmith_tracing/basic/activities.py @@ -0,0 +1,33 @@ +"""Basic LLM activity with LangSmith tracing.""" + +from dataclasses import dataclass + +from langsmith import traceable +from langsmith.wrappers import wrap_openai +from openai import AsyncOpenAI +from temporalio import activity + + +@dataclass +class OpenAIRequest: + model: str + input: str + instructions: str = "You are a helpful assistant." + + +@traceable(name="Call OpenAI", run_type="llm") +@activity.defn +async def call_openai(request: OpenAIRequest) -> str: + """Call OpenAI Responses API. Retries handled by Temporal, not the OpenAI client.""" + # wrap_openai patches the client so each API call (e.g. responses.create) + # creates its own child span with model parameters and token usage. + # max_retries=0 disables OpenAI's built-in retries — Temporal's activity + # retry policy handles retries instead, with full visibility in the UI. + client = wrap_openai(AsyncOpenAI(max_retries=0)) + response = await client.responses.create( + model=request.model, + instructions=request.instructions, + input=request.input, + timeout=30, + ) + return response.output_text diff --git a/langsmith_tracing/basic/starter.py b/langsmith_tracing/basic/starter.py new file mode 100644 index 000000000..4a2a103a4 --- /dev/null +++ b/langsmith_tracing/basic/starter.py @@ -0,0 +1,52 @@ +"""Starter for the basic LangSmith sample.""" + +import asyncio +import sys + +from langsmith import traceable +from temporalio.client import Client +from temporalio.contrib.langsmith import LangSmithPlugin +from temporalio.envconfig import ClientConfig + +from langsmith_tracing.basic.workflows import BasicLLMWorkflow + +PROJECT_NAME = "langsmith-basic" + + +@traceable( + name="Basic LLM Request", + run_type="chain", + # CRITICAL: Client-side @traceable runs outside the LangSmithPlugin's scope. + # Make sure client-side traces use the same project_name as what is given to + # the plugin. + project_name=PROJECT_NAME, + tags=["client-side"], +) +async def main() -> str: + add_temporal_runs = "--add-temporal-runs" in sys.argv + + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + + plugin = LangSmithPlugin( + project_name=PROJECT_NAME, + add_temporal_runs=add_temporal_runs, + ) + + client = await Client.connect( + **config, + plugins=[plugin], + ) + + result = await client.execute_workflow( + BasicLLMWorkflow.run, + "What is Temporal?", + id="langsmith-basic-workflow-id", + task_queue="langsmith-basic-task-queue", + ) + print(f"Workflow result: {result}") + return result + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/langsmith_tracing/basic/worker.py b/langsmith_tracing/basic/worker.py new file mode 100644 index 000000000..941b28c57 --- /dev/null +++ b/langsmith_tracing/basic/worker.py @@ -0,0 +1,47 @@ +"""Worker for the basic LangSmith sample.""" + +import asyncio +import logging +import sys + +from temporalio.client import Client +from temporalio.contrib.langsmith import LangSmithPlugin +from temporalio.envconfig import ClientConfig +from temporalio.worker import Worker + +from langsmith_tracing.basic.activities import call_openai +from langsmith_tracing.basic.workflows import BasicLLMWorkflow + + +async def main(): + logging.basicConfig(level=logging.INFO) + + # add_temporal_runs=True creates LangSmith runs for each Temporal + # workflow/activity execution. False (default) only propagates context. + add_temporal_runs = "--add-temporal-runs" in sys.argv + + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + + plugin = LangSmithPlugin( + project_name="langsmith-basic", + add_temporal_runs=add_temporal_runs, + ) + + client = await Client.connect(**config) + + worker = Worker( + client, + task_queue="langsmith-basic-task-queue", + workflows=[BasicLLMWorkflow], + activities=[call_openai], + plugins=[plugin], + ) + + label = "with" if add_temporal_runs else "without" + print(f"Worker started ({label} Temporal runs in traces), ctrl+c to exit") + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/langsmith_tracing/basic/workflows.py b/langsmith_tracing/basic/workflows.py new file mode 100644 index 000000000..c6cc14b2e --- /dev/null +++ b/langsmith_tracing/basic/workflows.py @@ -0,0 +1,33 @@ +"""Basic one-shot LLM workflow with LangSmith tracing.""" + +from datetime import timedelta + +from temporalio import workflow + +with workflow.unsafe.imports_passed_through(): + from langsmith import traceable + + from langsmith_tracing.basic.activities import OpenAIRequest, call_openai + + +@workflow.defn +class BasicLLMWorkflow: + # Do not decorate @workflow.run with @traceable — it would violate + # replay safety and produce duplicate or orphaned traces. Instead, + # wrap an inner function. + @workflow.run + async def run(self, prompt: str) -> str: + @traceable( + name=f"Ask: {prompt[:60]}", + run_type="chain", + metadata={"workflow_id": workflow.info().workflow_id}, + tags=["basic-llm"], + ) + async def _run() -> str: + return await workflow.execute_activity( + call_openai, + OpenAIRequest(model="gpt-4o-mini", input=prompt), + start_to_close_timeout=timedelta(seconds=60), + ) + + return await _run() diff --git a/langsmith_tracing/chatbot/README.md b/langsmith_tracing/chatbot/README.md new file mode 100644 index 000000000..b97849892 --- /dev/null +++ b/langsmith_tracing/chatbot/README.md @@ -0,0 +1,81 @@ +# Chatbot with Tool Calls + +A long-running conversational workflow with tool calls and update handlers. Demonstrates how LangSmith traces an agentic loop where the model calls tools across multiple turns. + +See the [parent README](../README.md) for prerequisites. + +## Running + +```bash +# Terminal 1 — start the worker +uv run --group langsmith-tracing python -m langsmith_tracing.chatbot.worker + +# Terminal 2 — interactive CLI +uv run --group langsmith-tracing python -m langsmith_tracing.chatbot.starter +``` + +Commands in the CLI: +- Type a message and press Enter to chat +- `exit` — end the session + +## Tools + +The model has two tools, both implemented as `@traceable` methods on the workflow class: + +- **`save_note(name, content)`** — Stores a note in the workflow's in-memory dict. The note is durable because workflow state survives crashes and restarts via Temporal's event history. +- **`read_note(name)`** — Reads a note from the workflow's in-memory dict. + +## Architecture + +The main `@workflow.run` method runs a loop that processes user messages (calls `_query_openai`, which handles the tool-call loop). The `message_from_user` update handler coordinates: it hands the message to the main loop via a shared `_pending_message` field, then waits for the response. + +This means: +- Activity calls and the tool loop happen inside the main workflow run +- The update handler's trace just shows the input/output of the coordination step + +## Trace Structure + +### `add_temporal_runs=False` (default) + +Only `@traceable` and `wrap_openai` spans appear. The client-side `@traceable` wraps `start_workflow` and each `execute_update`, so both workflow and update traces nest under it via context propagation. + +``` +Chatbot Session a1b2c3d4 (@traceable, client-side) +├── Session Apr 17 10:30 (@traceable, workflow main loop) +│ ├── Request: hello (@traceable, per-message in main loop) +│ │ └── Call OpenAI (@traceable, activity) +│ │ └── ChatOpenAI (automatic via wrap_openai) +│ └── Request: save that as note 15 (@traceable, per-message in main loop) +│ ├── Call OpenAI → function_call: save_note +│ ├── Save Note (@traceable, workflow method) +│ └── Call OpenAI → text response +├── Update: hello (@traceable, update handler) +└── Update: save that as note 15 (@traceable, update handler) +``` + +### `add_temporal_runs=True` + +With `--add-temporal-runs`, Temporal operation spans are added. `StartWorkflow`/`RunWorkflow` and `StartActivity`/`RunActivity` appear as sibling pairs. + +``` +Chatbot Session a1b2c3d4 (@traceable, client-side) +├── StartWorkflow:ChatbotWorkflow (automatic, Temporal plugin) +├── RunWorkflow:ChatbotWorkflow (automatic, Temporal plugin) +│ └── Session Apr 17 10:30 (@traceable, workflow main loop) +│ └── Request: save that as note 15 (@traceable, per-message) +│ ├── StartActivity:call_openai (automatic, Temporal plugin) +│ ├── RunActivity:call_openai (automatic, Temporal plugin) +│ │ └── Call OpenAI (@traceable, activity) +│ │ └── ChatOpenAI (automatic via wrap_openai) +│ ├── Save Note (@traceable, workflow method) +│ ├── StartActivity:call_openai (automatic, Temporal plugin) +│ └── RunActivity:call_openai (automatic, Temporal plugin) +│ └── Call OpenAI (@traceable, activity) +│ └── ChatOpenAI (automatic via wrap_openai) +├── StartWorkflowUpdate:message_from_user (automatic, Temporal plugin) +│ └── HandleUpdate:message_from_user (automatic, Temporal plugin) +│ └── Update: save that as note 15 (@traceable, update handler) +└── ... +``` + +Note that the `Request:` chain (with activity calls) lives under `RunWorkflow` (the main loop), while the `Update:` span lives under `HandleUpdate` (the update handler). They're connected by the shared workflow state but appear as separate subtrees. diff --git a/langsmith_tracing/chatbot/__init__.py b/langsmith_tracing/chatbot/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/langsmith_tracing/chatbot/activities.py b/langsmith_tracing/chatbot/activities.py new file mode 100644 index 000000000..5e979320e --- /dev/null +++ b/langsmith_tracing/chatbot/activities.py @@ -0,0 +1,66 @@ +"""Chatbot activities with LangSmith tracing.""" + +from dataclasses import dataclass +from typing import Any + +from langsmith import traceable +from langsmith.wrappers import wrap_openai +from openai import AsyncOpenAI +from pydantic import BaseModel, Field +from temporalio import activity + + +class ToolCall(BaseModel): + call_id: str + name: str + arguments: str + + +class ChatResponse(BaseModel): + id: str + output_text: str = "" + tool_calls: list[ToolCall] = Field(default_factory=list) + + +@dataclass +class OpenAIRequest: + model: str + # str for the initial user prompt, list for tool call results fed back + input: str | list[dict[str, Any]] + instructions: str = ( + "You are a helpful assistant with note-taking abilities. " + "Use save_note to remember things and read_note to recall them." + ) + tools: list[dict[str, Any]] | None = None + previous_response_id: str | None = None + + +@traceable(name="Call OpenAI", run_type="llm") +@activity.defn +async def call_openai(request: OpenAIRequest) -> ChatResponse: + """Call OpenAI Responses API. Retries handled by Temporal, not the OpenAI client.""" + # wrap_openai patches the client so each API call (e.g. responses.create) + # creates its own child span with model parameters and token usage. + # max_retries=0 disables OpenAI's built-in retries — Temporal's activity + # retry policy handles retries instead, with full visibility in the UI. + client = wrap_openai(AsyncOpenAI(max_retries=0)) + response_args: dict[str, Any] = { + "model": request.model, + "instructions": request.instructions, + "input": request.input, + "timeout": 30, + } + if request.tools: + response_args["tools"] = request.tools + if request.previous_response_id: + response_args["previous_response_id"] = request.previous_response_id + response = await client.responses.create(**response_args) + return ChatResponse( + id=response.id, + output_text=response.output_text or "", + tool_calls=[ + ToolCall(call_id=item.call_id, name=item.name, arguments=item.arguments) + for item in response.output + if item.type == "function_call" + ], + ) diff --git a/langsmith_tracing/chatbot/starter.py b/langsmith_tracing/chatbot/starter.py new file mode 100644 index 000000000..4d67e794f --- /dev/null +++ b/langsmith_tracing/chatbot/starter.py @@ -0,0 +1,76 @@ +"""Interactive CLI starter for the chatbot LangSmith sample.""" + +import asyncio +import readline # noqa: F401 — enables input() line editing +import sys +import uuid + +from langsmith import traceable +from temporalio.client import Client +from temporalio.contrib.langsmith import LangSmithPlugin +from temporalio.contrib.pydantic import pydantic_data_converter +from temporalio.envconfig import ClientConfig + +from langsmith_tracing.chatbot.workflows import ChatbotWorkflow + +PROJECT_NAME = "langsmith-chatbot" + + +async def main(): + add_temporal_runs = "--add-temporal-runs" in sys.argv + + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + + plugin = LangSmithPlugin( + project_name=PROJECT_NAME, + add_temporal_runs=add_temporal_runs, + ) + + client = await Client.connect( + **config, + data_converter=pydantic_data_converter, + plugins=[plugin], + ) + + wf_id = f"langsmith-chatbot-{uuid.uuid4().hex[:8]}" + + @traceable( + name=f"Chatbot Session {wf_id[-8:]}", + run_type="chain", + # CRITICAL: Client-side @traceable runs outside the LangSmithPlugin's scope. + # Make sure client-side traces use the same project_name as what is given to + # the plugin. + project_name=PROJECT_NAME, + tags=["client-side", "chatbot"], + ) + async def run_session(): + wf_handle = await client.start_workflow( + ChatbotWorkflow.run, + id=wf_id, + task_queue="langsmith-chatbot-task-queue", + ) + print(f"Started workflow: {wf_handle.id}") + print('Type a message or "exit" to quit.\n') + + while True: + user_input = input("> ").strip() + if not user_input: + continue + + if user_input.lower() == "exit": + await wf_handle.signal(ChatbotWorkflow.exit) + result = await wf_handle.result() + print(f"\nWorkflow finished: {result}") + return + + response = await wf_handle.execute_update( + ChatbotWorkflow.message_from_user, user_input + ) + print(f"Bot: {response}\n") + + await run_session() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/langsmith_tracing/chatbot/worker.py b/langsmith_tracing/chatbot/worker.py new file mode 100644 index 000000000..b2bd2c981 --- /dev/null +++ b/langsmith_tracing/chatbot/worker.py @@ -0,0 +1,49 @@ +"""Worker for the chatbot LangSmith sample.""" + +import asyncio +import logging +import sys + +from temporalio.client import Client +from temporalio.contrib.langsmith import LangSmithPlugin +from temporalio.contrib.pydantic import pydantic_data_converter +from temporalio.envconfig import ClientConfig +from temporalio.worker import Worker + +from langsmith_tracing.chatbot.activities import call_openai +from langsmith_tracing.chatbot.workflows import ChatbotWorkflow + + +async def main(): + logging.basicConfig(level=logging.INFO) + + add_temporal_runs = "--add-temporal-runs" in sys.argv + + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + + plugin = LangSmithPlugin( + project_name="langsmith-chatbot", + add_temporal_runs=add_temporal_runs, + ) + + client = await Client.connect( + **config, + data_converter=pydantic_data_converter, + ) + + worker = Worker( + client, + task_queue="langsmith-chatbot-task-queue", + workflows=[ChatbotWorkflow], + activities=[call_openai], + plugins=[plugin], + ) + + label = "with" if add_temporal_runs else "without" + print(f"Worker started ({label} Temporal runs in traces), ctrl+c to exit") + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/langsmith_tracing/chatbot/workflows.py b/langsmith_tracing/chatbot/workflows.py new file mode 100644 index 000000000..47205611c --- /dev/null +++ b/langsmith_tracing/chatbot/workflows.py @@ -0,0 +1,176 @@ +"""Conversational chatbot workflow with tool loop and LangSmith tracing.""" + +import json +from datetime import timedelta + +from temporalio import workflow +from temporalio.common import RetryPolicy + +with workflow.unsafe.imports_passed_through(): + from langsmith import traceable + + from langsmith_tracing.chatbot.activities import OpenAIRequest, call_openai + +TOOLS = [ + { + "type": "function", + "name": "save_note", + "description": "Save or update a note.", + "parameters": { + "type": "object", + "properties": { + "name": {"type": "string", "description": "Name/key for the note."}, + "content": { + "type": "string", + "description": "Content of the note.", + }, + }, + "required": ["name", "content"], + }, + }, + { + "type": "function", + "name": "read_note", + "description": "Read a previously saved note.", + "parameters": { + "type": "object", + "properties": { + "name": { + "type": "string", + "description": "Name/key of the note to read.", + }, + }, + "required": ["name"], + }, + }, +] + + +@workflow.defn +class ChatbotWorkflow: + def __init__(self): + self._pending_message: str | None = None + self._last_response: str = "" + self._previous_response_id: str | None = None + self._notes: dict[str, str] = {} + self._done = False + + @workflow.signal + async def exit(self) -> None: + self._done = True + + @workflow.query + def notes(self) -> dict[str, str]: + return dict(self._notes) + + @workflow.update + async def message_from_user(self, message: str) -> str: + """Hand the message to the main loop and wait for its response.""" + + # Inner @traceable captures the message as input and response as output. + @traceable(name=f"Update: {message[:60]}", run_type="chain") + async def _traced(msg: str) -> str: + # Wait until any previous message has finished processing + await workflow.wait_condition(lambda: self._pending_message is None) + self._pending_message = msg + # Main loop sets _last_response, then clears _pending_message to signal done + await workflow.wait_condition(lambda: self._pending_message is None) + return self._last_response + + return await _traced(message) + + # Do not decorate @workflow.run with @traceable — it would violate + # replay safety and produce duplicate or orphaned traces. Instead, + # wrap an inner function, eg. self._run_inner() in this case. + @workflow.run + async def run(self) -> str: + # Alternative to @traceable decorator: call traceable() as a function + # for dynamic trace names that depend on runtime values. + now = workflow.now().strftime("%b %d %H:%M") + return await traceable( + name=f"Session {now}", + run_type="chain", + metadata={"workflow_id": workflow.info().workflow_id}, + tags=["chatbot-session"], + )(self._run_inner)() + + async def _run_inner(self) -> str: + while not self._done: + await workflow.wait_condition( + lambda: self._pending_message is not None or self._done + ) + if self._done: + break + assert self._pending_message is not None + message = self._pending_message + self._last_response = await self._query_openai(message) + # Clear pending_message AFTER setting the response so the update + # handler reads the correct response when its wait_condition fires. + self._pending_message = None + + return "Session ended." + + # Unlike @workflow.run, other workflow methods can be decorated with + # @traceable directly — they run inside the plugin-managed context + # where trace I/O is handled safely during replay. + @traceable(name="Save Note", run_type="tool") + def _save_note(self, name: str, content: str) -> str: + self._notes[name] = content + return f"Saved note '{name}'." + + @traceable(name="Read Note", run_type="tool") + def _read_note(self, name: str) -> str: + return self._notes.get(name, "Note not found.") + + async def _query_openai(self, message: str) -> str: + @traceable( + name=f"Request: {message[:60]}", + run_type="chain", + tags=["user-message"], + ) + async def _traced(): + input_for_next: str | list = message + while True: + response = await workflow.execute_activity( + call_openai, + OpenAIRequest( + model="gpt-4o-mini", + input=input_for_next, + tools=TOOLS, + previous_response_id=self._previous_response_id, + ), + start_to_close_timeout=timedelta(seconds=60), + retry_policy=RetryPolicy( + initial_interval=timedelta(seconds=2), maximum_attempts=3 + ), + ) + self._previous_response_id = response.id + + tool_results = [] + for tc in response.tool_calls: + args = json.loads(tc.arguments) + if tc.name == "save_note": + result = self._save_note(args["name"], args["content"]) + tool_results.append( + { + "type": "function_call_output", + "call_id": tc.call_id, + "output": result, + } + ) + elif tc.name == "read_note": + result = self._read_note(args["name"]) + tool_results.append( + { + "type": "function_call_output", + "call_id": tc.call_id, + "output": result, + } + ) + + if not tool_results: + return response.output_text or "Done." + + input_for_next = tool_results + + return await _traced() diff --git a/litellm_activity/README.md b/litellm_activity/README.md new file mode 100644 index 000000000..0ca20afb3 --- /dev/null +++ b/litellm_activity/README.md @@ -0,0 +1,51 @@ +# LiteLLM Activity + +This sample calls an LLM provider through [LiteLLM](https://docs.litellm.ai/) from a Temporal Activity. + +LLM calls perform network I/O and return nondeterministic results, so they must not run in Workflow code. The Workflow only schedules the Activity and records its result, keeping replay deterministic. + +## Prerequisites + +Follow the [repository prerequisites](../README.md), then install the sample's dependencies: + +```bash +uv sync --group litellm +``` + +Set the API key expected by your provider. This example uses OpenAI by default: + +```bash +export OPENAI_API_KEY="your-api-key" +``` + +To use another [LiteLLM-supported provider](https://docs.litellm.ai/docs/providers), set its credentials and model name. For example: + +```bash +export ANTHROPIC_API_KEY="your-api-key" +export LITELLM_MODEL="anthropic/claude-sonnet-4-5-20250929" +``` + +Provider credentials stay in the Worker environment; they are not passed through the Workflow or stored in Event History. + +## Run the sample + +Start a local Temporal server, then run these commands in separate terminals: + +```bash +# Terminal 1: run the Worker +uv run --group litellm litellm_activity/worker.py + +# Terminal 2: start a Workflow +uv run --group litellm litellm_activity/starter.py \ + "Why should LLM calls run in Temporal Activities?" +``` + +The Activity gives each provider call a 30-second client timeout. The Workflow gives each Activity attempt 45 seconds, limits the entire Activity execution to two minutes, and retries failures for up to three total attempts with exponential backoff. LiteLLM's own retries are disabled so Temporal records and controls every attempt. + +## Tests + +The tests replace the provider call and Activity with deterministic fakes, so they do not require an API key or make live LLM requests: + +```bash +uv run --group litellm pytest tests/litellm_activity +``` diff --git a/litellm_activity/__init__.py b/litellm_activity/__init__.py new file mode 100644 index 000000000..ed0fc3745 --- /dev/null +++ b/litellm_activity/__init__.py @@ -0,0 +1 @@ +"""Call LiteLLM from a Temporal Activity.""" diff --git a/litellm_activity/activities.py b/litellm_activity/activities.py new file mode 100644 index 000000000..424604f0e --- /dev/null +++ b/litellm_activity/activities.py @@ -0,0 +1,28 @@ +from typing import cast + +from litellm import ModelResponse, acompletion +from temporalio import activity + +from litellm_activity.shared import LLMRequest + + +@activity.defn +async def call_litellm(request: LLMRequest) -> str: + """Make the nondeterministic network call outside Workflow code.""" + response = await acompletion( + model=request.model, + messages=[ + {"role": "system", "content": request.system_prompt}, + {"role": "user", "content": request.prompt}, + ], + timeout=30, + # Let Temporal own retries so every attempt is visible in Event History. + num_retries=0, + ) + + if not isinstance(response, ModelResponse): + raise TypeError( + f"Expected a non-streaming LiteLLM response, got {type(response).__name__}" + ) + + return cast(str, response.choices[0].message.content) diff --git a/litellm_activity/shared.py b/litellm_activity/shared.py new file mode 100644 index 000000000..4c8144485 --- /dev/null +++ b/litellm_activity/shared.py @@ -0,0 +1,13 @@ +from dataclasses import dataclass + +DEFAULT_MODEL = "openai/gpt-4o-mini" +TASK_QUEUE = "litellm-activity-task-queue" + + +@dataclass +class LLMRequest: + """Serializable input shared by the client, Workflow, and Activity.""" + + prompt: str + model: str = DEFAULT_MODEL + system_prompt: str = "You are a helpful assistant." diff --git a/litellm_activity/starter.py b/litellm_activity/starter.py new file mode 100644 index 000000000..0f57754bc --- /dev/null +++ b/litellm_activity/starter.py @@ -0,0 +1,33 @@ +import asyncio +import os +import sys +import uuid + +from temporalio.client import Client +from temporalio.envconfig import ClientConfig + +from litellm_activity.shared import DEFAULT_MODEL, TASK_QUEUE, LLMRequest +from litellm_activity.workflow import LiteLLMWorkflow + + +async def main() -> None: + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) + + prompt = " ".join(sys.argv[1:]) or "Explain Temporal in one sentence." + request = LLMRequest( + prompt=prompt, + model=os.getenv("LITELLM_MODEL", DEFAULT_MODEL), + ) + result = await client.execute_workflow( + LiteLLMWorkflow.run, + request, + id=f"litellm-activity-{uuid.uuid4()}", + task_queue=TASK_QUEUE, + ) + print(result) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/litellm_activity/worker.py b/litellm_activity/worker.py new file mode 100644 index 000000000..946b22116 --- /dev/null +++ b/litellm_activity/worker.py @@ -0,0 +1,30 @@ +import asyncio +import logging + +from temporalio.client import Client +from temporalio.envconfig import ClientConfig +from temporalio.worker import Worker + +from litellm_activity.activities import call_litellm +from litellm_activity.shared import TASK_QUEUE +from litellm_activity.workflow import LiteLLMWorkflow + + +async def main() -> None: + logging.basicConfig(level=logging.INFO) + + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) + + worker = Worker( + client, + task_queue=TASK_QUEUE, + workflows=[LiteLLMWorkflow], + activities=[call_litellm], + ) + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/litellm_activity/workflow.py b/litellm_activity/workflow.py new file mode 100644 index 000000000..c91e6696f --- /dev/null +++ b/litellm_activity/workflow.py @@ -0,0 +1,26 @@ +from datetime import timedelta + +from temporalio import workflow +from temporalio.common import RetryPolicy + +from litellm_activity.shared import LLMRequest + +with workflow.unsafe.imports_passed_through(): + from litellm_activity.activities import call_litellm + + +@workflow.defn +class LiteLLMWorkflow: + @workflow.run + async def run(self, request: LLMRequest) -> str: + return await workflow.execute_activity( + call_litellm, + request, + start_to_close_timeout=timedelta(seconds=45), + schedule_to_close_timeout=timedelta(minutes=2), + retry_policy=RetryPolicy( + initial_interval=timedelta(seconds=1), + maximum_interval=timedelta(seconds=10), + maximum_attempts=3, + ), + ) diff --git a/message_passing/__init__.py b/message_passing/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/message_passing/introduction/README.md b/message_passing/introduction/README.md new file mode 100644 index 000000000..e4e15b1ca --- /dev/null +++ b/message_passing/introduction/README.md @@ -0,0 +1,18 @@ +# Introduction to message-passing + +This sample provides an introduction to using Query, Signal, and Update. + +See https://docs.temporal.io/develop/python/message-passing. + +To run, first see the main [README.md](../../README.md) for prerequisites. + +Then create two terminals. + +Run the worker in one terminal: + + uv run message_passing/introduction/worker.py + +And execute the workflow in the other terminal: + + uv run message_passing/introduction/starter.py + diff --git a/message_passing/introduction/__init__.py b/message_passing/introduction/__init__.py new file mode 100644 index 000000000..79d4a7b1c --- /dev/null +++ b/message_passing/introduction/__init__.py @@ -0,0 +1,13 @@ +from enum import IntEnum + +TASK_QUEUE = "message-passing-introduction-task-queue" + + +class Language(IntEnum): + ARABIC = 1 + CHINESE = 2 + ENGLISH = 3 + FRENCH = 4 + HINDI = 5 + PORTUGUESE = 6 + SPANISH = 7 diff --git a/message_passing/introduction/activities.py b/message_passing/introduction/activities.py new file mode 100644 index 000000000..7f2c9c560 --- /dev/null +++ b/message_passing/introduction/activities.py @@ -0,0 +1,25 @@ +import asyncio +from typing import Optional + +from temporalio import activity + +from message_passing.introduction import Language + + +@activity.defn +async def call_greeting_service(to_language: Language) -> Optional[str]: + """ + An Activity that simulates a call to a remote greeting service. + The remote greeting service supports the full range of languages. + """ + greetings = { + Language.ARABIC: "مرحبا بالعالم", + Language.CHINESE: "你好，世界", + Language.ENGLISH: "Hello, world", + Language.FRENCH: "Bonjour, monde", + Language.HINDI: "नमस्ते दुनिया", + Language.PORTUGUESE: "Olá mundo", + Language.SPANISH: "Hola mundo", + } + await asyncio.sleep(0.2) # Simulate a network call + return greetings.get(to_language) diff --git a/message_passing/introduction/starter.py b/message_passing/introduction/starter.py new file mode 100644 index 000000000..aa5b8967c --- /dev/null +++ b/message_passing/introduction/starter.py @@ -0,0 +1,58 @@ +import asyncio +from typing import Optional + +from temporalio.client import Client, WorkflowUpdateStage +from temporalio.envconfig import ClientConfig + +from message_passing.introduction import TASK_QUEUE +from message_passing.introduction.workflows import ( + ApproveInput, + GetLanguagesInput, + GreetingWorkflow, + Language, + SetLanguageInput, +) + + +async def main(client: Optional[Client] = None): + if not client: + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) + + wf_handle = await client.start_workflow( + GreetingWorkflow.run, + id="greeting-workflow-1234", + task_queue=TASK_QUEUE, + ) + + # 👉 Send a Query + supported_languages = await wf_handle.query( + GreetingWorkflow.get_languages, GetLanguagesInput(include_unsupported=False) + ) + print(f"supported languages: {supported_languages}") + + # 👉 Execute an Update + previous_language = await wf_handle.execute_update( + GreetingWorkflow.set_language, SetLanguageInput(language=Language.CHINESE) + ) + assert await wf_handle.query(GreetingWorkflow.get_language) == Language.CHINESE + print(f"language changed: {previous_language.name} -> {Language.CHINESE.name}") + + # 👉 Start an Update and then wait for it to complete + update_handle = await wf_handle.start_update( + GreetingWorkflow.set_language_using_activity, + SetLanguageInput(language=Language.ARABIC), + wait_for_stage=WorkflowUpdateStage.ACCEPTED, + ) + previous_language = await update_handle.result() + assert await wf_handle.query(GreetingWorkflow.get_language) == Language.ARABIC + print(f"language changed: {previous_language.name} -> {Language.ARABIC.name}") + + # 👉 Send a Signal + await wf_handle.signal(GreetingWorkflow.approve, ApproveInput(name="")) + print(await wf_handle.result()) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/message_passing/introduction/worker.py b/message_passing/introduction/worker.py new file mode 100644 index 000000000..349748015 --- /dev/null +++ b/message_passing/introduction/worker.py @@ -0,0 +1,39 @@ +import asyncio +import logging + +from temporalio.client import Client +from temporalio.envconfig import ClientConfig +from temporalio.worker import Worker + +from message_passing.introduction import TASK_QUEUE +from message_passing.introduction.activities import call_greeting_service +from message_passing.introduction.workflows import GreetingWorkflow + +interrupt_event = asyncio.Event() + + +async def main(): + logging.basicConfig(level=logging.INFO) + + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) + + async with Worker( + client, + task_queue=TASK_QUEUE, + workflows=[GreetingWorkflow], + activities=[call_greeting_service], + ): + logging.info("Worker started, ctrl+c to exit") + await interrupt_event.wait() + logging.info("Shutting down") + + +if __name__ == "__main__": + loop = asyncio.new_event_loop() + try: + loop.run_until_complete(main()) + except KeyboardInterrupt: + interrupt_event.set() + loop.run_until_complete(loop.shutdown_asyncgens()) diff --git a/message_passing/introduction/workflows.py b/message_passing/introduction/workflows.py new file mode 100644 index 000000000..8988d5813 --- /dev/null +++ b/message_passing/introduction/workflows.py @@ -0,0 +1,124 @@ +import asyncio +from dataclasses import dataclass +from datetime import timedelta +from typing import List, Optional + +from temporalio import workflow +from temporalio.exceptions import ApplicationError + +with workflow.unsafe.imports_passed_through(): + from message_passing.introduction import Language + from message_passing.introduction.activities import call_greeting_service + + +@dataclass +class GetLanguagesInput: + include_unsupported: bool + + +@dataclass +class SetLanguageInput: + language: Language + + +@dataclass +class ApproveInput: + name: str + + +@workflow.defn +class GreetingWorkflow: + """ + A workflow that that returns a greeting in one of multiple supported + languages. + + It exposes a query to obtain the current language, a signal to approve the + workflow so that it is allowed to return its result, and two updates for + changing the current language and receiving the previous language in + response. + + One of the update handlers is not an `async def`, so it can only mutate and + return local workflow state; the other update handler is `async def` and + executes an activity which calls a remote service, giving access to language + translations which are not available in local workflow state. + """ + + def __init__(self) -> None: + self.approved_for_release = False + self.approver_name: Optional[str] = None + self.greetings = { + Language.CHINESE: "你好，世界", + Language.ENGLISH: "Hello, world", + } + self.language = Language.ENGLISH + self.lock = asyncio.Lock() # used by the async handler below + + @workflow.run + async def run(self) -> str: + # 👉 In addition to waiting for the `approve` Signal, we also wait for + # all handlers to finish. Otherwise, the Workflow might return its + # result while an async set_language_using_activity Update is in + # progress. + await workflow.wait_condition( + lambda: self.approved_for_release and workflow.all_handlers_finished() + ) + return self.greetings[self.language] + + @workflow.query + def get_languages(self, input: GetLanguagesInput) -> List[Language]: + # 👉 A Query handler returns a value: it can inspect but must not mutate the Workflow state. + if input.include_unsupported: + return sorted(Language) + else: + return sorted(self.greetings) + + @workflow.signal + def approve(self, input: ApproveInput) -> None: + # 👉 A Signal handler mutates the Workflow state but cannot return a value. + self.approved_for_release = True + self.approver_name = input.name + + @workflow.update + def set_language(self, input: SetLanguageInput) -> Language: + # 👉 An Update handler can mutate the Workflow state and return a value. + previous_language, self.language = self.language, input.language + return previous_language + + @set_language.validator + def validate_language(self, input: SetLanguageInput) -> None: + if input.language not in self.greetings: + # 👉 In an Update validator you raise any exception to reject the Update. + raise ValueError(f"{input.language.name} is not supported") + + @workflow.update + async def set_language_using_activity(self, input: SetLanguageInput) -> Language: + # 👉 This update handler is async, so it can execute an activity. + if input.language not in self.greetings: + # 👉 We use a lock so that, if this handler is executed multiple + # times, each execution can schedule the activity only when the + # previously scheduled activity has completed. This ensures that + # multiple calls to set_language are processed in order. + async with self.lock: + greeting = await workflow.execute_activity( + call_greeting_service, + input.language, + start_to_close_timeout=timedelta(seconds=10), + ) + # 👉 The requested language might not be supported by the remote + # service. If so, we raise ApplicationError, which will fail the + # Update. The WorkflowExecutionUpdateAccepted event will still + # be added to history. (Update validators can be used to reject + # updates before any event is written to history, but they + # cannot be async, and so we cannot use an update validator for + # this purpose.) + if greeting is None: + raise ApplicationError( + f"Greeting service does not support {input.language.name}" + ) + self.greetings[input.language] = greeting + previous_language, self.language = self.language, input.language + return previous_language + + @workflow.query + def get_language(self) -> Language: + return self.language diff --git a/message_passing/safe_message_handlers/README.md b/message_passing/safe_message_handlers/README.md new file mode 100644 index 000000000..f8e4db8aa --- /dev/null +++ b/message_passing/safe_message_handlers/README.md @@ -0,0 +1,21 @@ +# Atomic message handlers + +This sample shows off important techniques for handling signals and updates, aka messages. In particular, it illustrates how message handlers can interleave or not be completed before the workflow completes, and how you can manage that. + +* Here, using workflow.wait_condition, signal and update handlers will only operate when the workflow is within a certain state--between cluster_started and cluster_shutdown. +* Message handlers can block and their actions can be interleaved with one another and with the main workflow. This can easily cause bugs, so you can use a lock to protect shared state from interleaved access. +* An "Entity" workflow, i.e. a long-lived workflow, periodically "continues as new". It must do this to prevent its history from growing too large, and it passes its state to the next workflow. You can check `workflow.info().is_continue_as_new_suggested()` to see when it's time. +* Most people want their message handlers to finish before the workflow run completes or continues as new. Use `await workflow.wait_condition(lambda: workflow.all_handlers_finished())` to achieve this. +* Message handlers can be made idempotent. See update `ClusterManager.assign_nodes_to_job`. + +To run, first see [README.md](../../README.md) for prerequisites. + +Then, run the following from the root directory to run the worker: + + uv run message_passing/safe_message_handlers/worker.py + +Then, in another terminal, run the following to execute the workflow: + + uv run message_passing/safe_message_handlers/starter.py + +This will start a worker to run your workflow and activities, then start a ClusterManagerWorkflow and put it through its paces. diff --git a/message_passing/safe_message_handlers/__init__.py b/message_passing/safe_message_handlers/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/message_passing/safe_message_handlers/activities.py b/message_passing/safe_message_handlers/activities.py new file mode 100644 index 000000000..da8a8be00 --- /dev/null +++ b/message_passing/safe_message_handlers/activities.py @@ -0,0 +1,55 @@ +import asyncio +from dataclasses import dataclass +from typing import List, Set + +from temporalio import activity + + +@dataclass +class AssignNodesToJobInput: + nodes: List[str] + job_name: str + + +@dataclass +class ClusterState: + node_ids: List[str] + + +@activity.defn +async def start_cluster() -> ClusterState: + return ClusterState(node_ids=[f"node-{i}" for i in range(25)]) + + +@activity.defn +async def assign_nodes_to_job(input: AssignNodesToJobInput) -> None: + print(f"Assigning nodes {input.nodes} to job {input.job_name}") + await asyncio.sleep(0.1) + + +@dataclass +class UnassignNodesForJobInput: + nodes: List[str] + job_name: str + + +@activity.defn +async def unassign_nodes_for_job(input: UnassignNodesForJobInput) -> None: + print(f"Deallocating nodes {input.nodes} from job {input.job_name}") + await asyncio.sleep(0.1) + + +@dataclass +class FindBadNodesInput: + nodes_to_check: Set[str] + + +@activity.defn +async def find_bad_nodes(input: FindBadNodesInput) -> Set[str]: + await asyncio.sleep(0.1) + bad_nodes = set([id for id in input.nodes_to_check if hash(id) % 5 == 0]) + if bad_nodes: + print(f"Found bad nodes: {bad_nodes}") + else: + print("No new bad nodes found.") + return bad_nodes diff --git a/message_passing/safe_message_handlers/starter.py b/message_passing/safe_message_handlers/starter.py new file mode 100644 index 000000000..c8f1b5d39 --- /dev/null +++ b/message_passing/safe_message_handlers/starter.py @@ -0,0 +1,89 @@ +import argparse +import asyncio +import logging +import uuid +from typing import Optional + +from temporalio import common +from temporalio.client import Client, WorkflowHandle +from temporalio.envconfig import ClientConfig + +from message_passing.safe_message_handlers.workflow import ( + ClusterManagerAssignNodesToJobInput, + ClusterManagerDeleteJobInput, + ClusterManagerInput, + ClusterManagerWorkflow, +) + + +async def do_cluster_lifecycle(wf: WorkflowHandle, delay_seconds: Optional[int] = None): + cluster_status = await wf.execute_update( + ClusterManagerWorkflow.wait_until_cluster_started + ) + print(f"Cluster started with {len(cluster_status.nodes)} nodes") + + print("Assigning jobs to nodes...") + allocation_updates = [] + for i in range(6): + allocation_updates.append( + wf.execute_update( + ClusterManagerWorkflow.assign_nodes_to_job, + ClusterManagerAssignNodesToJobInput( + total_num_nodes=2, job_name=f"task-{i}" + ), + ) + ) + await asyncio.gather(*allocation_updates) + + print(f"Sleeping for {delay_seconds} second(s)") + if delay_seconds: + await asyncio.sleep(delay_seconds) + + print("Deleting jobs...") + deletion_updates = [] + for i in range(6): + deletion_updates.append( + wf.execute_update( + ClusterManagerWorkflow.delete_job, + ClusterManagerDeleteJobInput(job_name=f"task-{i}"), + ) + ) + await asyncio.gather(*deletion_updates) + + await wf.signal(ClusterManagerWorkflow.shutdown_cluster) + + +async def main(should_test_continue_as_new: bool): + # Connect to Temporal + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) + + print("Starting cluster") + cluster_manager_handle = await client.start_workflow( + ClusterManagerWorkflow.run, + ClusterManagerInput(test_continue_as_new=should_test_continue_as_new), + id=f"ClusterManagerWorkflow-{uuid.uuid4()}", + task_queue="safe-message-handlers-task-queue", + id_conflict_policy=common.WorkflowIDConflictPolicy.TERMINATE_EXISTING, + ) + delay_seconds = 10 if should_test_continue_as_new else 1 + await do_cluster_lifecycle(cluster_manager_handle, delay_seconds=delay_seconds) + result = await cluster_manager_handle.result() + print( + f"Cluster shut down successfully." + f" It had {result.num_currently_assigned_nodes} nodes assigned at the end." + ) + + +if __name__ == "__main__": + logging.basicConfig(level=logging.INFO) + parser = argparse.ArgumentParser(description="Atomic message handlers") + parser.add_argument( + "--test-continue-as-new", + help="Make the ClusterManagerWorkflow continue as new before shutting down", + action="store_true", + default=False, + ) + args = parser.parse_args() + asyncio.run(main(args.test_continue_as_new)) diff --git a/message_passing/safe_message_handlers/worker.py b/message_passing/safe_message_handlers/worker.py new file mode 100644 index 000000000..31e538d4a --- /dev/null +++ b/message_passing/safe_message_handlers/worker.py @@ -0,0 +1,47 @@ +import asyncio +import logging + +from temporalio.client import Client +from temporalio.envconfig import ClientConfig +from temporalio.worker import Worker + +from message_passing.safe_message_handlers.workflow import ( + ClusterManagerWorkflow, + assign_nodes_to_job, + find_bad_nodes, + start_cluster, + unassign_nodes_for_job, +) + +interrupt_event = asyncio.Event() + + +async def main(): + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) + + async with Worker( + client, + task_queue="safe-message-handlers-task-queue", + workflows=[ClusterManagerWorkflow], + activities=[ + assign_nodes_to_job, + unassign_nodes_for_job, + find_bad_nodes, + start_cluster, + ], + ): + logging.info("ClusterManagerWorkflow worker started, ctrl+c to exit") + await interrupt_event.wait() + logging.info("Shutting down") + + +if __name__ == "__main__": + logging.basicConfig(level=logging.INFO) + loop = asyncio.new_event_loop() + try: + loop.run_until_complete(main()) + except KeyboardInterrupt: + interrupt_event.set() + loop.run_until_complete(loop.shutdown_asyncgens()) diff --git a/message_passing/safe_message_handlers/workflow.py b/message_passing/safe_message_handlers/workflow.py new file mode 100644 index 000000000..ca549a61b --- /dev/null +++ b/message_passing/safe_message_handlers/workflow.py @@ -0,0 +1,265 @@ +import asyncio +import dataclasses +from dataclasses import dataclass +from datetime import timedelta +from typing import Dict, List, Optional, Set + +from temporalio import workflow +from temporalio.common import RetryPolicy +from temporalio.exceptions import ApplicationError + +from message_passing.safe_message_handlers.activities import ( + AssignNodesToJobInput, + FindBadNodesInput, + UnassignNodesForJobInput, + assign_nodes_to_job, + find_bad_nodes, + start_cluster, + unassign_nodes_for_job, +) + + +# In workflows that continue-as-new, it's convenient to store all your state in one serializable structure +# to make it easier to pass between runs +@dataclass +class ClusterManagerState: + cluster_started: bool = False + cluster_shutdown: bool = False + nodes: Dict[str, Optional[str]] = dataclasses.field(default_factory=dict) + jobs_assigned: Set[str] = dataclasses.field(default_factory=set) + + +@dataclass +class ClusterManagerInput: + state: Optional[ClusterManagerState] = None + test_continue_as_new: bool = False + + +@dataclass +class ClusterManagerResult: + num_currently_assigned_nodes: int + num_bad_nodes: int + + +# Be in the habit of storing message inputs and outputs in serializable structures. +# This makes it easier to add more over time in a backward-compatible way. +@dataclass +class ClusterManagerAssignNodesToJobInput: + # If larger or smaller than previous amounts, will resize the job. + total_num_nodes: int + job_name: str + + +@dataclass +class ClusterManagerDeleteJobInput: + job_name: str + + +@dataclass +class ClusterManagerAssignNodesToJobResult: + nodes_assigned: Set[str] + + +# ClusterManagerWorkflow keeps track of the assignments of a cluster of nodes. +# Via signals, the cluster can be started and shutdown. +# Via updates, clients can also assign jobs to nodes and delete jobs. +# These updates must run atomically. +@workflow.defn +class ClusterManagerWorkflow: + @workflow.init + def __init__(self, input: ClusterManagerInput) -> None: + if input.state: + self.state = input.state + else: + self.state = ClusterManagerState() + + if input.test_continue_as_new: + self.max_history_length: Optional[int] = 120 + self.sleep_interval_seconds = 1 + else: + self.max_history_length = None + self.sleep_interval_seconds = 600 + + # Protects workflow state from interleaved access + self.nodes_lock = asyncio.Lock() + + @workflow.update + async def wait_until_cluster_started(self) -> ClusterManagerState: + await workflow.wait_condition(lambda: self.state.cluster_started) + return self.state + + @workflow.signal + async def shutdown_cluster(self) -> None: + await workflow.wait_condition(lambda: self.state.cluster_started) + self.state.cluster_shutdown = True + workflow.logger.info("Cluster shut down") + + # This is an update as opposed to a signal because the client may want to wait for nodes to be allocated + # before sending work to those nodes. + # Returns the list of node names that were allocated to the job. + @workflow.update + async def assign_nodes_to_job( + self, input: ClusterManagerAssignNodesToJobInput + ) -> ClusterManagerAssignNodesToJobResult: + await workflow.wait_condition(lambda: self.state.cluster_started) + if self.state.cluster_shutdown: + # If you want the client to receive a failure, either add an update validator and throw the + # exception from there, or raise an ApplicationError. Other exceptions in the main handler + # will cause the workflow to keep retrying and get it stuck. + raise ApplicationError( + "Cannot assign nodes to a job: Cluster is already shut down" + ) + + async with self.nodes_lock: + # Idempotency guard. + if input.job_name in self.state.jobs_assigned: + return ClusterManagerAssignNodesToJobResult( + self.get_assigned_nodes(job_name=input.job_name) + ) + unassigned_nodes = self.get_unassigned_nodes() + if len(unassigned_nodes) < input.total_num_nodes: + # If you want the client to receive a failure, either add an update validator and throw the + # exception from there, or raise an ApplicationError. Other exceptions in the main handler + # will cause the workflow to keep retrying and get it stuck. + raise ApplicationError( + f"Cannot assign {input.total_num_nodes} nodes; have only {len(unassigned_nodes)} available" + ) + nodes_to_assign = unassigned_nodes[: input.total_num_nodes] + # This await would be dangerous without nodes_lock because it yields control and allows interleaving + # with delete_job and perform_health_checks, which both touch self.state.nodes. + await self._assign_nodes_to_job(nodes_to_assign, input.job_name) + return ClusterManagerAssignNodesToJobResult( + nodes_assigned=self.get_assigned_nodes(job_name=input.job_name) + ) + + async def _assign_nodes_to_job( + self, assigned_nodes: List[str], job_name: str + ) -> None: + await workflow.execute_activity( + assign_nodes_to_job, + AssignNodesToJobInput(nodes=assigned_nodes, job_name=job_name), + start_to_close_timeout=timedelta(seconds=10), + ) + for node in assigned_nodes: + self.state.nodes[node] = job_name + self.state.jobs_assigned.add(job_name) + + # Even though it returns nothing, this is an update because the client may want to track it, for example + # to wait for nodes to be unassigned before reassigning them. + @workflow.update + async def delete_job(self, input: ClusterManagerDeleteJobInput) -> None: + await workflow.wait_condition(lambda: self.state.cluster_started) + if self.state.cluster_shutdown: + # If you want the client to receive a failure, either add an update validator and throw the + # exception from there, or raise an ApplicationError. Other exceptions in the main handler + # will cause the workflow to keep retrying and get it stuck. + raise ApplicationError("Cannot delete a job: Cluster is already shut down") + + async with self.nodes_lock: + nodes_to_unassign = [ + k for k, v in self.state.nodes.items() if v == input.job_name + ] + # This await would be dangerous without nodes_lock because it yields control and allows interleaving + # with assign_nodes_to_job and perform_health_checks, which all touch self.state.nodes. + await self._unassign_nodes_for_job(nodes_to_unassign, input.job_name) + + async def _unassign_nodes_for_job( + self, nodes_to_unassign: List[str], job_name: str + ): + await workflow.execute_activity( + unassign_nodes_for_job, + UnassignNodesForJobInput(nodes=nodes_to_unassign, job_name=job_name), + start_to_close_timeout=timedelta(seconds=10), + ) + for node in nodes_to_unassign: + self.state.nodes[node] = None + + def get_unassigned_nodes(self) -> List[str]: + return [k for k, v in self.state.nodes.items() if v is None] + + def get_bad_nodes(self) -> Set[str]: + return set([k for k, v in self.state.nodes.items() if v == "BAD!"]) + + def get_assigned_nodes(self, *, job_name: Optional[str] = None) -> Set[str]: + if job_name: + return set([k for k, v in self.state.nodes.items() if v == job_name]) + else: + return set( + [ + k + for k, v in self.state.nodes.items() + if v is not None and v != "BAD!" + ] + ) + + async def perform_health_checks(self) -> None: + async with self.nodes_lock: + assigned_nodes = self.get_assigned_nodes() + try: + # This await would be dangerous without nodes_lock because it yields control and allows interleaving + # with assign_nodes_to_job and delete_job, which both touch self.state.nodes. + bad_nodes = await workflow.execute_activity( + find_bad_nodes, + FindBadNodesInput(nodes_to_check=assigned_nodes), + start_to_close_timeout=timedelta(seconds=10), + # This health check is optional, and our lock would block the whole workflow if we let it retry forever. + retry_policy=RetryPolicy(maximum_attempts=1), + ) + for node in bad_nodes: + self.state.nodes[node] = "BAD!" + except Exception as e: + workflow.logger.warn( + f"Health check failed with error {type(e).__name__}:{e}" + ) + + @workflow.run + async def run(self, input: ClusterManagerInput) -> ClusterManagerResult: + cluster_state = await workflow.execute_activity( + start_cluster, schedule_to_close_timeout=timedelta(seconds=10) + ) + self.state.nodes = {k: None for k in cluster_state.node_ids} + self.state.cluster_started = True + workflow.logger.info("Cluster started") + + # Perform health checks at intervals. + while True: + await self.perform_health_checks() + try: + await workflow.wait_condition( + lambda: self.state.cluster_shutdown + or self.should_continue_as_new(), + timeout=timedelta(seconds=self.sleep_interval_seconds), + ) + except asyncio.TimeoutError: + pass + if self.state.cluster_shutdown: + break + # The cluster manager is a long-running "entity" workflow so we need to periodically checkpoint its state and + # continue-as-new. + if self.should_continue_as_new(): + # We don't want to leave any job assignment or deletion handlers half-finished when we continue as new. + await workflow.wait_condition(lambda: workflow.all_handlers_finished()) + workflow.logger.info("Continuing as new") + workflow.continue_as_new( + ClusterManagerInput( + state=self.state, + test_continue_as_new=input.test_continue_as_new, + ) + ) + # Make sure we finish off handlers such as deleting jobs before we complete the workflow. + await workflow.wait_condition(lambda: workflow.all_handlers_finished()) + return ClusterManagerResult( + len(self.get_assigned_nodes()), + len(self.get_bad_nodes()), + ) + + def should_continue_as_new(self) -> bool: + if workflow.info().is_continue_as_new_suggested(): + return True + # This is just for ease-of-testing. In production, we trust temporal to tell us when to continue as new. + if ( + self.max_history_length + and workflow.info().get_current_history_length() > self.max_history_length + ): + return True + return False diff --git a/message_passing/update_with_start/lazy_initialization/README.md b/message_passing/update_with_start/lazy_initialization/README.md new file mode 100644 index 000000000..46f4e8130 --- /dev/null +++ b/message_passing/update_with_start/lazy_initialization/README.md @@ -0,0 +1,18 @@ +# Update With Start: Lazy init + +This sample illustrates the use of update-with-start to send Updates to a Workflow, starting the Workflow if +it is not running yet ("lazy init"). The Workflow represents a Shopping Cart in an e-commerce application, and +update-with-start is used to add items to the cart, receiving back the updated cart subtotal. + +To run, first see the main [README.md](../../../README.md) for prerequisites. + +Then run the following from the root directory: + + uv run message_passing/update_with_start/lazy_initialization/worker.py + +Then, in another terminal: + + uv run message_passing/update_with_start/lazy_initialization/starter.py + +This will start a worker to run your workflow and activities, then simulate a backend application receiving +requests to add items to a shopping cart, before finalizing the order. diff --git a/message_passing/update_with_start/lazy_initialization/__init__.py b/message_passing/update_with_start/lazy_initialization/__init__.py new file mode 100644 index 000000000..ac3632ee4 --- /dev/null +++ b/message_passing/update_with_start/lazy_initialization/__init__.py @@ -0,0 +1 @@ +TASK_QUEUE = "update-with-start-lazy-initialization" diff --git a/message_passing/update_with_start/lazy_initialization/activities.py b/message_passing/update_with_start/lazy_initialization/activities.py new file mode 100644 index 000000000..fa730e477 --- /dev/null +++ b/message_passing/update_with_start/lazy_initialization/activities.py @@ -0,0 +1,20 @@ +import asyncio +from dataclasses import dataclass +from typing import Optional + +from temporalio import activity + + +@dataclass +class ShoppingCartItem: + sku: str + quantity: int + + +@activity.defn +async def get_price(item: ShoppingCartItem) -> Optional[int]: + await asyncio.sleep(0.1) + price = None if item.sku == "sku-456" else 599 + if price is None: + return None + return price * item.quantity diff --git a/message_passing/update_with_start/lazy_initialization/starter.py b/message_passing/update_with_start/lazy_initialization/starter.py new file mode 100644 index 000000000..ec8749393 --- /dev/null +++ b/message_passing/update_with_start/lazy_initialization/starter.py @@ -0,0 +1,81 @@ +import asyncio +import uuid +from typing import Optional, Tuple + +from temporalio import common +from temporalio.client import ( + Client, + WithStartWorkflowOperation, + WorkflowHandle, + WorkflowUpdateFailedError, +) +from temporalio.envconfig import ClientConfig +from temporalio.exceptions import ApplicationError + +from message_passing.update_with_start.lazy_initialization import TASK_QUEUE +from message_passing.update_with_start.lazy_initialization.workflows import ( + ShoppingCartItem, + ShoppingCartWorkflow, +) + + +async def handle_add_item_request( + session_id: str, item_id: str, quantity: int, temporal_client: Client +) -> Tuple[Optional[int], WorkflowHandle]: + """ + Handle a client request to add an item to the shopping cart. The user is not logged in, but a session ID is + available from a cookie, and we use this as the cart ID. The Temporal client was created at service-start + time and is shared by all request handlers. + + A Workflow Type exists that can be used to represent a shopping cart. The method uses update-with-start to + add an item to the shopping cart, creating the cart if it doesn't already exist. + + Note that the workflow handle is available, even if the Update fails. + """ + cart_id = f"cart-{session_id}" + start_op = WithStartWorkflowOperation( + ShoppingCartWorkflow.run, + id=cart_id, + id_conflict_policy=common.WorkflowIDConflictPolicy.USE_EXISTING, + task_queue=TASK_QUEUE, + ) + try: + price = await temporal_client.execute_update_with_start_workflow( + ShoppingCartWorkflow.add_item, + ShoppingCartItem(sku=item_id, quantity=quantity), + start_workflow_operation=start_op, + ) + except WorkflowUpdateFailedError as err: + if ( + isinstance(err.cause, ApplicationError) + and err.cause.type == "ItemUnavailableError" + ): + price = None + else: + raise err + + workflow_handle = await start_op.workflow_handle() + + return price, workflow_handle + + +async def main(): + print("🛒") + session_id = f"session-{uuid.uuid4()}" + + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) + + subtotal_1, _ = await handle_add_item_request(session_id, "sku-123", 1, client) + subtotal_2, wf_handle = await handle_add_item_request( + session_id, "sku-456", 1, client + ) + print(f"subtotals were, {[subtotal_1, subtotal_2]}") + await wf_handle.signal(ShoppingCartWorkflow.checkout) + final_order = await wf_handle.result() + print(f"final order: {final_order}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/message_passing/update_with_start/lazy_initialization/worker.py b/message_passing/update_with_start/lazy_initialization/worker.py new file mode 100644 index 000000000..1cc4f6ff4 --- /dev/null +++ b/message_passing/update_with_start/lazy_initialization/worker.py @@ -0,0 +1,38 @@ +import asyncio +import logging + +from temporalio.client import Client +from temporalio.envconfig import ClientConfig +from temporalio.worker import Worker + +from message_passing.update_with_start.lazy_initialization import TASK_QUEUE, workflows +from message_passing.update_with_start.lazy_initialization.activities import get_price + +interrupt_event = asyncio.Event() + + +async def main(): + logging.basicConfig(level=logging.INFO) + + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) + + async with Worker( + client, + task_queue=TASK_QUEUE, + workflows=[workflows.ShoppingCartWorkflow], + activities=[get_price], + ): + logging.info("Worker started, ctrl+c to exit") + await interrupt_event.wait() + logging.info("Shutting down") + + +if __name__ == "__main__": + loop = asyncio.new_event_loop() + try: + loop.run_until_complete(main()) + except KeyboardInterrupt: + interrupt_event.set() + loop.run_until_complete(loop.shutdown_asyncgens()) diff --git a/message_passing/update_with_start/lazy_initialization/workflows.py b/message_passing/update_with_start/lazy_initialization/workflows.py new file mode 100644 index 000000000..49964ff2b --- /dev/null +++ b/message_passing/update_with_start/lazy_initialization/workflows.py @@ -0,0 +1,60 @@ +from dataclasses import dataclass +from datetime import timedelta +from typing import List, Tuple + +from temporalio import workflow +from temporalio.exceptions import ApplicationError + +with workflow.unsafe.imports_passed_through(): + from message_passing.update_with_start.lazy_initialization.activities import ( + ShoppingCartItem, + get_price, + ) + + +@dataclass +class FinalizedOrder: + id: str + items: List[Tuple[ShoppingCartItem, int]] + total: int + + +@workflow.defn +class ShoppingCartWorkflow: + def __init__(self): + self.items: List[Tuple[ShoppingCartItem, int]] = [] + self.order_submitted = False + + @workflow.run + async def run(self) -> FinalizedOrder: + await workflow.wait_condition( + lambda: workflow.all_handlers_finished() and self.order_submitted + ) + return FinalizedOrder( + id=workflow.info().workflow_id, + items=self.items, + total=sum(price for _, price in self.items), + ) + + @workflow.update + async def add_item(self, item: ShoppingCartItem) -> int: + price = await workflow.execute_activity( + get_price, item, start_to_close_timeout=timedelta(seconds=10) + ) + if price is None: + raise ApplicationError( + f"Item unavailable: {item}", + type="ItemUnavailableError", + ) + self.items.append((item, price)) + + return sum(price for _, price in self.items) + + @add_item.validator + def validate_add_item(self, item: ShoppingCartItem) -> None: + if self.order_submitted: + raise ApplicationError("Order already submitted") + + @workflow.signal + def checkout(self): + self.order_submitted = True diff --git a/message_passing/waiting_for_handlers/README.md b/message_passing/waiting_for_handlers/README.md new file mode 100644 index 000000000..9c9e3f7c6 --- /dev/null +++ b/message_passing/waiting_for_handlers/README.md @@ -0,0 +1,41 @@ +# Waiting for message handlers + +This workflow demonstrates how to wait for signal and update handlers to +finish in the following circumstances: + +- Before a successful return +- On failure +- On cancellation + +Your workflow can also exit via Continue-As-New. In that case you would +usually wait for the handlers to finish immediately before the call to +continue_as_new(); that's not illustrated in this sample. + + +To run, open two terminals. + +Run the worker in one terminal: + + uv run message_passing/waiting_for_handlers/worker.py + +And run the workflow-starter code in the other terminal: + + uv run message_passing/waiting_for_handlers/starter.py + + +Here's the output you'll see: + +``` +workflow exit type: SUCCESS + 🟢 caller received update result + 🟢 caller received workflow result + + +workflow exit type: FAILURE + 🟢 caller received update result + 🔴 caught exception while waiting for workflow result: Workflow execution failed: deliberately failing workflow + + +workflow exit type: CANCELLATION + 🟢 caller received update result +``` \ No newline at end of file diff --git a/message_passing/waiting_for_handlers/__init__.py b/message_passing/waiting_for_handlers/__init__.py new file mode 100644 index 000000000..1274f9bf6 --- /dev/null +++ b/message_passing/waiting_for_handlers/__init__.py @@ -0,0 +1,21 @@ +from dataclasses import dataclass +from enum import IntEnum + +TASK_QUEUE = "my-task-queue" +WORKFLOW_ID = "my-workflow-id" + + +class WorkflowExitType(IntEnum): + SUCCESS = 0 + FAILURE = 1 + CANCELLATION = 2 + + +@dataclass +class WorkflowInput: + exit_type: WorkflowExitType + + +@dataclass +class WorkflowResult: + data: str diff --git a/message_passing/waiting_for_handlers/activities.py b/message_passing/waiting_for_handlers/activities.py new file mode 100644 index 000000000..610db849b --- /dev/null +++ b/message_passing/waiting_for_handlers/activities.py @@ -0,0 +1,8 @@ +import asyncio + +from temporalio import activity + + +@activity.defn +async def activity_executed_by_update_handler(): + await asyncio.sleep(1) diff --git a/message_passing/waiting_for_handlers/starter.py b/message_passing/waiting_for_handlers/starter.py new file mode 100644 index 000000000..9d3fbc0cd --- /dev/null +++ b/message_passing/waiting_for_handlers/starter.py @@ -0,0 +1,75 @@ +import asyncio + +from temporalio import client, common +from temporalio.envconfig import ClientConfig + +from message_passing.waiting_for_handlers import ( + TASK_QUEUE, + WORKFLOW_ID, + WorkflowExitType, + WorkflowInput, +) +from message_passing.waiting_for_handlers.workflows import WaitingForHandlersWorkflow + + +async def starter(exit_type: WorkflowExitType): + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + cl = await client.Client.connect(**config) + + wf_handle = await cl.start_workflow( + WaitingForHandlersWorkflow.run, + WorkflowInput(exit_type=exit_type), + id=WORKFLOW_ID, + task_queue=TASK_QUEUE, + id_conflict_policy=common.WorkflowIDConflictPolicy.TERMINATE_EXISTING, + ) + await _check_run(wf_handle, exit_type) + + +async def _check_run( + wf_handle: client.WorkflowHandle, + exit_type: WorkflowExitType, +): + try: + up_handle = await wf_handle.start_update( + WaitingForHandlersWorkflow.my_update, + wait_for_stage=client.WorkflowUpdateStage.ACCEPTED, + ) + except Exception as e: + print( + f" 🔴 caught exception while starting update: {e}: {e.__cause__ or ''}" + ) + + if exit_type == WorkflowExitType.CANCELLATION: + await wf_handle.cancel() + + try: + await up_handle.result() + print(" 🟢 caller received update result") + except Exception as e: + print( + f" 🔴 caught exception while waiting for update result: {e}: {e.__cause__ or ''}" + ) + + try: + await wf_handle.result() + print(" 🟢 caller received workflow result") + except BaseException as e: + print( + f" 🔴 caught exception while waiting for workflow result: {e}: {e.__cause__ or ''}" + ) + + +async def main(): + for exit_type in [ + WorkflowExitType.SUCCESS, + WorkflowExitType.FAILURE, + WorkflowExitType.CANCELLATION, + ]: + print(f"\n\nworkflow exit type: {exit_type.name}") + await starter(exit_type) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/message_passing/waiting_for_handlers/worker.py b/message_passing/waiting_for_handlers/worker.py new file mode 100644 index 000000000..e32a2dcb0 --- /dev/null +++ b/message_passing/waiting_for_handlers/worker.py @@ -0,0 +1,43 @@ +import asyncio +import logging + +from temporalio.client import Client +from temporalio.envconfig import ClientConfig +from temporalio.worker import Worker + +from message_passing.waiting_for_handlers import TASK_QUEUE +from message_passing.waiting_for_handlers.activities import ( + activity_executed_by_update_handler, +) +from message_passing.waiting_for_handlers.workflows import WaitingForHandlersWorkflow + +interrupt_event = asyncio.Event() + + +async def main(): + logging.basicConfig(level=logging.INFO) + + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) + + async with Worker( + client, + task_queue=TASK_QUEUE, + workflows=[WaitingForHandlersWorkflow], + activities=[ + activity_executed_by_update_handler, + ], + ): + logging.info("Worker started, ctrl+c to exit") + await interrupt_event.wait() + logging.info("Shutting down") + + +if __name__ == "__main__": + loop = asyncio.new_event_loop() + try: + loop.run_until_complete(main()) + except KeyboardInterrupt: + interrupt_event.set() + loop.run_until_complete(loop.shutdown_asyncgens()) diff --git a/message_passing/waiting_for_handlers/workflows.py b/message_passing/waiting_for_handlers/workflows.py new file mode 100644 index 000000000..b4ed2991c --- /dev/null +++ b/message_passing/waiting_for_handlers/workflows.py @@ -0,0 +1,95 @@ +import asyncio +from datetime import timedelta + +from temporalio import exceptions, workflow + +from message_passing.waiting_for_handlers import ( + WorkflowExitType, + WorkflowInput, + WorkflowResult, +) +from message_passing.waiting_for_handlers.activities import ( + activity_executed_by_update_handler, +) + + +def is_workflow_exit_exception(e: BaseException) -> bool: + """ + True if the exception is of a type that will cause the workflow to exit. + + This is as opposed to exceptions that cause a workflow task failure, which + are retried automatically by Temporal. + """ + # 👉 If you have set additional failure_exception_types you should also + # check for these here. + return isinstance(e, (asyncio.CancelledError, exceptions.FailureError)) + + +@workflow.defn +class WaitingForHandlersWorkflow: + @workflow.run + async def run(self, input: WorkflowInput) -> WorkflowResult: + """ + This workflow.run method demonstrates a pattern that can be used to wait for signal and + update handlers to finish in the following circumstances: + + - On successful workflow return + - On workflow cancellation + - On workflow failure + + Your workflow can also exit via Continue-As-New. In that case you would usually wait for + the handlers to finish immediately before the call to continue_as_new(); that's not + illustrated in this sample. + + If you additionally need to perform cleanup or compensation on workflow failure or + cancellation, see the message_passing/waiting_for_handlers_and_compensation sample. + """ + try: + # 👉 Use this `try...except` style, instead of waiting for message + # handlers to finish in a `finally` block. The reason is that some + # exception types cause a workflow task failure as opposed to + # workflow exit, in which case we do *not* want to wait for message + # handlers to finish. + result = await self._my_workflow_application_logic(input) + await workflow.wait_condition(workflow.all_handlers_finished) + return result + # 👉 Catch BaseException since asyncio.CancelledError does not inherit + # from Exception. + except BaseException as e: + if is_workflow_exit_exception(e): + await workflow.wait_condition(workflow.all_handlers_finished) + raise + + # Methods below this point can be ignored unless you are interested in + # the implementation details of this sample. + + def __init__(self) -> None: + self._update_started = False + + @workflow.update + async def my_update(self) -> str: + self._update_started = True + await workflow.execute_activity( + activity_executed_by_update_handler, + start_to_close_timeout=timedelta(seconds=10), + ) + return "update-result" + + async def _my_workflow_application_logic( + self, input: WorkflowInput + ) -> WorkflowResult: + # The main workflow logic is implemented in a separate method in order + # to separate "platform-level" concerns (waiting for handlers to finish + # and error handling) from application logic. + + # Wait until handlers have started, so that we are demonstrating that we + # wait for them to finish. + await workflow.wait_condition(lambda: self._update_started) + if input.exit_type == WorkflowExitType.SUCCESS: + return WorkflowResult(data="workflow-result") + elif input.exit_type == WorkflowExitType.FAILURE: + raise exceptions.ApplicationError("deliberately failing workflow") + elif input.exit_type == WorkflowExitType.CANCELLATION: + # Block forever; the starter will send a workflow cancellation request. + await asyncio.Future() + raise AssertionError("unreachable") diff --git a/message_passing/waiting_for_handlers_and_compensation/README.md b/message_passing/waiting_for_handlers_and_compensation/README.md new file mode 100644 index 000000000..d23d484ed --- /dev/null +++ b/message_passing/waiting_for_handlers_and_compensation/README.md @@ -0,0 +1,39 @@ +# Waiting for message handlers, and performing compensation and cleanup in message handlers + +This sample demonstrates how to do the following: + +1. Ensure that all update/signal handlers are finished before a successful + workflow return, and on workflow cancellation and failure. +2. Perform compensation/cleanup in an update handler when the workflow is + cancelled or fails. + +For a simpler sample showing how to do (1) without (2), see [safe_message_handlers](../safe_message_handlers/README.md). + +To run, open two terminals. + +Run the worker in one terminal: + + uv run message_passing/waiting_for_handlers_and_compensation/worker.py + +And run the workflow-starter code in the other terminal: + + uv run message_passing/waiting_for_handlers_and_compensation/starter.py + + +Here's the output you'll see: + +``` +workflow exit type: SUCCESS + 🟢 caller received update result + 🟢 caller received workflow result + + +workflow exit type: FAILURE + 🔴 caught exception while waiting for update result: Workflow update failed: The update failed because the workflow run exited + 🔴 caught exception while waiting for workflow result: Workflow execution failed: deliberately failing workflow + + +workflow exit type: CANCELLATION + 🔴 caught exception while waiting for update result: Workflow update failed: The update failed because the workflow run exited + 🔴 caught exception while waiting for workflow result: Workflow execution failed: Workflow cancelled +``` \ No newline at end of file diff --git a/message_passing/waiting_for_handlers_and_compensation/__init__.py b/message_passing/waiting_for_handlers_and_compensation/__init__.py new file mode 100644 index 000000000..1274f9bf6 --- /dev/null +++ b/message_passing/waiting_for_handlers_and_compensation/__init__.py @@ -0,0 +1,21 @@ +from dataclasses import dataclass +from enum import IntEnum + +TASK_QUEUE = "my-task-queue" +WORKFLOW_ID = "my-workflow-id" + + +class WorkflowExitType(IntEnum): + SUCCESS = 0 + FAILURE = 1 + CANCELLATION = 2 + + +@dataclass +class WorkflowInput: + exit_type: WorkflowExitType + + +@dataclass +class WorkflowResult: + data: str diff --git a/message_passing/waiting_for_handlers_and_compensation/activities.py b/message_passing/waiting_for_handlers_and_compensation/activities.py new file mode 100644 index 000000000..36c5a5cbb --- /dev/null +++ b/message_passing/waiting_for_handlers_and_compensation/activities.py @@ -0,0 +1,18 @@ +import asyncio + +from temporalio import activity + + +@activity.defn +async def activity_executed_to_perform_workflow_compensation(): + await asyncio.sleep(1) + + +@activity.defn +async def activity_executed_by_update_handler(): + await asyncio.sleep(1) + + +@activity.defn +async def activity_executed_by_update_handler_to_perform_compensation(): + await asyncio.sleep(1) diff --git a/message_passing/waiting_for_handlers_and_compensation/starter.py b/message_passing/waiting_for_handlers_and_compensation/starter.py new file mode 100644 index 000000000..6f25c9a59 --- /dev/null +++ b/message_passing/waiting_for_handlers_and_compensation/starter.py @@ -0,0 +1,77 @@ +import asyncio + +from temporalio import client, common +from temporalio.envconfig import ClientConfig + +from message_passing.waiting_for_handlers_and_compensation import ( + TASK_QUEUE, + WORKFLOW_ID, + WorkflowExitType, + WorkflowInput, +) +from message_passing.waiting_for_handlers_and_compensation.workflows import ( + WaitingForHandlersAndCompensationWorkflow, +) + + +async def starter(exit_type: WorkflowExitType): + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + cl = await client.Client.connect(**config) + + wf_handle = await cl.start_workflow( + WaitingForHandlersAndCompensationWorkflow.run, + WorkflowInput(exit_type=exit_type), + id=WORKFLOW_ID, + task_queue=TASK_QUEUE, + id_conflict_policy=common.WorkflowIDConflictPolicy.TERMINATE_EXISTING, + ) + await _check_run(wf_handle, exit_type) + + +async def _check_run( + wf_handle: client.WorkflowHandle, + exit_type: WorkflowExitType, +): + try: + up_handle = await wf_handle.start_update( + WaitingForHandlersAndCompensationWorkflow.my_update, + wait_for_stage=client.WorkflowUpdateStage.ACCEPTED, + ) + except Exception as e: + print( + f" 🔴 caught exception while starting update: {e}: {e.__cause__ or ''}" + ) + + if exit_type == WorkflowExitType.CANCELLATION: + await wf_handle.cancel() + + try: + await up_handle.result() + print(" 🟢 caller received update result") + except Exception as e: + print( + f" 🔴 caught exception while waiting for update result: {e}: {e.__cause__ or ''}" + ) + + try: + await wf_handle.result() + print(" 🟢 caller received workflow result") + except Exception as e: + print( + f" 🔴 caught exception while waiting for workflow result: {e}: {e.__cause__ or ''}" + ) + + +async def main(): + for exit_type in [ + WorkflowExitType.SUCCESS, + WorkflowExitType.FAILURE, + WorkflowExitType.CANCELLATION, + ]: + print(f"\n\nworkflow exit type: {exit_type.name}") + await starter(exit_type) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/message_passing/waiting_for_handlers_and_compensation/worker.py b/message_passing/waiting_for_handlers_and_compensation/worker.py new file mode 100644 index 000000000..2a27769df --- /dev/null +++ b/message_passing/waiting_for_handlers_and_compensation/worker.py @@ -0,0 +1,48 @@ +import asyncio +import logging + +from temporalio.client import Client +from temporalio.envconfig import ClientConfig +from temporalio.worker import Worker + +from message_passing.waiting_for_handlers_and_compensation import TASK_QUEUE +from message_passing.waiting_for_handlers_and_compensation.activities import ( + activity_executed_by_update_handler, + activity_executed_by_update_handler_to_perform_compensation, + activity_executed_to_perform_workflow_compensation, +) +from message_passing.waiting_for_handlers_and_compensation.workflows import ( + WaitingForHandlersAndCompensationWorkflow, +) + +interrupt_event = asyncio.Event() + + +async def main(): + logging.basicConfig(level=logging.INFO) + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) + + async with Worker( + client, + task_queue=TASK_QUEUE, + workflows=[WaitingForHandlersAndCompensationWorkflow], + activities=[ + activity_executed_by_update_handler, + activity_executed_by_update_handler_to_perform_compensation, + activity_executed_to_perform_workflow_compensation, + ], + ): + logging.info("Worker started, ctrl+c to exit") + await interrupt_event.wait() + logging.info("Shutting down") + + +if __name__ == "__main__": + loop = asyncio.new_event_loop() + try: + loop.run_until_complete(main()) + except KeyboardInterrupt: + interrupt_event.set() + loop.run_until_complete(loop.shutdown_asyncgens()) diff --git a/message_passing/waiting_for_handlers_and_compensation/workflows.py b/message_passing/waiting_for_handlers_and_compensation/workflows.py new file mode 100644 index 000000000..450dff2c1 --- /dev/null +++ b/message_passing/waiting_for_handlers_and_compensation/workflows.py @@ -0,0 +1,176 @@ +import asyncio +from datetime import timedelta +from typing import cast + +from temporalio import exceptions, workflow + +from message_passing.waiting_for_handlers_and_compensation import ( + WorkflowExitType, + WorkflowInput, + WorkflowResult, +) +from message_passing.waiting_for_handlers_and_compensation.activities import ( + activity_executed_by_update_handler, + activity_executed_by_update_handler_to_perform_compensation, + activity_executed_to_perform_workflow_compensation, +) + + +@workflow.defn +class WaitingForHandlersAndCompensationWorkflow: + """ + This Workflow demonstrates how to wait for message handlers to finish and + perform compensation/cleanup: + + 1. It ensures that all signal and update handlers have finished before a + successful return, and on failure and cancellation. + 2. The update handler performs any necessary compensation/cleanup when the + workflow is cancelled or fails. + + If all you need to do is wait for handlers, without performing cleanup or compensation, + then see the simpler sample message_passing/waiting_for_handlers. + """ + + def __init__(self) -> None: + # 👉 If the workflow exits prematurely, this future will be completed + # with the associated exception as its value. Message handlers can then + # "race" this future against a task performing the message handler's own + # application logic; if this future completes before the message handler + # task then the handler should abort and perform compensation. + self.workflow_exit: asyncio.Future[None] = asyncio.Future() + + # The following attributes are implementation detail of this sample and can be + # ignored + self._update_started = False + self._update_compensation_done = False + self._workflow_compensation_done = False + + @workflow.run + async def run(self, input: WorkflowInput) -> WorkflowResult: + try: + # 👉 Use this `try...except` style, instead of waiting for message + # handlers to finish in a `finally` block. The reason is that some + # exception types cause a workflow task failure as opposed to + # workflow exit, in which case we do *not* want to wait for message + # handlers to finish. + + # 👉 The actual workflow application logic is implemented in a + # separate method in order to separate "platform-level" concerns + # (waiting for handlers to finish and ensuring that compensation is + # performed when appropriate) from application logic. + result = await self._my_workflow_application_logic(input) + self.workflow_exit.set_result(None) + await workflow.wait_condition(workflow.all_handlers_finished) + return result + # 👉 Catch BaseException since asyncio.CancelledError does not inherit + # from Exception. + except BaseException as e: + if is_workflow_exit_exception(e): + self.workflow_exit.set_exception(e) + await workflow.wait_condition(workflow.all_handlers_finished) + await self.workflow_compensation() + self._workflow_compensation_done = True + raise + + async def workflow_compensation(self): + await workflow.execute_activity( + activity_executed_to_perform_workflow_compensation, + start_to_close_timeout=timedelta(seconds=10), + ) + self._update_compensation_done = True + + @workflow.update + async def my_update(self) -> str: + """ + An update handler that handles exceptions raised in its own execution + and in that of the main workflow method. + + It ensures that: + - Compensation/cleanup is always performed when appropriate + - The update caller gets the update result, or WorkflowUpdateFailedError + """ + # 👉 "Race" the workflow_exit future against the handler's own + # application logic. Always use `workflow.wait` instead of + # `asyncio.wait` in Workflow code: asyncio's version is + # non-deterministic. (Note that coroutines must be wrapped in tasks in + # order to use workflow.wait.) + update_task = asyncio.create_task(self._my_update_application_logic()) + await workflow.wait( # type: ignore + [update_task, self.workflow_exit], return_when=asyncio.FIRST_EXCEPTION + ) + try: + if update_task.done(): + # 👉 The update has finished (whether successfully or not). + # Regardless of whether the main workflow method is about to + # exit or not, the update caller should receive a response + # informing them of the outcome of the update. So return the + # result, or raise the exception that caused the update handler + # to exit. + return await update_task + else: + # 👉 The main workflow method exited prematurely due to an + # error, and this happened before the update finished. Fail the + # update with the workflow exception as cause. + raise exceptions.ApplicationError( + "The update failed because the workflow run exited" + ) from cast(BaseException, self.workflow_exit.exception()) + # 👉 Catch BaseException since asyncio.CancelledError does not inherit + # from Exception. + except BaseException as e: + if is_workflow_exit_exception(e): + try: + await self.my_update_compensation() + except BaseException as e: + raise exceptions.ApplicationError( + "Update compensation failed" + ) from e + raise + + async def my_update_compensation(self): + await workflow.execute_activity( + activity_executed_by_update_handler_to_perform_compensation, + start_to_close_timeout=timedelta(seconds=10), + ) + self._update_compensation_done = True + + @workflow.query + def workflow_compensation_done(self) -> bool: + return self._workflow_compensation_done + + @workflow.query + def update_compensation_done(self) -> bool: + return self._update_compensation_done + + # Methods below this point are placeholders for the actual application logic + # that you would perform in your main workflow method or update handler. + # They can be ignored unless you are interested in the implementation + # details of this sample. + + async def _my_update_application_logic(self) -> str: + self._update_started = True + await workflow.execute_activity( + activity_executed_by_update_handler, + start_to_close_timeout=timedelta(seconds=10), + ) + return "update-result" + + async def _my_workflow_application_logic( + self, input: WorkflowInput + ) -> WorkflowResult: + # Wait until handlers have started, so that we are demonstrating that we + # wait for them to finish. + await workflow.wait_condition(lambda: self._update_started) + if input.exit_type == WorkflowExitType.SUCCESS: + return WorkflowResult(data="workflow-result") + elif input.exit_type == WorkflowExitType.FAILURE: + raise exceptions.ApplicationError("deliberately failing workflow") + elif input.exit_type == WorkflowExitType.CANCELLATION: + # Block forever; the starter will send a workflow cancellation request. + await asyncio.Future() + raise AssertionError("unreachable") + + +def is_workflow_exit_exception(e: BaseException) -> bool: + # 👉 If you have set additional failure_exception_types you should also + # check for these here. + return isinstance(e, (asyncio.CancelledError, exceptions.FailureError)) diff --git a/nexus_cancel/README.md b/nexus_cancel/README.md new file mode 100644 index 000000000..2f7f57030 --- /dev/null +++ b/nexus_cancel/README.md @@ -0,0 +1,50 @@ +# Nexus Cancellation + +This sample shows how a caller workflow can fan out multiple Nexus operations concurrently, take the first result, and cancel the rest using `WAIT_REQUESTED` cancellation semantics. + +With `WAIT_REQUESTED`, the caller proceeds once the handler has received the cancel request — it does not wait for the handler to finish processing the cancellation. + +Start a Temporal server. (See the main samples repo [README](../README.md)). + +Run the following: + +``` +temporal operator namespace create --namespace nexus-cancel-handler-namespace +temporal operator namespace create --namespace nexus-cancel-caller-namespace + +temporal operator nexus endpoint create \ + --name nexus-cancel-endpoint \ + --target-namespace nexus-cancel-handler-namespace \ + --target-task-queue nexus-cancel-handler-task-queue +``` + +Next, in separate terminal windows: + +## Nexus Handler Worker + +```bash +uv run nexus_cancel/handler/worker.py +``` + +## Nexus Caller App + +```bash +uv run nexus_cancel/caller/app.py +``` + +## Expected Output + +On the caller side, you should see a greeting in whichever language completed first: +``` +Hello Nexus 👋 +``` + +On the handler side, you should see cancellation log messages for the remaining operations: +``` +HelloHandlerWorkflow was cancelled successfully. +HelloHandlerWorkflow was cancelled successfully. +HelloHandlerWorkflow was cancelled successfully. +HelloHandlerWorkflow was cancelled successfully. +``` + +The caller workflow returns before all handler workflows have completed their cancellation cleanup. This demonstrates `WAIT_REQUESTED` semantics: the caller didn't wait for the handler workflows to finish, but still guaranteed that all handlers received the cancellation request. diff --git a/nexus_cancel/__init__.py b/nexus_cancel/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/nexus_cancel/caller/__init__.py b/nexus_cancel/caller/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/nexus_cancel/caller/app.py b/nexus_cancel/caller/app.py new file mode 100644 index 000000000..bb74e9e0b --- /dev/null +++ b/nexus_cancel/caller/app.py @@ -0,0 +1,43 @@ +import asyncio +import uuid +from typing import Optional + +from temporalio.client import Client +from temporalio.envconfig import ClientConfig +from temporalio.worker import Worker + +from nexus_cancel.caller.workflows import HelloCallerWorkflow + +NAMESPACE = "nexus-cancel-caller-namespace" +TASK_QUEUE = "nexus-cancel-caller-task-queue" + + +async def execute_caller_workflow( + client: Optional[Client] = None, +) -> str: + if client is None: + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + config.setdefault("namespace", NAMESPACE) + client = await Client.connect(**config) + + async with Worker( + client, + task_queue=TASK_QUEUE, + workflows=[HelloCallerWorkflow], + ): + return await client.execute_workflow( + HelloCallerWorkflow.run, + "Nexus", + id=f"hello-caller-{uuid.uuid4()}", + task_queue=TASK_QUEUE, + ) + + +if __name__ == "__main__": + loop = asyncio.new_event_loop() + try: + result = loop.run_until_complete(execute_caller_workflow()) + print(result) + except KeyboardInterrupt: + loop.run_until_complete(loop.shutdown_asyncgens()) diff --git a/nexus_cancel/caller/workflows.py b/nexus_cancel/caller/workflows.py new file mode 100644 index 000000000..f8a2e1ffc --- /dev/null +++ b/nexus_cancel/caller/workflows.py @@ -0,0 +1,69 @@ +""" +Caller workflow that demonstrates Nexus operation cancellation. + +Fans out 5 concurrent Nexus hello operations (one per language), takes the first +result, and cancels the rest using WAIT_REQUESTED cancellation semantics. +""" + +import asyncio +from datetime import timedelta + +from temporalio import workflow +from temporalio.exceptions import CancelledError, NexusOperationError + +with workflow.unsafe.imports_passed_through(): + from nexus_cancel.service import HelloInput, Language, NexusService + +NEXUS_ENDPOINT = "nexus-cancel-endpoint" + + +@workflow.defn +class HelloCallerWorkflow: + def __init__(self) -> None: + self.nexus_client = workflow.create_nexus_client( + service=NexusService, + endpoint=NEXUS_ENDPOINT, + ) + + @workflow.run + async def run(self, message: str) -> str: + # Fan out 5 concurrent Nexus calls, one per language. + # Each task starts and awaits its own operation so all race concurrently. + async def run_operation(language: Language): + handle = await self.nexus_client.start_operation( + NexusService.hello, + HelloInput(name=message, language=language), + schedule_to_close_timeout=timedelta(seconds=10), + cancellation_type=workflow.NexusOperationCancellationType.WAIT_REQUESTED, + ) + return await handle + + tasks = [asyncio.create_task(run_operation(lang)) for lang in Language] + + # Wait for the first operation to complete + workflow.logger.info( + f"Started {len(tasks)} operations, waiting for first to complete..." + ) + done, pending = await workflow.wait(tasks, return_when=asyncio.FIRST_COMPLETED) + + # Get the result from the first completed operation + result = await done.pop() + workflow.logger.info(f"First operation completed with: {result.message}") + + # Cancel all remaining operations + workflow.logger.info(f"Cancelling {len(pending)} remaining operations...") + for task in pending: + task.cancel() + + # Wait for all cancellations to be acknowledged. + # If the workflow completes before cancellation requests are delivered, + # the server drops them. Waiting ensures all handlers receive the + # cancellation. + for task in pending: + try: + await task + except (NexusOperationError, CancelledError): + # Expected: the operation was cancelled + workflow.logger.info("Operation was cancelled") + + return result.message diff --git a/nexus_cancel/handler/__init__.py b/nexus_cancel/handler/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/nexus_cancel/handler/service_handler.py b/nexus_cancel/handler/service_handler.py new file mode 100644 index 000000000..5d75e8a6f --- /dev/null +++ b/nexus_cancel/handler/service_handler.py @@ -0,0 +1,28 @@ +""" +Nexus service handler for the cancellation sample. + +The hello operation is backed by a workflow whose ID is derived from the +operation input (name + language), giving each fan-out branch a distinct, +meaningful business ID. +""" + +from __future__ import annotations + +import nexusrpc +from temporalio import nexus + +from nexus_cancel.handler.workflows import HelloHandlerWorkflow +from nexus_cancel.service import HelloInput, HelloOutput, NexusService + + +@nexusrpc.handler.service_handler(service=NexusService) +class NexusServiceHandler: + @nexus.workflow_run_operation + async def hello( + self, ctx: nexus.WorkflowRunOperationContext, input: HelloInput + ) -> nexus.WorkflowHandle[HelloOutput]: + return await ctx.start_workflow( + HelloHandlerWorkflow.run, + input, + id=f"hello-handler-{input.name}-{input.language.name}", + ) diff --git a/nexus_cancel/handler/worker.py b/nexus_cancel/handler/worker.py new file mode 100644 index 000000000..e29df3559 --- /dev/null +++ b/nexus_cancel/handler/worker.py @@ -0,0 +1,48 @@ +""" +Worker for the handler namespace that processes Nexus operations and workflows. +""" + +import asyncio +import logging +from typing import Optional + +from temporalio.client import Client +from temporalio.envconfig import ClientConfig +from temporalio.worker import Worker + +from nexus_cancel.handler.service_handler import NexusServiceHandler +from nexus_cancel.handler.workflows import HelloHandlerWorkflow + +interrupt_event = asyncio.Event() + +NAMESPACE = "nexus-cancel-handler-namespace" +TASK_QUEUE = "nexus-cancel-handler-task-queue" + + +async def main(client: Optional[Client] = None): + logging.basicConfig(level=logging.INFO) + + if not client: + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + config.setdefault("namespace", NAMESPACE) + client = await Client.connect(**config) + + async with Worker( + client, + task_queue=TASK_QUEUE, + workflows=[HelloHandlerWorkflow], + nexus_service_handlers=[NexusServiceHandler()], + ): + logging.info("Worker started, ctrl+c to exit") + await interrupt_event.wait() + logging.info("Shutting down") + + +if __name__ == "__main__": + loop = asyncio.new_event_loop() + try: + loop.run_until_complete(main()) + except KeyboardInterrupt: + interrupt_event.set() + loop.run_until_complete(loop.shutdown_asyncgens()) diff --git a/nexus_cancel/handler/workflows.py b/nexus_cancel/handler/workflows.py new file mode 100644 index 000000000..d799c62be --- /dev/null +++ b/nexus_cancel/handler/workflows.py @@ -0,0 +1,49 @@ +""" +Handler workflow started by the hello Nexus operation. + +Demonstrates how to handle cancellation from the caller workflow using a +detached cancellation scope (asyncio.shield) for cleanup work. +""" + +import asyncio + +from temporalio import workflow + +with workflow.unsafe.imports_passed_through(): + from nexus_cancel.service import HelloInput, HelloOutput, Language + +GREETINGS = { + Language.EN: "Hello {name} 👋", + Language.FR: "Bonjour {name} 👋", + Language.DE: "Hallo {name} 👋", + Language.ES: "¡Hola! {name} 👋", + Language.TR: "Merhaba {name} 👋", +} + + +@workflow.defn +class HelloHandlerWorkflow: + @workflow.run + async def run(self, input: HelloInput) -> HelloOutput: + try: + # Sleep for a random duration to simulate work (0-5 seconds) + random_seconds = workflow.random().randint(0, 5) + workflow.logger.info(f"Working for {random_seconds} seconds...") + await asyncio.sleep(random_seconds) + + # Return a greeting based on the language + greeting = GREETINGS[input.language].format(name=input.name) + return HelloOutput(message=greeting) + + except asyncio.CancelledError: + # Perform cleanup in a detached cancellation scope. + # asyncio.shield prevents the cleanup work from being cancelled. + workflow.logger.info("Received cancellation request, performing cleanup...") + try: + cleanup_seconds = workflow.random().randint(0, 5) + await asyncio.shield(asyncio.sleep(cleanup_seconds)) + except asyncio.CancelledError: + pass + workflow.logger.info("HelloHandlerWorkflow was cancelled successfully.") + # Re-raise the cancellation error + raise diff --git a/nexus_cancel/service.py b/nexus_cancel/service.py new file mode 100644 index 000000000..454a32f7b --- /dev/null +++ b/nexus_cancel/service.py @@ -0,0 +1,35 @@ +""" +Nexus service definition for the cancellation sample. + +Defines a NexusService with a single `hello` operation that takes a name and +language, and returns a greeting message. +""" + +from dataclasses import dataclass +from enum import IntEnum + +import nexusrpc + + +class Language(IntEnum): + EN = 0 + FR = 1 + DE = 2 + ES = 3 + TR = 4 + + +@dataclass +class HelloInput: + name: str + language: Language + + +@dataclass +class HelloOutput: + message: str + + +@nexusrpc.service +class NexusService: + hello: nexusrpc.Operation[HelloInput, HelloOutput] diff --git a/nexus_messaging/README.md b/nexus_messaging/README.md new file mode 100644 index 000000000..3103fc0fb --- /dev/null +++ b/nexus_messaging/README.md @@ -0,0 +1,16 @@ +This sample shows how to expose a long-running Workflow's queries, updates, and signals as Nexus +operations. There are two self-contained examples, each in its own directory: + +| | `callerpattern/` | `ondemandpattern/` | +|--------------------------------|--------------------------------------|--------------------------------------------------------------| +| **Pattern** | Signal an existing Workflow | Create and run Workflows on demand, and send signals to them | +| **Who creates the Workflow?** | The handler worker starts it on boot | The caller starts it via a Nexus operation | +| **Who knows the Workflow ID?** | Only the handler | The caller chooses and passes it in every operation | +| **Nexus service** | `NexusGreetingService` | `NexusRemoteGreetingService` | + +Each directory is fully self-contained for clarity. The `GreetingWorkflow`, activity, and +`Language` enum are **identical** between the two -- only the Nexus service definition and its +handler implementation differ. This highlights that the same Workflow can be exposed through +Nexus in different ways depending on whether the caller needs lifecycle control. + +See each directory's README for running instructions. diff --git a/nexus_messaging/__init__.py b/nexus_messaging/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/nexus_messaging/callerpattern/README.md b/nexus_messaging/callerpattern/README.md new file mode 100644 index 000000000..9458ae43b --- /dev/null +++ b/nexus_messaging/callerpattern/README.md @@ -0,0 +1,56 @@ +## Caller pattern + +The handler worker starts a `GreetingWorkflow` for a User ID. +`NexusGreetingServiceHandler` holds that ID and routes every Nexus operation to it. +The caller's input does not have that Workflow ID as the caller doesn't know it -- but the caller +sends in the User ID, and `NexusGreetingServiceHandler` knows how to get the desired Workflow ID +from that User ID (see the `get_workflow_id` call). + +The handler worker uses the same `get_workflow_id` call to generate a Workflow ID from a Wser ID +when it launches the Workflow. + +The caller Workflow: +1. Queries for supported languages (`get_languages` -- backed by a `@workflow.query`) +2. Changes the language to Arabic (`set_language` -- backed by a `@workflow.update` that calls an activity) +3. Confirms the change via a second query (`get_language`) +4. Approves the Workflow (`approve` -- backed by a `@workflow.signal`) + +### Running + +Start a Temporal server: + +```bash +temporal server start-dev +``` + +Create the namespaces and Nexus endpoint: + +```bash +temporal operator namespace create --namespace nexus-messaging-handler-namespace +temporal operator namespace create --namespace nexus-messaging-caller-namespace + +temporal operator nexus endpoint create \ + --name nexus-messaging-nexus-endpoint \ + --target-namespace nexus-messaging-handler-namespace \ + --target-task-queue nexus-messaging-handler-task-queue +``` + +In one terminal, start the handler worker: + +```bash +uv run python -m nexus_messaging.callerpattern.handler.worker +``` + +In another terminal, run the following command to start the example: + +```bash +uv run python -m nexus_messaging.callerpattern.caller.app +``` + +Expected output: + +``` +Supported languages: [, ] +Language changed: ENGLISH -> ARABIC +Workflow approved +``` diff --git a/nexus_messaging/callerpattern/__init__.py b/nexus_messaging/callerpattern/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/nexus_messaging/callerpattern/caller/__init__.py b/nexus_messaging/callerpattern/caller/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/nexus_messaging/callerpattern/caller/app.py b/nexus_messaging/callerpattern/caller/app.py new file mode 100644 index 000000000..933dcd5d5 --- /dev/null +++ b/nexus_messaging/callerpattern/caller/app.py @@ -0,0 +1,42 @@ +import asyncio +import uuid +from typing import Optional + +from temporalio.client import Client +from temporalio.envconfig import ClientConfig +from temporalio.worker import Worker + +from nexus_messaging.callerpattern.caller.workflows import CallerWorkflow + +NAMESPACE = "nexus-messaging-caller-namespace" +TASK_QUEUE = "nexus-messaging-caller-task-queue" + + +async def execute_caller_workflow(client: Optional[Client] = None) -> None: + if client is None: + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + config.setdefault("namespace", NAMESPACE) + client = await Client.connect(**config) + + async with Worker( + client, + task_queue=TASK_QUEUE, + workflows=[CallerWorkflow], + ): + log = await client.execute_workflow( + CallerWorkflow.run, + arg="user-1", + id=f"nexus-messaging-caller-{uuid.uuid4()}", + task_queue=TASK_QUEUE, + ) + for line in log: + print(line) + + +if __name__ == "__main__": + loop = asyncio.new_event_loop() + try: + loop.run_until_complete(execute_caller_workflow()) + except KeyboardInterrupt: + loop.run_until_complete(loop.shutdown_asyncgens()) diff --git a/nexus_messaging/callerpattern/caller/workflows.py b/nexus_messaging/callerpattern/caller/workflows.py new file mode 100644 index 000000000..04f79dada --- /dev/null +++ b/nexus_messaging/callerpattern/caller/workflows.py @@ -0,0 +1,73 @@ +""" +A caller workflow that executes Nexus operations. The caller does not have information +about how these operations are implemented by the Nexus service. +""" + +from temporalio import workflow +from temporalio.exceptions import ApplicationError + +from nexus_messaging.callerpattern.service import ( + ApproveInput, + GetLanguageInput, + GetLanguagesInput, + Language, + NexusGreetingService, + SetLanguageInput, +) + +NEXUS_ENDPOINT = "nexus-messaging-nexus-endpoint" + + +@workflow.defn +class CallerWorkflow: + @workflow.run + async def run(self, user_id: str) -> list[str]: + log: list[str] = [] + nexus_client = workflow.create_nexus_client( + service=NexusGreetingService, + endpoint=NEXUS_ENDPOINT, + ) + + # Call a Nexus operation backed by a query against the entity workflow. + # The workflow must already be running on the handler, otherwise you will + # get an error saying the workflow has already terminated. + languages_output = await nexus_client.execute_operation( + NexusGreetingService.get_languages, + GetLanguagesInput(include_unsupported=False, user_id=user_id), + ) + log.append(f"Supported languages: {languages_output.languages}") + workflow.logger.info("Supported languages: %s", languages_output.languages) + + # Following are examples for each of the three messaging types - + # update, query, then signal. + + # Call a Nexus operation backed by an update against the entity workflow. + previous_language = await nexus_client.execute_operation( + NexusGreetingService.set_language, + SetLanguageInput(language=Language.ARABIC, user_id=user_id), + ) + + # Call a Nexus operation backed by a query to confirm the language change. + current_language = await nexus_client.execute_operation( + NexusGreetingService.get_language, + GetLanguageInput(user_id=user_id), + ) + if current_language != Language.ARABIC: + raise ApplicationError(f"Expected language ARABIC, got {current_language}") + + log.append( + f"Language changed: {previous_language.name} -> {Language.ARABIC.name}" + ) + workflow.logger.info( + "Language changed from %s to %s", previous_language, Language.ARABIC + ) + + # Call a Nexus operation backed by a signal against the entity workflow. + await nexus_client.execute_operation( + NexusGreetingService.approve, + ApproveInput(name="caller", user_id=user_id), + ) + log.append("Workflow approved") + workflow.logger.info("Workflow approved") + + return log diff --git a/nexus_messaging/callerpattern/handler/__init__.py b/nexus_messaging/callerpattern/handler/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/nexus_messaging/callerpattern/handler/activities.py b/nexus_messaging/callerpattern/handler/activities.py new file mode 100644 index 000000000..4031b34fb --- /dev/null +++ b/nexus_messaging/callerpattern/handler/activities.py @@ -0,0 +1,22 @@ +import asyncio +from typing import Optional + +from temporalio import activity + +from nexus_messaging.callerpattern.service import Language + + +@activity.defn +async def call_greeting_service(language: Language) -> Optional[str]: + """Simulates a call to a remote greeting service. Returns None if unsupported.""" + greetings = { + Language.ARABIC: "\u0645\u0631\u062d\u0628\u0627 \u0628\u0627\u0644\u0639\u0627\u0644\u0645", + Language.CHINESE: "\u4f60\u597d\uff0c\u4e16\u754c", + Language.ENGLISH: "Hello, world", + Language.FRENCH: "Bonjour, monde", + Language.HINDI: "\u0928\u092e\u0938\u094d\u0924\u0947 \u0926\u0941\u0928\u093f\u092f\u093e", + Language.PORTUGUESE: "Ol\u00e1 mundo", + Language.SPANISH: "Hola mundo", + } + await asyncio.sleep(0.2) + return greetings.get(language) diff --git a/nexus_messaging/callerpattern/handler/service_handler.py b/nexus_messaging/callerpattern/handler/service_handler.py new file mode 100644 index 000000000..cbc57eadb --- /dev/null +++ b/nexus_messaging/callerpattern/handler/service_handler.py @@ -0,0 +1,80 @@ +""" +Nexus operation handler implementation for the entity pattern. Each operation receives a +user_id, which is mapped to a workflow ID. The operations are synchronous because queries +and updates against a running workflow complete quickly. +""" + +from __future__ import annotations + +import nexusrpc +from temporalio import nexus +from temporalio.client import WorkflowHandle + +from nexus_messaging.callerpattern.handler.workflows import GreetingWorkflow +from nexus_messaging.callerpattern.service import ( + ApproveInput, + ApproveOutput, + GetLanguageInput, + GetLanguagesInput, + GetLanguagesOutput, + Language, + NexusGreetingService, + SetLanguageInput, +) + +WORKFLOW_ID_PREFIX = "GreetingWorkflow_for_" + + +def get_workflow_id(user_id: str) -> str: + """Map a user ID to a workflow ID. + + This example assumes you might have multiple workflows, one for each user. + If you had a single workflow for all users, you could remove this function, + remove the user_id from each input, and just use a single workflow ID. + """ + return f"{WORKFLOW_ID_PREFIX}{user_id}" + + +@nexusrpc.handler.service_handler(service=NexusGreetingService) +class NexusGreetingServiceHandler: + def _get_workflow_handle( + self, user_id: str + ) -> WorkflowHandle[GreetingWorkflow, str]: + return nexus.client().get_workflow_handle_for( + GreetingWorkflow.run, get_workflow_id(user_id) + ) + + @nexusrpc.handler.sync_operation + async def get_languages( + self, ctx: nexusrpc.handler.StartOperationContext, input: GetLanguagesInput + ) -> GetLanguagesOutput: + return await self._get_workflow_handle(input.user_id).query( + GreetingWorkflow.get_languages, input + ) + + @nexusrpc.handler.sync_operation + async def get_language( + self, ctx: nexusrpc.handler.StartOperationContext, input: GetLanguageInput + ) -> Language: + return await self._get_workflow_handle(input.user_id).query( + GreetingWorkflow.get_language + ) + + # Routes to set_language_using_activity (not set_language) so that new languages not + # already in the greetings map can be fetched via an activity. + @nexusrpc.handler.sync_operation + async def set_language( + self, ctx: nexusrpc.handler.StartOperationContext, input: SetLanguageInput + ) -> Language: + return await self._get_workflow_handle(input.user_id).execute_update( + GreetingWorkflow.set_language_using_activity, input + ) + + @nexusrpc.handler.sync_operation + async def approve( + self, ctx: nexusrpc.handler.StartOperationContext, input: ApproveInput + ) -> ApproveOutput: + await self._get_workflow_handle(input.user_id).signal( + GreetingWorkflow.approve, input + ) + return ApproveOutput() diff --git a/nexus_messaging/callerpattern/handler/worker.py b/nexus_messaging/callerpattern/handler/worker.py new file mode 100644 index 000000000..fa8e2c0fa --- /dev/null +++ b/nexus_messaging/callerpattern/handler/worker.py @@ -0,0 +1,62 @@ +import asyncio +import logging +from typing import Optional + +from temporalio.client import Client +from temporalio.common import WorkflowIDConflictPolicy +from temporalio.envconfig import ClientConfig +from temporalio.worker import Worker + +from nexus_messaging.callerpattern.handler.activities import call_greeting_service +from nexus_messaging.callerpattern.handler.service_handler import ( + NexusGreetingServiceHandler, + get_workflow_id, +) +from nexus_messaging.callerpattern.handler.workflows import GreetingWorkflow + +interrupt_event = asyncio.Event() + +NAMESPACE = "nexus-messaging-handler-namespace" +TASK_QUEUE = "nexus-messaging-handler-task-queue" +USER_ID = "user-1" + + +async def main(client: Optional[Client] = None): + logging.basicConfig(level=logging.INFO) + + if client is None: + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + config.setdefault("namespace", NAMESPACE) + client = await Client.connect(**config) + + # Start the long-running entity workflow that backs the Nexus service, + # if not already running. + workflow_id = get_workflow_id(USER_ID) + await client.start_workflow( + GreetingWorkflow.run, + id=workflow_id, + task_queue=TASK_QUEUE, + id_conflict_policy=WorkflowIDConflictPolicy.USE_EXISTING, + ) + logging.info("Started greeting workflow: %s", workflow_id) + + async with Worker( + client, + task_queue=TASK_QUEUE, + workflows=[GreetingWorkflow], + activities=[call_greeting_service], + nexus_service_handlers=[NexusGreetingServiceHandler()], + ): + logging.info("Handler worker started, ctrl+c to exit") + await interrupt_event.wait() + logging.info("Shutting down") + + +if __name__ == "__main__": + loop = asyncio.new_event_loop() + try: + loop.run_until_complete(main()) + except KeyboardInterrupt: + interrupt_event.set() + loop.run_until_complete(loop.shutdown_asyncgens()) diff --git a/nexus_messaging/callerpattern/handler/workflows.py b/nexus_messaging/callerpattern/handler/workflows.py new file mode 100644 index 000000000..fb10d8c5d --- /dev/null +++ b/nexus_messaging/callerpattern/handler/workflows.py @@ -0,0 +1,87 @@ +""" +A long-running "entity" workflow that backs the NexusGreetingService Nexus operations. +The workflow exposes queries, an update, and a signal. These are private implementation +details of the Nexus service: the caller only interacts via Nexus operations. +""" + +import asyncio +from datetime import timedelta + +from temporalio import workflow +from temporalio.exceptions import ApplicationError + +from nexus_messaging.callerpattern.handler.activities import call_greeting_service +from nexus_messaging.callerpattern.service import ( + ApproveInput, + GetLanguagesInput, + GetLanguagesOutput, + Language, + SetLanguageInput, +) + + +@workflow.defn +class GreetingWorkflow: + def __init__(self) -> None: + self.approved_for_release = False + self.greetings: dict[Language, str] = { + Language.CHINESE: "\u4f60\u597d\uff0c\u4e16\u754c", + Language.ENGLISH: "Hello, world", + } + self.language = Language.ENGLISH + self.lock = asyncio.Lock() + + @workflow.run + async def run(self) -> str: + # Wait until approved and all in-flight update handlers have finished. + await workflow.wait_condition( + lambda: self.approved_for_release and workflow.all_handlers_finished() + ) + return self.greetings[self.language] + + @workflow.query + def get_languages(self, input: GetLanguagesInput) -> GetLanguagesOutput: + if input.include_unsupported: + languages = sorted(Language) + else: + languages = sorted(self.greetings) + return GetLanguagesOutput(languages=languages) + + @workflow.query + def get_language(self) -> Language: + return self.language + + @workflow.signal + def approve(self, input: ApproveInput) -> None: + workflow.logger.info("Approval signal received for user %s", input.user_id) + self.approved_for_release = True + + @workflow.update + def set_language(self, input: SetLanguageInput) -> Language: + workflow.logger.info("setLanguage update received for user %s", input.user_id) + previous_language, self.language = self.language, input.language + return previous_language + + @set_language.validator + def validate_set_language(self, input: SetLanguageInput) -> None: + if input.language not in self.greetings: + raise ValueError(f"{input.language.name} is not supported") + + # Changes the active language, calling an activity to fetch a greeting for new + # languages not already in the greetings map. + @workflow.update + async def set_language_using_activity(self, input: SetLanguageInput) -> Language: + if input.language not in self.greetings: + async with self.lock: + greeting = await workflow.execute_activity( + call_greeting_service, + input.language, + start_to_close_timeout=timedelta(seconds=10), + ) + if greeting is None: + raise ApplicationError( + f"Greeting service does not support {input.language.name}" + ) + self.greetings[input.language] = greeting + previous_language, self.language = self.language, input.language + return previous_language diff --git a/nexus_messaging/callerpattern/service.py b/nexus_messaging/callerpattern/service.py new file mode 100644 index 000000000..23a550fb7 --- /dev/null +++ b/nexus_messaging/callerpattern/service.py @@ -0,0 +1,67 @@ +""" +Nexus service definition for the caller (entity) pattern. Shared between the handler and +caller. The caller uses this to create a type-safe Nexus client; the handler implements +the operations. + +Every operation includes a user_id so the handler knows which entity workflow to target. +""" + +from dataclasses import dataclass +from enum import IntEnum + +import nexusrpc + + +class Language(IntEnum): + ARABIC = 1 + CHINESE = 2 + ENGLISH = 3 + FRENCH = 4 + HINDI = 5 + PORTUGUESE = 6 + SPANISH = 7 + + +@dataclass +class GetLanguagesInput: + include_unsupported: bool + user_id: str + + +@dataclass +class GetLanguagesOutput: + languages: list[Language] + + +@dataclass +class GetLanguageInput: + user_id: str + + +@dataclass +class SetLanguageInput: + language: Language + user_id: str + + +@dataclass +class ApproveInput: + name: str + user_id: str + + +@dataclass +class ApproveOutput: + pass + + +@nexusrpc.service +class NexusGreetingService: + # Returns the languages supported by the greeting workflow. + get_languages: nexusrpc.Operation[GetLanguagesInput, GetLanguagesOutput] + # Returns the currently active language. + get_language: nexusrpc.Operation[GetLanguageInput, Language] + # Changes the active language, returning the previous one. + set_language: nexusrpc.Operation[SetLanguageInput, Language] + # Approves the workflow, allowing it to complete. + approve: nexusrpc.Operation[ApproveInput, ApproveOutput] diff --git a/nexus_messaging/endpoint_description.md b/nexus_messaging/endpoint_description.md new file mode 100644 index 000000000..4184134ba --- /dev/null +++ b/nexus_messaging/endpoint_description.md @@ -0,0 +1,14 @@ +## Services + +### [NexusGreetingService](https://github.com/temporalio/samples-python/blob/main/nexus_messaging/callerpattern/service.py) (callerpattern) +- operation: `get_languages` +- operation: `get_language` +- operation: `set_language` +- operation: `approve` + +### [NexusRemoteGreetingService](https://github.com/temporalio/samples-python/blob/main/nexus_messaging/ondemandpattern/service.py) (ondemandpattern) +- operation: `run_from_remote` +- operation: `get_languages` +- operation: `get_language` +- operation: `set_language` +- operation: `approve` diff --git a/nexus_messaging/ondemandpattern/README.md b/nexus_messaging/ondemandpattern/README.md new file mode 100644 index 000000000..b9ba67ff7 --- /dev/null +++ b/nexus_messaging/ondemandpattern/README.md @@ -0,0 +1,60 @@ +## On-demand pattern + +No Workflow is pre-started. The caller creates and controls Workflow instances through Nexus +operations. `NexusRemoteGreetingService` adds a `run_from_remote` operation that starts a new +`GreetingWorkflow`, and every other operation includes a `user_id` so the handler knows which +instance to target. + +The caller Workflow: +1. Starts two remote `GreetingWorkflow` instances via `run_from_remote` (backed by `workflow_run_operation`) +2. Queries each for supported languages +3. Changes the language on each (Arabic and Hindi) +4. Confirms the changes via queries +5. Approves both Workflows +6. Waits for each to complete and returns their results + +### Running + +Start a Temporal server: + +```bash +temporal server start-dev +``` + +Create the namespaces and Nexus endpoint: + +```bash +temporal operator namespace create --namespace nexus-messaging-handler-namespace +temporal operator namespace create --namespace nexus-messaging-caller-namespace + +temporal operator nexus endpoint create \ + --name nexus-messaging-nexus-endpoint \ + --target-namespace nexus-messaging-handler-namespace \ + --target-task-queue nexus-messaging-handler-task-queue +``` + +In one terminal, start the handler worker: + +```bash +uv run python -m nexus_messaging.ondemandpattern.handler.worker +``` + +In another terminal, run the following command to start the example: + +```bash +uv run python -m nexus_messaging.ondemandpattern.caller.app +``` + +Expected output: + +``` +started remote greeting workflow: UserId One +started remote greeting workflow: UserId Two +Supported languages for UserId One: [, ] +Supported languages for UserId Two: [, ] +UserId One changed language: ENGLISH -> ARABIC +UserId Two changed language: ENGLISH -> HINDI +Workflows approved +Workflow one result: مرحبا بالعالم +Workflow two result: नमस्ते दुनिया +``` diff --git a/nexus_messaging/ondemandpattern/__init__.py b/nexus_messaging/ondemandpattern/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/nexus_messaging/ondemandpattern/caller/__init__.py b/nexus_messaging/ondemandpattern/caller/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/nexus_messaging/ondemandpattern/caller/app.py b/nexus_messaging/ondemandpattern/caller/app.py new file mode 100644 index 000000000..a1837babb --- /dev/null +++ b/nexus_messaging/ondemandpattern/caller/app.py @@ -0,0 +1,41 @@ +import asyncio +import uuid +from typing import Optional + +from temporalio.client import Client +from temporalio.envconfig import ClientConfig +from temporalio.worker import Worker + +from nexus_messaging.ondemandpattern.caller.workflows import CallerRemoteWorkflow + +NAMESPACE = "nexus-messaging-caller-namespace" +TASK_QUEUE = "nexus-messaging-caller-remote-task-queue" + + +async def execute_caller_workflow(client: Optional[Client] = None) -> None: + if client is None: + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + config.setdefault("namespace", NAMESPACE) + client = await Client.connect(**config) + + async with Worker( + client, + task_queue=TASK_QUEUE, + workflows=[CallerRemoteWorkflow], + ): + log = await client.execute_workflow( + CallerRemoteWorkflow.run, + id=f"nexus-messaging-remote-caller-{uuid.uuid4()}", + task_queue=TASK_QUEUE, + ) + for line in log: + print(line) + + +if __name__ == "__main__": + loop = asyncio.new_event_loop() + try: + loop.run_until_complete(execute_caller_workflow()) + except KeyboardInterrupt: + loop.run_until_complete(loop.shutdown_asyncgens()) diff --git a/nexus_messaging/ondemandpattern/caller/workflows.py b/nexus_messaging/ondemandpattern/caller/workflows.py new file mode 100644 index 000000000..8dbbd4b24 --- /dev/null +++ b/nexus_messaging/ondemandpattern/caller/workflows.py @@ -0,0 +1,148 @@ +""" +A caller workflow that creates and controls workflow instances through Nexus operations. +Unlike the entity (callerpattern), no workflow is pre-started; the caller creates them +on demand via the run_from_remote operation. +""" + +from temporalio import workflow + +from nexus_messaging.ondemandpattern.service import ( + ApproveInput, + GetLanguageInput, + GetLanguagesInput, + Language, + NexusRemoteGreetingService, + RunFromRemoteInput, + SetLanguageInput, +) + +NEXUS_ENDPOINT = "nexus-messaging-nexus-endpoint" + +REMOTE_WORKFLOW_ONE = "UserId One" +REMOTE_WORKFLOW_TWO = "UserId Two" + + +@workflow.defn +class CallerRemoteWorkflow: + def __init__(self) -> None: + self.nexus_client = workflow.create_nexus_client( + service=NexusRemoteGreetingService, + endpoint=NEXUS_ENDPOINT, + ) + + @workflow.run + async def run(self) -> list[str]: + log: list[str] = [] + + # Each call is performed twice in this example. This assumes there are two + # users we want to process. The first calls start two workflows, one for each + # user. Subsequent calls perform different actions between the two users. + + # This is an async Nexus operation -- starts a workflow on the handler and + # returns a handle. Unlike the sync operations below, this does not block + # until the workflow completes. It is backed by workflow_run_operation on the + # handler side. + handle_one = await self.nexus_client.start_operation( + NexusRemoteGreetingService.run_from_remote, + RunFromRemoteInput(user_id=REMOTE_WORKFLOW_ONE), + ) + log.append(f"started remote greeting workflow: {REMOTE_WORKFLOW_ONE}") + workflow.logger.info("started remote greeting workflow %s", REMOTE_WORKFLOW_ONE) + + handle_two = await self.nexus_client.start_operation( + NexusRemoteGreetingService.run_from_remote, + RunFromRemoteInput(user_id=REMOTE_WORKFLOW_TWO), + ) + log.append(f"started remote greeting workflow: {REMOTE_WORKFLOW_TWO}") + workflow.logger.info("started remote greeting workflow %s", REMOTE_WORKFLOW_TWO) + + # Query the remote workflows for supported languages. + languages_output = await self.nexus_client.execute_operation( + NexusRemoteGreetingService.get_languages, + GetLanguagesInput(include_unsupported=False, user_id=REMOTE_WORKFLOW_ONE), + ) + log.append( + f"Supported languages for {REMOTE_WORKFLOW_ONE}: " + f"{languages_output.languages}" + ) + workflow.logger.info( + "supported languages are %s for workflow %s", + languages_output.languages, + REMOTE_WORKFLOW_ONE, + ) + + languages_output = await self.nexus_client.execute_operation( + NexusRemoteGreetingService.get_languages, + GetLanguagesInput(include_unsupported=False, user_id=REMOTE_WORKFLOW_TWO), + ) + log.append( + f"Supported languages for {REMOTE_WORKFLOW_TWO}: " + f"{languages_output.languages}" + ) + workflow.logger.info( + "supported languages are %s for workflow %s", + languages_output.languages, + REMOTE_WORKFLOW_TWO, + ) + + # Update the language on each remote workflow. + previous_language_one = await self.nexus_client.execute_operation( + NexusRemoteGreetingService.set_language, + SetLanguageInput(language=Language.ARABIC, user_id=REMOTE_WORKFLOW_ONE), + ) + + previous_language_two = await self.nexus_client.execute_operation( + NexusRemoteGreetingService.set_language, + SetLanguageInput(language=Language.HINDI, user_id=REMOTE_WORKFLOW_TWO), + ) + + # Confirm the changes by querying. + current_language = await self.nexus_client.execute_operation( + NexusRemoteGreetingService.get_language, + GetLanguageInput(user_id=REMOTE_WORKFLOW_ONE), + ) + log.append( + f"{REMOTE_WORKFLOW_ONE} changed language: " + f"{previous_language_one.name} -> {current_language.name}" + ) + workflow.logger.info( + "Language changed from %s to %s for workflow %s", + previous_language_one, + current_language, + REMOTE_WORKFLOW_ONE, + ) + + current_language = await self.nexus_client.execute_operation( + NexusRemoteGreetingService.get_language, + GetLanguageInput(user_id=REMOTE_WORKFLOW_TWO), + ) + log.append( + f"{REMOTE_WORKFLOW_TWO} changed language: " + f"{previous_language_two.name} -> {current_language.name}" + ) + workflow.logger.info( + "Language changed from %s to %s for workflow %s", + previous_language_two, + current_language, + REMOTE_WORKFLOW_TWO, + ) + + # Approve both workflows so they can complete. + await self.nexus_client.execute_operation( + NexusRemoteGreetingService.approve, + ApproveInput(name="remote-caller", user_id=REMOTE_WORKFLOW_ONE), + ) + await self.nexus_client.execute_operation( + NexusRemoteGreetingService.approve, + ApproveInput(name="remote-caller", user_id=REMOTE_WORKFLOW_TWO), + ) + log.append("Workflows approved") + + # Wait for the remote workflows to finish and return their results. + result = await handle_one + log.append(f"Workflow one result: {result}") + + result = await handle_two + log.append(f"Workflow two result: {result}") + + return log diff --git a/nexus_messaging/ondemandpattern/handler/__init__.py b/nexus_messaging/ondemandpattern/handler/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/nexus_messaging/ondemandpattern/handler/activities.py b/nexus_messaging/ondemandpattern/handler/activities.py new file mode 100644 index 000000000..ba0284894 --- /dev/null +++ b/nexus_messaging/ondemandpattern/handler/activities.py @@ -0,0 +1,22 @@ +import asyncio +from typing import Optional + +from temporalio import activity + +from nexus_messaging.ondemandpattern.service import Language + + +@activity.defn +async def call_greeting_service(language: Language) -> Optional[str]: + """Simulates a call to a remote greeting service. Returns None if unsupported.""" + greetings = { + Language.ARABIC: "\u0645\u0631\u062d\u0628\u0627 \u0628\u0627\u0644\u0639\u0627\u0644\u0645", + Language.CHINESE: "\u4f60\u597d\uff0c\u4e16\u754c", + Language.ENGLISH: "Hello, world", + Language.FRENCH: "Bonjour, monde", + Language.HINDI: "\u0928\u092e\u0938\u094d\u0924\u0947 \u0926\u0941\u0928\u093f\u092f\u093e", + Language.PORTUGUESE: "Ol\u00e1 mundo", + Language.SPANISH: "Hola mundo", + } + await asyncio.sleep(0.2) + return greetings.get(language) diff --git a/nexus_messaging/ondemandpattern/handler/service_handler.py b/nexus_messaging/ondemandpattern/handler/service_handler.py new file mode 100644 index 000000000..1351aae7a --- /dev/null +++ b/nexus_messaging/ondemandpattern/handler/service_handler.py @@ -0,0 +1,84 @@ +""" +Nexus operation handler for the on-demand pattern. Each operation receives the target +userId in its input, and run_from_remote starts a brand-new GreetingWorkflow. +""" + +from __future__ import annotations + +import nexusrpc +from temporalio import nexus +from temporalio.client import WorkflowHandle + +from nexus_messaging.ondemandpattern.handler.workflows import GreetingWorkflow +from nexus_messaging.ondemandpattern.service import ( + ApproveInput, + ApproveOutput, + GetLanguageInput, + GetLanguagesInput, + GetLanguagesOutput, + Language, + NexusRemoteGreetingService, + RunFromRemoteInput, + SetLanguageInput, +) + +WORKFLOW_ID_PREFIX = "GreetingWorkflow_for_" + + +@nexusrpc.handler.service_handler(service=NexusRemoteGreetingService) +class NexusRemoteGreetingServiceHandler: + def _get_workflow_id(self, user_id: str) -> str: + return WORKFLOW_ID_PREFIX + user_id + + def _get_workflow_handle( + self, user_id: str + ) -> WorkflowHandle[GreetingWorkflow, str]: + return nexus.client().get_workflow_handle_for( + GreetingWorkflow.run, self._get_workflow_id(user_id) + ) + + # Starts a new GreetingWorkflow with the caller-specified user ID. + # This is an async Nexus operation backed by workflow_run_operation. + @nexus.workflow_run_operation + async def run_from_remote( + self, ctx: nexus.WorkflowRunOperationContext, input: RunFromRemoteInput + ) -> nexus.WorkflowHandle[str]: + return await ctx.start_workflow( + GreetingWorkflow.run, + id=self._get_workflow_id(input.user_id), + ) + + @nexusrpc.handler.sync_operation + async def get_languages( + self, ctx: nexusrpc.handler.StartOperationContext, input: GetLanguagesInput + ) -> GetLanguagesOutput: + return await self._get_workflow_handle(input.user_id).query( + GreetingWorkflow.get_languages, input + ) + + @nexusrpc.handler.sync_operation + async def get_language( + self, ctx: nexusrpc.handler.StartOperationContext, input: GetLanguageInput + ) -> Language: + return await self._get_workflow_handle(input.user_id).query( + GreetingWorkflow.get_language, + ) + + # Routes to set_language_using_activity so that new languages not already in the + # greetings map can be fetched via an activity. + @nexusrpc.handler.sync_operation + async def set_language( + self, ctx: nexusrpc.handler.StartOperationContext, input: SetLanguageInput + ) -> Language: + return await self._get_workflow_handle(input.user_id).execute_update( + GreetingWorkflow.set_language_using_activity, input + ) + + @nexusrpc.handler.sync_operation + async def approve( + self, ctx: nexusrpc.handler.StartOperationContext, input: ApproveInput + ) -> ApproveOutput: + await self._get_workflow_handle(input.user_id).signal( + GreetingWorkflow.approve, input + ) + return ApproveOutput() diff --git a/nexus_messaging/ondemandpattern/handler/worker.py b/nexus_messaging/ondemandpattern/handler/worker.py new file mode 100644 index 000000000..5eec9cfc2 --- /dev/null +++ b/nexus_messaging/ondemandpattern/handler/worker.py @@ -0,0 +1,48 @@ +import asyncio +import logging +from typing import Optional + +from temporalio.client import Client +from temporalio.envconfig import ClientConfig +from temporalio.worker import Worker + +from nexus_messaging.ondemandpattern.handler.activities import call_greeting_service +from nexus_messaging.ondemandpattern.handler.service_handler import ( + NexusRemoteGreetingServiceHandler, +) +from nexus_messaging.ondemandpattern.handler.workflows import GreetingWorkflow + +interrupt_event = asyncio.Event() + +NAMESPACE = "nexus-messaging-handler-namespace" +TASK_QUEUE = "nexus-messaging-handler-task-queue" + + +async def main(client: Optional[Client] = None): + logging.basicConfig(level=logging.INFO) + + if client is None: + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + config.setdefault("namespace", NAMESPACE) + client = await Client.connect(**config) + + async with Worker( + client, + task_queue=TASK_QUEUE, + workflows=[GreetingWorkflow], + activities=[call_greeting_service], + nexus_service_handlers=[NexusRemoteGreetingServiceHandler()], + ): + logging.info("Handler worker started, ctrl+c to exit") + await interrupt_event.wait() + logging.info("Shutting down") + + +if __name__ == "__main__": + loop = asyncio.new_event_loop() + try: + loop.run_until_complete(main()) + except KeyboardInterrupt: + interrupt_event.set() + loop.run_until_complete(loop.shutdown_asyncgens()) diff --git a/nexus_messaging/ondemandpattern/handler/workflows.py b/nexus_messaging/ondemandpattern/handler/workflows.py new file mode 100644 index 000000000..99fc57289 --- /dev/null +++ b/nexus_messaging/ondemandpattern/handler/workflows.py @@ -0,0 +1,88 @@ +""" +A long-running "entity" workflow that backs the NexusRemoteGreetingService Nexus +operations. The workflow exposes queries, an update, and a signal. These are private +implementation details of the Nexus service: the caller only interacts via Nexus +operations. +""" + +import asyncio +from datetime import timedelta + +from temporalio import workflow +from temporalio.exceptions import ApplicationError + +from nexus_messaging.ondemandpattern.handler.activities import call_greeting_service +from nexus_messaging.ondemandpattern.service import ( + ApproveInput, + GetLanguagesInput, + GetLanguagesOutput, + Language, + SetLanguageInput, +) + + +@workflow.defn +class GreetingWorkflow: + def __init__(self) -> None: + self.approved_for_release = False + self.greetings: dict[Language, str] = { + Language.CHINESE: "\u4f60\u597d\uff0c\u4e16\u754c", + Language.ENGLISH: "Hello, world", + } + self.language = Language.ENGLISH + self.lock = asyncio.Lock() + + @workflow.run + async def run(self) -> str: + # Wait until approved and all in-flight update handlers have finished. + await workflow.wait_condition( + lambda: self.approved_for_release and workflow.all_handlers_finished() + ) + return self.greetings[self.language] + + @workflow.query + def get_languages(self, input: GetLanguagesInput) -> GetLanguagesOutput: + if input.include_unsupported: + languages = sorted(Language) + else: + languages = sorted(self.greetings) + return GetLanguagesOutput(languages=languages) + + @workflow.query + def get_language(self) -> Language: + return self.language + + @workflow.signal + def approve(self, input: ApproveInput) -> None: + workflow.logger.info("Approval signal received for user %s", input.user_id) + self.approved_for_release = True + + @workflow.update + def set_language(self, input: SetLanguageInput) -> Language: + workflow.logger.info("setLanguage update received for user %s", input.user_id) + previous_language, self.language = self.language, input.language + return previous_language + + @set_language.validator + def validate_set_language(self, input: SetLanguageInput) -> None: + if input.language not in self.greetings: + raise ValueError(f"{input.language.name} is not supported") + + # Changes the active language, calling an activity to fetch a greeting for new + # languages not already in the greetings map. + @workflow.update + async def set_language_using_activity(self, input: SetLanguageInput) -> Language: + if input.language not in self.greetings: + async with self.lock: + greeting = await workflow.execute_activity( + call_greeting_service, + input.language, + start_to_close_timeout=timedelta(seconds=10), + ) + if greeting is None: + raise ApplicationError( + f"Greeting service does not support {input.language.name}" + ) + self.greetings[input.language] = greeting + previous_language, self.language = self.language, input.language + return previous_language diff --git a/nexus_messaging/ondemandpattern/service.py b/nexus_messaging/ondemandpattern/service.py new file mode 100644 index 000000000..8f347d32b --- /dev/null +++ b/nexus_messaging/ondemandpattern/service.py @@ -0,0 +1,72 @@ +""" +Nexus service definition for the on-demand pattern. Every operation includes a userId +so the caller controls which workflow instance is targeted. This also exposes a +run_from_remote operation that starts a new GreetingWorkflow. +""" + +from dataclasses import dataclass +from enum import IntEnum + +import nexusrpc + + +class Language(IntEnum): + ARABIC = 1 + CHINESE = 2 + ENGLISH = 3 + FRENCH = 4 + HINDI = 5 + PORTUGUESE = 6 + SPANISH = 7 + + +@dataclass +class RunFromRemoteInput: + user_id: str + + +@dataclass +class GetLanguagesInput: + include_unsupported: bool + user_id: str + + +@dataclass +class GetLanguagesOutput: + languages: list[Language] + + +@dataclass +class GetLanguageInput: + user_id: str + + +@dataclass +class SetLanguageInput: + language: Language + user_id: str + + +@dataclass +class ApproveInput: + name: str + user_id: str + + +@dataclass +class ApproveOutput: + pass + + +@nexusrpc.service +class NexusRemoteGreetingService: + # Starts a new GreetingWorkflow with the given workflow ID (asynchronous). + run_from_remote: nexusrpc.Operation[RunFromRemoteInput, str] + # Returns the languages supported by the specified workflow. + get_languages: nexusrpc.Operation[GetLanguagesInput, GetLanguagesOutput] + # Returns the currently active language of the specified workflow. + get_language: nexusrpc.Operation[GetLanguageInput, Language] + # Changes the active language on the specified workflow, returning the previous one. + set_language: nexusrpc.Operation[SetLanguageInput, Language] + # Approves the specified workflow, allowing it to complete. + approve: nexusrpc.Operation[ApproveInput, ApproveOutput] diff --git a/nexus_multiple_args/README.md b/nexus_multiple_args/README.md new file mode 100644 index 000000000..8da0f146f --- /dev/null +++ b/nexus_multiple_args/README.md @@ -0,0 +1,33 @@ +This sample shows how to map a Nexus operation to a handler workflow that takes multiple input arguments. The Nexus operation receives a single input object but unpacks it into multiple arguments when starting the workflow. + +### Sample directory structure + +- [service.py](./service.py) - shared Nexus service definition +- [caller](./caller) - a caller workflow that executes Nexus operations, together with a worker and starter code +- [handler](./handler) - Nexus operation handlers, together with a workflow used by the Nexus operation, and a worker that polls for both workflow and Nexus tasks. + +### Instructions + +Start a Temporal server. (See the main samples repo [README](../README.md)). + +Run the following: + +``` +temporal operator namespace create --namespace nexus-multiple-args-handler-namespace +temporal operator namespace create --namespace nexus-multiple-args-caller-namespace + +temporal operator nexus endpoint create \ + --name nexus-multiple-args-nexus-endpoint \ + --target-namespace nexus-multiple-args-handler-namespace \ + --target-task-queue nexus-multiple-args-handler-task-queue +``` + +In one terminal, run the Temporal worker in the handler namespace: +``` +uv run nexus_multiple_args/handler/worker.py +``` + +In another terminal, run the Temporal worker in the caller namespace and start the caller workflow: +``` +uv run nexus_multiple_args/caller/app.py +``` \ No newline at end of file diff --git a/nexus_multiple_args/__init__.py b/nexus_multiple_args/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/nexus_multiple_args/caller/__init__.py b/nexus_multiple_args/caller/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/nexus_multiple_args/caller/app.py b/nexus_multiple_args/caller/app.py new file mode 100644 index 000000000..b4d7ebc34 --- /dev/null +++ b/nexus_multiple_args/caller/app.py @@ -0,0 +1,55 @@ +import asyncio +import uuid +from typing import Optional + +from temporalio.client import Client +from temporalio.envconfig import ClientConfig +from temporalio.worker import Worker + +from nexus_multiple_args.caller.workflows import CallerWorkflow + +NAMESPACE = "nexus-multiple-args-caller-namespace" +TASK_QUEUE = "nexus-multiple-args-caller-task-queue" + + +async def execute_caller_workflow( + client: Optional[Client] = None, +) -> tuple[str, str]: + if client is None: + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + config.setdefault("namespace", NAMESPACE) + client = await Client.connect(**config) + + async with Worker( + client, + task_queue=TASK_QUEUE, + workflows=[CallerWorkflow], + ): + # Execute workflow with English language + result1 = await client.execute_workflow( + CallerWorkflow.run, + args=["Nexus", "en"], + id=str(uuid.uuid4()), + task_queue=TASK_QUEUE, + ) + + # Execute workflow with Spanish language + result2 = await client.execute_workflow( + CallerWorkflow.run, + args=["Nexus", "es"], + id=str(uuid.uuid4()), + task_queue=TASK_QUEUE, + ) + + return result1, result2 + + +if __name__ == "__main__": + loop = asyncio.new_event_loop() + try: + results = loop.run_until_complete(execute_caller_workflow()) + for result in results: + print(result) + except KeyboardInterrupt: + loop.run_until_complete(loop.shutdown_asyncgens()) diff --git a/nexus_multiple_args/caller/workflows.py b/nexus_multiple_args/caller/workflows.py new file mode 100644 index 000000000..940a032f1 --- /dev/null +++ b/nexus_multiple_args/caller/workflows.py @@ -0,0 +1,30 @@ +from temporalio import workflow + +with workflow.unsafe.imports_passed_through(): + from nexus_multiple_args.service import HelloInput, MyNexusService + +NEXUS_ENDPOINT = "nexus-multiple-args-nexus-endpoint" + + +# This is a workflow that calls a nexus operation with multiple arguments. +@workflow.defn +class CallerWorkflow: + # An __init__ method is always optional on a workflow class. Here we use it to set the + # nexus client, but that could alternatively be done in the run method. + def __init__(self): + self.nexus_client = workflow.create_nexus_client( + service=MyNexusService, + endpoint=NEXUS_ENDPOINT, + ) + + # The workflow run method demonstrates calling a nexus operation with multiple arguments + # packed into an input object. + @workflow.run + async def run(self, name: str, language: str) -> str: + # Start the nexus operation and wait for the result in one go, using execute_operation. + # The multiple arguments (name and language) are packed into a HelloInput object. + result = await self.nexus_client.execute_operation( + MyNexusService.hello, + HelloInput(name=name, language=language), + ) + return result.message diff --git a/nexus_multiple_args/handler/__init__.py b/nexus_multiple_args/handler/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/nexus_multiple_args/handler/service_handler.py b/nexus_multiple_args/handler/service_handler.py new file mode 100644 index 000000000..8c00ee310 --- /dev/null +++ b/nexus_multiple_args/handler/service_handler.py @@ -0,0 +1,37 @@ +from __future__ import annotations + +import nexusrpc +from temporalio import nexus + +from nexus_multiple_args.handler.workflows import HelloHandlerWorkflow +from nexus_multiple_args.service import HelloInput, HelloOutput, MyNexusService + + +# @@@SNIPSTART samples-python-nexus-handler-multiargs +@nexusrpc.handler.service_handler(service=MyNexusService) +class MyNexusServiceHandler: + """ + Service handler that demonstrates multiple argument handling in Nexus operations. + """ + + # This is a nexus operation that is backed by a Temporal workflow. + # The key feature here is that it demonstrates how to map a single input object + # (HelloInput) to a workflow that takes multiple individual arguments. + @nexus.workflow_run_operation + async def hello( + self, ctx: nexus.WorkflowRunOperationContext, input: HelloInput + ) -> nexus.WorkflowHandle[HelloOutput]: + """ + Start a workflow with multiple arguments unpacked from the input object. + """ + return await ctx.start_workflow( + HelloHandlerWorkflow.run, + args=[ + input.name, # First argument: name + input.language, # Second argument: language + ], + id=f"hello-multi-args-{input.name}-{input.language}", + ) + + +# @@@SNIPEND diff --git a/nexus_multiple_args/handler/worker.py b/nexus_multiple_args/handler/worker.py new file mode 100644 index 000000000..079d08ae8 --- /dev/null +++ b/nexus_multiple_args/handler/worker.py @@ -0,0 +1,47 @@ +import asyncio +import logging +from typing import Optional + +from temporalio.client import Client +from temporalio.envconfig import ClientConfig +from temporalio.worker import Worker + +from nexus_multiple_args.handler.service_handler import MyNexusServiceHandler +from nexus_multiple_args.handler.workflows import HelloHandlerWorkflow + +interrupt_event = asyncio.Event() + +NAMESPACE = "nexus-multiple-args-handler-namespace" +TASK_QUEUE = "nexus-multiple-args-handler-task-queue" + + +async def main(client: Optional[Client] = None): + logging.basicConfig(level=logging.INFO) + + if client is None: + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + config.setdefault("namespace", NAMESPACE) + client = await Client.connect(**config) + + # Start the worker, passing the Nexus service handler instance, in addition to the + # workflow classes that are started by your nexus operations, and any activities + # needed. This Worker will poll for both workflow tasks and Nexus tasks. + async with Worker( + client, + task_queue=TASK_QUEUE, + workflows=[HelloHandlerWorkflow], + nexus_service_handlers=[MyNexusServiceHandler()], + ): + logging.info("Worker started, ctrl+c to exit") + await interrupt_event.wait() + logging.info("Shutting down") + + +if __name__ == "__main__": + loop = asyncio.new_event_loop() + try: + loop.run_until_complete(main()) + except KeyboardInterrupt: + interrupt_event.set() + loop.run_until_complete(loop.shutdown_asyncgens()) diff --git a/nexus_multiple_args/handler/workflows.py b/nexus_multiple_args/handler/workflows.py new file mode 100644 index 000000000..15bd08240 --- /dev/null +++ b/nexus_multiple_args/handler/workflows.py @@ -0,0 +1,32 @@ +from temporalio import workflow + +with workflow.unsafe.imports_passed_through(): + from nexus_multiple_args.service import HelloOutput + + +# This is the workflow that is started by the `hello` nexus operation. +# It demonstrates handling multiple arguments passed from the Nexus service. +@workflow.defn +class HelloHandlerWorkflow: + @workflow.run + async def run(self, name: str, language: str) -> HelloOutput: + """ + Handle the hello workflow with multiple arguments. + + This method receives the individual arguments (name and language) + that were unpacked from the HelloInput in the service handler. + """ + if language == "en": + message = f"Hello {name} 👋" + elif language == "fr": + message = f"Bonjour {name} 👋" + elif language == "de": + message = f"Hallo {name} 👋" + elif language == "es": + message = f"¡Hola! {name} 👋" + elif language == "tr": + message = f"Merhaba {name} 👋" + else: + raise ValueError(f"Unsupported language: {language}") + + return HelloOutput(message=message) diff --git a/nexus_multiple_args/service.py b/nexus_multiple_args/service.py new file mode 100644 index 000000000..ccae11fd9 --- /dev/null +++ b/nexus_multiple_args/service.py @@ -0,0 +1,34 @@ +""" +This is a Nexus service definition that demonstrates multiple argument handling. + +A service definition defines a Nexus service as a named collection of operations, each +with input and output types. It does not implement operation handling: see the service +handler and operation handlers in nexus_multiple_args.handler.service_handler for that. + +A Nexus service definition is used by Nexus callers (e.g. a Temporal workflow) to create +type-safe clients, and it is used by Nexus handlers to validate that they implement +correctly-named operation handlers with the correct input and output types. + +The service defined in this file features one operation: hello, where hello +demonstrates handling multiple arguments through a single input object. +""" + +from dataclasses import dataclass + +import nexusrpc + + +@dataclass +class HelloInput: + name: str + language: str + + +@dataclass +class HelloOutput: + message: str + + +@nexusrpc.service +class MyNexusService: + hello: nexusrpc.Operation[HelloInput, HelloOutput] diff --git a/nexus_standalone_operations/README.md b/nexus_standalone_operations/README.md new file mode 100644 index 000000000..63a04b5ac --- /dev/null +++ b/nexus_standalone_operations/README.md @@ -0,0 +1,116 @@ +This sample demonstrates how to execute Nexus operations directly from client code, +without wrapping them in a workflow. It shows both synchronous and asynchronous +(workflow-backed) operations, plus listing and counting operations. + +The starter and worker connect to two different namespaces (a "caller" namespace and a "handler" +namespace) — this mirrors how Nexus is typically used to cross namespace boundaries. The client is +configured via the SDK's [environment configuration](https://docs.temporal.io/develop/environment-configuration) +support (`ClientConfig.load_client_connect_config()`), which reads `TEMPORAL_NAMESPACE`, +`TEMPORAL_ADDRESS`, etc. from the environment (and optionally profiles from `temporal.toml`). + +### Temporal Python SDK support for Standalone Nexus Operations is at [Pre-release](https://docs.temporal.io/evaluate/development-production-features/release-stages#pre-release). + +All APIs are experimental and may be subject to backwards-incompatible changes. + +Standalone Nexus operations require a server version that supports this feature. Use the dev server build at https://github.com/temporalio/cli/releases/tag/v1.7.4-standalone-nexus-operations. + +### Sample directory structure + +- [service.py](./service.py) - Nexus service definition with echo (sync) and hello (async) operations +- [handler.py](./handler.py) - Nexus operation handlers and the backing workflow for the async operation +- [worker.py](./worker.py) - Temporal worker that hosts the Nexus service +- [starter.py](./starter.py) - Client that executes standalone Nexus operations + +## Run locally against a dev server + +1. Start the [Temporal dev server build that supports standalone Nexus operations](https://docs.temporal.io/standalone-nexus-operation#temporal-cli-support) with the required namespaces pre-created: + + ```bash + ./temporal server start-dev \ + --namespace my-caller-namespace \ + --namespace my-handler-namespace + ``` + +2. Create a Nexus endpoint that routes to the handler namespace and the worker's task queue: + + ```bash + ./temporal operator nexus endpoint create \ + --name my-nexus-endpoint \ + --target-namespace my-handler-namespace \ + --target-task-queue nexus-handler-queue + ``` + +3. In a second terminal, start the worker in the handler namespace: + + ```bash + TEMPORAL_NAMESPACE=my-handler-namespace uv run nexus_standalone_operations/worker.py + ``` + +4. In a third terminal, run the starter in the caller namespace: + + ```bash + TEMPORAL_NAMESPACE=my-caller-namespace uv run nexus_standalone_operations/starter.py + ``` + +### Expected output + +``` +Echo result: hello +Echo result from existing operation handle: hello + +Started `MyNexusService.Hello`. OperationID: hello-... +`MyNexusService.Hello` result: Hello, World! + +Listing Nexus operations: + OperationId: echo-..., Operation: echo, Status: COMPLETED + OperationId: hello-..., Operation: hello, Status: COMPLETED + +Total Nexus operations: 2 +``` + +If you run the starter code multiple times, you should see additional operations in the listing results, as more operations are run. +The same goes for the total number of operations. + +## Run against Temporal Cloud + +1. Create two namespaces in Temporal Cloud (for example `my-caller-namespace.` and + `my-handler-namespace.`) and generate an API key (or mTLS cert) that can access both. + +2. Create a Nexus endpoint that targets the handler namespace and the worker's task queue. See the + Temporal Cloud instructions at https://docs.temporal.io/nexus/registry#create-a-nexus-endpoint. + Use: + - Endpoint name: `my-nexus-endpoint` + - Target namespace: `my-handler-namespace.` + - Target task queue: `nexus-handler-queue` + - Allowed caller namespaces: include `my-caller-namespace.` (endpoints reject callers + that are not on this list) + +3. Add two profiles to your [environment configuration file](https://docs.temporal.io/develop/environment-configuration), + one per namespace. Using API keys: + + ```toml + [profile.handler] + address = "..api.temporal.io:7233" + namespace = "my-handler-namespace." + api_key = "" + + [profile.caller] + address = "..api.temporal.io:7233" + namespace = "my-caller-namespace." + api_key = "" + ``` + + For mTLS instead of API keys, set `tls.client_cert_path` and `tls.client_key_path` on each profile + (see the [docs](https://docs.temporal.io/develop/environment-configuration) for the full schema). + +4. Run the worker and starter in separate terminals, selecting the appropriate profile in each: + + ```bash + # terminal 1 (worker, handler namespace) + TEMPORAL_PROFILE=handler uv run nexus_standalone_operations/worker.py + ``` + + ```bash + # terminal 2 (starter, caller namespace) + TEMPORAL_PROFILE=caller uv run nexus_standalone_operations/starter.py + ``` diff --git a/nexus_standalone_operations/__init__.py b/nexus_standalone_operations/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/nexus_standalone_operations/handler.py b/nexus_standalone_operations/handler.py new file mode 100644 index 000000000..da18e9477 --- /dev/null +++ b/nexus_standalone_operations/handler.py @@ -0,0 +1,45 @@ +"""Nexus service handler and backing workflow for standalone operations sample.""" + +from __future__ import annotations + +import uuid + +import nexusrpc.handler +from temporalio import nexus, workflow + +from nexus_standalone_operations.service import ( + EchoInput, + EchoOutput, + HelloInput, + HelloOutput, + MyNexusService, +) + + +@workflow.defn +class HelloWorkflow: + @workflow.run + async def run(self, input: HelloInput) -> HelloOutput: + return HelloOutput(greeting=f"Hello, {input.name}!") + + +@nexusrpc.handler.service_handler(service=MyNexusService) +class MyNexusServiceHandler: + @nexusrpc.handler.sync_operation + async def echo( + self, _ctx: nexusrpc.handler.StartOperationContext, input: EchoInput + ) -> EchoOutput: + return EchoOutput(message=input.message) + + @nexus.temporal_operation + async def hello( + self, + _ctx: nexus.TemporalStartOperationContext, + client: nexus.TemporalNexusClient, + input: HelloInput, + ) -> nexus.TemporalOperationResult[HelloOutput]: + return await client.start_workflow( + HelloWorkflow.run, + input, + id=str(uuid.uuid4()), + ) diff --git a/nexus_standalone_operations/service.py b/nexus_standalone_operations/service.py new file mode 100644 index 000000000..a8a906db2 --- /dev/null +++ b/nexus_standalone_operations/service.py @@ -0,0 +1,39 @@ +"""Nexus service definition for standalone operations sample. + +Defines a Nexus service with two operations: +- echo: a synchronous operation that echoes the input message +- hello: an asynchronous (workflow-backed) operation that returns a greeting + +This service definition is used by both the handler (to validate operation +signatures) and the client (to create type-safe nexus clients). +""" + +from dataclasses import dataclass + +import nexusrpc + + +@dataclass +class EchoInput: + message: str + + +@dataclass +class EchoOutput: + message: str + + +@dataclass +class HelloInput: + name: str + + +@dataclass +class HelloOutput: + greeting: str + + +@nexusrpc.service +class MyNexusService: + echo: nexusrpc.Operation[EchoInput, EchoOutput] + hello: nexusrpc.Operation[HelloInput, HelloOutput] diff --git a/nexus_standalone_operations/starter.py b/nexus_standalone_operations/starter.py new file mode 100644 index 000000000..df001cab7 --- /dev/null +++ b/nexus_standalone_operations/starter.py @@ -0,0 +1,83 @@ +"""Starter that demonstrates standalone Nexus operation execution. + +Unlike other Nexus samples that call operations from within a workflow, this +sample executes Nexus operations directly from client code using the standalone +Nexus operation APIs. +""" + +import asyncio +import uuid +from datetime import timedelta + +from temporalio.client import Client +from temporalio.envconfig import ClientConfig + +from nexus_standalone_operations.service import ( + EchoInput, + EchoOutput, + HelloInput, + MyNexusService, +) + +ENDPOINT_NAME = "my-nexus-endpoint" + + +async def main() -> None: + config = ClientConfig.load_client_connect_config() + _ = config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) + + # Create a typed NexusClient bound to the endpoint and service. + # The endpoint must be pre-created on the server (see README). + nexus_client = client.create_nexus_client( + service=MyNexusService, endpoint=ENDPOINT_NAME + ) + + # Start sync echo operation and await the result immediately. + operation_id = f"echo-{uuid.uuid4()}" + echo_result = await nexus_client.execute_operation( + MyNexusService.echo, + EchoInput(message="hello"), + id=operation_id, + schedule_to_close_timeout=timedelta(seconds=10), + ) + print(f"Echo result: {echo_result.message}") + + # Get a handle and the result of an existing operation + existing_op_handle = client.get_nexus_operation_handle( + operation_id, operation=MyNexusService.echo + ) + existing_result = await existing_op_handle.result() + print(f"Echo result from existing operation handle: {existing_result.message}") + + # Start async (workflow-backed) hello operation and get a NexusOperationHandle. + handle = await nexus_client.start_operation( + MyNexusService.hello, + HelloInput(name="World"), + id=f"hello-{uuid.uuid4()}", + schedule_to_close_timeout=timedelta(seconds=10), + ) + + print(f"\nStarted `MyNexusService.Hello`. OperationID: {handle.operation_id}") + + # Use the NexusOperationHandle to await the result of the operation. + hello_result = await handle.result() + print(f"`MyNexusService.Hello` result: {hello_result.greeting}") + + # List nexus operations. + print("\nListing Nexus operations:") + query = f'Endpoint = "{ENDPOINT_NAME}"' + async for op in client.list_nexus_operations(query): + print( + f" OperationId: {op.operation_id},", + f" Operation: {op.operation},", + f" Status: {op.status.name}", + ) + + # Count nexus operations. + count = await client.count_nexus_operations(query) + print(f"\nTotal Nexus operations: {count.count}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/nexus_standalone_operations/worker.py b/nexus_standalone_operations/worker.py new file mode 100644 index 000000000..2adcbdd90 --- /dev/null +++ b/nexus_standalone_operations/worker.py @@ -0,0 +1,41 @@ +"""Worker that hosts the Nexus service for standalone operations sample.""" + +import asyncio +import logging + +from temporalio.client import Client +from temporalio.envconfig import ClientConfig +from temporalio.worker import Worker + +from nexus_standalone_operations.handler import HelloWorkflow, MyNexusServiceHandler + +interrupt_event = asyncio.Event() + +TASK_QUEUE = "nexus-handler-queue" + + +async def main() -> None: + logging.basicConfig(level=logging.INFO) + + config = ClientConfig.load_client_connect_config() + _ = config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) + + async with Worker( + client, + task_queue=TASK_QUEUE, + workflows=[HelloWorkflow], + nexus_service_handlers=[MyNexusServiceHandler()], + ): + logging.info("Worker started, ctrl+c to exit") + _ = await interrupt_event.wait() + logging.info("Shutting down") + + +if __name__ == "__main__": + loop = asyncio.new_event_loop() + try: + loop.run_until_complete(main()) + except KeyboardInterrupt: + interrupt_event.set() + loop.run_until_complete(loop.shutdown_asyncgens()) diff --git a/open_telemetry/README.md b/open_telemetry/README.md index e742b3dd5..e95deacab 100644 --- a/open_telemetry/README.md +++ b/open_telemetry/README.md @@ -4,35 +4,34 @@ This sample shows how to configure OpenTelemetry to capture workflow traces and For this sample, the optional `open_telemetry` dependency group must be included. To include, run: - poetry install --with open_telemetry + uv sync --group open-telemetry -To run, first see [README.md](../README.md) for prerequisites. Then run the following to start a Jaeger container -with OTLP collector enabled to collect and view the trace results: +To run, first see [README.md](../README.md) for prerequisites. Then run the following to start an [Aspire](https://hub.docker.com/r/microsoft/dotnet-aspire-dashboard/) OTEL collector - docker run -d --name jaeger \ - -e COLLECTOR_OTLP_ENABLED=true \ - -p 16686:16686 \ - -p 4317:4317 \ - -p 4318:4318 \ - jaegertracing/all-in-one:latest + docker compose up -Now, from this directory, start the worker in its own terminal: +Now, start the worker in its own terminal: - poetry run python worker.py + uv run open_telemetry/worker.py -This will start the worker. Then, in another terminal, run the following to execute the workflow: +Then, in another terminal, run the following to execute the workflow: - poetry run python starter.py + uv run open_telemetry/starter.py -The workflow should complete with the hello result. The workflow trace can now be viewed in Jaeger at -http://localhost:16686/. Under service, select `my-service` and "Find Traces". The workflow should appear and when -clicked, may look something like: +The workflow should complete with the hello result. -![Jaeger Screenshot](jaeger-screenshot.png) +Now view the Aspire UI at http://localhost:18888/. + +To view metrics sent describing the worker and the workflow that was executed, select `Metrics` on the left and under "Select a resource" select "temporal-core-sdk". It may look like this: + +![Aspire metrics screenshot](aspire-metrics-screenshot.png) + + +To view workflow spans, select `Traces` on the left and under "Select a resource" select "temporal-core-sdk". It may look like this: + +![Aspire traces screenshot](aspire-traces-screenshot.png) Note, in-workflow spans do not have a time associated with them. This is by intention since due to replay. In OpenTelemetry, only the process that started the span may end it. But in Temporal a span may cross workers/processes. Therefore we intentionally start-then-end in-workflow spans immediately. So while the start time and hierarchy is -accurate, the duration is not. - -The metrics should have been dumped out in the terminal where the OpenTelemetry collector container is running. +accurate, the duration is not. \ No newline at end of file diff --git a/open_telemetry/aspire-metrics-screenshot.png b/open_telemetry/aspire-metrics-screenshot.png new file mode 100644 index 000000000..ed4ab8e93 Binary files /dev/null and b/open_telemetry/aspire-metrics-screenshot.png differ diff --git a/open_telemetry/aspire-traces-screenshot.png b/open_telemetry/aspire-traces-screenshot.png new file mode 100644 index 000000000..298c07ebd Binary files /dev/null and b/open_telemetry/aspire-traces-screenshot.png differ diff --git a/open_telemetry/docker-compose.yaml b/open_telemetry/docker-compose.yaml new file mode 100644 index 000000000..da3c02aed --- /dev/null +++ b/open_telemetry/docker-compose.yaml @@ -0,0 +1,8 @@ +services: + aspire-dashboard: + environment: + Dashboard__Frontend__AuthMode: Unsecured + image: mcr.microsoft.com/dotnet/aspire-dashboard:8.0 + ports: + - 4317:18889 + - 18888:18888 \ No newline at end of file diff --git a/open_telemetry/jaeger-screenshot.png b/open_telemetry/jaeger-screenshot.png deleted file mode 100644 index 8376d49c1..000000000 Binary files a/open_telemetry/jaeger-screenshot.png and /dev/null differ diff --git a/open_telemetry/otel-metrics-collector-config.yaml b/open_telemetry/otel-metrics-collector-config.yaml deleted file mode 100644 index 45265f88a..000000000 --- a/open_telemetry/otel-metrics-collector-config.yaml +++ /dev/null @@ -1,15 +0,0 @@ -receivers: - otlp: - protocols: - grpc: -exporters: - logging: - loglevel: debug -processors: - batch: -service: - pipelines: - metrics: - receivers: [otlp] - exporters: [logging] - processors: [batch] \ No newline at end of file diff --git a/open_telemetry/starter.py b/open_telemetry/starter.py index 863603685..9e8650b0a 100644 --- a/open_telemetry/starter.py +++ b/open_telemetry/starter.py @@ -2,6 +2,7 @@ from temporalio.client import Client from temporalio.contrib.opentelemetry import TracingInterceptor +from temporalio.envconfig import ClientConfig from open_telemetry.worker import GreetingWorkflow, init_runtime_with_telemetry @@ -9,9 +10,11 @@ async def main(): runtime = init_runtime_with_telemetry() + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") # Connect client client = await Client.connect( - "localhost:7233", + **config, # Use OpenTelemetry interceptor interceptors=[TracingInterceptor()], runtime=runtime, diff --git a/open_telemetry/worker.py b/open_telemetry/worker.py index f43471522..631bd008e 100644 --- a/open_telemetry/worker.py +++ b/open_telemetry/worker.py @@ -1,15 +1,15 @@ import asyncio from datetime import timedelta -import opentelemetry.context from opentelemetry import trace from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter -from opentelemetry.sdk.resources import SERVICE_NAME, Resource +from opentelemetry.sdk.resources import SERVICE_NAME, Resource # type: ignore from opentelemetry.sdk.trace import TracerProvider from opentelemetry.sdk.trace.export import BatchSpanProcessor from temporalio import activity, workflow from temporalio.client import Client from temporalio.contrib.opentelemetry import TracingInterceptor +from temporalio.envconfig import ClientConfig from temporalio.runtime import OpenTelemetryConfig, Runtime, TelemetryConfig from temporalio.worker import Worker @@ -51,9 +51,12 @@ def init_runtime_with_telemetry() -> Runtime: async def main(): runtime = init_runtime_with_telemetry() + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + # Connect client client = await Client.connect( - "localhost:7233", + **config, # Use OpenTelemetry interceptor interceptors=[TracingInterceptor()], runtime=runtime, diff --git a/openai_agents/README.md b/openai_agents/README.md new file mode 100644 index 000000000..f6857d795 --- /dev/null +++ b/openai_agents/README.md @@ -0,0 +1,42 @@ +# Temporal OpenAI Agents SDK Integration + +⚠️ **Public Preview** - This integration is experimental and its interfaces may change prior to General Availability. + +This directory contains samples demonstrating how to use the [OpenAI Agents SDK](https://github.com/openai/openai-agents-python) with Temporal's durable execution engine. +These samples are adapted from the [OpenAI Agents SDK examples](https://github.com/openai/openai-agents-python/tree/main/examples) and extended with Temporal's durability and orchestration capabilities. + +See the [module documentation](https://github.com/temporalio/sdk-python/blob/main/temporalio/contrib/openai_agents/README.md) for more information. + +## Overview + +The integration combines: +- **Temporal workflows** for orchestrating agent control flow and state management +- **OpenAI Agents SDK** for AI agent creation and tool interactions + +This approach ensures that AI agent workflows are durable, observable, and can handle failures gracefully. + +## Prerequisites + +- [Temporal CLI](https://docs.temporal.io/cli) with a local dev server running: + ``` + temporal server start-dev + ``` +- Required dependencies installed via `uv sync --group openai-agents` +- OpenAI API key set as environment variable: `export OPENAI_API_KEY=your_key_here` + +## Examples + +Each directory contains a complete example with its own README for detailed instructions: + +- **[Basic Examples](./basic/README.md)** - Simple agent examples including a hello world agent and a tools-enabled agent that can access external APIs like weather services. +- **[Agent Patterns](./agent_patterns/README.md)** - Advanced patterns for agent composition, including using agents as tools within other agents. +- **[Tools](./tools/README.md)** - Demonstrates available tools such as file search, image generation, and others. +- **[Handoffs](./handoffs/README.md)** - Agents collaborating via handoffs. +- **[Hosted MCP](./hosted_mcp/README.md)** - Using the MCP client functionality of the OpenAI Responses API. +- **[MCP](./mcp/README.md)** - Local MCP servers (filesystem/stdio, streamable HTTP, SSE, prompt server) integrated with Temporal workflows. +- **[Model Providers](./model_providers/README.md)** - Using custom LLM providers (e.g., Anthropic via LiteLLM). +- **[Research Bot](./research_bot/README.md)** - Multi-agent research system with specialized roles: a planner agent, search agent, and writer agent working together to conduct comprehensive research. +- **[Customer Service](./customer_service/README.md)** - Interactive customer service agent with escalation capabilities, demonstrating conversational workflows. +- **[Reasoning Content](./reasoning_content/README.md)** - Example of how to retrieve the thought process of reasoning models. +- **[Financial Research Agent](./financial_research_agent/README.md)** - Multi-agent financial research system with planner, search, analyst, writer, and verifier agents collaborating. +- **[Streaming](./streaming/README.md)** - `Runner.run_streamed` with buffered token streaming to external subscribers via `temporalio.contrib.workflow_streams`. **Experimental.** diff --git a/openai_agents/__init__.py b/openai_agents/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/openai_agents/agent_patterns/README.md b/openai_agents/agent_patterns/README.md new file mode 100644 index 000000000..c86fdfd21 --- /dev/null +++ b/openai_agents/agent_patterns/README.md @@ -0,0 +1,97 @@ +# Agent Patterns + +Common agentic patterns extended with Temporal's durable execution capabilities. + +*Adapted from [OpenAI Agents SDK agent patterns](https://github.com/openai/openai-agents-python/tree/main/examples/agent_patterns)* + +Before running these examples, be sure to review the [prerequisites and background on the integration](../README.md). + +## Running the Examples + +First, start the worker (supports all patterns): +```bash +uv run openai_agents/agent_patterns/run_worker.py +``` + +Then run individual examples in separate terminals: + +### Deterministic Flows +Sequential agent execution with validation gates - demonstrates breaking complex tasks into smaller steps: +```bash +uv run openai_agents/agent_patterns/run_deterministic_workflow.py +``` + +### Parallelization +Run multiple agents in parallel and select the best result - useful for improving quality or reducing latency: +```bash +uv run openai_agents/agent_patterns/run_parallelization_workflow.py +``` + +### LLM-as-a-Judge +Iterative improvement using feedback loops - generate content, evaluate it, and improve until satisfied: +```bash +uv run openai_agents/agent_patterns/run_llm_as_a_judge_workflow.py +``` + +### Agents as Tools +Use agents as callable tools within other agents - enables composition and specialized task delegation: +```bash +uv run openai_agents/agent_patterns/run_agents_as_tools_workflow.py +``` + +### Agent Routing and Handoffs +Route requests to specialized agents based on content analysis (adapted to consume the run's output in one piece; see [Streaming](../streaming/README.md) for streaming output to external subscribers): +```bash +uv run openai_agents/agent_patterns/run_routing_workflow.py +``` + +### Input Guardrails +Pre-execution validation to prevent unwanted requests - demonstrates safety mechanisms: +```bash +uv run openai_agents/agent_patterns/run_input_guardrails_workflow.py +``` + +### Output Guardrails +Post-execution validation to detect sensitive content - ensures safe responses: +```bash +uv run openai_agents/agent_patterns/run_output_guardrails_workflow.py +``` + +### Forcing Tool Use +Control tool execution strategies - choose between different approaches to tool usage: +```bash +uv run openai_agents/agent_patterns/run_forcing_tool_use_workflow.py +``` + +## Pattern Details + +### Deterministic Flows +A common tactic is to break down a task into a series of smaller steps. Each task can be performed by an agent, and the output of one agent is used as input to the next. For example, if your task was to generate a story, you could break it down into the following steps: + +1. Generate an outline +2. Check outline quality and genre +3. Write the story (only if outline passes validation) + +Each of these steps can be performed by an agent. The output of one agent is used as input to the next. + +### Parallelization +Running multiple agents in parallel is a common pattern. This can be useful for both latency (e.g. if you have multiple steps that don't depend on each other) and also for other reasons e.g. generating multiple responses and picking the best one. + +### LLM-as-a-Judge +LLMs can often improve the quality of their output if given feedback. A common pattern is to generate a response using a model, and then use a second model to provide feedback. You can even use a small model for the initial generation and a larger model for the feedback, to optimize cost. + +### Agents as Tools +The mental model for handoffs is that the new agent "takes over". It sees the previous conversation history, and owns the conversation from that point onwards. However, this is not the only way to use agents. You can also use agents as a tool - the tool agent goes off and runs on its own, and then returns the result to the original agent. + +### Guardrails +Related to parallelization, you often want to run input guardrails to make sure the inputs to your agents are valid. For example, if you have a customer support agent, you might want to make sure that the user isn't trying to ask for help with a math problem. + +You can definitely do this without any special Agents SDK features by using parallelization, but we support a special guardrail primitive. Guardrails can have a "tripwire" - if the tripwire is triggered, the agent execution will immediately stop and a `GuardrailTripwireTriggered` exception will be raised. + +This is really useful for latency: for example, you might have a very fast model that runs the guardrail and a slow model that runs the actual agent. You wouldn't want to wait for the slow model to finish, so guardrails let you quickly reject invalid inputs. + +## Omitted Examples + +The following patterns from the [reference repository](https://github.com/openai/openai-agents-python/tree/main/examples/agent_patterns) are not included in this Temporal adaptation: + +- **Streaming Guardrails**: The pattern interrupts generation by inspecting deltas from inside the run loop. The Temporal integration does support streaming (see [Streaming](../streaming/README.md)), but the model call runs in an activity and the workflow only sees its events once that activity returns, so there is no in-run delta to act on mid-response. \ No newline at end of file diff --git a/openai_agents/agent_patterns/run_agents_as_tools_workflow.py b/openai_agents/agent_patterns/run_agents_as_tools_workflow.py new file mode 100644 index 000000000..8b0c1345b --- /dev/null +++ b/openai_agents/agent_patterns/run_agents_as_tools_workflow.py @@ -0,0 +1,32 @@ +import asyncio + +from temporalio.client import Client +from temporalio.contrib.openai_agents import OpenAIAgentsPlugin + +from openai_agents.agent_patterns.workflows.agents_as_tools_workflow import ( + AgentsAsToolsWorkflow, +) + + +async def main(): + # Create client connected to server at the given address + client = await Client.connect( + "localhost:7233", + plugins=[ + OpenAIAgentsPlugin(), + ], + ) + + # Execute a workflow + result = await client.execute_workflow( + AgentsAsToolsWorkflow.run, + "Please translate 'Good morning, how are you?' to Spanish and French", + id="agents-as-tools-workflow-example", + task_queue="openai-agents-patterns-task-queue", + ) + + print(f"Result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/openai_agents/agent_patterns/run_deterministic_workflow.py b/openai_agents/agent_patterns/run_deterministic_workflow.py new file mode 100644 index 000000000..abb2e4de3 --- /dev/null +++ b/openai_agents/agent_patterns/run_deterministic_workflow.py @@ -0,0 +1,31 @@ +import asyncio + +from temporalio.client import Client +from temporalio.contrib.openai_agents import OpenAIAgentsPlugin + +from openai_agents.agent_patterns.workflows.deterministic_workflow import ( + DeterministicWorkflow, +) + + +async def main(): + # Create client connected to server at the given address + client = await Client.connect( + "localhost:7233", + plugins=[ + OpenAIAgentsPlugin(), + ], + ) + + # Execute a workflow + result = await client.execute_workflow( + DeterministicWorkflow.run, + "Write a science fiction story about time travel", + id="deterministic-workflow-example", + task_queue="openai-agents-patterns-task-queue", + ) + print(f"Result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/openai_agents/agent_patterns/run_forcing_tool_use_workflow.py b/openai_agents/agent_patterns/run_forcing_tool_use_workflow.py new file mode 100644 index 000000000..5dfa42c52 --- /dev/null +++ b/openai_agents/agent_patterns/run_forcing_tool_use_workflow.py @@ -0,0 +1,50 @@ +import asyncio + +from temporalio.client import Client +from temporalio.contrib.openai_agents import OpenAIAgentsPlugin + +from openai_agents.agent_patterns.workflows.forcing_tool_use_workflow import ( + ForcingToolUseWorkflow, +) + + +async def main(): + # Create client connected to server at the given address + client = await Client.connect( + "localhost:7233", + plugins=[ + OpenAIAgentsPlugin(), + ], + ) + + # Execute workflows with different tool use behaviors + print("Testing default behavior:") + result1 = await client.execute_workflow( + ForcingToolUseWorkflow.run, + "default", + id="forcing-tool-use-workflow-default", + task_queue="openai-agents-patterns-task-queue", + ) + print(f"Default result: {result1}") + + print("\nTesting first_tool behavior:") + result2 = await client.execute_workflow( + ForcingToolUseWorkflow.run, + "first_tool", + id="forcing-tool-use-workflow-first-tool", + task_queue="openai-agents-patterns-task-queue", + ) + print(f"First tool result: {result2}") + + print("\nTesting custom behavior:") + result3 = await client.execute_workflow( + ForcingToolUseWorkflow.run, + "custom", + id="forcing-tool-use-workflow-custom", + task_queue="openai-agents-patterns-task-queue", + ) + print(f"Custom result: {result3}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/openai_agents/agent_patterns/run_input_guardrails_workflow.py b/openai_agents/agent_patterns/run_input_guardrails_workflow.py new file mode 100644 index 000000000..82536e268 --- /dev/null +++ b/openai_agents/agent_patterns/run_input_guardrails_workflow.py @@ -0,0 +1,40 @@ +import asyncio + +from temporalio.client import Client +from temporalio.contrib.openai_agents import OpenAIAgentsPlugin + +from openai_agents.agent_patterns.workflows.input_guardrails_workflow import ( + InputGuardrailsWorkflow, +) + + +async def main(): + # Create client connected to server at the given address + client = await Client.connect( + "localhost:7233", + plugins=[ + OpenAIAgentsPlugin(), + ], + ) + + # Execute a workflow with a normal question (should pass) + result1 = await client.execute_workflow( + InputGuardrailsWorkflow.run, + "What's the capital of California?", + id="input-guardrails-workflow-normal", + task_queue="openai-agents-patterns-task-queue", + ) + print(f"Normal question result: {result1}") + + # Execute a workflow with a math homework question (should be blocked) + result2 = await client.execute_workflow( + InputGuardrailsWorkflow.run, + "Can you help me solve for x: 2x + 5 = 11?", + id="input-guardrails-workflow-blocked", + task_queue="openai-agents-patterns-task-queue", + ) + print(f"Math homework result: {result2}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/openai_agents/agent_patterns/run_llm_as_a_judge_workflow.py b/openai_agents/agent_patterns/run_llm_as_a_judge_workflow.py new file mode 100644 index 000000000..aa6d97a66 --- /dev/null +++ b/openai_agents/agent_patterns/run_llm_as_a_judge_workflow.py @@ -0,0 +1,31 @@ +import asyncio + +from temporalio.client import Client +from temporalio.contrib.openai_agents import OpenAIAgentsPlugin + +from openai_agents.agent_patterns.workflows.llm_as_a_judge_workflow import ( + LLMAsAJudgeWorkflow, +) + + +async def main(): + # Create client connected to server at the given address + client = await Client.connect( + "localhost:7233", + plugins=[ + OpenAIAgentsPlugin(), + ], + ) + + # Execute a workflow + result = await client.execute_workflow( + LLMAsAJudgeWorkflow.run, + "A thrilling adventure story about pirates searching for treasure", + id="llm-as-a-judge-workflow-example", + task_queue="openai-agents-patterns-task-queue", + ) + print(f"Result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/openai_agents/agent_patterns/run_output_guardrails_workflow.py b/openai_agents/agent_patterns/run_output_guardrails_workflow.py new file mode 100644 index 000000000..16d647645 --- /dev/null +++ b/openai_agents/agent_patterns/run_output_guardrails_workflow.py @@ -0,0 +1,40 @@ +import asyncio + +from temporalio.client import Client +from temporalio.contrib.openai_agents import OpenAIAgentsPlugin + +from openai_agents.agent_patterns.workflows.output_guardrails_workflow import ( + OutputGuardrailsWorkflow, +) + + +async def main(): + # Create client connected to server at the given address + client = await Client.connect( + "localhost:7233", + plugins=[ + OpenAIAgentsPlugin(), + ], + ) + + # Execute a workflow with a normal question (should pass) + result1 = await client.execute_workflow( + OutputGuardrailsWorkflow.run, + "What's the capital of California?", + id="output-guardrails-workflow-normal", + task_queue="openai-agents-patterns-task-queue", + ) + print(f"Normal question result: {result1}") + + # Execute a workflow with input that might trigger sensitive data output + result2 = await client.execute_workflow( + OutputGuardrailsWorkflow.run, + "My phone number is 650-123-4567. Where do you think I live?", + id="output-guardrails-workflow-sensitive", + task_queue="openai-agents-patterns-task-queue", + ) + print(f"Sensitive data result: {result2}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/openai_agents/agent_patterns/run_parallelization_workflow.py b/openai_agents/agent_patterns/run_parallelization_workflow.py new file mode 100644 index 000000000..5b8d9f5d1 --- /dev/null +++ b/openai_agents/agent_patterns/run_parallelization_workflow.py @@ -0,0 +1,31 @@ +import asyncio + +from temporalio.client import Client +from temporalio.contrib.openai_agents import OpenAIAgentsPlugin + +from openai_agents.agent_patterns.workflows.parallelization_workflow import ( + ParallelizationWorkflow, +) + + +async def main(): + # Create client connected to server at the given address + client = await Client.connect( + "localhost:7233", + plugins=[ + OpenAIAgentsPlugin(), + ], + ) + + # Execute a workflow + result = await client.execute_workflow( + ParallelizationWorkflow.run, + "Hello, world! How are you today?", + id="parallelization-workflow-example", + task_queue="openai-agents-patterns-task-queue", + ) + print(f"Result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/openai_agents/agent_patterns/run_routing_workflow.py b/openai_agents/agent_patterns/run_routing_workflow.py new file mode 100644 index 000000000..51c282331 --- /dev/null +++ b/openai_agents/agent_patterns/run_routing_workflow.py @@ -0,0 +1,29 @@ +import asyncio + +from temporalio.client import Client +from temporalio.contrib.openai_agents import OpenAIAgentsPlugin + +from openai_agents.agent_patterns.workflows.routing_workflow import RoutingWorkflow + + +async def main(): + # Create client connected to server at the given address + client = await Client.connect( + "localhost:7233", + plugins=[ + OpenAIAgentsPlugin(), + ], + ) + + # Execute a workflow + result = await client.execute_workflow( + RoutingWorkflow.run, + "Bonjour! Comment allez-vous aujourd'hui?", + id="routing-workflow-example", + task_queue="openai-agents-patterns-task-queue", + ) + print(f"Result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/openai_agents/agent_patterns/run_worker.py b/openai_agents/agent_patterns/run_worker.py new file mode 100644 index 000000000..4edb8ae4d --- /dev/null +++ b/openai_agents/agent_patterns/run_worker.py @@ -0,0 +1,65 @@ +from __future__ import annotations + +import asyncio +from datetime import timedelta + +from temporalio.client import Client +from temporalio.contrib.openai_agents import ModelActivityParameters, OpenAIAgentsPlugin +from temporalio.worker import Worker + +from openai_agents.agent_patterns.workflows.agents_as_tools_workflow import ( + AgentsAsToolsWorkflow, +) +from openai_agents.agent_patterns.workflows.deterministic_workflow import ( + DeterministicWorkflow, +) +from openai_agents.agent_patterns.workflows.forcing_tool_use_workflow import ( + ForcingToolUseWorkflow, +) +from openai_agents.agent_patterns.workflows.input_guardrails_workflow import ( + InputGuardrailsWorkflow, +) +from openai_agents.agent_patterns.workflows.llm_as_a_judge_workflow import ( + LLMAsAJudgeWorkflow, +) +from openai_agents.agent_patterns.workflows.output_guardrails_workflow import ( + OutputGuardrailsWorkflow, +) +from openai_agents.agent_patterns.workflows.parallelization_workflow import ( + ParallelizationWorkflow, +) +from openai_agents.agent_patterns.workflows.routing_workflow import RoutingWorkflow + + +async def main(): + # Create client connected to server at the given address + client = await Client.connect( + "localhost:7233", + plugins=[ + OpenAIAgentsPlugin( + model_params=ModelActivityParameters( + start_to_close_timeout=timedelta(seconds=30) + ) + ), + ], + ) + + worker = Worker( + client, + task_queue="openai-agents-patterns-task-queue", + workflows=[ + AgentsAsToolsWorkflow, + DeterministicWorkflow, + ParallelizationWorkflow, + LLMAsAJudgeWorkflow, + ForcingToolUseWorkflow, + InputGuardrailsWorkflow, + OutputGuardrailsWorkflow, + RoutingWorkflow, + ], + ) + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/openai_agents/agent_patterns/workflows/agents_as_tools_workflow.py b/openai_agents/agent_patterns/workflows/agents_as_tools_workflow.py new file mode 100644 index 000000000..db849c1cd --- /dev/null +++ b/openai_agents/agent_patterns/workflows/agents_as_tools_workflow.py @@ -0,0 +1,85 @@ +from agents import Agent, ItemHelpers, MessageOutputItem, RunConfig, Runner, trace +from temporalio import workflow + +""" +This example shows the agents-as-tools pattern. The frontline agent receives a user message and +then picks which agents to call, as tools. In this case, it picks from a set of translation +agents. +""" + + +def orchestrator_agent() -> Agent: + spanish_agent = Agent( + name="spanish_agent", + instructions="You translate the user's message to Spanish", + handoff_description="An english to spanish translator", + ) + + french_agent = Agent( + name="french_agent", + instructions="You translate the user's message to French", + handoff_description="An english to french translator", + ) + + italian_agent = Agent( + name="italian_agent", + instructions="You translate the user's message to Italian", + handoff_description="An english to italian translator", + ) + + orchestrator_agent = Agent( + name="orchestrator_agent", + instructions=( + "You are a translation agent. You use the tools given to you to translate." + "If asked for multiple translations, you call the relevant tools in order." + "You never translate on your own, you always use the provided tools." + ), + tools=[ + spanish_agent.as_tool( + tool_name="translate_to_spanish", + tool_description="Translate the user's message to Spanish", + ), + french_agent.as_tool( + tool_name="translate_to_french", + tool_description="Translate the user's message to French", + ), + italian_agent.as_tool( + tool_name="translate_to_italian", + tool_description="Translate the user's message to Italian", + ), + ], + ) + return orchestrator_agent + + +def synthesizer_agent() -> Agent: + return Agent( + name="synthesizer_agent", + instructions="You inspect translations, correct them if needed, and produce a final concatenated response.", + ) + + +@workflow.defn +class AgentsAsToolsWorkflow: + @workflow.run + async def run(self, msg: str) -> str: + config = RunConfig() + + # Run the entire orchestration in a single trace + with trace("Orchestrator evaluator"): + orchestrator = orchestrator_agent() + synthesizer = synthesizer_agent() + + orchestrator_result = await Runner.run(orchestrator, msg, run_config=config) + + for item in orchestrator_result.new_items: + if isinstance(item, MessageOutputItem): + text = ItemHelpers.text_message_output(item) + if text: + workflow.logger.info(f" - Translation step: {text}") + + synthesizer_result = await Runner.run( + synthesizer, orchestrator_result.to_input_list(), run_config=config + ) + + return synthesizer_result.final_output diff --git a/openai_agents/agent_patterns/workflows/deterministic_workflow.py b/openai_agents/agent_patterns/workflows/deterministic_workflow.py new file mode 100644 index 000000000..339e7a0f9 --- /dev/null +++ b/openai_agents/agent_patterns/workflows/deterministic_workflow.py @@ -0,0 +1,90 @@ +from agents import Agent, RunConfig, Runner, trace +from pydantic import BaseModel +from temporalio import workflow + +""" +This example demonstrates a deterministic flow, where each step is performed by an agent. +1. The first agent generates a story outline +2. We feed the outline into the second agent +3. The second agent checks if the outline is good quality and if it is a scifi story +4. If the outline is not good quality or not a scifi story, we stop here +5. If the outline is good quality and a scifi story, we feed the outline into the third agent +6. The third agent writes the story + +*Adapted from the OpenAI Agents SDK deterministic pattern example* +""" + + +class OutlineCheckerOutput(BaseModel): + good_quality: bool + is_scifi: bool + + +def story_outline_agent() -> Agent: + return Agent( + name="story_outline_agent", + instructions="Generate a very short story outline based on the user's input.", + ) + + +def outline_checker_agent() -> Agent: + return Agent( + name="outline_checker_agent", + instructions="Read the given story outline, and judge the quality. Also, determine if it is a scifi story.", + output_type=OutlineCheckerOutput, + ) + + +def story_agent() -> Agent: + return Agent( + name="story_agent", + instructions="Write a short story based on the given outline.", + output_type=str, + ) + + +@workflow.defn +class DeterministicWorkflow: + @workflow.run + async def run(self, input_prompt: str) -> str: + config = RunConfig() + + # Ensure the entire workflow is a single trace + with trace("Deterministic story flow"): + # 1. Generate an outline + outline_result = await Runner.run( + story_outline_agent(), + input_prompt, + run_config=config, + ) + workflow.logger.info("Outline generated") + + # 2. Check the outline + outline_checker_result = await Runner.run( + outline_checker_agent(), + outline_result.final_output, + run_config=config, + ) + + # 3. Add a gate to stop if the outline is not good quality or not a scifi story + assert isinstance(outline_checker_result.final_output, OutlineCheckerOutput) + if not outline_checker_result.final_output.good_quality: + workflow.logger.info("Outline is not good quality, so we stop here.") + return "Story generation stopped: Outline quality insufficient." + + if not outline_checker_result.final_output.is_scifi: + workflow.logger.info("Outline is not a scifi story, so we stop here.") + return "Story generation stopped: Outline is not science fiction." + + workflow.logger.info( + "Outline is good quality and a scifi story, so we continue to write the story." + ) + + # 4. Write the story + story_result = await Runner.run( + story_agent(), + outline_result.final_output, + run_config=config, + ) + + return f"Final story: {story_result.final_output}" diff --git a/openai_agents/agent_patterns/workflows/forcing_tool_use_workflow.py b/openai_agents/agent_patterns/workflows/forcing_tool_use_workflow.py new file mode 100644 index 000000000..9d3aee3b7 --- /dev/null +++ b/openai_agents/agent_patterns/workflows/forcing_tool_use_workflow.py @@ -0,0 +1,84 @@ +from typing import Any, Literal + +from agents import ( + Agent, + FunctionToolResult, + ModelSettings, + RunConfig, + RunContextWrapper, + Runner, + ToolsToFinalOutputFunction, + ToolsToFinalOutputResult, + function_tool, +) +from pydantic import BaseModel +from temporalio import workflow + +""" +This example shows how to force the agent to use a tool. It uses `ModelSettings(tool_choice="required")` +to force the agent to use any tool. + +You can run it with 3 options: +1. `default`: The default behavior, which is to send the tool output to the LLM. In this case, + `tool_choice` is not set, because otherwise it would result in an infinite loop - the LLM would + call the tool, the tool would run and send the results to the LLM, and that would repeat + (because the model is forced to use a tool every time.) +2. `first_tool_result`: The first tool result is used as the final output. +3. `custom`: A custom tool use behavior function is used. The custom function receives all the tool + results, and chooses to use the first tool result to generate the final output. + +*Adapted from the OpenAI Agents SDK forcing_tool_use pattern example* +""" + + +class Weather(BaseModel): + city: str + temperature_range: str + conditions: str + + +@function_tool +def get_weather(city: str) -> Weather: + workflow.logger.info("[debug] get_weather called") + return Weather(city=city, temperature_range="14-20C", conditions="Sunny with wind") + + +async def custom_tool_use_behavior( + context: RunContextWrapper[Any], results: list[FunctionToolResult] +) -> ToolsToFinalOutputResult: + weather: Weather = results[0].output + return ToolsToFinalOutputResult( + is_final_output=True, final_output=f"{weather.city} is {weather.conditions}." + ) + + +@workflow.defn +class ForcingToolUseWorkflow: + @workflow.run + async def run(self, tool_use_behavior: str = "default") -> str: + config = RunConfig() + + if tool_use_behavior == "default": + behavior: ( + Literal["run_llm_again", "stop_on_first_tool"] + | ToolsToFinalOutputFunction + ) = "run_llm_again" + elif tool_use_behavior == "first_tool": + behavior = "stop_on_first_tool" + elif tool_use_behavior == "custom": + behavior = custom_tool_use_behavior + + agent = Agent( + name="Weather agent", + instructions="You are a helpful agent.", + tools=[get_weather], + tool_use_behavior=behavior, + model_settings=ModelSettings( + tool_choice="required" if tool_use_behavior != "default" else None + ), + ) + + result = await Runner.run( + agent, input="What's the weather in Tokyo?", run_config=config + ) + return str(result.final_output) diff --git a/openai_agents/agent_patterns/workflows/input_guardrails_workflow.py b/openai_agents/agent_patterns/workflows/input_guardrails_workflow.py new file mode 100644 index 000000000..836778406 --- /dev/null +++ b/openai_agents/agent_patterns/workflows/input_guardrails_workflow.py @@ -0,0 +1,87 @@ +from agents import ( + Agent, + GuardrailFunctionOutput, + InputGuardrailTripwireTriggered, + RunConfig, + RunContextWrapper, + Runner, + TResponseInputItem, + input_guardrail, +) +from pydantic import BaseModel +from temporalio import workflow + +""" +This example shows how to use input guardrails. + +Guardrails are checks that run in parallel to the agent's execution. +They can be used to do things like: +- Check if input messages are off-topic +- Check that input messages don't violate any policies +- Take over control of the agent's execution if an unexpected input is detected + +In this example, we'll setup an input guardrail that trips if the user is asking to do math homework. +If the guardrail trips, we'll respond with a refusal message. + +*Adapted from the OpenAI Agents SDK input_guardrails pattern example* +""" + + +class MathHomeworkOutput(BaseModel): + reasoning: str + is_math_homework: bool + + +guardrail_agent = Agent( + name="Guardrail check", + instructions="Check if the user is asking you to do their math homework.", + output_type=MathHomeworkOutput, +) + + +@input_guardrail +async def math_guardrail( + context: RunContextWrapper[None], + agent: Agent, + input: str | list[TResponseInputItem], +) -> GuardrailFunctionOutput: + """This is an input guardrail function, which happens to call an agent to check if the input + is a math homework question. + """ + result = await Runner.run(guardrail_agent, input, context=context.context) + final_output = result.final_output_as(MathHomeworkOutput) + + return GuardrailFunctionOutput( + output_info=final_output, + tripwire_triggered=final_output.is_math_homework, + ) + + +@workflow.defn +class InputGuardrailsWorkflow: + @workflow.run + async def run(self, user_input: str) -> str: + config = RunConfig() + agent = Agent( + name="Customer support agent", + instructions="You are a customer support agent. You help customers with their questions.", + input_guardrails=[math_guardrail], + ) + + input_data: list[TResponseInputItem] = [ + { + "role": "user", + "content": user_input, + } + ] + + try: + result = await Runner.run(agent, input_data, run_config=config) + return str(result.final_output) + except InputGuardrailTripwireTriggered: + # If the guardrail triggered, we instead return a refusal message + message = "Sorry, I can't help you with your math homework." + workflow.logger.info( + "Input guardrail triggered - refusing to help with math homework" + ) + return message diff --git a/openai_agents/agent_patterns/workflows/llm_as_a_judge_workflow.py b/openai_agents/agent_patterns/workflows/llm_as_a_judge_workflow.py new file mode 100644 index 000000000..7ad536f2e --- /dev/null +++ b/openai_agents/agent_patterns/workflows/llm_as_a_judge_workflow.py @@ -0,0 +1,86 @@ +from dataclasses import dataclass +from typing import Literal + +from agents import Agent, ItemHelpers, RunConfig, Runner, TResponseInputItem, trace +from temporalio import workflow + +""" +This example shows the LLM as a judge pattern. The first agent generates an outline for a story. +The second agent judges the outline and provides feedback. We loop until the judge is satisfied +with the outline. + +*Adapted from the OpenAI Agents SDK llm_as_a_judge pattern example* +""" + + +@dataclass +class EvaluationFeedback: + feedback: str + score: Literal["pass", "needs_improvement", "fail"] + + +def story_outline_generator() -> Agent: + return Agent[None]( + name="story_outline_generator", + instructions=( + "You generate a very short story outline based on the user's input." + "If there is any feedback provided, use it to improve the outline." + ), + ) + + +def evaluator() -> Agent: + return Agent[None]( + name="evaluator", + instructions=( + "You evaluate a story outline and decide if it's good enough." + "If it's not good enough, you provide feedback on what needs to be improved." + "Never give it a pass on the first try. After 5 attempts, you can give it a pass if story outline is good enough - do not go for perfection" + ), + output_type=EvaluationFeedback, + ) + + +@workflow.defn +class LLMAsAJudgeWorkflow: + @workflow.run + async def run(self, msg: str) -> str: + config = RunConfig() + input_items: list[TResponseInputItem] = [{"content": msg, "role": "user"}] + latest_outline: str | None = None + + # We'll run the entire workflow in a single trace + with trace("LLM as a judge"): + while True: + story_outline_result = await Runner.run( + story_outline_generator(), + input_items, + run_config=config, + ) + + input_items = story_outline_result.to_input_list() + latest_outline = ItemHelpers.text_message_outputs( + story_outline_result.new_items + ) + workflow.logger.info("Story outline generated") + + evaluator_result = await Runner.run( + evaluator(), + input_items, + run_config=config, + ) + result: EvaluationFeedback = evaluator_result.final_output + + workflow.logger.info(f"Evaluator score: {result.score}") + + if result.score == "pass": + workflow.logger.info("Story outline is good enough, exiting.") + break + + workflow.logger.info("Re-running with feedback") + + input_items.append( + {"content": f"Feedback: {result.feedback}", "role": "user"} + ) + + return f"Final story outline: {latest_outline}" diff --git a/openai_agents/agent_patterns/workflows/output_guardrails_workflow.py b/openai_agents/agent_patterns/workflows/output_guardrails_workflow.py new file mode 100644 index 000000000..58a306c92 --- /dev/null +++ b/openai_agents/agent_patterns/workflows/output_guardrails_workflow.py @@ -0,0 +1,78 @@ +from agents import ( + Agent, + GuardrailFunctionOutput, + OutputGuardrailTripwireTriggered, + RunConfig, + RunContextWrapper, + Runner, + output_guardrail, +) +from pydantic import BaseModel, Field +from temporalio import workflow + +""" +This example shows how to use output guardrails. + +Output guardrails are checks that run on the final output of an agent. +They can be used to do things like: +- Check if the output contains sensitive data +- Check if the output is a valid response to the user's message + +In this example, we'll use a (contrived) example where we check if the agent's response contains +a phone number. + +*Adapted from the OpenAI Agents SDK output_guardrails pattern example* +""" + + +class MessageOutput(BaseModel): + reasoning: str = Field( + description="Thoughts on how to respond to the user's message" + ) + response: str = Field(description="The response to the user's message") + user_name: str | None = Field( + description="The name of the user who sent the message, if known" + ) + + +@output_guardrail +async def sensitive_data_check( + context: RunContextWrapper, agent: Agent, output: MessageOutput +) -> GuardrailFunctionOutput: + phone_number_in_response = "650" in output.response + phone_number_in_reasoning = "650" in output.reasoning + + return GuardrailFunctionOutput( + output_info={ + "phone_number_in_response": phone_number_in_response, + "phone_number_in_reasoning": phone_number_in_reasoning, + }, + tripwire_triggered=phone_number_in_response or phone_number_in_reasoning, + ) + + +def assistant_agent() -> Agent: + return Agent( + name="Assistant", + instructions="You are a helpful assistant.", + output_type=MessageOutput, + output_guardrails=[sensitive_data_check], + ) + + +@workflow.defn +class OutputGuardrailsWorkflow: + @workflow.run + async def run(self, user_input: str) -> str: + config = RunConfig() + agent = assistant_agent() + + try: + result = await Runner.run(agent, user_input, run_config=config) + output = result.final_output_as(MessageOutput) + return f"Response: {output.response}" + except OutputGuardrailTripwireTriggered as e: + workflow.logger.info( + f"Output guardrail triggered. Info: {e.guardrail_result.output.output_info}" + ) + return f"Output guardrail triggered due to sensitive data detection. Info: {e.guardrail_result.output.output_info}" diff --git a/openai_agents/agent_patterns/workflows/parallelization_workflow.py b/openai_agents/agent_patterns/workflows/parallelization_workflow.py new file mode 100644 index 000000000..5a07a0305 --- /dev/null +++ b/openai_agents/agent_patterns/workflows/parallelization_workflow.py @@ -0,0 +1,70 @@ +import asyncio + +from agents import Agent, ItemHelpers, RunConfig, Runner, trace +from temporalio import workflow + +""" +This example shows the parallelization pattern. We run the agent three times in parallel, and pick +the best result. + +*Adapted from the OpenAI Agents SDK parallelization pattern example* +""" + + +def spanish_agent() -> Agent: + return Agent( + name="spanish_agent", + instructions="You translate the user's message to Spanish", + ) + + +def translation_picker() -> Agent: + return Agent( + name="translation_picker", + instructions="You pick the best Spanish translation from the given options.", + ) + + +@workflow.defn +class ParallelizationWorkflow: + @workflow.run + async def run(self, msg: str) -> str: + config = RunConfig() + + # Ensure the entire workflow is a single trace + with trace("Parallel translation"): + # Run three translation agents in parallel + res_1, res_2, res_3 = await asyncio.gather( + Runner.run( + spanish_agent(), + msg, + run_config=config, + ), + Runner.run( + spanish_agent(), + msg, + run_config=config, + ), + Runner.run( + spanish_agent(), + msg, + run_config=config, + ), + ) + + outputs = [ + ItemHelpers.text_message_outputs(res_1.new_items), + ItemHelpers.text_message_outputs(res_2.new_items), + ItemHelpers.text_message_outputs(res_3.new_items), + ] + + translations = "\n\n".join(outputs) + workflow.logger.info(f"Generated translations:\n{translations}") + + best_translation = await Runner.run( + translation_picker(), + f"Input: {msg}\n\nTranslations:\n{translations}", + run_config=config, + ) + + return f"Best translation: {best_translation.final_output}" diff --git a/openai_agents/agent_patterns/workflows/routing_workflow.py b/openai_agents/agent_patterns/workflows/routing_workflow.py new file mode 100644 index 000000000..4d8213490 --- /dev/null +++ b/openai_agents/agent_patterns/workflows/routing_workflow.py @@ -0,0 +1,67 @@ +from agents import Agent, RunConfig, Runner, TResponseInputItem, trace +from temporalio import workflow + +""" +This example shows the handoffs/routing pattern. The triage agent receives the first message, and +then hands off to the appropriate agent based on the language of the request. + +Note: This is adapted from the original streaming version to work with Temporal's non-streaming approach. + +*Adapted from the OpenAI Agents SDK routing pattern example* +""" + + +def french_agent() -> Agent: + return Agent( + name="french_agent", + instructions="You only speak French", + ) + + +def spanish_agent() -> Agent: + return Agent( + name="spanish_agent", + instructions="You only speak Spanish", + ) + + +def english_agent() -> Agent: + return Agent( + name="english_agent", + instructions="You only speak English", + ) + + +def triage_agent() -> Agent: + return Agent( + name="triage_agent", + instructions="Handoff to the appropriate agent based on the language of the request.", + handoffs=[french_agent(), spanish_agent(), english_agent()], + ) + + +@workflow.defn +class RoutingWorkflow: + @workflow.run + async def run(self, msg: str) -> str: + config = RunConfig() + + with trace("Routing example"): + inputs: list[TResponseInputItem] = [{"content": msg, "role": "user"}] + + # Run the triage agent to determine which language agent to handoff to + result = await Runner.run( + triage_agent(), + input=inputs, + run_config=config, + ) + + # Get the final response after handoff + # Note: current_agent attribute may not be available in all SDK versions + workflow.logger.info("Handoff completed") + + # Convert result to proper input format for next agent + inputs = result.to_input_list() + + # Return the result from the handoff (either the handoff agent's response or triage response) + return f"Response: {result.final_output}" diff --git a/openai_agents/basic/README.md b/openai_agents/basic/README.md new file mode 100644 index 000000000..128ec7cac --- /dev/null +++ b/openai_agents/basic/README.md @@ -0,0 +1,80 @@ +# Basic Agent Examples + +Simple examples to get started with OpenAI Agents SDK integrated with Temporal workflows. + +*Adapted from [OpenAI Agents SDK basic examples](https://github.com/openai/openai-agents-python/tree/main/examples/basic)* + +Before running these examples, be sure to review the [prerequisites and background on the integration](../README.md). + +## Running the Examples + +First, start the worker (supports all basic examples): +```bash +uv run openai_agents/basic/run_worker.py +``` + +Then run individual examples in separate terminals: + +### Hello World Agent +Basic agent that only responds in haikus: +```bash +uv run openai_agents/basic/run_hello_world_workflow.py +``` + +### Tools Agent +Agent with access to external tools (simulated weather API): +```bash +uv run openai_agents/basic/run_tools_workflow.py +``` + +### Agent Lifecycle with Hooks +Demonstrates agent lifecycle events and handoffs between agents: +```bash +uv run openai_agents/basic/run_agent_lifecycle_workflow.py +``` + +### Lifecycle with Usage Tracking +Shows detailed usage tracking with RunHooks (requests, tokens, etc.): +```bash +uv run openai_agents/basic/run_lifecycle_workflow.py +``` + +### Dynamic System Prompts +Agent with dynamic instruction generation based on context (haiku/pirate/robot): +```bash +uv run openai_agents/basic/run_dynamic_system_prompt_workflow.py +``` + +### Non-Strict Output Types +Demonstrates different JSON schema validation approaches: +```bash +uv run openai_agents/basic/run_non_strict_output_workflow.py +``` + +Note: `CustomOutputSchema` is not supported by the Temporal OpenAI Agents SDK integration and is omitted in this example. + +### Image Processing - Local +Process local image files with AI vision: +```bash +uv run openai_agents/basic/run_local_image_workflow.py +``` + +### Image Processing - Remote +Process remote image URLs with AI vision: +```bash +uv run openai_agents/basic/run_remote_image_workflow.py +``` + +### Previous Response ID +Demonstrates conversation continuity using response IDs: +```bash +uv run openai_agents/basic/run_previous_response_id_workflow.py +``` + +## Omitted Examples + +The following examples from the [reference repository](https://github.com/openai/openai-agents-python/tree/main/examples/basic) are not included in this Temporal adaptation: + +- **Session** - Stores state in local SQLite database, not appropriate for distributed workflows + +**Stream Items/Stream Text** are adapted in [`../streaming/`](../streaming/README.md) rather than here. They need `streaming_topic` set on the plugin's `ModelActivityParameters`, so they run on their own worker instead of sharing this directory's. \ No newline at end of file diff --git a/openai_agents/basic/activities/get_weather_activity.py b/openai_agents/basic/activities/get_weather_activity.py new file mode 100644 index 000000000..c8be473c4 --- /dev/null +++ b/openai_agents/basic/activities/get_weather_activity.py @@ -0,0 +1,18 @@ +from dataclasses import dataclass + +from temporalio import activity + + +@dataclass +class Weather: + city: str + temperature_range: str + conditions: str + + +@activity.defn +async def get_weather(city: str) -> Weather: + """ + Get the weather for a given city. + """ + return Weather(city=city, temperature_range="14-20C", conditions="Sunny with wind.") diff --git a/openai_agents/basic/activities/image_activities.py b/openai_agents/basic/activities/image_activities.py new file mode 100644 index 000000000..23c770b1b --- /dev/null +++ b/openai_agents/basic/activities/image_activities.py @@ -0,0 +1,19 @@ +import base64 + +from temporalio import activity + + +@activity.defn +async def read_image_as_base64(image_path: str) -> str: + """ + Read an image file and convert it to base64 string. + + Args: + image_path: Path to the image file + + Returns: + Base64 encoded string of the image + """ + with open(image_path, "rb") as image_file: + encoded_string = base64.b64encode(image_file.read()).decode("utf-8") + return encoded_string diff --git a/openai_agents/basic/activities/math_activities.py b/openai_agents/basic/activities/math_activities.py new file mode 100644 index 000000000..1e875b23f --- /dev/null +++ b/openai_agents/basic/activities/math_activities.py @@ -0,0 +1,15 @@ +import random + +from temporalio import activity + + +@activity.defn +async def random_number(max_value: int) -> int: + """Generate a random number up to the provided maximum.""" + return random.randint(0, max_value) + + +@activity.defn +async def multiply_by_two(x: int) -> int: + """Simple multiplication by two.""" + return x * 2 diff --git a/openai_agents/basic/media/image_bison.jpg b/openai_agents/basic/media/image_bison.jpg new file mode 100644 index 000000000..b113c91f6 Binary files /dev/null and b/openai_agents/basic/media/image_bison.jpg differ diff --git a/openai_agents/basic/run_agent_lifecycle_workflow.py b/openai_agents/basic/run_agent_lifecycle_workflow.py new file mode 100644 index 000000000..2cd16c600 --- /dev/null +++ b/openai_agents/basic/run_agent_lifecycle_workflow.py @@ -0,0 +1,31 @@ +import asyncio + +from temporalio.client import Client +from temporalio.envconfig import ClientConfig + +from openai_agents.basic.workflows.agent_lifecycle_workflow import ( + AgentLifecycleWorkflow, +) + + +async def main() -> None: + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) + + user_input = input("Enter a max number: ") + max_number = int(user_input) + + result = await client.execute_workflow( + AgentLifecycleWorkflow.run, + max_number, + id="agent-lifecycle-workflow", + task_queue="openai-agents-basic-task-queue", + ) + + print(f"Final result: {result}") + print("Done!") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/openai_agents/basic/run_dynamic_system_prompt_workflow.py b/openai_agents/basic/run_dynamic_system_prompt_workflow.py new file mode 100644 index 000000000..46c53c6d6 --- /dev/null +++ b/openai_agents/basic/run_dynamic_system_prompt_workflow.py @@ -0,0 +1,41 @@ +import asyncio + +from temporalio.client import Client +from temporalio.contrib.openai_agents import OpenAIAgentsPlugin + +from openai_agents.basic.workflows.dynamic_system_prompt_workflow import ( + DynamicSystemPromptWorkflow, +) + + +async def main(): + client = await Client.connect( + "localhost:7233", + plugins=[ + OpenAIAgentsPlugin(), + ], + ) + + user_message = "Tell me a joke." + + result = await client.execute_workflow( + DynamicSystemPromptWorkflow.run, + user_message, + id="dynamic-prompt-workflow", + task_queue="openai-agents-basic-task-queue", + ) + print(result) + print() + + # Run with specific style + result = await client.execute_workflow( + DynamicSystemPromptWorkflow.run, + args=[user_message, "pirate"], + id="dynamic-prompt-pirate-workflow", + task_queue="openai-agents-basic-task-queue", + ) + print(result) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/openai_agents/basic/run_hello_world_workflow.py b/openai_agents/basic/run_hello_world_workflow.py new file mode 100644 index 000000000..0662a4fa3 --- /dev/null +++ b/openai_agents/basic/run_hello_world_workflow.py @@ -0,0 +1,29 @@ +import asyncio + +from temporalio.client import Client +from temporalio.contrib.openai_agents import OpenAIAgentsPlugin + +from openai_agents.basic.workflows.hello_world_workflow import HelloWorldAgent + + +async def main(): + # Create client connected to server at the given address + client = await Client.connect( + "localhost:7233", + plugins=[ + OpenAIAgentsPlugin(), + ], + ) + + # Execute a workflow + result = await client.execute_workflow( + HelloWorldAgent.run, + "Tell me about recursion in programming.", + id="my-workflow-id", + task_queue="openai-agents-basic-task-queue", + ) + print(f"Result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/openai_agents/basic/run_lifecycle_workflow.py b/openai_agents/basic/run_lifecycle_workflow.py new file mode 100644 index 000000000..00286ece8 --- /dev/null +++ b/openai_agents/basic/run_lifecycle_workflow.py @@ -0,0 +1,31 @@ +import asyncio + +from temporalio.client import Client +from temporalio.contrib.openai_agents import OpenAIAgentsPlugin + +from openai_agents.basic.workflows.lifecycle_workflow import LifecycleWorkflow + + +async def main(): + client = await Client.connect( + "localhost:7233", + plugins=[ + OpenAIAgentsPlugin(), + ], + ) + + user_input = input("Enter a max number: ") + max_number = int(user_input) + + result = await client.execute_workflow( + LifecycleWorkflow.run, + max_number, + id="lifecycle-workflow", + task_queue="openai-agents-basic-task-queue", + ) + + print(f"Final result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/openai_agents/basic/run_local_image_workflow.py b/openai_agents/basic/run_local_image_workflow.py new file mode 100644 index 000000000..6dfec809c --- /dev/null +++ b/openai_agents/basic/run_local_image_workflow.py @@ -0,0 +1,32 @@ +import asyncio +import os + +from temporalio.client import Client +from temporalio.contrib.openai_agents import OpenAIAgentsPlugin + +from openai_agents.basic.workflows.local_image_workflow import LocalImageWorkflow + + +async def main(): + client = await Client.connect( + "localhost:7233", + plugins=[ + OpenAIAgentsPlugin(), + ], + ) + + # Use the media file from the original example + image_path = os.path.join(os.path.dirname(__file__), "media/image_bison.jpg") + + result = await client.execute_workflow( + LocalImageWorkflow.run, + args=[image_path, "What do you see in this image?"], + id="local-image-workflow", + task_queue="openai-agents-basic-task-queue", + ) + + print(f"Agent response: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/openai_agents/basic/run_non_strict_output_workflow.py b/openai_agents/basic/run_non_strict_output_workflow.py new file mode 100644 index 000000000..192e12065 --- /dev/null +++ b/openai_agents/basic/run_non_strict_output_workflow.py @@ -0,0 +1,35 @@ +import asyncio + +from temporalio.client import Client +from temporalio.contrib.openai_agents import OpenAIAgentsPlugin + +from openai_agents.basic.workflows.non_strict_output_workflow import ( + NonStrictOutputWorkflow, +) + + +async def main(): + client = await Client.connect( + "localhost:7233", + plugins=[ + OpenAIAgentsPlugin(), + ], + ) + + input_message = "Tell me 3 short jokes." + + result = await client.execute_workflow( + NonStrictOutputWorkflow.run, + input_message, + id="non-strict-output-workflow", + task_queue="openai-agents-basic-task-queue", + ) + + print("=== Non-Strict Output Type Results ===") + for key, value in result.items(): + print(f"{key}: {value}") + print() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/openai_agents/basic/run_previous_response_id_workflow.py b/openai_agents/basic/run_previous_response_id_workflow.py new file mode 100644 index 000000000..ddae51079 --- /dev/null +++ b/openai_agents/basic/run_previous_response_id_workflow.py @@ -0,0 +1,35 @@ +import asyncio + +from temporalio.client import Client +from temporalio.contrib.openai_agents import OpenAIAgentsPlugin + +from openai_agents.basic.workflows.previous_response_id_workflow import ( + PreviousResponseIdWorkflow, +) + + +async def main(): + client = await Client.connect( + "localhost:7233", + plugins=[ + OpenAIAgentsPlugin(), + ], + ) + + first_question = "What is the largest country in South America?" + follow_up_question = "What is the capital of that country?" + + result = await client.execute_workflow( + PreviousResponseIdWorkflow.run, + args=[first_question, follow_up_question], + id="previous-response-id-workflow", + task_queue="openai-agents-basic-task-queue", + ) + + print("\nFinal results:") + print(f"1. {result[0]}") + print(f"2. {result[1]}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/openai_agents/basic/run_remote_image_workflow.py b/openai_agents/basic/run_remote_image_workflow.py new file mode 100644 index 000000000..f2175f7f2 --- /dev/null +++ b/openai_agents/basic/run_remote_image_workflow.py @@ -0,0 +1,35 @@ +import asyncio + +from temporalio.client import Client +from temporalio.contrib.openai_agents import OpenAIAgentsPlugin +from temporalio.envconfig import ClientConfig + +from openai_agents.basic.workflows.remote_image_workflow import RemoteImageWorkflow + + +async def main(): + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + + client = await Client.connect( + **config, + plugins=[OpenAIAgentsPlugin()], + ) + + # Use the URL from the original example + image_url = ( + "https://upload.wikimedia.org/wikipedia/commons/0/0c/GoldenGateBridge-001.jpg" + ) + + result = await client.execute_workflow( + RemoteImageWorkflow.run, + args=[image_url, "What do you see in this image?"], + id="remote-image-workflow", + task_queue="openai-agents-basic-task-queue", + ) + + print(f"Agent response: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/openai_agents/basic/run_tools_workflow.py b/openai_agents/basic/run_tools_workflow.py new file mode 100644 index 000000000..9aed2dbfb --- /dev/null +++ b/openai_agents/basic/run_tools_workflow.py @@ -0,0 +1,30 @@ +import asyncio + +from temporalio.client import Client +from temporalio.contrib.openai_agents import OpenAIAgentsPlugin + +from openai_agents.basic.workflows.tools_workflow import ToolsWorkflow + + +async def main(): + # Create client connected to server at the given address + client = await Client.connect( + "localhost:7233", + plugins=[ + OpenAIAgentsPlugin(), + ], + ) + + # Execute a workflow + result = await client.execute_workflow( + ToolsWorkflow.run, + "What is the weather in Tokio?", + id="tools-workflow", + task_queue="openai-agents-basic-task-queue", + ) + + print(f"Result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/openai_agents/basic/run_worker.py b/openai_agents/basic/run_worker.py new file mode 100644 index 000000000..94d6a8823 --- /dev/null +++ b/openai_agents/basic/run_worker.py @@ -0,0 +1,73 @@ +from __future__ import annotations + +import asyncio +from datetime import timedelta + +from temporalio.client import Client +from temporalio.contrib.openai_agents import ModelActivityParameters, OpenAIAgentsPlugin +from temporalio.worker import Worker + +from openai_agents.basic.activities.get_weather_activity import get_weather +from openai_agents.basic.activities.image_activities import read_image_as_base64 +from openai_agents.basic.activities.math_activities import ( + multiply_by_two, + random_number, +) +from openai_agents.basic.workflows.agent_lifecycle_workflow import ( + AgentLifecycleWorkflow, +) +from openai_agents.basic.workflows.dynamic_system_prompt_workflow import ( + DynamicSystemPromptWorkflow, +) +from openai_agents.basic.workflows.hello_world_workflow import HelloWorldAgent +from openai_agents.basic.workflows.lifecycle_workflow import LifecycleWorkflow +from openai_agents.basic.workflows.local_image_workflow import LocalImageWorkflow +from openai_agents.basic.workflows.non_strict_output_workflow import ( + NonStrictOutputWorkflow, +) +from openai_agents.basic.workflows.previous_response_id_workflow import ( + PreviousResponseIdWorkflow, +) +from openai_agents.basic.workflows.remote_image_workflow import RemoteImageWorkflow +from openai_agents.basic.workflows.tools_workflow import ToolsWorkflow + + +async def main(): + # Create client connected to server at the given address + client = await Client.connect( + "localhost:7233", + plugins=[ + OpenAIAgentsPlugin( + model_params=ModelActivityParameters( + start_to_close_timeout=timedelta(seconds=30) + ) + ), + ], + ) + + worker = Worker( + client, + task_queue="openai-agents-basic-task-queue", + workflows=[ + HelloWorldAgent, + ToolsWorkflow, + AgentLifecycleWorkflow, + DynamicSystemPromptWorkflow, + NonStrictOutputWorkflow, + LocalImageWorkflow, + RemoteImageWorkflow, + LifecycleWorkflow, + PreviousResponseIdWorkflow, + ], + activities=[ + get_weather, + multiply_by_two, + random_number, + read_image_as_base64, + ], + ) + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/openai_agents/basic/workflows/agent_lifecycle_workflow.py b/openai_agents/basic/workflows/agent_lifecycle_workflow.py new file mode 100644 index 000000000..32bce03b4 --- /dev/null +++ b/openai_agents/basic/workflows/agent_lifecycle_workflow.py @@ -0,0 +1,96 @@ +from typing import Any + +from agents import Agent, AgentHooks, RunContextWrapper, Runner, function_tool +from pydantic import BaseModel +from temporalio import workflow + + +class CustomAgentHooks(AgentHooks): + def __init__(self, display_name: str): + self.event_counter = 0 + self.display_name = display_name + + async def on_start(self, context: RunContextWrapper, agent: Agent) -> None: + self.event_counter += 1 + print( + f"### ({self.display_name}) {self.event_counter}: Agent {agent.name} started" + ) + + async def on_end( + self, context: RunContextWrapper, agent: Agent, output: Any + ) -> None: + self.event_counter += 1 + print( + f"### ({self.display_name}) {self.event_counter}: Agent {agent.name} ended with output {output}" + ) + + async def on_handoff( + self, context: RunContextWrapper, agent: Agent, source: Agent + ) -> None: + self.event_counter += 1 + print( + f"### ({self.display_name}) {self.event_counter}: Agent {source.name} handed off to {agent.name}" + ) + + async def on_tool_start( + self, context: RunContextWrapper, agent: Agent, tool + ) -> None: + self.event_counter += 1 + print( + f"### ({self.display_name}) {self.event_counter}: Agent {agent.name} started tool {tool.name}" + ) + + async def on_tool_end( + self, context: RunContextWrapper, agent: Agent, tool, result: Any + ) -> None: + self.event_counter += 1 + print( + f"### ({self.display_name}) {self.event_counter}: Agent {agent.name} ended tool {tool.name} with result {result}" + ) + + +@function_tool +def random_number_tool(max: int) -> int: + """ + Generate a random number up to the provided maximum. + """ + return workflow.random().randint(0, max) + + +@function_tool +def multiply_by_two_tool(x: int) -> int: + """Simple multiplication by two.""" + return x * 2 + + +class FinalResult(BaseModel): + number: int + + +@workflow.defn +class AgentLifecycleWorkflow: + @workflow.run + async def run(self, max_number: int) -> FinalResult: + multiply_agent = Agent( + name="Multiply Agent", + instructions="Multiply the number by 2 and then return the final result.", + tools=[multiply_by_two_tool], + output_type=FinalResult, + hooks=CustomAgentHooks(display_name="Multiply Agent"), + ) + + start_agent = Agent( + name="Start Agent", + instructions="Generate a random number. If it's even, stop. If it's odd, hand off to the multiply agent.", + tools=[random_number_tool], + output_type=FinalResult, + handoffs=[multiply_agent], + hooks=CustomAgentHooks(display_name="Start Agent"), + ) + + result = await Runner.run( + start_agent, + input=f"Generate a random number between 0 and {max_number}.", + ) + + return result.final_output diff --git a/openai_agents/basic/workflows/dynamic_system_prompt_workflow.py b/openai_agents/basic/workflows/dynamic_system_prompt_workflow.py new file mode 100644 index 000000000..fb7ce109b --- /dev/null +++ b/openai_agents/basic/workflows/dynamic_system_prompt_workflow.py @@ -0,0 +1,48 @@ +from typing import Literal, Optional + +from agents import Agent, RunContextWrapper, Runner +from temporalio import workflow + + +class CustomContext: + def __init__(self, style: Literal["haiku", "pirate", "robot"]): + self.style = style + + +def custom_instructions( + run_context: RunContextWrapper[CustomContext], agent: Agent[CustomContext] +) -> str: + context = run_context.context + if context.style == "haiku": + return "Only respond in haikus." + elif context.style == "pirate": + return "Respond as a pirate." + else: + return "Respond as a robot and say 'beep boop' a lot." + + +@workflow.defn +class DynamicSystemPromptWorkflow: + @workflow.run + async def run(self, user_message: str, style: Optional[str] = None) -> str: + if style is None: + selected_style: Literal["haiku", "pirate", "robot"] = ( + workflow.random().choice(["haiku", "pirate", "robot"]) + ) + else: + # Validate that the provided style is one of the allowed values + if style not in ["haiku", "pirate", "robot"]: + raise ValueError( + f"Invalid style: {style}. Must be one of: haiku, pirate, robot" + ) + selected_style = style # type: ignore + + context = CustomContext(style=selected_style) + + agent = Agent( + name="Chat agent", + instructions=custom_instructions, + ) + + result = await Runner.run(agent, user_message, context=context) + return f"Style: {selected_style}\nResponse: {result.final_output}" diff --git a/openai_agents/basic/workflows/hello_world_workflow.py b/openai_agents/basic/workflows/hello_world_workflow.py new file mode 100644 index 000000000..dd6b2e41b --- /dev/null +++ b/openai_agents/basic/workflows/hello_world_workflow.py @@ -0,0 +1,15 @@ +from agents import Agent, Runner +from temporalio import workflow + + +@workflow.defn +class HelloWorldAgent: + @workflow.run + async def run(self, prompt: str) -> str: + agent = Agent( + name="Assistant", + instructions="You only respond in haikus.", + ) + + result = await Runner.run(agent, input=prompt) + return result.final_output diff --git a/openai_agents/basic/workflows/lifecycle_workflow.py b/openai_agents/basic/workflows/lifecycle_workflow.py new file mode 100644 index 000000000..ded120a61 --- /dev/null +++ b/openai_agents/basic/workflows/lifecycle_workflow.py @@ -0,0 +1,106 @@ +from typing import Any + +from agents import ( + Agent, + RunContextWrapper, + RunHooks, + Runner, + Tool, + Usage, + function_tool, +) +from pydantic import BaseModel +from temporalio import workflow + + +class ExampleHooks(RunHooks): + def __init__(self): + self.event_counter = 0 + + def _usage_to_str(self, usage: Usage) -> str: + return f"{usage.requests} requests, {usage.input_tokens} input tokens, {usage.output_tokens} output tokens, {usage.total_tokens} total tokens" + + async def on_agent_start(self, context: RunContextWrapper, agent: Agent) -> None: + self.event_counter += 1 + print( + f"### {self.event_counter}: Agent {agent.name} started. Usage: {self._usage_to_str(context.usage)}" + ) + + async def on_agent_end( + self, context: RunContextWrapper, agent: Agent, output: Any + ) -> None: + self.event_counter += 1 + print( + f"### {self.event_counter}: Agent {agent.name} ended with output {output}. Usage: {self._usage_to_str(context.usage)}" + ) + + async def on_tool_start( + self, context: RunContextWrapper, agent: Agent, tool: Tool + ) -> None: + self.event_counter += 1 + print( + f"### {self.event_counter}: Tool {tool.name} started. Usage: {self._usage_to_str(context.usage)}" + ) + + async def on_tool_end( + self, context: RunContextWrapper, agent: Agent, tool: Tool, result: Any + ) -> None: + self.event_counter += 1 + print( + f"### {self.event_counter}: Tool {tool.name} ended with result {result}. Usage: {self._usage_to_str(context.usage)}" + ) + + async def on_handoff( + self, context: RunContextWrapper, from_agent: Agent, to_agent: Agent + ) -> None: + self.event_counter += 1 + print( + f"### {self.event_counter}: Handoff from {from_agent.name} to {to_agent.name}. Usage: {self._usage_to_str(context.usage)}" + ) + + +@function_tool +def random_number(max: int) -> int: + """Generate a random number up to the provided max.""" + return workflow.random().randint(0, max) + + +@function_tool +def multiply_by_two(x: int) -> int: + """Return x times two.""" + return x * 2 + + +class FinalResult(BaseModel): + number: int + + +@workflow.defn +class LifecycleWorkflow: + @workflow.run + async def run(self, max_number: int) -> FinalResult: + hooks = ExampleHooks() + + multiply_agent = Agent( + name="Multiply Agent", + instructions="Multiply the number by 2 and then return the final result.", + tools=[multiply_by_two], + output_type=FinalResult, + ) + + start_agent = Agent( + name="Start Agent", + instructions="Generate a random number. If it's even, stop. If it's odd, hand off to the multiplier agent.", + tools=[random_number], + output_type=FinalResult, + handoffs=[multiply_agent], + ) + + result = await Runner.run( + start_agent, + hooks=hooks, + input=f"Generate a random number between 0 and {max_number}.", + ) + + print("Done!") + return result.final_output diff --git a/openai_agents/basic/workflows/local_image_workflow.py b/openai_agents/basic/workflows/local_image_workflow.py new file mode 100644 index 000000000..9c82a5991 --- /dev/null +++ b/openai_agents/basic/workflows/local_image_workflow.py @@ -0,0 +1,56 @@ +from datetime import timedelta + +from agents import Agent, Runner +from temporalio import workflow + +from openai_agents.basic.activities.image_activities import read_image_as_base64 + + +@workflow.defn +class LocalImageWorkflow: + @workflow.run + async def run( + self, image_path: str, question: str = "What do you see in this image?" + ) -> str: + """ + Process a local image file with an AI agent. + + Args: + image_path: Path to the local image file + question: Question to ask about the image + + Returns: + Agent's response about the image + """ + # Convert image to base64 using activity + b64_image = await workflow.execute_activity( + read_image_as_base64, + image_path, + start_to_close_timeout=timedelta(seconds=30), + ) + + agent = Agent( + name="Assistant", + instructions="You are a helpful assistant.", + ) + + result = await Runner.run( + agent, + [ + { + "role": "user", + "content": [ + { + "type": "input_image", + "detail": "auto", + "image_url": f"data:image/jpeg;base64,{b64_image}", + } + ], + }, + { + "role": "user", + "content": question, + }, + ], + ) + return result.final_output diff --git a/openai_agents/basic/workflows/non_strict_output_workflow.py b/openai_agents/basic/workflows/non_strict_output_workflow.py new file mode 100644 index 000000000..f56716c13 --- /dev/null +++ b/openai_agents/basic/workflows/non_strict_output_workflow.py @@ -0,0 +1,86 @@ +import json +from dataclasses import dataclass +from typing import Any + +from agents import Agent, AgentOutputSchema, AgentOutputSchemaBase, Runner +from temporalio import workflow + + +@dataclass +class OutputType: + jokes: dict[int, str] + """A list of jokes, indexed by joke number.""" + + +class CustomOutputSchema(AgentOutputSchemaBase): + """A demonstration of a custom output schema.""" + + def is_plain_text(self) -> bool: + return False + + def name(self) -> str: + return "CustomOutputSchema" + + def json_schema(self) -> dict[str, Any]: + return { + "type": "object", + "properties": { + "jokes": {"type": "object", "properties": {"joke": {"type": "string"}}} + }, + } + + def is_strict_json_schema(self) -> bool: + return False + + def validate_json(self, json_str: str) -> Any: + json_obj = json.loads(json_str) + # Just for demonstration, we'll return a list. + return list(json_obj["jokes"].values()) + + +@workflow.defn +class NonStrictOutputWorkflow: + @workflow.run + async def run(self, input_text: str) -> dict[str, Any]: + """ + Demonstrates non-strict output types that require special handling. + + Args: + input_text: The input message to the agent + + Returns: + Dictionary with results from different output type approaches + """ + results = {} + + agent = Agent( + name="Assistant", + instructions="You are a helpful assistant.", + output_type=OutputType, + ) + + # First, try with strict output type (this should fail) + try: + result = await Runner.run(agent, input_text) + results["strict_result"] = "Unexpected success" + except Exception as e: + results["strict_error"] = str(e) + + # Now try with non-strict output type + try: + agent.output_type = AgentOutputSchema(OutputType, strict_json_schema=False) + result = await Runner.run(agent, input_text) + results["non_strict_result"] = result.final_output + except Exception as e: + results["non_strict_error"] = str(e) + + # Finally, try with custom output type + # Not presently supported by Temporal + # try: + # agent.output_type = CustomOutputSchema() + # result = await Runner.run(agent, input_text) + # results["custom_result"] = result.final_output + # except Exception as e: + # results["custom_error"] = str(e) + + return results diff --git a/openai_agents/basic/workflows/previous_response_id_workflow.py b/openai_agents/basic/workflows/previous_response_id_workflow.py new file mode 100644 index 000000000..64015159b --- /dev/null +++ b/openai_agents/basic/workflows/previous_response_id_workflow.py @@ -0,0 +1,48 @@ +from typing import Tuple + +from agents import Agent, Runner +from temporalio import workflow + + +@workflow.defn +class PreviousResponseIdWorkflow: + @workflow.run + async def run( + self, first_question: str, follow_up_question: str + ) -> Tuple[str, str]: + """ + Demonstrates usage of the `previous_response_id` parameter to continue a conversation. + The second run passes the previous response ID to the model, which allows it to continue the + conversation without re-sending the previous messages. + + Notes: + 1. This only applies to the OpenAI Responses API. Other models will ignore this parameter. + 2. Responses are only stored for 30 days as of this writing, so in production you should + store the response ID along with an expiration date; if the response is no longer valid, + you'll need to re-send the previous conversation history. + + Args: + first_question: The initial question to ask + follow_up_question: The follow-up question that references the first response + + Returns: + Tuple of (first_response, second_response) + """ + agent = Agent( + name="Assistant", + instructions="You are a helpful assistant. be VERY concise.", + ) + + # First question + result1 = await Runner.run(agent, first_question) + first_response = result1.final_output + + # Follow-up question using previous response ID + result2 = await Runner.run( + agent, + follow_up_question, + previous_response_id=result1.last_response_id, + ) + second_response = result2.final_output + + return first_response, second_response diff --git a/openai_agents/basic/workflows/remote_image_workflow.py b/openai_agents/basic/workflows/remote_image_workflow.py new file mode 100644 index 000000000..ce07bd21b --- /dev/null +++ b/openai_agents/basic/workflows/remote_image_workflow.py @@ -0,0 +1,45 @@ +from agents import Agent, Runner +from temporalio import workflow + + +@workflow.defn +class RemoteImageWorkflow: + @workflow.run + async def run( + self, image_url: str, question: str = "What do you see in this image?" + ) -> str: + """ + Process a remote image URL with an AI agent. + + Args: + image_url: URL of the remote image + question: Question to ask about the image + + Returns: + Agent's response about the image + """ + agent = Agent( + name="Assistant", + instructions="You are a helpful assistant.", + ) + + result = await Runner.run( + agent, + [ + { + "role": "user", + "content": [ + { + "type": "input_image", + "detail": "auto", + "image_url": image_url, + } + ], + }, + { + "role": "user", + "content": question, + }, + ], + ) + return result.final_output diff --git a/openai_agents/basic/workflows/tools_workflow.py b/openai_agents/basic/workflows/tools_workflow.py new file mode 100644 index 000000000..70964dc09 --- /dev/null +++ b/openai_agents/basic/workflows/tools_workflow.py @@ -0,0 +1,27 @@ +from __future__ import annotations + +from datetime import timedelta + +from agents import Agent, Runner +from temporalio import workflow +from temporalio.contrib import openai_agents as temporal_agents + +from openai_agents.basic.activities.get_weather_activity import get_weather + + +@workflow.defn +class ToolsWorkflow: + @workflow.run + async def run(self, question: str) -> str: + agent = Agent( + name="Hello world", + instructions="You are a helpful agent.", + tools=[ + temporal_agents.workflow.activity_as_tool( + get_weather, start_to_close_timeout=timedelta(seconds=10) + ) + ], + ) + + result = await Runner.run(agent, input=question) + return result.final_output diff --git a/openai_agents/customer_service/README.md b/openai_agents/customer_service/README.md new file mode 100644 index 000000000..34b9504d1 --- /dev/null +++ b/openai_agents/customer_service/README.md @@ -0,0 +1,21 @@ +# Customer Service + +Interactive customer service agent with escalation capabilities, extended with Temporal's durable conversational workflows. + +*Adapted from [OpenAI Agents SDK customer service](https://github.com/openai/openai-agents-python/tree/main/examples/customer_service)* + +This example demonstrates how to build persistent, stateful conversations where each conversation maintains state across multiple interactions and can survive system restarts and failures. + +## Running the Example + +First, start the worker: +```bash +uv run openai_agents/customer_service/run_worker.py +``` + +Then start a customer service conversation: +```bash +uv run openai_agents/customer_service/run_customer_service_client.py --conversation-id my-conversation-123 +``` + +You can start a new conversation with any unique conversation ID, or resume existing conversations by using the same conversation ID. The conversation state is persisted in the Temporal workflow, allowing you to resume conversations even after restarting the client. \ No newline at end of file diff --git a/openai_agents/customer_service/customer_service.py b/openai_agents/customer_service/customer_service.py new file mode 100644 index 000000000..459970332 --- /dev/null +++ b/openai_agents/customer_service/customer_service.py @@ -0,0 +1,136 @@ +from __future__ import annotations as _annotations + +from typing import Dict, Tuple + +from agents import Agent, RunContextWrapper, function_tool, handoff +from agents.extensions.handoff_prompt import RECOMMENDED_PROMPT_PREFIX +from pydantic import BaseModel +from temporalio import workflow + +### CONTEXT + + +class AirlineAgentContext(BaseModel): + passenger_name: str | None = None + confirmation_number: str | None = None + seat_number: str | None = None + flight_number: str | None = None + + +### TOOLS + + +@function_tool( + name_override="faq_lookup_tool", + description_override="Lookup frequently asked questions.", +) +async def faq_lookup_tool(question: str) -> str: + question_lower = question.lower() + if "bag" in question_lower or "baggage" in question_lower: + return ( + "You are allowed to bring one bag on the plane. " + "It must be under 50 pounds and 22 inches x 14 inches x 9 inches." + ) + elif "seats" in question_lower or "plane" in question_lower: + return ( + "There are 120 seats on the plane. " + "There are 22 business class seats and 98 economy seats. " + "Exit rows are rows 4 and 16. " + "Rows 5-8 are Economy Plus, with extra legroom. " + ) + elif "wifi" in question_lower: + return "We have free wifi on the plane, join Airline-Wifi" + return "I'm sorry, I don't know the answer to that question." + + +@function_tool +async def update_seat( + context: RunContextWrapper[AirlineAgentContext], + confirmation_number: str, + new_seat: str, +) -> str: + """ + Update the seat for a given confirmation number. + + Args: + confirmation_number: The confirmation number for the flight. + new_seat: The new seat to update to. + """ + # Update the context based on the customer's input + context.context.confirmation_number = confirmation_number + context.context.seat_number = new_seat + # Ensure that the flight number has been set by the incoming handoff + assert context.context.flight_number is not None, "Flight number is required" + return f"Updated seat to {new_seat} for confirmation number {confirmation_number}" + + +### HOOKS + + +async def on_seat_booking_handoff( + context: RunContextWrapper[AirlineAgentContext], +) -> None: + flight_number = f"FLT-{workflow.random().randint(100, 999)}" + context.context.flight_number = flight_number + + +### AGENTS + + +def init_agents() -> ( + Tuple[Agent[AirlineAgentContext], Dict[str, Agent[AirlineAgentContext]]] +): + """ + Initialize the agents for the airline customer service workflow. + :return: triage agent + """ + faq_agent = Agent[AirlineAgentContext]( + name="FAQ Agent", + handoff_description="A helpful agent that can answer questions about the airline.", + instructions=f"""{RECOMMENDED_PROMPT_PREFIX} + You are an FAQ agent. If you are speaking to a customer, you probably were transferred to from the triage agent. + Use the following routine to support the customer. + # Routine + 1. Identify the last question asked by the customer. + 2. Use the faq lookup tool to answer the question. Do not rely on your own knowledge. + 3. If you cannot answer the question, transfer back to the triage agent.""", + tools=[faq_lookup_tool], + ) + + seat_booking_agent = Agent[AirlineAgentContext]( + name="Seat Booking Agent", + handoff_description="A helpful agent that can update a seat on a flight.", + instructions=f"""{RECOMMENDED_PROMPT_PREFIX} + You are a seat booking agent. If you are speaking to a customer, you probably were transferred to from the triage agent. + Use the following routine to support the customer. + # Routine + 1. Ask for their confirmation number. + 2. Ask the customer what their desired seat number is. + 3. Use the update seat tool to update the seat on the flight. + If the customer asks a question that is not related to the routine, transfer back to the triage agent. """, + tools=[update_seat], + ) + + triage_agent = Agent[AirlineAgentContext]( + name="Triage Agent", + handoff_description="A triage agent that can delegate a customer's request to the appropriate agent.", + instructions=( + f"{RECOMMENDED_PROMPT_PREFIX} " + "You are a helpful triaging agent. You can use your tools to delegate questions to other appropriate agents." + ), + handoffs=[ + faq_agent, + handoff(agent=seat_booking_agent, on_handoff=on_seat_booking_handoff), + ], + ) + + faq_agent.handoffs.append(triage_agent) + seat_booking_agent.handoffs.append(triage_agent) + return triage_agent, { + agent.name: agent for agent in [faq_agent, seat_booking_agent, triage_agent] + } + + +class ProcessUserMessageInput(BaseModel): + user_input: str + chat_length: int diff --git a/openai_agents/customer_service/run_customer_service_client.py b/openai_agents/customer_service/run_customer_service_client.py new file mode 100644 index 000000000..044e0775c --- /dev/null +++ b/openai_agents/customer_service/run_customer_service_client.py @@ -0,0 +1,82 @@ +import argparse +import asyncio + +from temporalio.client import ( + Client, + WorkflowQueryRejectedError, + WorkflowUpdateFailedError, +) +from temporalio.common import QueryRejectCondition +from temporalio.contrib.openai_agents import OpenAIAgentsPlugin +from temporalio.service import RPCError, RPCStatusCode + +from openai_agents.customer_service.workflows.customer_service_workflow import ( + CustomerServiceWorkflow, + ProcessUserMessageInput, +) + + +async def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--conversation-id", type=str, required=True) + args = parser.parse_args() + + # Create client connected to server at the given address + client = await Client.connect( + "localhost:7233", + plugins=[ + OpenAIAgentsPlugin(), + ], + ) + + handle = client.get_workflow_handle(args.conversation_id) + + # Query the workflow for the chat history + # If the workflow is not open, start a new one + start = False + history = [] + try: + history = await handle.query( + CustomerServiceWorkflow.get_chat_history, + reject_condition=QueryRejectCondition.NOT_OPEN, + ) + except WorkflowQueryRejectedError: + start = True + except RPCError as e: + if e.status == RPCStatusCode.NOT_FOUND: + start = True + else: + raise e + if start: + await client.start_workflow( + CustomerServiceWorkflow.run, + id=args.conversation_id, + task_queue="openai-agents-task-queue", + ) + history = [] + print(*history, sep="\n") + + # Loop to send messages to the workflow + while True: + user_input = input("Enter your message: ") + message_input = ProcessUserMessageInput( + user_input=user_input, chat_length=len(history) + ) + try: + new_history = await handle.execute_update( + CustomerServiceWorkflow.process_user_message, message_input + ) + history.extend(new_history) + print(*new_history[1:], sep="\n") + except WorkflowUpdateFailedError: + print("** Stale conversation. Reloading...") + length = len(history) + history = await handle.query( + CustomerServiceWorkflow.get_chat_history, + reject_condition=QueryRejectCondition.NOT_OPEN, + ) + print(*history[length:], sep="\n") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/openai_agents/customer_service/run_worker.py b/openai_agents/customer_service/run_worker.py new file mode 100644 index 000000000..b82f69195 --- /dev/null +++ b/openai_agents/customer_service/run_worker.py @@ -0,0 +1,39 @@ +from __future__ import annotations + +import asyncio +from datetime import timedelta + +from temporalio.client import Client +from temporalio.contrib.openai_agents import ModelActivityParameters, OpenAIAgentsPlugin +from temporalio.worker import Worker + +from openai_agents.customer_service.workflows.customer_service_workflow import ( + CustomerServiceWorkflow, +) + + +async def main(): + # Create client connected to server at the given address + client = await Client.connect( + "localhost:7233", + plugins=[ + OpenAIAgentsPlugin( + model_params=ModelActivityParameters( + start_to_close_timeout=timedelta(seconds=30) + ) + ), + ], + ) + + worker = Worker( + client, + task_queue="openai-agents-task-queue", + workflows=[ + CustomerServiceWorkflow, + ], + ) + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/openai_agents/customer_service/workflows/customer_service_workflow.py b/openai_agents/customer_service/workflows/customer_service_workflow.py new file mode 100644 index 000000000..0157d0508 --- /dev/null +++ b/openai_agents/customer_service/workflows/customer_service_workflow.py @@ -0,0 +1,130 @@ +from __future__ import annotations as _annotations + +from agents import ( + HandoffCallItem, + HandoffOutputItem, + ItemHelpers, + MessageOutputItem, + RunConfig, + Runner, + ToolCallItem, + ToolCallOutputItem, + TResponseInputItem, + trace, +) +from pydantic import dataclasses +from temporalio import workflow + +from openai_agents.customer_service.customer_service import ( + AirlineAgentContext, + ProcessUserMessageInput, + init_agents, +) + + +@dataclasses.dataclass +class CustomerServiceWorkflowState: + printed_history: list[str] + current_agent_name: str + context: AirlineAgentContext + input_items: list[TResponseInputItem] + + +@workflow.defn +class CustomerServiceWorkflow: + @workflow.init + def __init__( + self, customer_service_state: CustomerServiceWorkflowState | None = None + ): + self.run_config = RunConfig() + + starting_agent, self.agent_map = init_agents() + self.current_agent = ( + self.agent_map[customer_service_state.current_agent_name] + if customer_service_state + else starting_agent + ) + self.context = ( + customer_service_state.context + if customer_service_state + else AirlineAgentContext() + ) + self.printed_history: list[str] = ( + customer_service_state.printed_history if customer_service_state else [] + ) + self.input_items = ( + customer_service_state.input_items if customer_service_state else [] + ) + + @workflow.run + async def run( + self, customer_service_state: CustomerServiceWorkflowState | None = None + ): + await workflow.wait_condition( + lambda: workflow.info().is_continue_as_new_suggested() + and workflow.all_handlers_finished() + ) + workflow.continue_as_new( + CustomerServiceWorkflowState( + printed_history=self.printed_history, + current_agent_name=self.current_agent.name, + context=self.context, + input_items=self.input_items, + ) + ) + + @workflow.query + def get_chat_history(self) -> list[str]: + return self.printed_history + + @workflow.update + async def process_user_message(self, input: ProcessUserMessageInput) -> list[str]: + length = len(self.printed_history) + self.printed_history.append(f"User: {input.user_input}") + with trace("Customer service", group_id=workflow.info().workflow_id): + self.input_items.append({"content": input.user_input, "role": "user"}) + result = await Runner.run( + self.current_agent, + self.input_items, + context=self.context, + run_config=self.run_config, + ) + + for new_item in result.new_items: + agent_name = new_item.agent.name + if isinstance(new_item, MessageOutputItem): + self.printed_history.append( + f"{agent_name}: {ItemHelpers.text_message_output(new_item)}" + ) + elif isinstance(new_item, HandoffOutputItem): + self.printed_history.append( + f"Handed off from {new_item.source_agent.name} to {new_item.target_agent.name}" + ) + elif isinstance(new_item, HandoffCallItem): + self.printed_history.append( + f"{agent_name}: Handed off to tool {new_item.raw_item.name}" + ) + elif isinstance(new_item, ToolCallItem): + self.printed_history.append(f"{agent_name}: Calling a tool") + elif isinstance(new_item, ToolCallOutputItem): + self.printed_history.append( + f"{agent_name}: Tool call output: {new_item.output}" + ) + else: + self.printed_history.append( + f"{agent_name}: Skipping item: {new_item.__class__.__name__}" + ) + self.input_items = result.to_input_list() + self.current_agent = result.last_agent + workflow.set_current_details("\n\n".join(self.printed_history)) + + return self.printed_history[length:] + + @process_user_message.validator + def validate_process_user_message(self, input: ProcessUserMessageInput) -> None: + if not input.user_input: + raise ValueError("User input cannot be empty.") + if len(input.user_input) > 1000: + raise ValueError("User input is too long. Please limit to 1000 characters.") + if input.chat_length != len(self.printed_history): + raise ValueError("Stale chat history. Please refresh the chat.") diff --git a/openai_agents/financial_research_agent/README.md b/openai_agents/financial_research_agent/README.md new file mode 100644 index 000000000..fed8e5b2d --- /dev/null +++ b/openai_agents/financial_research_agent/README.md @@ -0,0 +1,61 @@ +# Financial Research Agent + +Multi-agent financial research system with specialized roles, extended with Temporal's durable execution. + +*Adapted from [OpenAI Agents SDK financial research agent](https://github.com/openai/openai-agents-python/tree/main/examples/financial_research_agent)* + +## Architecture + +This example shows how you might compose a richer financial research agent using the Agents SDK. The pattern is similar to the `research_bot` example, but with more specialized sub-agents and a verification step. + +The flow is: + +1. **Planning**: A planner agent turns the end user's request into a list of search terms relevant to financial analysis – recent news, earnings calls, corporate filings, industry commentary, etc. +2. **Search**: A search agent uses the built-in `WebSearchTool` to retrieve terse summaries for each search term. (You could also add `FileSearchTool` if you have indexed PDFs or 10-Ks.) +3. **Sub-analysts**: Additional agents (e.g. a fundamentals analyst and a risk analyst) are exposed as tools so the writer can call them inline and incorporate their outputs. +4. **Writing**: A senior writer agent brings together the search snippets and any sub-analyst summaries into a long-form markdown report plus a short executive summary. +5. **Verification**: A final verifier agent audits the report for obvious inconsistencies or missing sourcing. + +## Running the Example + +First, start the worker: +```bash +uv run openai_agents/financial_research_agent/run_worker.py +``` + +Then run the financial research workflow: +```bash +uv run openai_agents/financial_research_agent/run_financial_research_workflow.py +``` + +Enter a query like: +``` +Write up an analysis of Apple Inc.'s most recent quarter. +``` + +You can also just hit enter to run this query, which is provided as the default. + +## Components + +### Agents + +- **Planner Agent**: Creates a search plan with 5-15 relevant search terms +- **Search Agent**: Uses web search to gather financial information +- **Financials Agent**: Analyzes company fundamentals (revenue, profit, margins) +- **Risk Agent**: Identifies potential red flags and risk factors +- **Writer Agent**: Synthesizes information into a comprehensive report +- **Verifier Agent**: Audits the final report for consistency and accuracy + +### Writer Agent Tools + +The writer agent has access to tools that invoke the specialist analysts: +- `fundamentals_analysis`: Get financial performance analysis +- `risk_analysis`: Get risk factor assessment + +## Temporal Integration + +The example demonstrates several Temporal patterns: +- Durable execution of multi-step research workflows +- Parallel execution of web searches using `asyncio.create_task` +- Use of `workflow.as_completed` for handling concurrent tasks +- Proper import handling with `workflow.unsafe.imports_passed_through()` diff --git a/openai_agents/financial_research_agent/agents/financials_agent.py b/openai_agents/financial_research_agent/agents/financials_agent.py new file mode 100644 index 000000000..72a2be959 --- /dev/null +++ b/openai_agents/financial_research_agent/agents/financials_agent.py @@ -0,0 +1,23 @@ +from agents import Agent +from pydantic import BaseModel + +# A sub-agent focused on analyzing a company's fundamentals. +FINANCIALS_PROMPT = ( + "You are a financial analyst focused on company fundamentals such as revenue, " + "profit, margins and growth trajectory. Given a collection of web (and optional file) " + "search results about a company, write a concise analysis of its recent financial " + "performance. Pull out key metrics or quotes. Keep it under 2 paragraphs." +) + + +class AnalysisSummary(BaseModel): + summary: str + """Short text summary for this aspect of the analysis.""" + + +def new_financials_agent() -> Agent: + return Agent( + name="FundamentalsAnalystAgent", + instructions=FINANCIALS_PROMPT, + output_type=AnalysisSummary, + ) diff --git a/openai_agents/financial_research_agent/agents/planner_agent.py b/openai_agents/financial_research_agent/agents/planner_agent.py new file mode 100644 index 000000000..8c7ffcb92 --- /dev/null +++ b/openai_agents/financial_research_agent/agents/planner_agent.py @@ -0,0 +1,35 @@ +from agents import Agent +from pydantic import BaseModel + +# Generate a plan of searches to ground the financial analysis. +# For a given financial question or company, we want to search for +# recent news, official filings, analyst commentary, and other +# relevant background. +PROMPT = ( + "You are a financial research planner. Given a request for financial analysis, " + "produce a set of web searches to gather the context needed. Aim for recent " + "headlines, earnings calls or 10-K snippets, analyst commentary, and industry background. " + "Output between 5 and 15 search terms to query for." +) + + +class FinancialSearchItem(BaseModel): + reason: str + """Your reasoning for why this search is relevant.""" + + query: str + """The search term to feed into a web (or file) search.""" + + +class FinancialSearchPlan(BaseModel): + searches: list[FinancialSearchItem] + """A list of searches to perform.""" + + +def new_planner_agent() -> Agent: + return Agent( + name="FinancialPlannerAgent", + instructions=PROMPT, + model="o3-mini", + output_type=FinancialSearchPlan, + ) diff --git a/openai_agents/financial_research_agent/agents/risk_agent.py b/openai_agents/financial_research_agent/agents/risk_agent.py new file mode 100644 index 000000000..c73e94efb --- /dev/null +++ b/openai_agents/financial_research_agent/agents/risk_agent.py @@ -0,0 +1,22 @@ +from agents import Agent +from pydantic import BaseModel + +# A sub-agent specializing in identifying risk factors or concerns. +RISK_PROMPT = ( + "You are a risk analyst looking for potential red flags in a company's outlook. " + "Given background research, produce a short analysis of risks such as competitive threats, " + "regulatory issues, supply chain problems, or slowing growth. Keep it under 2 paragraphs." +) + + +class AnalysisSummary(BaseModel): + summary: str + """Short text summary for this aspect of the analysis.""" + + +def new_risk_agent() -> Agent: + return Agent( + name="RiskAnalystAgent", + instructions=RISK_PROMPT, + output_type=AnalysisSummary, + ) diff --git a/openai_agents/financial_research_agent/agents/search_agent.py b/openai_agents/financial_research_agent/agents/search_agent.py new file mode 100644 index 000000000..e40e357e0 --- /dev/null +++ b/openai_agents/financial_research_agent/agents/search_agent.py @@ -0,0 +1,20 @@ +from agents import Agent, WebSearchTool +from agents.model_settings import ModelSettings + +# Given a search term, use web search to pull back a brief summary. +# Summaries should be concise but capture the main financial points. +INSTRUCTIONS = ( + "You are a research assistant specializing in financial topics. " + "Given a search term, use web search to retrieve up-to-date context and " + "produce a short summary of at most 300 words. Focus on key numbers, events, " + "or quotes that will be useful to a financial analyst." +) + + +def new_search_agent() -> Agent: + return Agent( + name="FinancialSearchAgent", + instructions=INSTRUCTIONS, + tools=[WebSearchTool()], + model_settings=ModelSettings(tool_choice="required"), + ) diff --git a/openai_agents/financial_research_agent/agents/verifier_agent.py b/openai_agents/financial_research_agent/agents/verifier_agent.py new file mode 100644 index 000000000..9d3f0a012 --- /dev/null +++ b/openai_agents/financial_research_agent/agents/verifier_agent.py @@ -0,0 +1,27 @@ +from agents import Agent +from pydantic import BaseModel + +# Agent to sanity-check a synthesized report for consistency and recall. +# This can be used to flag potential gaps or obvious mistakes. +VERIFIER_PROMPT = ( + "You are a meticulous auditor. You have been handed a financial analysis report. " + "Your job is to verify the report is internally consistent, clearly sourced, and makes " + "no unsupported claims. Point out any issues or uncertainties." +) + + +class VerificationResult(BaseModel): + verified: bool + """Whether the report seems coherent and plausible.""" + + issues: str + """If not verified, describe the main issues or concerns.""" + + +def new_verifier_agent() -> Agent: + return Agent( + name="VerificationAgent", + instructions=VERIFIER_PROMPT, + model="gpt-4o", + output_type=VerificationResult, + ) diff --git a/openai_agents/financial_research_agent/agents/writer_agent.py b/openai_agents/financial_research_agent/agents/writer_agent.py new file mode 100644 index 000000000..9accc2021 --- /dev/null +++ b/openai_agents/financial_research_agent/agents/writer_agent.py @@ -0,0 +1,34 @@ +from agents import Agent +from pydantic import BaseModel + +# Writer agent brings together the raw search results and optionally calls out +# to sub-analyst tools for specialized commentary, then returns a cohesive markdown report. +WRITER_PROMPT = ( + "You are a senior financial analyst. You will be provided with the original query and " + "a set of raw search summaries. Your task is to synthesize these into a long-form markdown " + "report (at least several paragraphs) including a short executive summary and follow-up " + "questions. If needed, you can call the available analysis tools (e.g. fundamentals_analysis, " + "risk_analysis) to get short specialist write-ups to incorporate." +) + + +class FinancialReportData(BaseModel): + short_summary: str + """A short 2-3 sentence executive summary.""" + + markdown_report: str + """The full markdown report.""" + + follow_up_questions: list[str] + """Suggested follow-up questions for further research.""" + + +# Note: We will attach tools to specialist analyst agents at runtime in the manager. +# This shows how an agent can use tools to delegate to specialized subagents. +def new_writer_agent() -> Agent: + return Agent( + name="FinancialWriterAgent", + instructions=WRITER_PROMPT, + model="gpt-4.1-2025-04-14", + output_type=FinancialReportData, + ) diff --git a/openai_agents/financial_research_agent/financial_research_manager.py b/openai_agents/financial_research_agent/financial_research_manager.py new file mode 100644 index 000000000..6f2050b27 --- /dev/null +++ b/openai_agents/financial_research_agent/financial_research_manager.py @@ -0,0 +1,142 @@ +from __future__ import annotations + +import asyncio +from collections.abc import Sequence + +from agents import RunConfig, Runner, RunResult, RunResultStreaming, custom_span, trace +from temporalio import workflow + +from openai_agents.financial_research_agent.agents.financials_agent import ( + new_financials_agent, +) +from openai_agents.financial_research_agent.agents.planner_agent import ( + FinancialSearchItem, + FinancialSearchPlan, + new_planner_agent, +) +from openai_agents.financial_research_agent.agents.risk_agent import new_risk_agent +from openai_agents.financial_research_agent.agents.search_agent import new_search_agent +from openai_agents.financial_research_agent.agents.verifier_agent import ( + VerificationResult, + new_verifier_agent, +) +from openai_agents.financial_research_agent.agents.writer_agent import ( + FinancialReportData, + new_writer_agent, +) + + +async def _summary_extractor(run_result: RunResult | RunResultStreaming) -> str: + """Custom output extractor for sub-agents that return an AnalysisSummary.""" + # The financial/risk analyst agents emit an AnalysisSummary with a `summary` field. + # We want the tool call to return just that summary text so the writer can drop it inline. + return str(run_result.final_output.summary) + + +class FinancialResearchManager: + """ + Orchestrates the full flow: planning, searching, sub-analysis, writing, and verification. + """ + + def __init__(self) -> None: + self.run_config = RunConfig() + self.planner_agent = new_planner_agent() + self.search_agent = new_search_agent() + self.financials_agent = new_financials_agent() + self.risk_agent = new_risk_agent() + self.writer_agent = new_writer_agent() + self.verifier_agent = new_verifier_agent() + + async def run(self, query: str) -> str: + with trace("Financial research trace"): + search_plan = await self._plan_searches(query) + search_results = await self._perform_searches(search_plan) + report = await self._write_report(query, search_results) + verification = await self._verify_report(report) + + # Return formatted output + result = f"""=====REPORT===== + +{report.markdown_report} + +=====FOLLOW UP QUESTIONS===== + +{chr(10).join(report.follow_up_questions)} + +=====VERIFICATION===== + +Verified: {verification.verified} +Issues: {verification.issues}""" + + return result + + async def _plan_searches(self, query: str) -> FinancialSearchPlan: + result = await Runner.run( + self.planner_agent, + f"Query: {query}", + run_config=self.run_config, + ) + return result.final_output_as(FinancialSearchPlan) + + async def _perform_searches( + self, search_plan: FinancialSearchPlan + ) -> Sequence[str]: + with custom_span("Search the web"): + tasks = [ + asyncio.create_task(self._search(item)) for item in search_plan.searches + ] + results: list[str] = [] + for task in workflow.as_completed(tasks): + result = await task + if result is not None: + results.append(result) + return results + + async def _search(self, item: FinancialSearchItem) -> str | None: + input_data = f"Search term: {item.query}\nReason: {item.reason}" + try: + result = await Runner.run( + self.search_agent, + input_data, + run_config=self.run_config, + ) + return str(result.final_output) + except Exception: + return None + + async def _write_report( + self, query: str, search_results: Sequence[str] + ) -> FinancialReportData: + # Expose the specialist analysts as tools so the writer can invoke them inline + # and still produce the final FinancialReportData output. + fundamentals_tool = self.financials_agent.as_tool( + tool_name="fundamentals_analysis", + tool_description="Use to get a short write-up of key financial metrics", + custom_output_extractor=_summary_extractor, + ) + risk_tool = self.risk_agent.as_tool( + tool_name="risk_analysis", + tool_description="Use to get a short write-up of potential red flags", + custom_output_extractor=_summary_extractor, + ) + writer_with_tools = self.writer_agent.clone( + tools=[fundamentals_tool, risk_tool] + ) + + input_data = ( + f"Original query: {query}\nSummarized search results: {search_results}" + ) + result = await Runner.run( + writer_with_tools, + input_data, + run_config=self.run_config, + ) + return result.final_output_as(FinancialReportData) + + async def _verify_report(self, report: FinancialReportData) -> VerificationResult: + result = await Runner.run( + self.verifier_agent, + report.markdown_report, + run_config=self.run_config, + ) + return result.final_output_as(VerificationResult) diff --git a/openai_agents/financial_research_agent/run_financial_research_workflow.py b/openai_agents/financial_research_agent/run_financial_research_workflow.py new file mode 100644 index 000000000..80adc86bb --- /dev/null +++ b/openai_agents/financial_research_agent/run_financial_research_workflow.py @@ -0,0 +1,41 @@ +#!/usr/bin/env python3 + +import asyncio + +from temporalio.client import Client +from temporalio.contrib.openai_agents import OpenAIAgentsPlugin + +from openai_agents.financial_research_agent.workflows.financial_research_workflow import ( + FinancialResearchWorkflow, +) + + +async def main(): + # Get the query from user input + query = input("Enter a financial research query: ") + if not query.strip(): + query = "Write up an analysis of Apple Inc.'s most recent quarter." + print(f"Using default query: {query}") + + client = await Client.connect( + "localhost:7233", + plugins=[ + OpenAIAgentsPlugin(), + ], + ) + + print(f"Starting financial research for: {query}") + print("This may take several minutes to complete...\n") + + result = await client.execute_workflow( + FinancialResearchWorkflow.run, + query, + id=f"financial-research-{hash(query)}", + task_queue="financial-research-task-queue", + ) + + print(result) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/openai_agents/financial_research_agent/run_worker.py b/openai_agents/financial_research_agent/run_worker.py new file mode 100644 index 000000000..507bd77ca --- /dev/null +++ b/openai_agents/financial_research_agent/run_worker.py @@ -0,0 +1,33 @@ +#!/usr/bin/env python3 + +import asyncio + +from temporalio.client import Client +from temporalio.contrib.openai_agents import OpenAIAgentsPlugin +from temporalio.worker import Worker + +from openai_agents.financial_research_agent.workflows.financial_research_workflow import ( + FinancialResearchWorkflow, +) + + +async def main(): + client = await Client.connect( + "localhost:7233", + plugins=[ + OpenAIAgentsPlugin(), + ], + ) + + worker = Worker( + client, + task_queue="financial-research-task-queue", + workflows=[FinancialResearchWorkflow], + ) + + print("Starting financial research worker...") + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/openai_agents/financial_research_agent/workflows/financial_research_workflow.py b/openai_agents/financial_research_agent/workflows/financial_research_workflow.py new file mode 100644 index 000000000..487e3fd57 --- /dev/null +++ b/openai_agents/financial_research_agent/workflows/financial_research_workflow.py @@ -0,0 +1,13 @@ +from temporalio import workflow + +from openai_agents.financial_research_agent.financial_research_manager import ( + FinancialResearchManager, +) + + +@workflow.defn +class FinancialResearchWorkflow: + @workflow.run + async def run(self, query: str) -> str: + manager = FinancialResearchManager() + return await manager.run(query) diff --git a/openai_agents/handoffs/README.md b/openai_agents/handoffs/README.md new file mode 100644 index 000000000..21717e9ff --- /dev/null +++ b/openai_agents/handoffs/README.md @@ -0,0 +1,44 @@ +# Handoffs Examples + +Agent handoff patterns with message filtering in Temporal workflows. + +*Adapted from [OpenAI Agents SDK handoffs examples](https://github.com/openai/openai-agents-python/tree/main/examples/handoffs)* + +Before running these examples, be sure to review the [prerequisites and background on the integration](../README.md). + +## Running the Examples + +First, start the worker: +```bash +uv run openai_agents/handoffs/run_worker.py +``` + +Then run the workflow: + +### Message Filter Workflow +Demonstrates agent handoffs with message history filtering: +```bash +uv run openai_agents/handoffs/run_message_filter_workflow.py +``` + +## Workflow Pattern + +The workflow demonstrates a 4-step conversation with message filtering: + +1. **Introduction**: User greets first agent with name +2. **Tool Usage**: First agent generates random number using function tool +3. **Agent Switch**: Conversation moves to second agent for general questions +4. **Spanish Handoff**: Second agent detects Spanish and hands off to Spanish specialist + +During the Spanish handoff, message filtering occurs: +- All tool-related messages are removed from history +- First two messages are dropped (demonstration of selective context) +- Filtered conversation continues with Spanish agent + +The workflow returns both the final response and complete message history for inspection. + +## Omitted Examples + +The following patterns from the [reference repository](https://github.com/openai/openai-agents-python/tree/main/examples/handoffs) are not included in this Temporal adaptation: + +- **Message Filter Streaming**: Differs from the included message-filter example only in rendering the same run's output as it streams. The Temporal integration does support that — see [Streaming](../streaming/README.md) — but it is demonstrated there rather than duplicated here. \ No newline at end of file diff --git a/openai_agents/handoffs/run_message_filter_workflow.py b/openai_agents/handoffs/run_message_filter_workflow.py new file mode 100644 index 000000000..c2b2ba3db --- /dev/null +++ b/openai_agents/handoffs/run_message_filter_workflow.py @@ -0,0 +1,42 @@ +import asyncio +import json + +from temporalio.client import Client +from temporalio.contrib.openai_agents import OpenAIAgentsPlugin +from temporalio.envconfig import ClientConfig + +from openai_agents.handoffs.workflows.message_filter_workflow import ( + MessageFilterWorkflow, +) + + +async def main(): + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + + # Create client connected to server at the given address + client = await Client.connect( + **config, + plugins=[ + OpenAIAgentsPlugin(), + ], + ) + + # Execute a workflow + result = await client.execute_workflow( + MessageFilterWorkflow.run, + "Sora", + id="message-filter-workflow", + task_queue="openai-agents-handoffs-task-queue", + ) + + print(f"Final output: {result.final_output}") + print("\n===Final messages===\n") + + # Print the final message history to see the effect of the message filter + for message in result.final_messages: + print(json.dumps(message, indent=2)) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/openai_agents/handoffs/run_worker.py b/openai_agents/handoffs/run_worker.py new file mode 100644 index 000000000..8941f4d85 --- /dev/null +++ b/openai_agents/handoffs/run_worker.py @@ -0,0 +1,42 @@ +from __future__ import annotations + +import asyncio +from datetime import timedelta + +from temporalio.client import Client +from temporalio.contrib.openai_agents import ModelActivityParameters, OpenAIAgentsPlugin +from temporalio.worker import Worker + +from openai_agents.handoffs.workflows.message_filter_workflow import ( + MessageFilterWorkflow, +) + + +async def main(): + # Create client connected to server at the given address + client = await Client.connect( + "localhost:7233", + plugins=[ + OpenAIAgentsPlugin( + model_params=ModelActivityParameters( + start_to_close_timeout=timedelta(seconds=60) + ) + ), + ], + ) + + worker = Worker( + client, + task_queue="openai-agents-handoffs-task-queue", + workflows=[ + MessageFilterWorkflow, + ], + activities=[ + # No custom activities needed for these workflows + ], + ) + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/openai_agents/handoffs/workflows/message_filter_workflow.py b/openai_agents/handoffs/workflows/message_filter_workflow.py new file mode 100644 index 000000000..8adc9453f --- /dev/null +++ b/openai_agents/handoffs/workflows/message_filter_workflow.py @@ -0,0 +1,112 @@ +from __future__ import annotations + +from dataclasses import dataclass +from typing import List + +from agents import Agent, HandoffInputData, Runner, function_tool, handoff +from agents.extensions import handoff_filters +from agents.items import TResponseInputItem +from temporalio import workflow + + +@dataclass +class MessageFilterResult: + final_output: str + final_messages: List[TResponseInputItem] + + +@function_tool +def random_number_tool(max: int) -> int: + """Return a random integer between 0 and the given maximum.""" + return workflow.random().randint(0, max) + + +def spanish_handoff_message_filter( + handoff_message_data: HandoffInputData, +) -> HandoffInputData: + # First, we'll remove any tool-related messages from the message history + handoff_message_data = handoff_filters.remove_all_tools(handoff_message_data) + + # Second, we'll also remove the first two items from the history, just for demonstration + history = ( + tuple(handoff_message_data.input_history[2:]) + if isinstance(handoff_message_data.input_history, tuple) + else handoff_message_data.input_history + ) + + return HandoffInputData( + input_history=history, + pre_handoff_items=tuple(handoff_message_data.pre_handoff_items), + new_items=tuple(handoff_message_data.new_items), + ) + + +@workflow.defn +class MessageFilterWorkflow: + @workflow.run + async def run(self, user_name: str = "Sora") -> MessageFilterResult: + first_agent = Agent( + name="Assistant", + instructions="Be extremely concise.", + tools=[random_number_tool], + ) + + spanish_agent = Agent( + name="Spanish Assistant", + instructions="You only speak Spanish and are extremely concise.", + handoff_description="A Spanish-speaking assistant.", + ) + + second_agent = Agent( + name="Assistant", + instructions=( + "Be a helpful assistant. If the user speaks Spanish, handoff to the Spanish assistant." + ), + handoffs=[ + handoff(spanish_agent, input_filter=spanish_handoff_message_filter) + ], + ) + + # 1. Send a regular message to the first agent + result = await Runner.run(first_agent, input=f"Hi, my name is {user_name}.") + + # 2. Ask it to generate a number + result = await Runner.run( + first_agent, + input=result.to_input_list() + + [ + { + "content": "Can you generate a random number between 0 and 100?", + "role": "user", + } + ], + ) + + # 3. Call the second agent + result = await Runner.run( + second_agent, + input=result.to_input_list() + + [ + { + "content": "I live in New York City. What's the population of the city?", + "role": "user", + } + ], + ) + + # 4. Cause a handoff to occur + result = await Runner.run( + second_agent, + input=result.to_input_list() + + [ + { + "content": "Por favor habla en español. ¿Cuál es mi nombre y dónde vivo?", + "role": "user", + } + ], + ) + + # Return the final result and message history + return MessageFilterResult( + final_output=result.final_output, final_messages=result.to_input_list() + ) diff --git a/openai_agents/hosted_mcp/README.md b/openai_agents/hosted_mcp/README.md new file mode 100644 index 000000000..78a371311 --- /dev/null +++ b/openai_agents/hosted_mcp/README.md @@ -0,0 +1,39 @@ +# Hosted MCP Examples + +Integration with hosted MCP (Model Context Protocol) servers using OpenAI agents in Temporal workflows. + +*Adapted from [OpenAI Agents SDK hosted_mcp examples](https://github.com/openai/openai-agents-python/tree/main/examples/hosted_mcp)* + +Before running these examples, be sure to review the [prerequisites and background on the integration](../README.md). + +## Running the Examples + +First, start the worker (supports all MCP workflows): +```bash +uv run openai_agents/hosted_mcp/run_worker.py +``` + +Then run individual examples in separate terminals: + +### Simple MCP Connection +Connect to a hosted MCP server without approval requirements (trusted servers): +```bash +uv run openai_agents/hosted_mcp/run_simple_mcp_workflow.py +``` + +### MCP with Approval Callbacks +Connect to a hosted MCP server with approval workflow for tool execution: +```bash +uv run openai_agents/hosted_mcp/run_approval_mcp_workflow.py +``` + +## MCP Server Configuration + +Both examples default to using the GitMCP server (`https://gitmcp.io/openai/codex`) which provides repository analysis capabilities. The workflows can be easily modified to use different MCP servers by changing the `server_url` parameter. + +### Approval Workflow Notes + +The approval example demonstrates the callback structure for tool approvals in a Temporal context. In this implementation: + +- The approval callback automatically approves requests for demonstration purposes +- In production environments, approvals would typically be handled by communicating with a human user. Because the approval executes in the Temporal workflow, you can use signals or updates to communicate approval status. diff --git a/openai_agents/hosted_mcp/run_approval_mcp_workflow.py b/openai_agents/hosted_mcp/run_approval_mcp_workflow.py new file mode 100644 index 000000000..8821c78a7 --- /dev/null +++ b/openai_agents/hosted_mcp/run_approval_mcp_workflow.py @@ -0,0 +1,30 @@ +import asyncio + +from temporalio.client import Client +from temporalio.contrib.openai_agents import OpenAIAgentsPlugin + +from openai_agents.hosted_mcp.workflows.approval_mcp_workflow import ApprovalMCPWorkflow + + +async def main(): + # Create client connected to server at the given address + client = await Client.connect( + "localhost:7233", + plugins=[ + OpenAIAgentsPlugin(), + ], + ) + + # Execute a workflow + result = await client.execute_workflow( + ApprovalMCPWorkflow.run, + "Which language is this repo written in?", + id="approval-mcp-workflow", + task_queue="openai-agents-hosted-mcp-task-queue", + ) + + print(f"Result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/openai_agents/hosted_mcp/run_simple_mcp_workflow.py b/openai_agents/hosted_mcp/run_simple_mcp_workflow.py new file mode 100644 index 000000000..5e0c064f5 --- /dev/null +++ b/openai_agents/hosted_mcp/run_simple_mcp_workflow.py @@ -0,0 +1,30 @@ +import asyncio + +from temporalio.client import Client +from temporalio.contrib.openai_agents import OpenAIAgentsPlugin + +from openai_agents.hosted_mcp.workflows.simple_mcp_workflow import SimpleMCPWorkflow + + +async def main(): + # Create client connected to server at the given address + client = await Client.connect( + "localhost:7233", + plugins=[ + OpenAIAgentsPlugin(), + ], + ) + + # Execute a workflow + result = await client.execute_workflow( + SimpleMCPWorkflow.run, + "Which language is this repo written in?", + id="simple-mcp-workflow", + task_queue="openai-agents-hosted-mcp-task-queue", + ) + + print(f"Result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/openai_agents/hosted_mcp/run_worker.py b/openai_agents/hosted_mcp/run_worker.py new file mode 100644 index 000000000..fb25f7b6b --- /dev/null +++ b/openai_agents/hosted_mcp/run_worker.py @@ -0,0 +1,42 @@ +from __future__ import annotations + +import asyncio +from datetime import timedelta + +from temporalio.client import Client +from temporalio.contrib.openai_agents import ModelActivityParameters, OpenAIAgentsPlugin +from temporalio.worker import Worker + +from openai_agents.hosted_mcp.workflows.approval_mcp_workflow import ApprovalMCPWorkflow +from openai_agents.hosted_mcp.workflows.simple_mcp_workflow import SimpleMCPWorkflow + + +async def main(): + # Create client connected to server at the given address + client = await Client.connect( + "localhost:7233", + plugins=[ + OpenAIAgentsPlugin( + model_params=ModelActivityParameters( + start_to_close_timeout=timedelta(seconds=60) + ) + ), + ], + ) + + worker = Worker( + client, + task_queue="openai-agents-hosted-mcp-task-queue", + workflows=[ + SimpleMCPWorkflow, + ApprovalMCPWorkflow, + ], + activities=[ + # No custom activities needed for these workflows + ], + ) + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/openai_agents/hosted_mcp/workflows/approval_mcp_workflow.py b/openai_agents/hosted_mcp/workflows/approval_mcp_workflow.py new file mode 100644 index 000000000..1b5b7b6f9 --- /dev/null +++ b/openai_agents/hosted_mcp/workflows/approval_mcp_workflow.py @@ -0,0 +1,48 @@ +from __future__ import annotations + +from agents import ( + Agent, + HostedMCPTool, + MCPToolApprovalFunctionResult, + MCPToolApprovalRequest, + Runner, +) +from temporalio import workflow + + +def approval_callback(request: MCPToolApprovalRequest) -> MCPToolApprovalFunctionResult: + """Simple approval callback that logs the request and approves by default. + + In a real application, user input would be provided through a UI or API. + The approval callback executes within the Temporal workflow, so the application + can use signals or updates to receive user input. + """ + workflow.logger.info(f"MCP tool approval requested for: {request.data.name}") + + result: MCPToolApprovalFunctionResult = {"approve": True} + return result + + +@workflow.defn +class ApprovalMCPWorkflow: + @workflow.run + async def run( + self, question: str, server_url: str = "https://gitmcp.io/openai/codex" + ) -> str: + agent = Agent( + name="Assistant", + tools=[ + HostedMCPTool( + tool_config={ + "type": "mcp", + "server_label": "gitmcp", + "server_url": server_url, + "require_approval": "always", + }, + on_approval_request=approval_callback, + ) + ], + ) + + result = await Runner.run(agent, question) + return result.final_output diff --git a/openai_agents/hosted_mcp/workflows/simple_mcp_workflow.py b/openai_agents/hosted_mcp/workflows/simple_mcp_workflow.py new file mode 100644 index 000000000..2fac64bc5 --- /dev/null +++ b/openai_agents/hosted_mcp/workflows/simple_mcp_workflow.py @@ -0,0 +1,28 @@ +from __future__ import annotations + +from agents import Agent, HostedMCPTool, Runner +from temporalio import workflow + + +@workflow.defn +class SimpleMCPWorkflow: + @workflow.run + async def run( + self, question: str, server_url: str = "https://gitmcp.io/openai/codex" + ) -> str: + agent = Agent( + name="Assistant", + tools=[ + HostedMCPTool( + tool_config={ + "type": "mcp", + "server_label": "gitmcp", + "server_url": server_url, + "require_approval": "never", + } + ) + ], + ) + + result = await Runner.run(agent, question) + return result.final_output diff --git a/openai_agents/mcp/README.md b/openai_agents/mcp/README.md new file mode 100644 index 000000000..d9a172f30 --- /dev/null +++ b/openai_agents/mcp/README.md @@ -0,0 +1,91 @@ +# MCP Examples + +Integration with MCP (Model Context Protocol) servers using OpenAI agents in Temporal workflows. + +*Adapted from [OpenAI Agents SDK MCP examples](https://github.com/openai/openai-agents-python/tree/main/examples/mcp)* + +Before running these examples, be sure to review the [prerequisites and background on the integration](../README.md). + + +## Running the Examples + +### Stdio MCP + +First, start the worker: +```bash +uv run openai_agents/mcp/run_file_system_worker.py +``` + +Run the workflow: +```bash +uv run openai_agents/mcp/run_file_system_workflow.py +``` + +This sample assumes that the worker and `run_file_system_workflow.py` are on the same machine. + + +### Streamable HTTP MCP + +First, start the worker: +```bash +uv run openai_agents/mcp/servers/tools_server.py --transport=streamable-http +``` + +Then start the worker: +```bash +uv run openai_agents/mcp/run_streamable_http_worker.py +``` + +Finally, run the workflow: +```bash +uv run openai_agents/mcp/run_streamable_http_workflow.py +``` + +### SSE MCP + +First, start the MCP server: +```bash +uv run openai_agents/mcp/servers/tools_server.py --transport=sse +``` + +Then start the worker: +```bash +uv run openai_agents/mcp/run_sse_worker.py +``` + +Finally, run the workflow: +```bash +uv run openai_agents/mcp/run_sse_workflow.py +``` + +### Prompt Server MCP + +First, start the MCP server: +```bash +uv run openai_agents/mcp/servers/prompt_server.py +``` + +Then start the worker: +```bash +uv run openai_agents/mcp/run_prompt_server_worker.py +``` + +Finally, run the workflow: +```bash +uv run openai_agents/mcp/run_prompt_server_workflow.py +``` + + +### Memory MCP (Research Scratchpad) + +Demonstrates durable note-taking with the Memory MCP server: write seed notes, query by tags, synthesize a brief with citations, then update and delete notes. + +Start the worker: +```bash +uv run openai_agents/mcp/run_memory_research_scratchpad_worker.py +``` + +Run the research scratchpad workflow: +```bash +uv run openai_agents/mcp/run_memory_research_scratchpad_workflow.py +``` diff --git a/openai_agents/mcp/run_file_system_worker.py b/openai_agents/mcp/run_file_system_worker.py new file mode 100644 index 000000000..2ed8dffdf --- /dev/null +++ b/openai_agents/mcp/run_file_system_worker.py @@ -0,0 +1,66 @@ +from __future__ import annotations + +import asyncio +import logging +import os +from datetime import timedelta + +from agents.mcp import MCPServerStdio +from temporalio.client import Client +from temporalio.contrib.openai_agents import ( + ModelActivityParameters, + OpenAIAgentsPlugin, + StatelessMCPServerProvider, +) +from temporalio.envconfig import ClientConfig +from temporalio.worker import Worker + +from openai_agents.mcp.workflows.file_system_workflow import FileSystemWorkflow + + +async def main(): + logging.basicConfig(level=logging.INFO) + current_dir = os.path.dirname(os.path.abspath(__file__)) + samples_dir = os.path.join(current_dir, "sample_files") + + file_system_server = StatelessMCPServerProvider( + "FileSystemServer", + lambda: MCPServerStdio( + name="FileSystemServer", + params={ + "command": "npx", + "args": ["-y", "@modelcontextprotocol/server-filesystem", samples_dir], + }, + ), + ) + + # Create client connected to server at the given address + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect( + **config, + plugins=[ + OpenAIAgentsPlugin( + model_params=ModelActivityParameters( + start_to_close_timeout=timedelta(seconds=60) + ), + mcp_server_providers=[file_system_server], + ), + ], + ) + + worker = Worker( + client, + task_queue=f"openai-agents-mcp-filesystem-task-queue", + workflows=[ + FileSystemWorkflow, + ], + activities=[ + # No custom activities needed for these workflows + ], + ) + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/openai_agents/mcp/run_file_system_workflow.py b/openai_agents/mcp/run_file_system_workflow.py new file mode 100644 index 000000000..d076d3d5c --- /dev/null +++ b/openai_agents/mcp/run_file_system_workflow.py @@ -0,0 +1,32 @@ +import asyncio + +from temporalio.client import Client +from temporalio.contrib.openai_agents import OpenAIAgentsPlugin +from temporalio.envconfig import ClientConfig + +from openai_agents.mcp.workflows.file_system_workflow import FileSystemWorkflow + + +async def main(): + # Create client connected to server at the given address + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect( + **config, + plugins=[ + OpenAIAgentsPlugin(), + ], + ) + + # Execute a workflow + result = await client.execute_workflow( + FileSystemWorkflow.run, + id="file-system-workflow", + task_queue="openai-agents-mcp-filesystem-task-queue", + ) + + print(f"Result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/openai_agents/mcp/run_memory_research_scratchpad_worker.py b/openai_agents/mcp/run_memory_research_scratchpad_worker.py new file mode 100644 index 000000000..536ab9745 --- /dev/null +++ b/openai_agents/mcp/run_memory_research_scratchpad_worker.py @@ -0,0 +1,63 @@ +from __future__ import annotations + +import asyncio +import logging +from datetime import timedelta + +from agents.mcp import MCPServerStdio +from temporalio.client import Client +from temporalio.contrib.openai_agents import ( + ModelActivityParameters, + OpenAIAgentsPlugin, + StatefulMCPServerProvider, +) +from temporalio.envconfig import ClientConfig +from temporalio.worker import Worker + +from openai_agents.mcp.workflows.memory_research_scratchpad_workflow import ( + MemoryResearchScratchpadWorkflow, +) + + +async def main(): + logging.basicConfig(level=logging.INFO) + + memory_server_provider = StatefulMCPServerProvider( + "MemoryServer", + lambda _: MCPServerStdio( + name="MemoryServer", + params={ + "command": "npx", + "args": ["-y", "@modelcontextprotocol/server-memory"], + }, + ), + ) + + # Create client connected to server at the given address + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect( + **config, + plugins=[ + OpenAIAgentsPlugin( + model_params=ModelActivityParameters( + start_to_close_timeout=timedelta(seconds=60) + ), + mcp_server_providers=[memory_server_provider], + ), + ], + ) + + worker = Worker( + client, + task_queue="openai-agents-mcp-memory-task-queue", + workflows=[ + MemoryResearchScratchpadWorkflow, + ], + activities=[], + ) + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/openai_agents/mcp/run_memory_research_scratchpad_workflow.py b/openai_agents/mcp/run_memory_research_scratchpad_workflow.py new file mode 100644 index 000000000..bf724f9a9 --- /dev/null +++ b/openai_agents/mcp/run_memory_research_scratchpad_workflow.py @@ -0,0 +1,34 @@ +from __future__ import annotations + +import asyncio + +from temporalio.client import Client +from temporalio.contrib.openai_agents import OpenAIAgentsPlugin +from temporalio.envconfig import ClientConfig + +from openai_agents.mcp.workflows.memory_research_scratchpad_workflow import ( + MemoryResearchScratchpadWorkflow, +) + + +async def main(): + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect( + **config, + plugins=[ + OpenAIAgentsPlugin(), + ], + ) + + result = await client.execute_workflow( + MemoryResearchScratchpadWorkflow.run, + id="memory-research-scratchpad-workflow", + task_queue="openai-agents-mcp-memory-task-queue", + ) + + print(f"Result:\n{result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/openai_agents/mcp/run_prompt_server_worker.py b/openai_agents/mcp/run_prompt_server_worker.py new file mode 100644 index 000000000..08b68faee --- /dev/null +++ b/openai_agents/mcp/run_prompt_server_worker.py @@ -0,0 +1,68 @@ +from __future__ import annotations + +import asyncio +import logging +from datetime import timedelta + +from agents.mcp import MCPServerStreamableHttp +from temporalio.client import Client +from temporalio.contrib.openai_agents import ( + ModelActivityParameters, + OpenAIAgentsPlugin, + StatelessMCPServerProvider, +) +from temporalio.envconfig import ClientConfig +from temporalio.worker import Worker + +from openai_agents.mcp.workflows.prompt_server_workflow import PromptServerWorkflow + + +async def main(): + logging.basicConfig(level=logging.INFO) + + print("Setting up worker...\n") + + try: + prompt_server_provider = StatelessMCPServerProvider( + "PromptServer", + lambda: MCPServerStreamableHttp( + name="PromptServer", + params={ + "url": "http://localhost:8000/mcp", + }, + ), + ) + + # Create client connected to server at the given address + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect( + **config, + plugins=[ + OpenAIAgentsPlugin( + model_params=ModelActivityParameters( + start_to_close_timeout=timedelta(seconds=120) + ), + mcp_server_providers=[prompt_server_provider], + ), + ], + ) + + worker = Worker( + client, + task_queue="openai-agents-mcp-prompt-task-queue", + workflows=[ + PromptServerWorkflow, + ], + activities=[ + # No custom activities needed for these workflows + ], + ) + await worker.run() + except Exception as e: + print(f"Worker failed: {e}") + raise + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/openai_agents/mcp/run_prompt_server_workflow.py b/openai_agents/mcp/run_prompt_server_workflow.py new file mode 100644 index 000000000..9cad27250 --- /dev/null +++ b/openai_agents/mcp/run_prompt_server_workflow.py @@ -0,0 +1,32 @@ +from __future__ import annotations + +import asyncio + +from temporalio.client import Client +from temporalio.contrib.openai_agents import OpenAIAgentsPlugin +from temporalio.envconfig import ClientConfig + +from openai_agents.mcp.workflows.prompt_server_workflow import PromptServerWorkflow + + +async def main(): + # Create client connected to server at the given address + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect( + **config, + plugins=[OpenAIAgentsPlugin()], + ) + + # Execute a workflow + result = await client.execute_workflow( + PromptServerWorkflow.run, + id="prompt-server-workflow", + task_queue="openai-agents-mcp-prompt-task-queue", + ) + + print(f"Result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/openai_agents/mcp/run_sse_worker.py b/openai_agents/mcp/run_sse_worker.py new file mode 100644 index 000000000..8ed719bdc --- /dev/null +++ b/openai_agents/mcp/run_sse_worker.py @@ -0,0 +1,68 @@ +from __future__ import annotations + +import asyncio +import logging +from datetime import timedelta + +from agents.mcp import MCPServerSse +from temporalio.client import Client +from temporalio.contrib.openai_agents import ( + ModelActivityParameters, + OpenAIAgentsPlugin, + StatelessMCPServerProvider, +) +from temporalio.envconfig import ClientConfig +from temporalio.worker import Worker + +from openai_agents.mcp.workflows.sse_workflow import SseWorkflow + + +async def main(): + logging.basicConfig(level=logging.INFO) + + print("Setting up worker...\n") + + try: + sse_server_provider = StatelessMCPServerProvider( + "SseServer", + lambda: MCPServerSse( + name="SseServer", + params={ + "url": "http://localhost:8000/sse", + }, + ), + ) + + # Create client connected to server at the given address + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect( + **config, + plugins=[ + OpenAIAgentsPlugin( + model_params=ModelActivityParameters( + start_to_close_timeout=timedelta(seconds=60) + ), + mcp_server_providers=[sse_server_provider], + ), + ], + ) + + worker = Worker( + client, + task_queue="openai-agents-mcp-sse-task-queue", + workflows=[ + SseWorkflow, + ], + activities=[ + # No custom activities needed for these workflows + ], + ) + await worker.run() + except Exception as e: + print(f"Worker failed: {e}") + raise + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/openai_agents/mcp/run_sse_workflow.py b/openai_agents/mcp/run_sse_workflow.py new file mode 100644 index 000000000..8effd5fe1 --- /dev/null +++ b/openai_agents/mcp/run_sse_workflow.py @@ -0,0 +1,32 @@ +from __future__ import annotations + +import asyncio + +from temporalio.client import Client +from temporalio.contrib.openai_agents import OpenAIAgentsPlugin +from temporalio.envconfig import ClientConfig + +from openai_agents.mcp.workflows.sse_workflow import SseWorkflow + + +async def main(): + # Create client connected to server at the given address + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect( + **config, + plugins=[OpenAIAgentsPlugin()], + ) + + # Execute a workflow + result = await client.execute_workflow( + SseWorkflow.run, + id="sse-workflow", + task_queue="openai-agents-mcp-sse-task-queue", + ) + + print(f"Result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/openai_agents/mcp/run_streamable_http_worker.py b/openai_agents/mcp/run_streamable_http_worker.py new file mode 100644 index 000000000..724147273 --- /dev/null +++ b/openai_agents/mcp/run_streamable_http_worker.py @@ -0,0 +1,68 @@ +from __future__ import annotations + +import asyncio +import logging +from datetime import timedelta + +from agents.mcp import MCPServerStreamableHttp +from temporalio.client import Client +from temporalio.contrib.openai_agents import ( + ModelActivityParameters, + OpenAIAgentsPlugin, + StatelessMCPServerProvider, +) +from temporalio.envconfig import ClientConfig +from temporalio.worker import Worker + +from openai_agents.mcp.workflows.streamable_http_workflow import StreamableHttpWorkflow + + +async def main(): + logging.basicConfig(level=logging.INFO) + + print("Setting up worker...\n") + + try: + streamable_http_server_provider = StatelessMCPServerProvider( + "StreamableHttpServer", + lambda: MCPServerStreamableHttp( + name="StreamableHttpServer", + params={ + "url": "http://localhost:8000/mcp", + }, + ), + ) + + # Create client connected to server at the given address + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect( + **config, + plugins=[ + OpenAIAgentsPlugin( + model_params=ModelActivityParameters( + start_to_close_timeout=timedelta(seconds=60) + ), + mcp_server_providers=[streamable_http_server_provider], + ), + ], + ) + + worker = Worker( + client, + task_queue="openai-agents-mcp-streamable-http-task-queue", + workflows=[ + StreamableHttpWorkflow, + ], + activities=[ + # No custom activities needed for these workflows + ], + ) + await worker.run() + except Exception as e: + print(f"Worker failed: {e}") + raise + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/openai_agents/mcp/run_streamable_http_workflow.py b/openai_agents/mcp/run_streamable_http_workflow.py new file mode 100644 index 000000000..b691e456d --- /dev/null +++ b/openai_agents/mcp/run_streamable_http_workflow.py @@ -0,0 +1,32 @@ +from __future__ import annotations + +import asyncio + +from temporalio.client import Client +from temporalio.contrib.openai_agents import OpenAIAgentsPlugin +from temporalio.envconfig import ClientConfig + +from openai_agents.mcp.workflows.streamable_http_workflow import StreamableHttpWorkflow + + +async def main(): + # Create client connected to server at the given address + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect( + **config, + plugins=[OpenAIAgentsPlugin()], + ) + + # Execute a workflow + result = await client.execute_workflow( + StreamableHttpWorkflow.run, + id="streamable-http-workflow", + task_queue="openai-agents-mcp-streamable-http-task-queue", + ) + + print(f"Result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/openai_agents/mcp/sample_files/favorite_books.txt b/openai_agents/mcp/sample_files/favorite_books.txt new file mode 100644 index 000000000..c55f457ec --- /dev/null +++ b/openai_agents/mcp/sample_files/favorite_books.txt @@ -0,0 +1,20 @@ +1. To Kill a Mockingbird – Harper Lee +2. Pride and Prejudice – Jane Austen +3. 1984 – George Orwell +4. The Hobbit – J.R.R. Tolkien +5. Harry Potter and the Sorcerer’s Stone – J.K. Rowling +6. The Great Gatsby – F. Scott Fitzgerald +7. Charlotte’s Web – E.B. White +8. Anne of Green Gables – Lucy Maud Montgomery +9. The Alchemist – Paulo Coelho +10. Little Women – Louisa May Alcott +11. The Catcher in the Rye – J.D. Salinger +12. Animal Farm – George Orwell +13. The Chronicles of Narnia: The Lion, the Witch, and the Wardrobe – C.S. Lewis +14. The Book Thief – Markus Zusak +15. A Wrinkle in Time – Madeleine L’Engle +16. The Secret Garden – Frances Hodgson Burnett +17. Moby-Dick – Herman Melville +18. Fahrenheit 451 – Ray Bradbury +19. Jane Eyre – Charlotte Brontë +20. The Little Prince – Antoine de Saint-Exupéry \ No newline at end of file diff --git a/openai_agents/mcp/sample_files/favorite_cities.txt b/openai_agents/mcp/sample_files/favorite_cities.txt new file mode 100644 index 000000000..1d3354f22 --- /dev/null +++ b/openai_agents/mcp/sample_files/favorite_cities.txt @@ -0,0 +1,4 @@ +- In the summer, I love visiting London. +- In the winter, Tokyo is great. +- In the spring, San Francisco. +- In the fall, New York is the best. \ No newline at end of file diff --git a/openai_agents/mcp/sample_files/favorite_songs.txt b/openai_agents/mcp/sample_files/favorite_songs.txt new file mode 100644 index 000000000..d659bb589 --- /dev/null +++ b/openai_agents/mcp/sample_files/favorite_songs.txt @@ -0,0 +1,10 @@ +1. "Here Comes the Sun" – The Beatles +2. "Imagine" – John Lennon +3. "Bohemian Rhapsody" – Queen +4. "Shake It Off" – Taylor Swift +5. "Billie Jean" – Michael Jackson +6. "Uptown Funk" – Mark Ronson ft. Bruno Mars +7. "Don’t Stop Believin’" – Journey +8. "Dancing Queen" – ABBA +9. "Happy" – Pharrell Williams +10. "Wonderwall" – Oasis diff --git a/openai_agents/mcp/servers/prompt_server.py b/openai_agents/mcp/servers/prompt_server.py new file mode 100644 index 000000000..07f025ce6 --- /dev/null +++ b/openai_agents/mcp/servers/prompt_server.py @@ -0,0 +1,58 @@ +from mcp.server.fastmcp import FastMCP + +# Create server +mcp = FastMCP("Prompt Server") + + +# Instruction-generating prompts (user-controlled) +@mcp.prompt() +def generate_code_review_instructions( + focus: str = "general code quality", language: str = "python" +) -> str: + """Generate agent instructions for code review tasks""" + print(f"[debug-server] generate_code_review_instructions({focus}, {language})") + + return f"""You are a senior {language} code review specialist. Your role is to provide comprehensive code analysis with focus on {focus}. + +INSTRUCTIONS: +- Analyze code for quality, security, performance, and best practices +- Provide specific, actionable feedback with examples +- Identify potential bugs, vulnerabilities, and optimization opportunities +- Suggest improvements with code examples when applicable +- Be constructive and educational in your feedback +- Focus particularly on {focus} aspects + +RESPONSE FORMAT: +1. Overall Assessment +2. Specific Issues Found +3. Security Considerations +4. Performance Notes +5. Recommended Improvements +6. Best Practices Suggestions + +Use the available tools to check current time if you need timestamps for your analysis.""" + + +@mcp.prompt() +def generate_review_rubric(target: str = "application code") -> str: + """Generate a scoring rubric for reviewing code or plans""" + return f"""You are evaluating {target}. Score each category 1-5 and justify briefly. + +CATEGORIES: +- Correctness and Reliability +- Security and Risk +- Performance and Efficiency +- Readability and Maintainability +- Testability and Coverage +- Compliance with Style/Guidelines + +FORMAT: +- Overall Score (1-5) +- Category Scores (bulleted) +- Top 3 Issues (with impact level and suggested fix) +- Quick Wins (3 bullets) +""" + + +if __name__ == "__main__": + mcp.run(transport="streamable-http") diff --git a/openai_agents/mcp/servers/tools_server.py b/openai_agents/mcp/servers/tools_server.py new file mode 100644 index 000000000..a329c7ee3 --- /dev/null +++ b/openai_agents/mcp/servers/tools_server.py @@ -0,0 +1,44 @@ +import argparse +import random + +import requests +from mcp.server.fastmcp import FastMCP + +# Create server +mcp = FastMCP("Tools Server") + + +@mcp.tool() +def add(a: int, b: int) -> int: + """Add two numbers""" + print(f"[debug-server] add({a}, {b})") + return a + b + + +@mcp.tool() +def get_secret_word() -> str: + print("[debug-server] get_secret_word()") + return random.choice(["apple", "banana", "cherry"]) + + +@mcp.tool() +def get_current_weather(city: str) -> str: + print(f"[debug-server] get_current_weather({city})") + + endpoint = "https://wttr.in" + response = requests.get(f"{endpoint}/{city}") + return response.text + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="MCP Tools Server") + parser.add_argument( + "--transport", + choices=["streamable-http", "sse"], + default="streamable-http", + help="Transport type (default: streamable-http)", + ) + args = parser.parse_args() + + print(f"Starting Tools Server with {args.transport} transport...") + mcp.run(transport=args.transport) diff --git a/openai_agents/mcp/workflows/file_system_workflow.py b/openai_agents/mcp/workflows/file_system_workflow.py new file mode 100644 index 000000000..b3528c185 --- /dev/null +++ b/openai_agents/mcp/workflows/file_system_workflow.py @@ -0,0 +1,40 @@ +from __future__ import annotations + +from agents import Agent, Runner, trace +from agents.mcp import MCPServer +from temporalio import workflow +from temporalio.contrib import openai_agents + + +@workflow.defn +class FileSystemWorkflow: + @workflow.run + async def run(self) -> str: + with trace(workflow_name="MCP File System Example"): + server: MCPServer = openai_agents.workflow.stateless_mcp_server( + "FileSystemServer" + ) + agent = Agent( + name="Assistant", + instructions="Use the tools to read the filesystem and answer questions based on those files.", + mcp_servers=[server], + ) + + # List the files it can read + message = "Read the files and list them." + workflow.logger.info(f"Running: {message}") + result1 = await Runner.run(starting_agent=agent, input=message) + + # Ask about books + message = "What is my #1 favorite book?" + workflow.logger.info(f"Running: {message}") + result2 = await Runner.run(starting_agent=agent, input=message) + + # Ask a question that reads then reasons. + message = ( + "Look at my favorite songs. Suggest one new song that I might like." + ) + workflow.logger.info(f"Running: {message}") + result3 = await Runner.run(starting_agent=agent, input=message) + + return f"{result1.final_output}\n\n{result2.final_output}\n\n{result3.final_output}" diff --git a/openai_agents/mcp/workflows/memory_research_scratchpad_workflow.py b/openai_agents/mcp/workflows/memory_research_scratchpad_workflow.py new file mode 100644 index 000000000..1812a4521 --- /dev/null +++ b/openai_agents/mcp/workflows/memory_research_scratchpad_workflow.py @@ -0,0 +1,122 @@ +from __future__ import annotations + +from agents import Agent, Runner, trace +from agents.model_settings import ModelSettings +from temporalio import workflow +from temporalio.contrib import openai_agents as temporal_openai_agents + +SEED_NOTES = [ + ( + "scratchpad/ai-sum/001", + "Study A (2024-04)", + "Summaries reduced triage time by 22% (n=60).", + ["ai-summarization", "email", "kpi"], + ), + ( + "scratchpad/ai-sum/002", + "User preference", + "Users prefer action-first summaries with 5–8 bullets max.", + ["ai-summarization", "email", "ux"], + ), + ( + "scratchpad/ai-sum/003", + "Risk: misleading summaries", + "Hallucination risk; mitigation: confidence thresholds + easy fallback to original email.", + ["ai-summarization", "email", "risk"], + ), + ( + "scratchpad/ai-sum/004", + "Latency consideration", + "Cold-start latency noticeable on first open; can cache or precompute in background.", + ["ai-summarization", "email", "perf"], + ), + ( + "scratchpad/ai-sum/005", + "Adoption insight", + "Admin controls improve enterprise adoption; opt-in increases trust and perceived control.", + ["ai-summarization", "email", "adoption"], + ), +] + + +@workflow.defn +class MemoryResearchScratchpadWorkflow: + @workflow.run + async def run(self) -> str: + async with temporal_openai_agents.workflow.stateful_mcp_server( + "MemoryServer", + ) as server: + with trace(workflow_name="MCP Memory Scratchpad Example"): + agent = Agent( + name="Research Scratchpad Agent", + instructions=( + "Use the Memory MCP tools to persist, query, update, and delete notes." + " Keep IDs short and consistent. Synthesis must rely only on recalled notes and include simple" + " citations of the form '(Note: id)'. Keep the brief to 5 bullets." + ), + mcp_servers=[server], + model_settings=ModelSettings(tool_choice="required"), + ) + + # Step 1: Write seed notes to memory + write_prompt_lines = [ + "Store the following notes in memory. Use the given id and tags for each entry.", + "After storing, confirm each (id, tags) that was written.", + "", + ] + for note_id, title, content, tags in SEED_NOTES: + # Store tags as separate observation lines so search can reliably match them + tag_obs = ", ".join([f"tag: {t}" for t in tags]) + write_prompt_lines.append( + f"- id: {note_id}; title: {title}; content: {content}; observations: [{tag_obs}]" + ) + write_prompt = "\n".join(write_prompt_lines) + workflow.logger.info("Writing seed notes to memory") + r1 = await Runner.run(starting_agent=agent, input=write_prompt) + + # Step 2: Query by tags + query_prompt = ( + "Search memory for notes that contain BOTH observations 'tag: ai-summarization' and 'tag: email'. " + "If the search returns empty, list entities with the name prefix 'scratchpad/ai-sum/' and filter to those that have both tag observations. " + "For the resulting ids, call retrieve_entities to fetch their observations, then return a normalized list of (id, title, 1–2 key points) based on the retrieved entities." + ) + workflow.logger.info("Querying notes by tags") + r2 = await Runner.run( + starting_agent=agent, + input=query_prompt, + previous_response_id=r1.last_response_id, + ) + + # Step 3: Synthesis with citations + synth_prompt = ( + "Using only the recalled notes, produce a 5-bullet brief. " + "Include one citation per bullet in the form '(Note: id)'. Do not introduce new facts." + ) + workflow.logger.info("Synthesizing brief from recalled notes") + r3 = await Runner.run( + starting_agent=agent, + input=synth_prompt, + previous_response_id=r2.last_response_id, + ) + + # Step 4: Update and re-query (optional demonstration) + update_prompt = ( + "Update the note 'scratchpad/ai-sum/003' to include more precise mitigation:" + " 'threshold=0.7; fallback to full email on low confidence'. Then delete the note" + " 'scratchpad/ai-sum/005'. Finally, list only the remaining 'risk' notes with (id, updated content)." + ) + workflow.logger.info( + "Updating one note and deleting another, then re-listing risk notes" + ) + r4 = await Runner.run( + starting_agent=agent, + input=update_prompt, + previous_response_id=r3.last_response_id, + ) + + return ( + f"WRITE CONFIRMATIONS:\n{r1.final_output}\n\n" + f"QUERY RESULTS:\n{r2.final_output}\n\n" + f"SYNTHESIS:\n{r3.final_output}\n\n" + f"UPDATES:\n{r4.final_output}" + ) diff --git a/openai_agents/mcp/workflows/prompt_server_workflow.py b/openai_agents/mcp/workflows/prompt_server_workflow.py new file mode 100644 index 000000000..0a8735610 --- /dev/null +++ b/openai_agents/mcp/workflows/prompt_server_workflow.py @@ -0,0 +1,79 @@ +from __future__ import annotations + +from agents import Agent, Runner, trace +from agents.mcp import MCPServer +from temporalio import workflow +from temporalio.contrib import openai_agents + + +@workflow.defn +class PromptServerWorkflow: + @workflow.run + async def run(self) -> str: + with trace(workflow_name="Prompt Server Example"): + outputs: list[str] = [] + server: MCPServer = openai_agents.workflow.stateless_mcp_server( + "PromptServer" + ) + + # Show available prompts + workflow.logger.info("=== AVAILABLE PROMPTS ===") + outputs.append("=== AVAILABLE PROMPTS ===") + prompts_result = await server.list_prompts() + workflow.logger.info("User can select from these prompts:") + outputs.append("User can select from these prompts:") + for i, prompt in enumerate(prompts_result.prompts, 1): + line = f" {i}. {prompt.name} - {prompt.description}" + workflow.logger.info(line) + outputs.append(line) + workflow.logger.info("") + outputs.append("") + + # Demo code review with user-selected prompt + workflow.logger.info("=== CODE REVIEW DEMO ===") + + # Get instructions from prompt + workflow.logger.info( + "Getting instructions from prompt: generate_code_review_instructions" + ) + try: + prompt_result = await server.get_prompt( + "generate_code_review_instructions", + {"focus": "security vulnerabilities", "language": "python"}, + ) + content = prompt_result.messages[0].content + instructions = ( + content.text if hasattr(content, "text") else str(content) + ) + workflow.logger.info("Generated instructions") + preview = instructions[:200].replace("\n", " ") + ( + "..." if len(instructions) > 200 else "" + ) + outputs.append("=== INSTRUCTIONS (PREVIEW) ===") + outputs.append(preview) + except Exception as e: + workflow.logger.info(f"Failed to get instructions: {e}") + instructions = f"You are a helpful assistant. Error: {e}" + outputs.append(f"Failed to get instructions: {e}") + + agent = Agent( + name="Code Reviewer Agent", + instructions=instructions, + ) + + message = """Please review this code: + +def process_user_input(user_input): + command = f"echo {user_input}" + os.system(command) + return "Command executed" + +""" + + workflow.logger.info(f"Running: {message[:60]}...") + outputs.append("=== REVIEW OUTPUT ===") + result = await Runner.run(starting_agent=agent, input=message) + workflow.logger.info(result.final_output) + outputs.append(result.final_output) + workflow.logger.info("\n" + "=" * 50 + "\n") + return "\n".join(outputs) diff --git a/openai_agents/mcp/workflows/sse_workflow.py b/openai_agents/mcp/workflows/sse_workflow.py new file mode 100644 index 000000000..c8b80e033 --- /dev/null +++ b/openai_agents/mcp/workflows/sse_workflow.py @@ -0,0 +1,38 @@ +from __future__ import annotations + +from agents import Agent, Runner, trace +from agents.mcp import MCPServer +from agents.model_settings import ModelSettings +from temporalio import workflow +from temporalio.contrib import openai_agents + + +@workflow.defn +class SseWorkflow: + @workflow.run + async def run(self) -> str: + with trace(workflow_name="SSE Example"): + server: MCPServer = openai_agents.workflow.stateless_mcp_server("SseServer") + agent = Agent( + name="Assistant", + instructions="Use the tools to answer the questions.", + mcp_servers=[server], + model_settings=ModelSettings(tool_choice="required"), + ) + + # Use the `add` tool to add two numbers + message = "Add these numbers: 7 and 22." + workflow.logger.info(f"Running: {message}") + result1 = await Runner.run(starting_agent=agent, input=message) + + # Run the `get_weather` tool + message = "What's the weather in Tokyo?" + workflow.logger.info(f"Running: {message}") + result2 = await Runner.run(starting_agent=agent, input=message) + + # Run the `get_secret_word` tool + message = "What's the secret word?" + workflow.logger.info(f"Running: {message}") + result3 = await Runner.run(starting_agent=agent, input=message) + + return f"{result1.final_output}\n\n{result2.final_output}\n\n{result3.final_output}" diff --git a/openai_agents/mcp/workflows/streamable_http_workflow.py b/openai_agents/mcp/workflows/streamable_http_workflow.py new file mode 100644 index 000000000..98b7d5766 --- /dev/null +++ b/openai_agents/mcp/workflows/streamable_http_workflow.py @@ -0,0 +1,40 @@ +from __future__ import annotations + +from agents import Agent, Runner, trace +from agents.mcp import MCPServer +from agents.model_settings import ModelSettings +from temporalio import workflow +from temporalio.contrib import openai_agents + + +@workflow.defn +class StreamableHttpWorkflow: + @workflow.run + async def run(self) -> str: + with trace(workflow_name="Streamable HTTP Example"): + server: MCPServer = openai_agents.workflow.stateless_mcp_server( + "StreamableHttpServer" + ) + agent = Agent( + name="Assistant", + instructions="Use the tools to answer the questions.", + mcp_servers=[server], + model_settings=ModelSettings(tool_choice="required"), + ) + + # Use the `add` tool to add two numbers + message = "Add these numbers: 7 and 22." + workflow.logger.info(f"Running: {message}") + result1 = await Runner.run(starting_agent=agent, input=message) + + # Run the `get_weather` tool + message = "What's the weather in Tokyo?" + workflow.logger.info(f"Running: {message}") + result2 = await Runner.run(starting_agent=agent, input=message) + + # Run the `get_secret_word` tool + message = "What's the secret word?" + workflow.logger.info(f"Running: {message}") + result3 = await Runner.run(starting_agent=agent, input=message) + + return f"{result1.final_output}\n\n{result2.final_output}\n\n{result3.final_output}" diff --git a/openai_agents/model_providers/README.md b/openai_agents/model_providers/README.md new file mode 100644 index 000000000..df8f286e8 --- /dev/null +++ b/openai_agents/model_providers/README.md @@ -0,0 +1,67 @@ +# Model Providers Examples + +Custom LLM provider integration examples for OpenAI Agents SDK with Temporal workflows. + +*Adapted from [OpenAI Agents SDK model providers examples](https://github.com/openai/openai-agents-python/tree/main/examples/model_providers)* + +Before running these examples, be sure to review the [prerequisites and background on the integration](../README.md). + +## Running the Examples + +### Currently Implemented + +#### LiteLLM Auto +Uses built-in LiteLLM support to connect to various model providers. + +Start the LiteLLM provider worker: +```bash +# Set the required environment variable for your chosen provider +export ANTHROPIC_API_KEY="your_anthropic_api_key" # For Anthropic + +uv run openai_agents/model_providers/run_litellm_provider_worker.py +``` + +Then run the example in a separate terminal: +```bash +uv run openai_agents/model_providers/run_litellm_auto_workflow.py +``` + +The example uses Anthropic Claude by default but can be modified to use other LiteLLM-supported providers. + +Find more LiteLLM providers at: https://docs.litellm.ai/docs/providers + +### Extra + +#### GPT-OSS with Ollama + +This example demonstrates tool calling using the gpt-oss reasoning model with a local Ollama server. +Running this example requires sufficiently powerful hardware (and involves a 14 GB model download. +It is adapted from the [OpenAI Cookbook example](https://cookbook.openai.com/articles/gpt-oss/run-locally-ollama#agents-sdk-integration). + + +Make sure you have [Ollama](https://ollama.com/) installed: +```bash +ollama serve +``` + +Download the `gpt-oss` model: +```bash +ollama pull gpt-oss:20b +``` + +Start the gpt-oss worker: +```bash +uv run openai_agents/model_providers/run_gpt_oss_worker.py +``` + +Then run the example in a separate terminal: +```bash +uv run openai_agents/model_providers/run_gpt_oss_workflow.py +``` + +### Not Yet Implemented + +- **Custom Example Agent** - Custom OpenAI client integration +- **Custom Example Global** - Global default client configuration +- **Custom Example Provider** - Custom ModelProvider pattern +- **LiteLLM Provider** - Interactive model/API key input \ No newline at end of file diff --git a/openai_agents/model_providers/run_gpt_oss_worker.py b/openai_agents/model_providers/run_gpt_oss_worker.py new file mode 100644 index 000000000..59798a1d4 --- /dev/null +++ b/openai_agents/model_providers/run_gpt_oss_worker.py @@ -0,0 +1,68 @@ +import asyncio +import logging +from datetime import timedelta +from typing import Optional + +from agents import ( + Model, + ModelProvider, + OpenAIChatCompletionsModel, + set_tracing_disabled, +) +from openai import AsyncOpenAI +from temporalio.client import Client +from temporalio.contrib.openai_agents import ModelActivityParameters, OpenAIAgentsPlugin +from temporalio.worker import Worker + +from openai_agents.model_providers.workflows.gpt_oss_workflow import GptOssWorkflow + +ollama_client = AsyncOpenAI( + base_url="http://localhost:11434/v1", # Local Ollama API endpoint + api_key="ollama", # Ignored by Ollama +) + + +class CustomModelProvider(ModelProvider): + def get_model(self, model_name: Optional[str]) -> Model: + model = OpenAIChatCompletionsModel( + model=model_name if model_name else "gpt-oss:20b", + openai_client=ollama_client, + ) + return model + + +async def main(): + # Disable Agents SDK tracing — the default exporter sends traces to OpenAI's + # backend, which requires an OpenAI API key not available in these samples. + # Call here rather than in the workflow because it's a global side effect. + set_tracing_disabled(disabled=True) + + # Configure logging to show workflow debug messages + logging.basicConfig(level=logging.WARNING) + logging.getLogger("temporalio.workflow").setLevel(logging.DEBUG) + + # Create client connected to server at the given address + client = await Client.connect( + "localhost:7233", + plugins=[ + OpenAIAgentsPlugin( + model_params=ModelActivityParameters( + start_to_close_timeout=timedelta(seconds=30) + ), + model_provider=CustomModelProvider(), + ), + ], + ) + + worker = Worker( + client, + task_queue="openai-agents-model-providers-task-queue", + workflows=[ + GptOssWorkflow, + ], + ) + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/openai_agents/model_providers/run_gpt_oss_workflow.py b/openai_agents/model_providers/run_gpt_oss_workflow.py new file mode 100644 index 000000000..35df5979d --- /dev/null +++ b/openai_agents/model_providers/run_gpt_oss_workflow.py @@ -0,0 +1,27 @@ +import asyncio + +from temporalio.client import Client +from temporalio.contrib.openai_agents import OpenAIAgentsPlugin + +from openai_agents.model_providers.workflows.gpt_oss_workflow import GptOssWorkflow + + +async def main(): + client = await Client.connect( + "localhost:7233", + plugins=[ + OpenAIAgentsPlugin(), + ], + ) + + result = await client.execute_workflow( + GptOssWorkflow.run, + "What's the weather in Tokyo?", + id="litellm-gpt-oss-workflow-id", + task_queue="openai-agents-model-providers-task-queue", + ) + print(f"Result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/openai_agents/model_providers/run_litellm_auto_workflow.py b/openai_agents/model_providers/run_litellm_auto_workflow.py new file mode 100644 index 000000000..0e4e05f14 --- /dev/null +++ b/openai_agents/model_providers/run_litellm_auto_workflow.py @@ -0,0 +1,29 @@ +import asyncio + +from temporalio.client import Client +from temporalio.contrib.openai_agents import OpenAIAgentsPlugin + +from openai_agents.model_providers.workflows.litellm_auto_workflow import ( + LitellmAutoWorkflow, +) + + +async def main(): + client = await Client.connect( + "localhost:7233", + plugins=[ + OpenAIAgentsPlugin(), + ], + ) + + result = await client.execute_workflow( + LitellmAutoWorkflow.run, + "What's the weather in Tokyo?", + id="litellm-auto-workflow-id", + task_queue="openai-agents-model-providers-task-queue", + ) + print(f"Result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/openai_agents/model_providers/run_litellm_provider_worker.py b/openai_agents/model_providers/run_litellm_provider_worker.py new file mode 100644 index 000000000..3441013c7 --- /dev/null +++ b/openai_agents/model_providers/run_litellm_provider_worker.py @@ -0,0 +1,45 @@ +import asyncio +from datetime import timedelta + +from agents import set_tracing_disabled +from agents.extensions.models.litellm_provider import LitellmProvider +from temporalio.client import Client +from temporalio.contrib.openai_agents import ModelActivityParameters, OpenAIAgentsPlugin +from temporalio.worker import Worker + +from openai_agents.model_providers.workflows.litellm_auto_workflow import ( + LitellmAutoWorkflow, +) + + +async def main(): + # Disable Agents SDK tracing — the default exporter sends traces to OpenAI's + # backend, which requires an OpenAI API key not available in these samples. + # Call here rather than in the workflow because it's a global side effect. + set_tracing_disabled(disabled=True) + + # Create client connected to server at the given address + client = await Client.connect( + "localhost:7233", + plugins=[ + OpenAIAgentsPlugin( + model_params=ModelActivityParameters( + start_to_close_timeout=timedelta(seconds=30) + ), + model_provider=LitellmProvider(), + ), + ], + ) + + worker = Worker( + client, + task_queue="openai-agents-model-providers-task-queue", + workflows=[ + LitellmAutoWorkflow, + ], + ) + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/openai_agents/model_providers/workflows/gpt_oss_workflow.py b/openai_agents/model_providers/workflows/gpt_oss_workflow.py new file mode 100644 index 000000000..821844b3e --- /dev/null +++ b/openai_agents/model_providers/workflows/gpt_oss_workflow.py @@ -0,0 +1,24 @@ +from __future__ import annotations + +from agents import Agent, Runner, function_tool +from temporalio import workflow + + +@workflow.defn +class GptOssWorkflow: + @workflow.run + async def run(self, prompt: str) -> str: + @function_tool + def get_weather(city: str): + workflow.logger.debug(f"Getting weather for {city}") + return f"The weather in {city} is sunny." + + agent = Agent( + name="Assistant", + instructions="You only respond in haikus. When asked about the weather always use the tool to get the current weather..", + model="gpt-oss:20b", + tools=[get_weather], + ) + + result = await Runner.run(agent, prompt) + return result.final_output diff --git a/openai_agents/model_providers/workflows/litellm_auto_workflow.py b/openai_agents/model_providers/workflows/litellm_auto_workflow.py new file mode 100644 index 000000000..9fe3d40b7 --- /dev/null +++ b/openai_agents/model_providers/workflows/litellm_auto_workflow.py @@ -0,0 +1,23 @@ +from __future__ import annotations + +from agents import Agent, Runner, function_tool +from temporalio import workflow + + +@workflow.defn +class LitellmAutoWorkflow: + @workflow.run + async def run(self, prompt: str) -> str: + @function_tool + def get_weather(city: str): + return f"The weather in {city} is sunny." + + agent = Agent( + name="Assistant", + instructions="You only respond in haikus.", + model="anthropic/claude-3-5-sonnet-20240620", + tools=[get_weather], + ) + + result = await Runner.run(agent, prompt) + return result.final_output diff --git a/openai_agents/reasoning_content/README.md b/openai_agents/reasoning_content/README.md new file mode 100644 index 000000000..52ad48372 --- /dev/null +++ b/openai_agents/reasoning_content/README.md @@ -0,0 +1,37 @@ +# Reasoning Content + +Example demonstrating how to use the reasoning content feature with models that support it, running in the context of Temporal's durable execution. + +*Adapted from [OpenAI Agents SDK reasoning content](https://github.com/openai/openai-agents-python/tree/main/examples/reasoning_content)* + +## Overview + +Some models, like deepseek-reasoner, provide a reasoning_content field in addition to the regular content. This example shows how to access and use this reasoning content within Temporal workflows. The reasoning content contains the model's step-by-step thinking process before providing the final answer. + +## Architecture + +This example uses an activity to handle the OpenAI model calls. The workflow orchestrates the process by calling the `get_reasoning_response` activity, which uses the OpenAI provider to get a response from a reasoning-capable model and extracts both reasoning content and regular content. + +The model calls are run in an activity rather than directly in the workflow because Temporal's the involve I/O. + +## Running the Example + +First, start the worker: +```bash +uv run openai_agents/reasoning_content/run_worker.py +``` + +Then run the reasoning content workflow: +```bash +uv run openai_agents/reasoning_content/run_reasoning_content_workflow.py +``` + +## Requirements + +- Set your `OPENAI_API_KEY` environment variable +- Use a model that supports reasoning content (e.g., `deepseek-reasoner`) +- Optionally set `EXAMPLE_MODEL_NAME` environment variable to specify the model + +## Note on Streaming + +The original OpenAI Agents SDK example includes a streaming variant. This example keeps only the non-streaming approach for brevity; the integration does support streaming model output to external subscribers, which is covered in [Streaming](../streaming/README.md). \ No newline at end of file diff --git a/openai_agents/reasoning_content/activities/reasoning_activities.py b/openai_agents/reasoning_content/activities/reasoning_activities.py new file mode 100644 index 000000000..1f7ef9ef8 --- /dev/null +++ b/openai_agents/reasoning_content/activities/reasoning_activities.py @@ -0,0 +1,53 @@ +import os +from typing import Any, cast + +from agents import ModelSettings +from agents.models.interface import ModelTracing +from agents.models.openai_provider import OpenAIProvider +from openai.types.responses import ResponseOutputRefusal, ResponseOutputText +from temporalio import activity + + +@activity.defn +async def get_reasoning_response( + prompt: str, model_name: str | None = None +) -> tuple[str | None, str | None]: + """ + Activity to get response from a reasoning-capable model. + Returns tuple of (reasoning_content, regular_content). + """ + model_name = model_name or os.getenv("EXAMPLE_MODEL_NAME") or "deepseek-reasoner" + + provider = OpenAIProvider() + model = provider.get_model(model_name) + + response = await model.get_response( + system_instructions="You are a helpful assistant that explains your reasoning step by step.", + input=prompt, + model_settings=ModelSettings(), + tools=[], + output_schema=None, + handoffs=[], + tracing=ModelTracing.DISABLED, + previous_response_id=None, + prompt=None, + conversation_id=None, + ) + + # Extract reasoning content and regular content from the response + reasoning_content = None + regular_content = None + + for item in response.output: + if hasattr(item, "type") and item.type == "reasoning": + reasoning_content = item.summary[0].text + elif hasattr(item, "type") and item.type == "message": + if item.content and len(item.content) > 0: + content_item = item.content[0] + if isinstance(content_item, ResponseOutputText): + regular_content = content_item.text + elif isinstance(content_item, ResponseOutputRefusal): + refusal_item = cast(Any, content_item) + regular_content = refusal_item.refusal + + return reasoning_content, regular_content diff --git a/openai_agents/reasoning_content/run_reasoning_content_workflow.py b/openai_agents/reasoning_content/run_reasoning_content_workflow.py new file mode 100644 index 000000000..79e5d7baa --- /dev/null +++ b/openai_agents/reasoning_content/run_reasoning_content_workflow.py @@ -0,0 +1,54 @@ +#!/usr/bin/env python3 + +import asyncio +import os + +from temporalio.client import Client +from temporalio.contrib.openai_agents import OpenAIAgentsPlugin + +from openai_agents.reasoning_content.workflows.reasoning_content_workflow import ( + ReasoningContentWorkflow, + ReasoningResult, +) + + +async def main(): + client = await Client.connect( + "localhost:7233", + plugins=[ + OpenAIAgentsPlugin(), + ], + ) + + # Demo prompts that benefit from reasoning + demo_prompts = [ + "What is the square root of 841? Please explain your reasoning.", + "Explain the concept of recursion in programming", + "Write a haiku about recursion in programming", + ] + + model_name = os.getenv("EXAMPLE_MODEL_NAME") or "deepseek-reasoner" + print(f"Using model: {model_name}") + print("Note: This example requires a model that supports reasoning content.") + print("You may need to use a specific model like deepseek-reasoner or similar.\n") + + for i, prompt in enumerate(demo_prompts, 1): + print(f"=== Example {i}: {prompt} ===") + + result: ReasoningResult = await client.execute_workflow( + ReasoningContentWorkflow.run, + args=[prompt, model_name], + id=f"reasoning-content-{i}", + task_queue="reasoning-content-task-queue", + ) + + print(f"\nPrompt: {result.prompt}") + print("\nReasoning Content:") + print(result.reasoning_content or "No reasoning content provided") + print("\nRegular Content:") + print(result.regular_content or "No regular content provided") + print("-" * 50 + "\n") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/openai_agents/reasoning_content/run_worker.py b/openai_agents/reasoning_content/run_worker.py new file mode 100644 index 000000000..51393b2e0 --- /dev/null +++ b/openai_agents/reasoning_content/run_worker.py @@ -0,0 +1,37 @@ +#!/usr/bin/env python3 + +import asyncio + +from temporalio.client import Client +from temporalio.contrib.openai_agents import OpenAIAgentsPlugin +from temporalio.worker import Worker + +from openai_agents.reasoning_content.activities.reasoning_activities import ( + get_reasoning_response, +) +from openai_agents.reasoning_content.workflows.reasoning_content_workflow import ( + ReasoningContentWorkflow, +) + + +async def main(): + client = await Client.connect( + "localhost:7233", + plugins=[ + OpenAIAgentsPlugin(), + ], + ) + + worker = Worker( + client, + task_queue="reasoning-content-task-queue", + workflows=[ReasoningContentWorkflow], + activities=[get_reasoning_response], + ) + + print("Starting reasoning content worker...") + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/openai_agents/reasoning_content/workflows/reasoning_content_workflow.py b/openai_agents/reasoning_content/workflows/reasoning_content_workflow.py new file mode 100644 index 000000000..2a584a4bc --- /dev/null +++ b/openai_agents/reasoning_content/workflows/reasoning_content_workflow.py @@ -0,0 +1,33 @@ +from dataclasses import dataclass +from datetime import timedelta + +from temporalio import workflow + +from openai_agents.reasoning_content.activities.reasoning_activities import ( + get_reasoning_response, +) + + +@dataclass +class ReasoningResult: + reasoning_content: str | None + regular_content: str | None + prompt: str + + +@workflow.defn +class ReasoningContentWorkflow: + @workflow.run + async def run(self, prompt: str, model_name: str | None = None) -> ReasoningResult: + # Call the activity to get the reasoning response + reasoning_content, regular_content = await workflow.execute_activity( + get_reasoning_response, + args=[prompt, model_name], + start_to_close_timeout=timedelta(minutes=5), + ) + + return ReasoningResult( + reasoning_content=reasoning_content, + regular_content=regular_content, + prompt=prompt, + ) diff --git a/openai_agents/research_bot/README.md b/openai_agents/research_bot/README.md new file mode 100644 index 000000000..f4f4a074f --- /dev/null +++ b/openai_agents/research_bot/README.md @@ -0,0 +1,35 @@ +# Research Bot + +Multi-agent research system with specialized roles, extended with Temporal's durable execution. + +*Adapted from [OpenAI Agents SDK research bot](https://github.com/openai/openai-agents-python/tree/main/examples/research_bot)* + +## Architecture + +The flow is: + +1. User enters their research topic +2. `planner_agent` comes up with a plan to search the web for information. The plan is a list of search queries, with a search term and a reason for each query. +3. For each search item, we run a `search_agent`, which uses the Web Search tool to search for that term and summarize the results. These all run in parallel. +4. Finally, the `writer_agent` receives the search summaries, and creates a written report. + +## Running the Example + +First, start the worker: +```bash +uv run openai_agents/research_bot/run_worker.py +``` + +Then run the research workflow: +```bash +uv run openai_agents/research_bot/run_research_workflow.py +``` + +## Suggested Improvements + +If you're building your own research bot, some ideas to add to this are: + +1. Retrieval: Add support for fetching relevant information from a vector store. You could use the File Search tool for this. +2. Image and file upload: Allow users to attach PDFs or other files, as baseline context for the research. +3. More planning and thinking: Models often produce better results given more time to think. Improve the planning process to come up with a better plan, and add an evaluation step so that the model can choose to improve its results, search for more stuff, etc. +4. Code execution: Allow running code, which is useful for data analysis. \ No newline at end of file diff --git a/openai_agents/research_bot/agents/planner_agent.py b/openai_agents/research_bot/agents/planner_agent.py new file mode 100644 index 000000000..3e2f26de3 --- /dev/null +++ b/openai_agents/research_bot/agents/planner_agent.py @@ -0,0 +1,29 @@ +from agents import Agent +from pydantic import BaseModel + +PROMPT = ( + "You are a helpful research assistant. Given a query, come up with a set of web searches " + "to perform to best answer the query. Output between 5 and 20 terms to query for." +) + + +class WebSearchItem(BaseModel): + reason: str + "Your reasoning for why this search is important to the query." + + query: str + "The search term to use for the web search." + + +class WebSearchPlan(BaseModel): + searches: list[WebSearchItem] + """A list of web searches to perform to best answer the query.""" + + +def new_planner_agent(): + return Agent( + name="PlannerAgent", + instructions=PROMPT, + model="gpt-4o", + output_type=WebSearchPlan, + ) diff --git a/openai_agents/research_bot/agents/research_manager.py b/openai_agents/research_bot/agents/research_manager.py new file mode 100644 index 000000000..62a77a070 --- /dev/null +++ b/openai_agents/research_bot/agents/research_manager.py @@ -0,0 +1,80 @@ +from __future__ import annotations + +import asyncio + +from temporalio import workflow + +with workflow.unsafe.imports_passed_through(): + # TODO: Restore progress updates + from agents import RunConfig, Runner, custom_span, trace + + from openai_agents.research_bot.agents.planner_agent import ( + WebSearchItem, + WebSearchPlan, + new_planner_agent, + ) + from openai_agents.research_bot.agents.search_agent import new_search_agent + from openai_agents.research_bot.agents.writer_agent import ( + ReportData, + new_writer_agent, + ) + + +class ResearchManager: + def __init__(self): + self.run_config = RunConfig() + self.search_agent = new_search_agent() + self.planner_agent = new_planner_agent() + self.writer_agent = new_writer_agent() + + async def run(self, query: str) -> str: + with trace("Research trace"): + search_plan = await self._plan_searches(query) + search_results = await self._perform_searches(search_plan) + report = await self._write_report(query, search_results) + + return report.markdown_report + + async def _plan_searches(self, query: str) -> WebSearchPlan: + result = await Runner.run( + self.planner_agent, + f"Query: {query}", + run_config=self.run_config, + ) + return result.final_output_as(WebSearchPlan) + + async def _perform_searches(self, search_plan: WebSearchPlan) -> list[str]: + with custom_span("Search the web"): + num_completed = 0 + tasks = [ + asyncio.create_task(self._search(item)) for item in search_plan.searches + ] + results = [] + for task in workflow.as_completed(tasks): + result = await task + if result is not None: + results.append(result) + num_completed += 1 + return results + + async def _search(self, item: WebSearchItem) -> str | None: + input = f"Search term: {item.query}\nReason for searching: {item.reason}" + try: + result = await Runner.run( + self.search_agent, + input, + run_config=self.run_config, + ) + return str(result.final_output) + except Exception: + return None + + async def _write_report(self, query: str, search_results: list[str]) -> ReportData: + input = f"Original query: {query}\nSummarized search results: {search_results}" + result = await Runner.run( + self.writer_agent, + input, + run_config=self.run_config, + ) + + return result.final_output_as(ReportData) diff --git a/openai_agents/research_bot/agents/search_agent.py b/openai_agents/research_bot/agents/search_agent.py new file mode 100644 index 000000000..43c30ed88 --- /dev/null +++ b/openai_agents/research_bot/agents/search_agent.py @@ -0,0 +1,20 @@ +from agents import Agent, WebSearchTool +from agents.model_settings import ModelSettings + +INSTRUCTIONS = ( + "You are a research assistant. Given a search term, you search the web for that term and " + "produce a concise summary of the results. The summary must 2-3 paragraphs and less than 300 " + "words. Capture the main points. Write succinctly, no need to have complete sentences or good " + "grammar. This will be consumed by someone synthesizing a report, so its vital you capture the " + "essence and ignore any fluff. Do not include any additional commentary other than the summary " + "itself." +) + + +def new_search_agent(): + return Agent( + name="Search agent", + instructions=INSTRUCTIONS, + tools=[WebSearchTool()], + model_settings=ModelSettings(tool_choice="required"), + ) diff --git a/openai_agents/research_bot/agents/writer_agent.py b/openai_agents/research_bot/agents/writer_agent.py new file mode 100644 index 000000000..9d8a34b0b --- /dev/null +++ b/openai_agents/research_bot/agents/writer_agent.py @@ -0,0 +1,33 @@ +# Agent used to synthesize a final report from the individual summaries. +from agents import Agent +from pydantic import BaseModel + +PROMPT = ( + "You are a senior researcher tasked with writing a cohesive report for a research query. " + "You will be provided with the original query, and some initial research done by a research " + "assistant.\n" + "You should first come up with an outline for the report that describes the structure and " + "flow of the report. Then, generate the report and return that as your final output.\n" + "The final output should be in markdown format, and it should be lengthy and detailed. Aim " + "for 5-10 pages of content, at least 1000 words." +) + + +class ReportData(BaseModel): + short_summary: str + """A short 2-3 sentence summary of the findings.""" + + markdown_report: str + """The final report""" + + follow_up_questions: list[str] + """Suggested topics to research further""" + + +def new_writer_agent(): + return Agent( + name="WriterAgent", + instructions=PROMPT, + model="o3-mini", + output_type=ReportData, + ) diff --git a/openai_agents/research_bot/run_research_workflow.py b/openai_agents/research_bot/run_research_workflow.py new file mode 100644 index 000000000..e2739ef55 --- /dev/null +++ b/openai_agents/research_bot/run_research_workflow.py @@ -0,0 +1,30 @@ +import asyncio + +from temporalio.client import Client +from temporalio.contrib.openai_agents import OpenAIAgentsPlugin + +from openai_agents.research_bot.workflows.research_bot_workflow import ResearchWorkflow + + +async def main(): + # Create client connected to server at the given address + client = await Client.connect( + "localhost:7233", + plugins=[ + OpenAIAgentsPlugin(), + ], + ) + + # Execute a workflow + result = await client.execute_workflow( + ResearchWorkflow.run, + "Caribbean vacation spots in April, optimizing for surfing, hiking and water sports", + id="research-workflow", + task_queue="openai-agents-task-queue", + ) + + print(f"Result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/openai_agents/research_bot/run_worker.py b/openai_agents/research_bot/run_worker.py new file mode 100644 index 000000000..fd6827d6e --- /dev/null +++ b/openai_agents/research_bot/run_worker.py @@ -0,0 +1,37 @@ +from __future__ import annotations + +import asyncio +from datetime import timedelta + +from temporalio.client import Client +from temporalio.contrib.openai_agents import ModelActivityParameters, OpenAIAgentsPlugin +from temporalio.worker import Worker + +from openai_agents.research_bot.workflows.research_bot_workflow import ResearchWorkflow + + +async def main(): + # Create client connected to server at the given address + client = await Client.connect( + "localhost:7233", + plugins=[ + OpenAIAgentsPlugin( + model_params=ModelActivityParameters( + start_to_close_timeout=timedelta(seconds=120) + ) + ), + ], + ) + + worker = Worker( + client, + task_queue="openai-agents-task-queue", + workflows=[ + ResearchWorkflow, + ], + ) + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/openai_agents/research_bot/workflows/research_bot_workflow.py b/openai_agents/research_bot/workflows/research_bot_workflow.py new file mode 100644 index 000000000..c2523c8c9 --- /dev/null +++ b/openai_agents/research_bot/workflows/research_bot_workflow.py @@ -0,0 +1,10 @@ +from temporalio import workflow + +from openai_agents.research_bot.agents.research_manager import ResearchManager + + +@workflow.defn +class ResearchWorkflow: + @workflow.run + async def run(self, query: str) -> str: + return await ResearchManager().run(query) diff --git a/openai_agents/streaming/README.md b/openai_agents/streaming/README.md new file mode 100644 index 000000000..0085953c3 --- /dev/null +++ b/openai_agents/streaming/README.md @@ -0,0 +1,130 @@ +# Streaming OpenAI Agents + +> **Experimental.** These samples use the streaming support in +> `temporalio.contrib.openai_agents` together with +> `temporalio.contrib.workflow_streams`. Both are experimental and their APIs +> may change in future versions. + +*Adapted from the [OpenAI Agents SDK basic examples](https://github.com/openai/openai-agents-python/tree/main/examples/basic)* + +Before running these examples, be sure to review the [prerequisites and background on the integration](../README.md). + +The OpenAI Agents SDK streams model output via `Runner.run_streamed`, which +yields events as the model produces them. Inside a Temporal workflow the model +call runs in an activity, so the workflow cannot iterate the live HTTP stream +directly. Instead the plugin runs `model.stream_response()` in a streaming +activity, and that activity publishes each event to the workflow's +[`WorkflowStream`](../../workflow_streams/README.md) so external subscribers +see events as they are produced. + +Publishing is batched: the activity coalesces events over +`ModelActivityParameters.streaming_batch_interval` (default 100ms) before +signalling the workflow. Call this **buffered token streaming** — deltas reach +subscribers within a batch window of being produced, not on every byte. At +typical model speeds one batch carries several tokens, so output arrives in +small bursts rather than glyph-by-glyph. Lower the interval for smoother +output at the cost of more signals. + +Two things to know before reading the samples: + +* `streaming_topic` is **required** for `Runner.run_streamed`. If it is unset, + `run_streamed` raises before scheduling any activity. +* The workflow must host a `WorkflowStream`. It has to be constructed from a + method named `__init__` — `WorkflowStream` inspects its caller's frame and + raises otherwise — and `@workflow.init` is what makes the workflow's run + argument (carrying `stream_state` for continue-as-new) available there. + +## Running the Examples + +First, start the worker (supports both examples): + +```bash +uv run openai_agents/streaming/run_worker.py +``` + +Then run either example in another terminal. + +### `stream_text` — buffered text deltas + +Adapted from [`examples/basic/stream_text.py`][upstream-text]. The workflow +just calls `Runner.run_streamed`; the subscriber renders the +`ResponseTextDeltaEvent`s the streaming activity publishes on the `events` +topic. + +Subscribers receive **native OpenAI events** (`TResponseStreamEvent`), because +the activity publishes them straight from `Model.stream_response`. That differs +from `stream_events()` inside the workflow, which yields the agents-SDK +`StreamEvent` union — raw model events arrive there wrapped as +`RawResponsesStreamEvent.data`. + +[upstream-text]: https://github.com/openai/openai-agents-python/blob/main/examples/basic/stream_text.py + +```bash +uv run openai_agents/streaming/run_stream_text_workflow.py +``` + +### `stream_items` — agent-level events with a tool call + +Adapted from [`examples/basic/stream_items.py`][upstream-items]. Renders agent +updates, tool calls, tool outputs, and message outputs as a play-by-play. + +The agents SDK builds those higher-level events from the model output, so they +exist only inside the workflow — the streaming activity never sees them. This +workflow therefore does its own publishing: it iterates +`result.stream_events()` and forwards each event of interest to an `items` +topic as a small serializable `ItemEvent`. (The agents-SDK event types carry +the originating `Agent`, which holds tool callables and so cannot be +serialized.) `stream_events()` resolves a turn at a time — each model call is +one activity — so a multi-turn run like this one reaches the subscriber +progressively rather than in one lump. + +[upstream-items]: https://github.com/openai/openai-agents-python/blob/main/examples/basic/stream_items.py + +```bash +uv run openai_agents/streaming/run_stream_items_workflow.py +``` + +## How it works + +1. The workflow constructs a `WorkflowStream` in `@workflow.init`. +2. `OpenAIAgentsPlugin` is configured with `streaming_topic="events"`, which + routes `Runner.run_streamed` to `invoke_model_activity_streaming`. +3. Inside that activity each event from the live HTTP stream is both collected + (returned to the workflow when the activity completes) and published to the + stream via `WorkflowStreamClient.from_within_activity()`. +4. Just before returning, the workflow publishes a terminator on a separate + `done` topic, then sleeps briefly so the subscriber's next poll can drain + the tail of the stream — the log lives in workflow memory and disappears + when the run completes. +5. External code subscribes with + `WorkflowStreamClient.create(...).subscribe([...], result_type=RawValue)` + and breaks on the terminator. `RawValue` keeps the payloads undecoded so + each topic can be decoded against its own type. If the workflow reaches a + terminal state without publishing a terminator (a failure, say), the + iterator exhausts on its own and the following `handle.result()` raises. + +In the workflow, `stream_events()` resolves only after the model activity +returns, so the workflow itself does not see deltas as they arrive — the +streaming benefit is for external observers. + +## Notes + +* Streaming is incompatible with `use_local_activity=True`: local activities + support neither heartbeats nor the workflow stream signal channel. +* The streaming activity heartbeats on a background task, so set + `heartbeat_timeout` well below `start_to_close_timeout` to detect a stuck + model call early. +* Delivery is at-least-once per activity attempt. An attempt that fails + mid-response leaves its partial events on the stream — they are flushed + before the failure is reported — and the retry publishes a whole new + response. `stream_events()` in the workflow only sees the successful attempt, + so the workflow's return value stays correct while a naive subscriber renders + the truncated attempt followed by the full one. + + The plugin's streaming activity publishes no retry marker, so subscribers + detect this in band: every OpenAI stream event carries a `sequence_number` + that starts at 0 per response, and a number that fails to advance means a new + attempt. `run_stream_text_workflow.py` prints a notice at that seam; + `workflow_streams/run_llm.py` shows the fuller treatment, where an activity + you own publishes an explicit `RetryEvent` from `activity.info().attempt` and + the consumer erases the failed attempt's output. diff --git a/openai_agents/streaming/__init__.py b/openai_agents/streaming/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/openai_agents/streaming/activities/__init__.py b/openai_agents/streaming/activities/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/openai_agents/streaming/activities/joke_activities.py b/openai_agents/streaming/activities/joke_activities.py new file mode 100644 index 000000000..7fe1c4992 --- /dev/null +++ b/openai_agents/streaming/activities/joke_activities.py @@ -0,0 +1,11 @@ +from __future__ import annotations + +import random + +from temporalio import activity + + +@activity.defn +async def how_many_jokes() -> int: + """Return a random integer of jokes to tell between 1 and 10 (inclusive).""" + return random.randint(1, 10) diff --git a/openai_agents/streaming/run_stream_items_workflow.py b/openai_agents/streaming/run_stream_items_workflow.py new file mode 100644 index 000000000..93698d358 --- /dev/null +++ b/openai_agents/streaming/run_stream_items_workflow.py @@ -0,0 +1,65 @@ +"""Start StreamItemsWorkflow and render its run as a play-by-play.""" + +from __future__ import annotations + +import asyncio +import uuid + +from temporalio.client import Client +from temporalio.common import RawValue +from temporalio.contrib.openai_agents import OpenAIAgentsPlugin +from temporalio.contrib.workflow_streams import WorkflowStreamClient + +from openai_agents.streaming.shared import ( + TASK_QUEUE, + TOPIC_DONE, + TOPIC_ITEMS, + ItemEvent, +) +from openai_agents.streaming.workflows.stream_items_workflow import ( + StreamItemsInput, + StreamItemsWorkflow, +) + + +async def main() -> None: + client = await Client.connect( + "localhost:7233", + plugins=[OpenAIAgentsPlugin()], + ) + + workflow_id = f"stream-items-{uuid.uuid4().hex[:8]}" + handle = await client.start_workflow( + StreamItemsWorkflow.run, + StreamItemsInput(), + id=workflow_id, + task_queue=TASK_QUEUE, + ) + + stream = WorkflowStreamClient.create(client, workflow_id) + converter = client.data_converter.payload_converter + + print("=== Run starting ===") + # result_type=RawValue so the two topics can be decoded per item.topic. + # The raw model events the streaming activity publishes on TOPIC_EVENTS are + # on the stream too; this subscriber just isn't interested in them. + async for item in stream.subscribe([TOPIC_ITEMS, TOPIC_DONE], result_type=RawValue): + if item.topic == TOPIC_DONE: + break + event = converter.from_payload(item.data.payload, ItemEvent) + if event.kind == "agent_updated": + print(f"Agent updated: {event.detail}") + elif event.kind == "tool_call": + print(f"-- Tool was called: {event.detail}") + elif event.kind == "tool_output": + print(f"-- Tool output: {event.detail}") + elif event.kind == "message_output": + print(f"-- Message output:\n {event.detail}") + + result = await handle.result() + print("=== Run complete ===") + print(result) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/openai_agents/streaming/run_stream_text_workflow.py b/openai_agents/streaming/run_stream_text_workflow.py new file mode 100644 index 000000000..5b3181a2d --- /dev/null +++ b/openai_agents/streaming/run_stream_text_workflow.py @@ -0,0 +1,98 @@ +"""Start StreamTextWorkflow and render its model output as it streams. + +Delivery is at-least-once per model-activity attempt: an attempt that fails +mid-response leaves its partial deltas on the stream, and the retry publishes +a whole new response. Unlike ``workflow_streams/activities/llm_activity.py``, +which publishes an explicit ``RetryEvent`` on ``activity.info().attempt > 1``, +the plugin's streaming activity emits no retry marker — so this subscriber +infers a new attempt from the stream itself and says so, rather than silently +running the two responses together. (``workflow_streams/run_llm.py`` goes a +step further and erases the failed attempt's output with ANSI escapes.) +""" + +from __future__ import annotations + +import asyncio +import uuid +from typing import Any, cast + +from agents.items import TResponseStreamEvent +from openai.types.responses import ResponseCompletedEvent, ResponseTextDeltaEvent +from temporalio.client import Client +from temporalio.common import RawValue +from temporalio.contrib.openai_agents import OpenAIAgentsPlugin +from temporalio.contrib.workflow_streams import WorkflowStreamClient + +from openai_agents.streaming.shared import TASK_QUEUE, TOPIC_DONE, TOPIC_EVENTS +from openai_agents.streaming.workflows.stream_text_workflow import ( + StreamTextInput, + StreamTextWorkflow, +) + +# TResponseStreamEvent is a typing.Annotated union rather than a class, so it +# needs a cast to satisfy from_payload's type[T] signature. The plugin's +# pydantic converter resolves the union's discriminator at runtime. +EVENT_TYPE = cast(type, TResponseStreamEvent) + + +async def main() -> None: + # The plugin's data converter is what decodes the OpenAI event payloads + # published on TOPIC_EVENTS. + client = await Client.connect( + "localhost:7233", + plugins=[OpenAIAgentsPlugin()], + ) + + workflow_id = f"stream-text-{uuid.uuid4().hex[:8]}" + handle = await client.start_workflow( + StreamTextWorkflow.run, + StreamTextInput(prompt="Please tell me 5 jokes."), + id=workflow_id, + task_queue=TASK_QUEUE, + ) + + stream = WorkflowStreamClient.create(client, workflow_id) + converter = client.data_converter.payload_converter + + # A single iterator over both topics — one subscriber, no cancellation race + # between concurrent ones. result_type=RawValue delivers the underlying + # Payload so heterogeneous topics can be decoded per item.topic. The loop + # ends on the in-band terminator, or by the iterator exhausting if the + # workflow reaches a terminal state without publishing one (e.g. on + # failure); either way handle.result() below surfaces the outcome. + last_sequence = -1 + response_in_flight = False + async for item in stream.subscribe( + [TOPIC_EVENTS, TOPIC_DONE], result_type=RawValue + ): + if item.topic == TOPIC_DONE: + break + # Subscribers receive native OpenAI events, not the agents-SDK + # StreamEvent wrappers that stream_events() yields in the workflow. + event: Any = converter.from_payload(item.data.payload, EVENT_TYPE) + + # Every event carries a sequence_number that starts at 0 per response, + # so a number that does not advance means a new response is streaming. + # That is a retry only if the previous one never completed: each turn + # of a multi-turn run is its own response and restarts the count too. + # The retry is an independently sampled answer rather than a + # continuation, so mark the seam instead of letting the failed + # attempt's partial text run into the new one. The workflow's return + # value is unaffected — stream_events() there sees only the attempt + # that succeeded. + sequence = event.sequence_number + if sequence <= last_sequence and response_in_flight: + print("\n\n[model activity retried — output restarts here]\n") + last_sequence = sequence + response_in_flight = not isinstance(event, ResponseCompletedEvent) + + if isinstance(event, ResponseTextDeltaEvent): + print(event.delta, end="", flush=True) + + result = await handle.result() + print("\n--- final result ---") + print(result) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/openai_agents/streaming/run_worker.py b/openai_agents/streaming/run_worker.py new file mode 100644 index 000000000..99a754702 --- /dev/null +++ b/openai_agents/streaming/run_worker.py @@ -0,0 +1,54 @@ +from __future__ import annotations + +import asyncio +import logging +from datetime import timedelta + +from temporalio.client import Client +from temporalio.contrib.openai_agents import ( + ModelActivityParameters, + OpenAIAgentsPlugin, +) +from temporalio.worker import Worker + +from openai_agents.streaming.activities.joke_activities import how_many_jokes +from openai_agents.streaming.shared import TASK_QUEUE, TOPIC_EVENTS +from openai_agents.streaming.workflows.stream_items_workflow import ( + StreamItemsWorkflow, +) +from openai_agents.streaming.workflows.stream_text_workflow import ( + StreamTextWorkflow, +) + + +async def main() -> None: + logging.basicConfig(level=logging.INFO) + client = await Client.connect( + "localhost:7233", + plugins=[ + OpenAIAgentsPlugin( + model_params=ModelActivityParameters( + # The streaming activity heartbeats on a background task, + # so a heartbeat_timeout well under start_to_close_timeout + # detects a stuck model call early. + heartbeat_timeout=timedelta(seconds=10), + start_to_close_timeout=timedelta(minutes=5), + # Required for Runner.run_streamed: the topic the streaming + # activity publishes raw model events to. + streaming_topic=TOPIC_EVENTS, + ), + ), + ], + ) + + worker = Worker( + client, + task_queue=TASK_QUEUE, + workflows=[StreamTextWorkflow, StreamItemsWorkflow], + activities=[how_many_jokes], + ) + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/openai_agents/streaming/shared.py b/openai_agents/streaming/shared.py new file mode 100644 index 000000000..467ec3ce1 --- /dev/null +++ b/openai_agents/streaming/shared.py @@ -0,0 +1,42 @@ +from __future__ import annotations + +from dataclasses import dataclass +from datetime import timedelta + +TASK_QUEUE = "openai-agents-streaming-task-queue" + +# Topic the streaming activity publishes raw model stream events to. Must match +# OpenAIAgentsPlugin(model_params=ModelActivityParameters(streaming_topic=...)). +# Events on this topic are native OpenAI `TResponseStreamEvent`s, not the +# agents-SDK `StreamEvent` wrappers that `stream_events()` yields. +TOPIC_EVENTS = "events" + +# Topic the stream_items workflow publishes its own higher-level events to. The +# agents SDK builds those from the model output inside the workflow, so the +# workflow — not the activity — is what publishes them. +TOPIC_ITEMS = "items" + +# Topic the workflow publishes a terminator to once Runner.run_streamed has +# finished. Subscribers watch both topics and break on the terminator, rather +# than racing handle.result() against their next poll. +TOPIC_DONE = "done" + +# How long a workflow holds its run open after publishing the terminator, so a +# subscriber's next poll can drain the tail of the stream. The log lives in +# workflow memory, so it disappears when the run completes. +DRAIN_INTERVAL = timedelta(milliseconds=500) + + +@dataclass +class ItemEvent: + """One step of a run, as published on TOPIC_ITEMS. + + The agents-SDK event types (`RunItemStreamEvent` and friends) carry the + originating `Agent`, which holds tool callables and so is not + serializable. Samples publish their own flattened event instead. + """ + + kind: str + """One of "agent_updated", "tool_call", "tool_output", "message_output".""" + + detail: str diff --git a/openai_agents/streaming/workflows/__init__.py b/openai_agents/streaming/workflows/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/openai_agents/streaming/workflows/stream_items_workflow.py b/openai_agents/streaming/workflows/stream_items_workflow.py new file mode 100644 index 000000000..57aec09b0 --- /dev/null +++ b/openai_agents/streaming/workflows/stream_items_workflow.py @@ -0,0 +1,101 @@ +"""Streaming counterpart to the OpenAI Agents SDK ``stream_items.py`` example. + +Adapted from https://github.com/openai/openai-agents-python/blob/main/examples/basic/stream_items.py + +The upstream example renders higher-level events as they arrive: agent +updates, tool calls, tool outputs, and message outputs. Those are built by the +agents SDK from the model's output, so unlike the raw model events in +``stream_text_workflow`` they exist only inside the workflow — the streaming +activity never sees them. + +So this workflow does the publishing itself: it iterates +``result.stream_events()`` and forwards each interesting event to its own +topic. ``stream_events()`` resolves a turn at a time (each model call is one +activity), so a multi-turn run like this one — model call, tool call, model +call — reaches the subscriber as a play-by-play rather than in one lump. +""" + +from __future__ import annotations + +from dataclasses import dataclass +from datetime import timedelta + +from agents import Agent, ItemHelpers, Runner +from temporalio import workflow +from temporalio.contrib import openai_agents as temporal_agents +from temporalio.contrib.workflow_streams import WorkflowStream, WorkflowStreamState + +from openai_agents.streaming.activities.joke_activities import how_many_jokes +from openai_agents.streaming.shared import ( + DRAIN_INTERVAL, + TOPIC_DONE, + TOPIC_ITEMS, + ItemEvent, +) + + +@dataclass +class StreamItemsInput: + prompt: str = "Hello" + # Carries stream state across continue-as-new. None on a fresh start. + stream_state: WorkflowStreamState | None = None + + +@workflow.defn +class StreamItemsWorkflow: + @workflow.init + def __init__(self, input: StreamItemsInput) -> None: + # WorkflowStream requires construction from a method named __init__ + # (it checks its caller's frame and raises otherwise), and + # @workflow.init is what makes the run argument — and the + # stream_state it carries across continue-as-new — available here. + self.stream = WorkflowStream(prior_state=input.stream_state) + self.items = self.stream.topic(TOPIC_ITEMS, type=ItemEvent) + self.done = self.stream.topic(TOPIC_DONE, type=bool) + + @workflow.run + async def run(self, input: StreamItemsInput) -> str: + agent = Agent( + name="Joker", + instructions=( + "First call the `how_many_jokes` tool, then tell that many jokes." + ), + tools=[ + temporal_agents.workflow.activity_as_tool( + how_many_jokes, start_to_close_timeout=timedelta(seconds=10) + ) + ], + ) + result = Runner.run_streamed(agent, input=input.prompt) + + messages: list[str] = [] + async for event in result.stream_events(): + if event.type == "agent_updated_stream_event": + self.items.publish( + ItemEvent(kind="agent_updated", detail=event.new_agent.name) + ) + elif event.type == "run_item_stream_event": + item = event.item + if item.type == "tool_call_item": + name = getattr(item.raw_item, "name", "Unknown Tool") + self.items.publish(ItemEvent(kind="tool_call", detail=name)) + elif item.type == "tool_call_output_item": + self.items.publish( + ItemEvent(kind="tool_output", detail=str(item.output)) + ) + elif item.type == "message_output_item": + text = ItemHelpers.text_message_output(item) + messages.append(text) + self.items.publish(ItemEvent(kind="message_output", detail=text)) + + self.done.publish(True) + # Brief pause so the subscriber's next poll can drain the tail of the + # stream — the log lives in workflow memory and is gone once this run + # completes. + await workflow.sleep(DRAIN_INTERVAL) + # final_output is typed Any and is None when a run ends without + # message output, so assert the str this signature promises rather + # than letting a None through. + if not messages: + return result.final_output_as(str, raise_if_incorrect_type=True) + return "\n\n".join(messages) diff --git a/openai_agents/streaming/workflows/stream_text_workflow.py b/openai_agents/streaming/workflows/stream_text_workflow.py new file mode 100644 index 000000000..abbd8b23c --- /dev/null +++ b/openai_agents/streaming/workflows/stream_text_workflow.py @@ -0,0 +1,83 @@ +"""Streaming counterpart to the OpenAI Agents SDK ``stream_text.py`` example. + +Adapted from https://github.com/openai/openai-agents-python/blob/main/examples/basic/stream_text.py + +The upstream example calls ``Runner.run_streamed`` and iterates raw +``ResponseTextDeltaEvent``s as they arrive over HTTP. Inside a Temporal +workflow the model call runs in an activity, so the workflow cannot iterate +the live HTTP stream directly. The plugin's streaming support runs +``model.stream_response()`` inside the activity and publishes each event to +the workflow's stream, where external subscribers see them as they are +produced. + +The workflow itself only needs to: + +1. host a ``WorkflowStream`` so the streaming activity has somewhere to + publish to; +2. call ``Runner.run_streamed`` (rather than ``Runner.run``) so the agents + framework drives the streaming activity. + +``stream_events()`` inside the workflow resolves only once the activity +returns, so in-workflow consumption is over the final list — not +deltas-as-they-arrive. Streaming is for external observers. +""" + +from __future__ import annotations + +from dataclasses import dataclass + +from agents import Agent, Runner +from openai.types.responses import ResponseTextDeltaEvent +from temporalio import workflow +from temporalio.contrib.workflow_streams import WorkflowStream, WorkflowStreamState + +from openai_agents.streaming.shared import DRAIN_INTERVAL, TOPIC_DONE + + +@dataclass +class StreamTextInput: + prompt: str + # Carries stream state across continue-as-new. None on a fresh start. + stream_state: WorkflowStreamState | None = None + + +@workflow.defn +class StreamTextWorkflow: + @workflow.init + def __init__(self, input: StreamTextInput) -> None: + # WorkflowStream requires construction from a method named __init__ + # (it checks its caller's frame and raises otherwise), and + # @workflow.init is what makes the run argument — and the + # stream_state it carries across continue-as-new — available here. + self.stream = WorkflowStream(prior_state=input.stream_state) + self.done = self.stream.topic(TOPIC_DONE, type=bool) + + @workflow.run + async def run(self, input: StreamTextInput) -> str: + agent = Agent( + name="Joker", + instructions="You are a helpful assistant.", + ) + result = Runner.run_streamed(agent, input=input.prompt) + + # The workflow only sees these events once the activity returns, so + # the loop just counts them. External subscribers receive them as the + # activity publishes them. + deltas = 0 + async for event in result.stream_events(): + if event.type == "raw_response_event" and isinstance( + event.data, ResponseTextDeltaEvent + ): + deltas += 1 + workflow.logger.info("collected %d delta events", deltas) + + # In-band terminator so the subscriber can stop without racing the + # workflow's completion, then a brief pause to let its next poll + # deliver the tail of the stream — the log lives in workflow memory + # and is gone once this run completes. + self.done.publish(True) + await workflow.sleep(DRAIN_INTERVAL) + # final_output is typed Any and is None when a run ends without + # message output, so assert the str this signature promises rather + # than letting a None through. + return result.final_output_as(str, raise_if_incorrect_type=True) diff --git a/openai_agents/tools/README.md b/openai_agents/tools/README.md new file mode 100644 index 000000000..6f65e432d --- /dev/null +++ b/openai_agents/tools/README.md @@ -0,0 +1,61 @@ +# Tools Examples + +Demonstrations of various OpenAI agent tools integrated with Temporal workflows. + +*Adapted from [OpenAI Agents SDK tools examples](https://github.com/openai/openai-agents-python/tree/main/examples/tools)* + +Before running these examples, be sure to review the [prerequisites and background on the integration](../README.md). + +## Setup + +### Knowledge Base Setup (Required for File Search) +Create a vector store with sample documents for file search testing: +```bash +uv run openai_agents/tools/setup_knowledge_base.py +``` + +This script: +- Creates 6 sample documents +- Uploads files to OpenAI with proper cleanup using context managers +- Creates an assistant with vector store for file search capabilities +- Updates workflow files with the new vector store ID automatically + +## Running the Examples + +First, start the worker (supports all tools): +```bash +uv run openai_agents/tools/run_worker.py +``` + +Then run individual examples in separate terminals: + +### Code Interpreter +Execute Python code for mathematical calculations and data analysis: +```bash +uv run openai_agents/tools/run_code_interpreter_workflow.py +``` + +### File Search +Search through uploaded documents using vector similarity: +```bash +uv run openai_agents/tools/run_file_search_workflow.py +``` + +### Image Generation +Generate images: +```bash +uv run openai_agents/tools/run_image_generator_workflow.py +``` + +### Web Search +Search the web for current information with location context: +```bash +uv run openai_agents/tools/run_web_search_workflow.py +``` + + +## Omitted Examples + +The following tools from the [reference repository](https://github.com/openai/openai-agents-python/tree/main/examples/tools) are not included in this Temporal adaptation: + +- **Computer Use**: Complex browser automation not suitable for distributed systems implementation. \ No newline at end of file diff --git a/openai_agents/tools/run_code_interpreter_workflow.py b/openai_agents/tools/run_code_interpreter_workflow.py new file mode 100644 index 000000000..2b8197124 --- /dev/null +++ b/openai_agents/tools/run_code_interpreter_workflow.py @@ -0,0 +1,32 @@ +import asyncio + +from temporalio.client import Client +from temporalio.contrib.openai_agents import OpenAIAgentsPlugin + +from openai_agents.tools.workflows.code_interpreter_workflow import ( + CodeInterpreterWorkflow, +) + + +async def main(): + # Create client connected to server at the given address + client = await Client.connect( + "localhost:7233", + plugins=[ + OpenAIAgentsPlugin(), + ], + ) + + # Execute a workflow + result = await client.execute_workflow( + CodeInterpreterWorkflow.run, + "What is the square root of 273 * 312821 plus 1782?", + id="code-interpreter-workflow", + task_queue="openai-agents-tools-task-queue", + ) + + print(f"Result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/openai_agents/tools/run_file_search_workflow.py b/openai_agents/tools/run_file_search_workflow.py new file mode 100644 index 000000000..09b8bb574 --- /dev/null +++ b/openai_agents/tools/run_file_search_workflow.py @@ -0,0 +1,33 @@ +import asyncio + +from temporalio.client import Client +from temporalio.contrib.openai_agents import OpenAIAgentsPlugin + +from openai_agents.tools.workflows.file_search_workflow import FileSearchWorkflow + + +async def main(): + # Create client connected to server at the given address + client = await Client.connect( + "localhost:7233", + plugins=[ + OpenAIAgentsPlugin(), + ], + ) + + # Execute a workflow + result = await client.execute_workflow( + FileSearchWorkflow.run, + args=[ + "Be concise, and tell me 1 sentence about Arrakis I might not know.", + "vs_68855c27140c8191849b5f1887d8d335", # Vector store with Arrakis knowledge + ], + id="file-search-workflow", + task_queue="openai-agents-tools-task-queue", + ) + + print(f"Result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/openai_agents/tools/run_image_generator_workflow.py b/openai_agents/tools/run_image_generator_workflow.py new file mode 100644 index 000000000..77a60d267 --- /dev/null +++ b/openai_agents/tools/run_image_generator_workflow.py @@ -0,0 +1,60 @@ +import asyncio +import base64 +import os +import subprocess +import sys +import tempfile + +from temporalio.client import Client +from temporalio.contrib.openai_agents import OpenAIAgentsPlugin + +from openai_agents.tools.workflows.image_generator_workflow import ( + ImageGeneratorWorkflow, +) + + +def open_file(path: str) -> None: + if sys.platform.startswith("darwin"): + subprocess.run(["open", path], check=False) # macOS + elif os.name == "nt": # Windows + os.startfile(path) # type: ignore + elif os.name == "posix": + subprocess.run(["xdg-open", path], check=False) # Linux/Unix + else: + print(f"Don't know how to open files on this platform: {sys.platform}") + + +async def main(): + # Create client connected to server at the given address + client = await Client.connect( + "localhost:7233", + plugins=[ + OpenAIAgentsPlugin(), + ], + ) + + # Execute a workflow + result = await client.execute_workflow( + ImageGeneratorWorkflow.run, + "Create an image of a frog eating a pizza, comic book style.", + id="image-generator-workflow", + task_queue="openai-agents-tools-task-queue", + ) + + print(f"Text result: {result.final_output}") + + if result.image_data: + # Save and open the image + with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp: + tmp.write(base64.b64decode(result.image_data)) + temp_path = tmp.name + + print(f"Image saved to: {temp_path}") + # Open the image + open_file(temp_path) + else: + print("No image data found in result") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/openai_agents/tools/run_web_search_workflow.py b/openai_agents/tools/run_web_search_workflow.py new file mode 100644 index 000000000..dd7f992f0 --- /dev/null +++ b/openai_agents/tools/run_web_search_workflow.py @@ -0,0 +1,33 @@ +import asyncio + +from temporalio.client import Client +from temporalio.contrib.openai_agents import OpenAIAgentsPlugin + +from openai_agents.tools.workflows.web_search_workflow import WebSearchWorkflow + + +async def main(): + # Create client connected to server at the given address + client = await Client.connect( + "localhost:7233", + plugins=[ + OpenAIAgentsPlugin(), + ], + ) + + # Execute a workflow + result = await client.execute_workflow( + WebSearchWorkflow.run, + args=[ + "search the web for 'local sports news' and give me 1 interesting update in a sentence.", + "New York", + ], + id="web-search-workflow", + task_queue="openai-agents-tools-task-queue", + ) + + print(f"Result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/openai_agents/tools/run_worker.py b/openai_agents/tools/run_worker.py new file mode 100644 index 000000000..a58df4706 --- /dev/null +++ b/openai_agents/tools/run_worker.py @@ -0,0 +1,50 @@ +from __future__ import annotations + +import asyncio +from datetime import timedelta + +from temporalio.client import Client +from temporalio.contrib.openai_agents import ModelActivityParameters, OpenAIAgentsPlugin +from temporalio.worker import Worker + +from openai_agents.tools.workflows.code_interpreter_workflow import ( + CodeInterpreterWorkflow, +) +from openai_agents.tools.workflows.file_search_workflow import FileSearchWorkflow +from openai_agents.tools.workflows.image_generator_workflow import ( + ImageGeneratorWorkflow, +) +from openai_agents.tools.workflows.web_search_workflow import WebSearchWorkflow + + +async def main(): + # Create client connected to server at the given address + client = await Client.connect( + "localhost:7233", + plugins=[ + OpenAIAgentsPlugin( + model_params=ModelActivityParameters( + start_to_close_timeout=timedelta(seconds=60) + ) + ), + ], + ) + + worker = Worker( + client, + task_queue="openai-agents-tools-task-queue", + workflows=[ + CodeInterpreterWorkflow, + FileSearchWorkflow, + ImageGeneratorWorkflow, + WebSearchWorkflow, + ], + activities=[ + # No custom activities needed for these workflows + ], + ) + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/openai_agents/tools/setup_knowledge_base.py b/openai_agents/tools/setup_knowledge_base.py new file mode 100644 index 000000000..fc467805e --- /dev/null +++ b/openai_agents/tools/setup_knowledge_base.py @@ -0,0 +1,270 @@ +#!/usr/bin/env python3 +""" +Setup script to create vector store with sample documents for testing FileSearchWorkflow. +Creates documents about Arrakis/Dune and uploads them to OpenAI for file search testing. +""" + +import asyncio +import os +import tempfile +from contextlib import asynccontextmanager +from pathlib import Path +from typing import Dict, List + +from openai import AsyncOpenAI + +# Sample knowledge base content +KNOWLEDGE_BASE = { + "arrakis_overview": """ +Arrakis: The Desert Planet + +Arrakis, also known as Dune, is the third planet of the Canopus system. This harsh desert world is the sole source of the spice melange, the most valuable substance in the known universe. + +Key characteristics: +- Single biome: Desert covering the entire planet +- No natural precipitation +- Extreme temperature variations between day and night +- Home to the giant sandworms (Shai-Hulud) +- Indigenous population: the Fremen + +The planet's ecology is entirely dependent on the sandworms, which produce the spice as a byproduct of their life cycle. Water is incredibly scarce, leading to the development of stillsuits and other water conservation technologies. +""", + "spice_melange": """ +The Spice Melange + +Melange, commonly known as "the spice," is the most important substance in the Dune universe. This geriatric spice extends life, expands consciousness, and is essential for space navigation. + +Properties of Spice: +- Extends human lifespan significantly +- Enhances mental abilities and prescient vision +- Required for Guild Navigators to fold space +- Highly addictive with fatal withdrawal symptoms +- Turns eyes blue over time (the "Eyes of Ibad") + +Production: +The spice is created through the interaction of sandworms with pre-spice masses in the deep desert. The presence of water is toxic to sandworms, making Arrakis the only known source of spice in the universe. + +Economic Impact: +Control of spice production grants immense political and economic power, making Arrakis the most strategically important planet in the Imperium. +""", + "sandworms": """ +Sandworms of Arrakis + +The sandworms, known to the Fremen as Shai-Hulud ("Old Man of the Desert"), are colossal creatures that dominate Arrakis. These massive beings can grow to lengths of over 400 meters and live for thousands of years. + +Characteristics: +- Enormous size: up to 400+ meters in length +- Extreme sensitivity to water and moisture +- Produce the spice melange as part of their life cycle +- Territorial and attracted to rhythmic vibrations +- Crystalline teeth capable of crushing rock and metal + +Life Cycle: +Sandworms begin as sandtrout, small creatures that sequester water. They eventually metamorphose into the giant sandworms through a complex process involving spice production. + +Cultural Significance: +The Fremen worship sandworms as semi-divine beings and have developed elaborate rituals around them, including the dangerous practice of sandworm riding. +""", + "fremen_culture": """ +The Fremen of Arrakis + +The Fremen are the indigenous people of Arrakis, perfectly adapted to life in the harsh desert environment. Their culture revolves around water conservation, survival, and reverence for the sandworms. + +Cultural Practices: +- Water discipline: Every drop of moisture is preserved +- Stillsuits: Advanced technology to recycle body moisture +- Desert survival skills passed down through generations +- Ritualistic relationship with sandworms +- Sietch communities: Hidden underground settlements + +Religious Beliefs: +The Fremen follow a syncretic religion combining elements of Islam, Buddhism, and Christianity, adapted to their desert environment. They believe in prophecies of a messiah who will transform Arrakis. + +Military Prowess: +Despite their seemingly primitive lifestyle, the Fremen are formidable warriors, using their intimate knowledge of the desert and unconventional tactics to great effect. +""", + "house_atreides": """ +House Atreides and Arrakis + +House Atreides, led by Duke Leto Atreides, was granted control of Arrakis by Emperor Shaddam IV in a political trap designed to destroy the noble house. This transition from House Harkonnen marked the beginning of the events in Dune. + +Key Figures: +- Duke Leto Atreides: Noble leader focused on honor and justice +- Lady Jessica: Bene Gesserit concubine and mother of Paul +- Paul Atreides: Heir to the duchy and potential Kwisatz Haderach +- Duncan Idaho: Loyal swordmaster and warrior +- Gurney Halleck: Weapons master and troubadour + +The Atreides approach to ruling Arrakis differed dramatically from the Harkonnens, seeking to work with the Fremen rather than exploit them. This philosophy, while noble, ultimately led to their downfall when the Emperor and Harkonnens betrayed them. + +Legacy: +Though House Atreides was destroyed in the coup, Paul's survival and alliance with the Fremen would eventually lead to an even greater destiny. +""", + "ecology_arrakis": """ +The Ecology of Arrakis + +Arrakis presents a unique ecosystem entirely based on the water cycle created by sandworms and sandtrout. This closed ecological system has evolved over millennia to support life in extreme desert conditions. + +Water Cycle: +- Sandtrout sequester all available water deep underground +- This creates the desert conditions necessary for spice production +- Adult sandworms are killed by water, maintaining the cycle +- Plants and animals have evolved extreme water conservation + +Flora and Fauna: +- Desert plants with deep root systems and water storage +- Small desert animals adapted to minimal water consumption +- No large surface water bodies exist naturally +- All life forms show evolutionary adaptation to water scarcity + +The ecosystem is incredibly fragile - any significant introduction of water could disrupt the entire balance and potentially eliminate spice production, fundamentally changing the planet and the universe's economy. +""", +} + + +@asynccontextmanager +async def temporary_files(content_dict: Dict[str, str]): + """Context manager to create and cleanup temporary files.""" + temp_files = [] + try: + for name, content in content_dict.items(): + temp_file = tempfile.NamedTemporaryFile( + mode="w", suffix=".txt", prefix=f"{name}_", delete=False + ) + temp_file.write(content) + temp_file.close() + temp_files.append((name, temp_file.name)) + + yield temp_files + finally: + for _, temp_path in temp_files: + try: + os.unlink(temp_path) + except OSError: + pass + + +async def upload_files_to_openai(temp_files: List[tuple[str, str]]) -> List[str]: + """Upload temporary files to OpenAI and return file IDs.""" + client = AsyncOpenAI() + file_ids = [] + + for name, temp_path in temp_files: + try: + with open(temp_path, "rb") as f: + file_obj = await client.files.create(file=f, purpose="assistants") + file_ids.append(file_obj.id) + print(f"Uploaded {name}: {file_obj.id}") + except Exception as e: + print(f"Error uploading {name}: {e}") + + return file_ids + + +async def create_vector_store_with_assistant(file_ids: List[str]) -> str: + """Create an assistant with vector store containing the uploaded files.""" + client = AsyncOpenAI() + + try: + assistant = await client.beta.assistants.create( + name="Arrakis Knowledge Assistant", + instructions="You are an expert on Arrakis and the Dune universe. Use the uploaded files to answer questions about the desert planet, spice, sandworms, Fremen culture, and related topics.", + model="gpt-4o", + tools=[{"type": "file_search"}], + tool_resources={ + "file_search": { + "vector_stores": [ + { + "file_ids": file_ids, + "metadata": {"name": "Arrakis Knowledge Base"}, + } + ] + } + }, + ) + + # Extract vector store ID from assistant + if assistant.tool_resources and assistant.tool_resources.file_search: + vector_store_ids = assistant.tool_resources.file_search.vector_store_ids + if vector_store_ids: + return vector_store_ids[0] + + raise Exception("No vector store ID found in assistant response") + + except Exception as e: + print(f"Error creating assistant: {e}") + raise + + +def update_workflow_files(vector_store_id: str): + """Update workflow files with the new vector store ID.""" + import re + + files_to_update = ["run_file_search_workflow.py"] + + # Pattern to match any vector store ID with the specific comment + pattern = r'(vs_[a-f0-9]+)",\s*#\s*Vector store with Arrakis knowledge' + replacement = f'{vector_store_id}", # Vector store with Arrakis knowledge' + + for filename in files_to_update: + file_path = Path(__file__).parent / filename + if file_path.exists(): + try: + content = file_path.read_text() + if re.search(pattern, content): + updated_content = re.sub(pattern, replacement, content) + file_path.write_text(updated_content) + print(f"Updated {filename} with vector store ID") + else: + print(f"No matching pattern found in {filename}") + except Exception as e: + print(f"Error updating {filename}: {e}") + + +async def main(): + """Main function to set up the knowledge base.""" + # Check for API key + if not os.getenv("OPENAI_API_KEY"): + print("Error: OPENAI_API_KEY environment variable not set") + print("Please set your OpenAI API key:") + print("export OPENAI_API_KEY='your-api-key-here'") + return + + print("Setting up Arrakis knowledge base...") + + try: + # Create temporary files and upload them + async with temporary_files(KNOWLEDGE_BASE) as temp_files: + print(f"Created {len(temp_files)} temporary files") + + file_ids = await upload_files_to_openai(temp_files) + + if not file_ids: + print("Error: No files were successfully uploaded") + return + + print(f"Successfully uploaded {len(file_ids)} files") + + # Create vector store via assistant + vector_store_id = await create_vector_store_with_assistant(file_ids) + + print(f"Created vector store: {vector_store_id}") + + # Update workflow files + update_workflow_files(vector_store_id) + + print() + print("=" * 60) + print("KNOWLEDGE BASE SETUP COMPLETE") + print("=" * 60) + print(f"Vector Store ID: {vector_store_id}") + print(f"Files indexed: {len(file_ids)}") + print("Content: Arrakis/Dune universe knowledge") + print("=" * 60) + + except Exception as e: + print(f"Setup failed: {e}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/openai_agents/tools/workflows/code_interpreter_workflow.py b/openai_agents/tools/workflows/code_interpreter_workflow.py new file mode 100644 index 000000000..6715bc505 --- /dev/null +++ b/openai_agents/tools/workflows/code_interpreter_workflow.py @@ -0,0 +1,25 @@ +from __future__ import annotations + +from agents import Agent, CodeInterpreterTool, Runner +from temporalio import workflow + + +@workflow.defn +class CodeInterpreterWorkflow: + @workflow.run + async def run(self, question: str) -> str: + agent = Agent( + name="Code interpreter", + instructions="You love doing math.", + tools=[ + CodeInterpreterTool( + tool_config={ + "type": "code_interpreter", + "container": {"type": "auto"}, + }, + ) + ], + ) + + result = await Runner.run(agent, question) + return result.final_output diff --git a/openai_agents/tools/workflows/file_search_workflow.py b/openai_agents/tools/workflows/file_search_workflow.py new file mode 100644 index 000000000..915dcec42 --- /dev/null +++ b/openai_agents/tools/workflows/file_search_workflow.py @@ -0,0 +1,24 @@ +from __future__ import annotations + +from agents import Agent, FileSearchTool, Runner +from temporalio import workflow + + +@workflow.defn +class FileSearchWorkflow: + @workflow.run + async def run(self, question: str, vector_store_id: str) -> str: + agent = Agent( + name="File searcher", + instructions="You are a helpful agent.", + tools=[ + FileSearchTool( + max_num_results=3, + vector_store_ids=[vector_store_id], + include_search_results=True, + ) + ], + ) + + result = await Runner.run(agent, question) + return result.final_output diff --git a/openai_agents/tools/workflows/image_generator_workflow.py b/openai_agents/tools/workflows/image_generator_workflow.py new file mode 100644 index 000000000..202341256 --- /dev/null +++ b/openai_agents/tools/workflows/image_generator_workflow.py @@ -0,0 +1,46 @@ +from __future__ import annotations + +from dataclasses import dataclass +from typing import Optional + +from agents import Agent, ImageGenerationTool, Runner +from openai.types.responses.response_output_item import ImageGenerationCall +from temporalio import workflow + + +@dataclass +class ImageGenerationResult: + final_output: str + image_data: Optional[str] = None + + +@workflow.defn +class ImageGeneratorWorkflow: + @workflow.run + async def run(self, prompt: str) -> ImageGenerationResult: + agent = Agent( + name="Image generator", + instructions="You are a helpful agent.", + tools=[ + ImageGenerationTool( + tool_config={"type": "image_generation", "quality": "low"}, + ) + ], + ) + + result = await Runner.run(agent, prompt) + + # Extract image data if available + image_data = None + for item in result.new_items: + if ( + item.type == "tool_call_item" + and isinstance(item.raw_item, ImageGenerationCall) + and (img_result := item.raw_item.result) + ): + image_data = img_result + break + + return ImageGenerationResult( + final_output=result.final_output, image_data=image_data + ) diff --git a/openai_agents/tools/workflows/web_search_workflow.py b/openai_agents/tools/workflows/web_search_workflow.py new file mode 100644 index 000000000..8b505ac14 --- /dev/null +++ b/openai_agents/tools/workflows/web_search_workflow.py @@ -0,0 +1,20 @@ +from __future__ import annotations + +from agents import Agent, Runner, WebSearchTool +from temporalio import workflow + + +@workflow.defn +class WebSearchWorkflow: + @workflow.run + async def run(self, question: str, user_city: str = "New York") -> str: + agent = Agent( + name="Web searcher", + instructions="You are a helpful agent.", + tools=[ + WebSearchTool(user_location={"type": "approximate", "city": user_city}) + ], + ) + + result = await Runner.run(agent, question) + return result.final_output diff --git a/patching/README.md b/patching/README.md index 61106f317..24744075d 100644 --- a/patching/README.md +++ b/patching/README.md @@ -8,15 +8,15 @@ To run, first see [README.md](../README.md) for prerequisites. Then follow the p This stage is for existing running workflows. To simulate our initial workflow, run the worker in a separate terminal: - poetry run python worker.py --workflow initial + uv run patching/worker.py --workflow initial Now we can start this workflow: - poetry run python starter.py --start-workflow initial-workflow-id + uv run patching/starter.py --start-workflow initial-workflow-id This will output "Started workflow with ID initial-workflow-id and ...". Now query this workflow: - poetry run python starter.py --query-workflow initial-workflow-id + uv run patching/starter.py --query-workflow initial-workflow-id This will output "Query result for ID initial-workflow-id: pre-patch". @@ -25,21 +25,21 @@ This will output "Query result for ID initial-workflow-id: pre-patch". This stage is for needing to run old and new workflows at the same time. To simulate our patched workflow, stop the worker from before and start it again with the patched workflow: - poetry run python worker.py --workflow patched + uv run patching/worker.py --workflow patched Now let's start another workflow with this patched code: - poetry run python starter.py --start-workflow patched-workflow-id + uv run patching/starter.py --start-workflow patched-workflow-id This will output "Started workflow with ID patched-workflow-id and ...". Now query the old workflow that's still running: - poetry run python starter.py --query-workflow initial-workflow-id + uv run patching/starter.py --query-workflow initial-workflow-id This will output "Query result for ID initial-workflow-id: pre-patch" since it is pre-patch. But if we execute a query against the new code: - poetry run python starter.py --query-workflow patched-workflow-id + uv run patching/starter.py --query-workflow patched-workflow-id We get "Query result for ID patched-workflow-id: post-patch". This is how old workflow code can take old paths and new workflow code can take new paths. @@ -50,22 +50,22 @@ Once we know that all workflows that started with the initial code from "Stage 1 the patch so we can deprecate it. To use the patch deprecated workflow, stop the workflow from before and start it again with: - poetry run python worker.py --workflow patch-deprecated + uv run patching/worker.py --workflow patch-deprecated All workflows in "Stage 2" and any new workflows will work. Now let's start another workflow with this patch deprecated code: - poetry run python starter.py --start-workflow patch-deprecated-workflow-id + uv run patching/starter.py --start-workflow patch-deprecated-workflow-id This will output "Started workflow with ID patch-deprecated-workflow-id and ...". Now query the patched workflow that's still running: - poetry run python starter.py --query-workflow patched-workflow-id + uv run patching/starter.py --query-workflow patched-workflow-id This will output "Query result for ID patched-workflow-id: post-patch". And if we execute a query against the latest workflow: - poetry run python starter.py --query-workflow patch-deprecated-workflow-id + uv run patching/starter.py --query-workflow patch-deprecated-workflow-id As expected, this will output "Query result for ID patch-deprecated-workflow-id: post-patch". @@ -75,22 +75,22 @@ Once we know we don't even have any workflows running on "Stage 2" or before (i. both code paths), we can just remove the patch deprecation altogether. To use the patch complete workflow, stop the workflow from before and start it again with: - poetry run python worker.py --workflow patch-complete + uv run patching/worker.py --workflow patch-complete All workflows in "Stage 3" and any new workflows will work. Now let's start another workflow with this patch complete code: - poetry run python starter.py --start-workflow patch-complete-workflow-id + uv run patching/starter.py --start-workflow patch-complete-workflow-id This will output "Started workflow with ID patch-complete-workflow-id and ...". Now query the patch deprecated workflow that's still running: - poetry run python starter.py --query-workflow patch-deprecated-workflow-id + uv run patching/starter.py --query-workflow patch-deprecated-workflow-id This will output "Query result for ID patch-deprecated-workflow-id: post-patch". And if we execute a query against the latest workflow: - poetry run python starter.py --query-workflow patch-complete-workflow-id + uv run patching/starter.py --query-workflow patch-complete-workflow-id As expected, this will output "Query result for ID patch-complete-workflow-id: post-patch". diff --git a/patching/starter.py b/patching/starter.py index 9e6d7f31d..f1b92f324 100644 --- a/patching/starter.py +++ b/patching/starter.py @@ -2,6 +2,7 @@ import asyncio from temporalio.client import Client +from temporalio.envconfig import ClientConfig # Since it's just used for typing purposes, it doesn't matter which one we # import @@ -17,7 +18,9 @@ async def main(): raise RuntimeError("Either --start-workflow or --query-workflow is required") # Connect client - client = await Client.connect("localhost:7233") + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) if args.start_workflow: handle = await client.start_workflow( diff --git a/patching/worker.py b/patching/worker.py index 8f1e3c820..417c8ef9c 100644 --- a/patching/worker.py +++ b/patching/worker.py @@ -2,6 +2,7 @@ import asyncio from temporalio.client import Client +from temporalio.envconfig import ClientConfig from temporalio.worker import Worker from patching.activities import post_patch_activity, pre_patch_activity @@ -30,7 +31,9 @@ async def main(): raise RuntimeError("Unrecognized workflow") # Connect client - 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- -@dataclass -class ComposeGreetingInput: - greeting: str - name: str +from polling.test_service import ComposeGreetingInput, get_service_result @activity.defn async def compose_greeting(input: ComposeGreetingInput) -> str: - test_service = TestService() while True: try: - result = test_service.get_service_result(input) - return result - except Exception: + try: + result = await get_service_result(input) + activity.logger.info(f"Exiting activity ${result}") + return result + except Exception: + # swallow exception since service is down + activity.logger.debug("Failed, trying again shortly", exc_info=True) + activity.heartbeat("Invoking activity") + await asyncio.sleep(1) + except asyncio.CancelledError: + # activity was either cancelled or workflow was completed or worker shut down + # if you need to clean up you can catch this. + # Here we are just reraising the exception + raise diff --git a/polling/frequent/run_frequent.py b/polling/frequent/run_frequent.py index bc43ccfde..420480925 100644 --- a/polling/frequent/run_frequent.py +++ b/polling/frequent/run_frequent.py @@ -1,11 +1,16 @@ import asyncio from temporalio.client import Client -from workflows import GreetingWorkflow +from temporalio.envconfig import ClientConfig + +from polling.frequent.workflows import GreetingWorkflow async def main(): - client = await Client.connect("localhost:7233") + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) + result = await client.execute_workflow( GreetingWorkflow.run, "World", diff --git a/polling/frequent/run_worker.py b/polling/frequent/run_worker.py index 97b327fff..cf5ccb789 100644 --- a/polling/frequent/run_worker.py +++ b/polling/frequent/run_worker.py @@ -1,13 +1,17 @@ import asyncio -from activities import compose_greeting from temporalio.client import Client +from temporalio.envconfig import ClientConfig from temporalio.worker import Worker -from workflows import GreetingWorkflow + +from polling.frequent.activities import compose_greeting +from polling.frequent.workflows import GreetingWorkflow async def main(): - client = await Client.connect("localhost:7233") + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) worker = Worker( client, diff --git a/polling/frequent/workflows.py b/polling/frequent/workflows.py index c5329ad9a..0dac0a494 100644 --- a/polling/frequent/workflows.py +++ b/polling/frequent/workflows.py @@ -3,7 +3,7 @@ from temporalio import workflow with workflow.unsafe.imports_passed_through(): - from activities import ComposeGreetingInput, compose_greeting + from polling.frequent.activities import ComposeGreetingInput, compose_greeting @workflow.defn diff --git a/polling/infrequent/README.md b/polling/infrequent/README.md index 7c0a32256..7e86ea438 100644 --- a/polling/infrequent/README.md +++ b/polling/infrequent/README.md @@ -11,12 +11,14 @@ This will enable the Activity to be retried exactly on the set interval. To run, first see [README.md](../../README.md) for prerequisites. -Then, run the following from this directory to run the sample: +Then, run the following from the root directory to run the sample: + + uv run polling/infrequent/run_worker.py + +Then, in another terminal, run the following to execute the workflow: + + uv run polling/infrequent/run_infrequent.py -```bash -poetry run python run_worker.py -poetry run python run_infrequent.py -``` Since the test service simulates being _down_ for four polling attempts and then returns _OK_ on the fifth poll attempt, the Workflow will perform four Activity retries with a 60-second poll interval, and then return the service result on the successful fifth attempt. diff --git a/polling/infrequent/activities.py b/polling/infrequent/activities.py index 2bd715878..b3db1aed7 100644 --- a/polling/infrequent/activities.py +++ b/polling/infrequent/activities.py @@ -1,19 +1,10 @@ -from dataclasses import dataclass - from temporalio import activity -from polling.test_service import TestService - - -@dataclass -class ComposeGreetingInput: - greeting: str - name: str +from polling.test_service import ComposeGreetingInput, get_service_result @activity.defn async def compose_greeting(input: ComposeGreetingInput) -> str: - test_service = TestService() # If this raises an exception because it's not done yet, the activity will # continually be scheduled for retry - return await test_service.get_service_result(input) + return await get_service_result(input) diff --git a/polling/infrequent/run_infrequent.py b/polling/infrequent/run_infrequent.py index 0056a36c2..8a0ea8713 100644 --- a/polling/infrequent/run_infrequent.py +++ b/polling/infrequent/run_infrequent.py @@ -1,11 +1,16 @@ import asyncio from temporalio.client import Client -from workflows import GreetingWorkflow +from temporalio.envconfig import ClientConfig + +from polling.infrequent.workflows import GreetingWorkflow async def main(): - client = await Client.connect("localhost:7233") + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) + result = await client.execute_workflow( GreetingWorkflow.run, "World", diff --git a/polling/infrequent/run_worker.py b/polling/infrequent/run_worker.py index 27db61de7..f52a8082b 100644 --- a/polling/infrequent/run_worker.py +++ b/polling/infrequent/run_worker.py @@ -1,13 +1,17 @@ import asyncio -from activities import compose_greeting from temporalio.client import Client +from temporalio.envconfig import ClientConfig from temporalio.worker import Worker -from workflows import GreetingWorkflow + +from polling.infrequent.activities import compose_greeting +from polling.infrequent.workflows import GreetingWorkflow async def main(): - client = await Client.connect("localhost:7233") + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) worker = Worker( client, diff --git a/polling/infrequent/workflows.py b/polling/infrequent/workflows.py index 013a3b91b..35769573b 100644 --- a/polling/infrequent/workflows.py +++ b/polling/infrequent/workflows.py @@ -4,7 +4,7 @@ from temporalio.common import RetryPolicy with workflow.unsafe.imports_passed_through(): - from activities import ComposeGreetingInput, compose_greeting + from polling.infrequent.activities import ComposeGreetingInput, compose_greeting @workflow.defn diff --git a/polling/periodic_sequence/README.md b/polling/periodic_sequence/README.md index 65fe06698..8c5cb9501 100644 --- a/polling/periodic_sequence/README.md +++ b/polling/periodic_sequence/README.md @@ -6,12 +6,14 @@ This is a rare scenario where polling requires execution of a Sequence of Activi To run, first see [README.md](../../README.md) for prerequisites. -Then, run the following from this directory to run the sample: +Then, run the following from the root directory to run the sample: + + uv run polling/periodic_sequence/run_worker.py + +Then, in another terminal, run the following to execute the workflow: + + uv run polling/periodic_sequence/run_periodic.py -```bash -poetry run python run_worker.py -poetry run python run_periodic.py -``` This will start a Workflow and Child Workflow to periodically poll an Activity. The Parent Workflow is not aware about the Child Workflow calling Continue-As-New, and it gets notified when it completes (or fails). \ No newline at end of file diff --git a/polling/periodic_sequence/activities.py b/polling/periodic_sequence/activities.py index 1a1196c6c..87b698904 100644 --- a/polling/periodic_sequence/activities.py +++ b/polling/periodic_sequence/activities.py @@ -1,14 +1,8 @@ -from dataclasses import dataclass +from typing import Any, NoReturn from temporalio import activity -@dataclass -class ComposeGreetingInput: - greeting: str - name: str - - @activity.defn -async def compose_greeting(input: ComposeGreetingInput) -> str: +async def compose_greeting(input: Any) -> NoReturn: raise RuntimeError("Service is down") diff --git a/polling/periodic_sequence/run_periodic.py b/polling/periodic_sequence/run_periodic.py index 5853d4676..393fb8be4 100644 --- a/polling/periodic_sequence/run_periodic.py +++ b/polling/periodic_sequence/run_periodic.py @@ -1,11 +1,16 @@ import asyncio from temporalio.client import Client -from workflows import GreetingWorkflow +from temporalio.envconfig import ClientConfig + +from polling.periodic_sequence.workflows import GreetingWorkflow async def main(): - client = await Client.connect("localhost:7233") + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) + result = await client.execute_workflow( GreetingWorkflow.run, "World", diff --git a/polling/periodic_sequence/run_worker.py b/polling/periodic_sequence/run_worker.py index 8c0591f9e..9689ef2fd 100644 --- a/polling/periodic_sequence/run_worker.py +++ b/polling/periodic_sequence/run_worker.py @@ -1,13 +1,17 @@ import asyncio -from activities import compose_greeting from temporalio.client import Client +from temporalio.envconfig import ClientConfig from temporalio.worker import Worker -from workflows import ChildWorkflow, GreetingWorkflow + +from polling.periodic_sequence.activities import compose_greeting +from polling.periodic_sequence.workflows import ChildWorkflow, GreetingWorkflow async def main(): - client = await Client.connect("localhost:7233") + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) worker = Worker( client, diff --git a/polling/periodic_sequence/workflows.py b/polling/periodic_sequence/workflows.py index 12045bc8f..3c6d7555c 100644 --- a/polling/periodic_sequence/workflows.py +++ b/polling/periodic_sequence/workflows.py @@ -6,7 +6,8 @@ from temporalio.exceptions import ActivityError with workflow.unsafe.imports_passed_through(): - from activities import ComposeGreetingInput, compose_greeting + from polling.periodic_sequence.activities import compose_greeting + from polling.test_service import ComposeGreetingInput @workflow.defn @@ -37,6 +38,5 @@ async def run(self, name: str) -> str: except ActivityError: workflow.logger.error("Activity failed, retrying in 1 seconds") await asyncio.sleep(1) - workflow.continue_as_new(name) - raise Exception("Polling failed after all attempts") + workflow.continue_as_new(name) diff --git a/polling/test_service.py b/polling/test_service.py index 5bcec7cdf..994b98ce4 100644 --- a/polling/test_service.py +++ b/polling/test_service.py @@ -1,14 +1,30 @@ -class TestService: - def __init__(self): - self.try_attempts = 0 - self.error_attempts = 5 - - def get_service_result(self, input): - print( - f"Attempt {self.try_attempts}" - f" of {self.error_attempts} to invoke service" - ) - self.try_attempts += 1 - if self.try_attempts % self.error_attempts == 0: - return f"{input.greeting}, {input.name}!" - raise Exception("service is down") +from dataclasses import dataclass +from typing import Counter + +from temporalio import activity +from temporalio.exceptions import ApplicationError, ApplicationErrorCategory + +attempts = Counter[str | None]() +ERROR_ATTEMPTS = 5 + + +@dataclass +class ComposeGreetingInput: + greeting: str + name: str + + +async def get_service_result(input): + workflow_id = activity.info().workflow_id + attempts[workflow_id] += 1 + + print(f"Attempt {attempts[workflow_id]} of {ERROR_ATTEMPTS} to invoke service") + if attempts[workflow_id] == ERROR_ATTEMPTS: + return f"{input.greeting}, {input.name}!" + raise ApplicationError( + message="service is down", + # Set the error as BENIGN to indicate it is an expected error. + # BENIGN errors have activity failure logs downgraded to DEBUG level + # and do not emit activity failure metrics. + category=ApplicationErrorCategory.BENIGN, + ) diff --git a/prometheus/README.md b/prometheus/README.md index 6f605d62f..c0b5eb8f2 100644 --- a/prometheus/README.md +++ b/prometheus/README.md @@ -2,16 +2,16 @@ This sample shows how to have SDK Prometheus metrics made available via HTTP. -To run, first see [README.md](../README.md) for prerequisites. Then, run the following from this directory to start the +To run, first see [README.md](../README.md) for prerequisites. Then, run the following from the root directory to start the worker: - poetry run python worker.py + uv run prometheus/worker.py This will start the worker and the metrics will be visible for this process at http://127.0.0.1:9000/metrics. Then, in another terminal, run the following to execute a workflow: - poetry run python starter.py + uv run prometheus/starter.py After executing the workflow, the process will stay open so the metrics if this separate process can be accessed at http://127.0.0.1:9001/metrics. \ No newline at end of file diff --git a/prometheus/starter.py b/prometheus/starter.py index b94f56014..571aee077 100644 --- a/prometheus/starter.py +++ b/prometheus/starter.py @@ -1,6 +1,7 @@ import asyncio from temporalio.client import Client +from temporalio.envconfig import ClientConfig from prometheus.worker import GreetingWorkflow, init_runtime_with_prometheus @@ -10,9 +11,12 @@ async def main(): runtime = init_runtime_with_prometheus(9001) + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + # Connect client client = await Client.connect( - "localhost:7233", + **config, runtime=runtime, ) diff --git a/prometheus/worker.py b/prometheus/worker.py index 5e7d64ab1..b41b75c53 100644 --- a/prometheus/worker.py +++ b/prometheus/worker.py @@ -3,6 +3,7 @@ from temporalio import activity, workflow from temporalio.client import Client +from temporalio.envconfig import ClientConfig from temporalio.runtime import PrometheusConfig, Runtime, TelemetryConfig from temporalio.worker import Worker @@ -38,9 +39,12 @@ def init_runtime_with_prometheus(port: int) -> Runtime: async def main(): runtime = init_runtime_with_prometheus(9000) + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + # Connect client client = await Client.connect( - "localhost:7233", + **config, runtime=runtime, ) diff --git a/pydantic_converter/README.md b/pydantic_converter/README.md index 5914fa3d2..e215e7359 100644 --- a/pydantic_converter/README.md +++ b/pydantic_converter/README.md @@ -1,31 +1,19 @@ # Pydantic Converter Sample -This sample shows how to create a custom Pydantic converter to properly serialize Pydantic models. +This sample shows how to use the Pydantic data converter. -For this sample, the optional `pydantic` dependency group must be included. To include, run: +For this sample, the optional `pydantic_converter` dependency group must be included. To include, run: - poetry install --with pydantic + uv sync --group pydantic-converter -To run, first see [README.md](../README.md) for prerequisites. Then, run the following from this directory to start the +To run, first see [README.md](../README.md) for prerequisites. Then, run the following from the root directory to start the worker: - poetry run python worker.py + uv run pydantic_converter/worker.py This will start the worker. Then, in another terminal, run the following to execute the workflow: - poetry run python starter.py + uv run pydantic_converter/starter.py In the worker terminal, the workflow and its activity will log that it received the Pydantic models. In the starter terminal, the Pydantic models in the workflow result will be logged. - -### Notes - -This is the preferred way to use Pydantic models with Temporal Python SDK. The converter code is small and meant to -embed into other projects. - -This sample also demonstrates use of `datetime` inside of Pydantic models. Due to a known issue with the Temporal -sandbox, this class is seen by Pydantic as `date` instead of `datetime` upon deserialization. This is due to a -[known Python issue](https://github.com/python/cpython/issues/89010) where, when we proxy the `datetime` class in the -sandbox to prevent non-deterministic calls like `now()`, `issubclass` fails for the proxy type causing Pydantic to think -it's a `date` instead. In `worker.py`, we have shown a workaround of disabling restrictions on `datetime` which solves -this issue but no longer protects against workflow developers making non-deterministic calls in that module. \ No newline at end of file diff --git a/pydantic_converter/starter.py b/pydantic_converter/starter.py index 5aceca6e0..47766be66 100644 --- a/pydantic_converter/starter.py +++ b/pydantic_converter/starter.py @@ -4,16 +4,23 @@ from ipaddress import IPv4Address from temporalio.client import Client +from temporalio.contrib.pydantic import pydantic_data_converter +from temporalio.envconfig import ClientConfig -from pydantic_converter.converter import pydantic_data_converter from pydantic_converter.worker import MyPydanticModel, MyWorkflow async def main(): logging.basicConfig(level=logging.INFO) + + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + # Connect client using the Pydantic converter + client = await Client.connect( - "localhost:7233", data_converter=pydantic_data_converter + **config, + data_converter=pydantic_data_converter, ) # Run workflow @@ -29,7 +36,7 @@ async def main(): some_date=datetime(2001, 2, 3, 4, 5, 6), ), ], - id=f"pydantic_converter-workflow-id", + id="pydantic_converter-workflow-id", task_queue="pydantic_converter-task-queue", ) logging.info("Got models from client: %s" % result) diff --git a/pydantic_converter/worker.py b/pydantic_converter/worker.py index b7e0bedf9..8acc11254 100644 --- a/pydantic_converter/worker.py +++ b/pydantic_converter/worker.py @@ -1,5 +1,4 @@ import asyncio -import dataclasses import logging from datetime import datetime, timedelta from ipaddress import IPv4Address @@ -7,18 +6,14 @@ from temporalio import activity, workflow from temporalio.client import Client +from temporalio.envconfig import ClientConfig from temporalio.worker import Worker -from temporalio.worker.workflow_sandbox import ( - SandboxedWorkflowRunner, - SandboxRestrictions, -) -# We always want to pass through external modules to the sandbox that we know -# are safe for workflow use +# Always pass through external modules to the sandbox that you know are safe for +# workflow use with workflow.unsafe.imports_passed_through(): from pydantic import BaseModel - - from pydantic_converter.converter import pydantic_data_converter + from temporalio.contrib.pydantic import pydantic_data_converter class MyPydanticModel(BaseModel): @@ -42,37 +37,19 @@ async def run(self, models: List[MyPydanticModel]) -> List[MyPydanticModel]: ) -# Due to known issues with Pydantic's use of issubclass and our inability to -# override the check in sandbox, Pydantic will think datetime is actually date -# in the sandbox. At the expense of protecting against datetime.now() use in -# workflows, we're going to remove datetime module restrictions. See sdk-python -# README's discussion of known sandbox issues for more details. -def new_sandbox_runner() -> SandboxedWorkflowRunner: - # TODO(cretz): Use with_child_unrestricted when https://github.com/temporalio/sdk-python/issues/254 - # is fixed and released - invalid_module_member_children = dict( - SandboxRestrictions.invalid_module_members_default.children - ) - del invalid_module_member_children["datetime"] - return SandboxedWorkflowRunner( - restrictions=dataclasses.replace( - SandboxRestrictions.default, - invalid_module_members=dataclasses.replace( - SandboxRestrictions.invalid_module_members_default, - children=invalid_module_member_children, - ), - ) - ) - - interrupt_event = asyncio.Event() async def main(): logging.basicConfig(level=logging.INFO) + + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + # Connect client using the Pydantic converter client = await Client.connect( - "localhost:7233", data_converter=pydantic_data_converter + **config, + data_converter=pydantic_data_converter, ) # Run a worker for the workflow @@ -81,7 +58,6 @@ async def main(): task_queue="pydantic_converter-task-queue", workflows=[MyWorkflow], activities=[my_activity], - workflow_runner=new_sandbox_runner(), ): # Wait until interrupted print("Worker started, ctrl+c to exit") diff --git a/pydantic_converter_v1/README.md b/pydantic_converter_v1/README.md new file mode 100644 index 000000000..526e69303 --- /dev/null +++ b/pydantic_converter_v1/README.md @@ -0,0 +1,31 @@ +# Pydantic v1 Converter Sample + +**This sample shows how to use Pydantic v1 with Temporal. This is not recommended: use Pydantic v2 if possible, and use the +main [pydantic_converter](../pydantic_converter/README.md) sample.** + +To install, run: + + uv sync --group pydantic-converter + uv run pip uninstall pydantic pydantic-core + uv run pip install pydantic==1.10 + +To run, first see the root [README.md](../README.md) for prerequisites. Then, run the following from the root directory to start the +worker: + + uv run pydantic_converter_v1/worker.py + +This will start the worker. Then, in another terminal, run the following to execute the workflow: + + uv run pydantic_converter_v1/starter.py + +In the worker terminal, the workflow and its activity will log that it received the Pydantic models. In the starter +terminal, the Pydantic models in the workflow result will be logged. + +### Notes + +This sample also demonstrates use of `datetime` inside of Pydantic v1 models. Due to a known issue with the Temporal +sandbox, this class is seen by Pydantic v1 as `date` instead of `datetime` upon deserialization. This is due to a +[known Python issue](https://github.com/python/cpython/issues/89010) where, when we proxy the `datetime` class in the +sandbox to prevent non-deterministic calls like `now()`, `issubclass` fails for the proxy type causing Pydantic v1 to think +it's a `date` instead. In `worker.py`, we have shown a workaround of disabling restrictions on `datetime` which solves +this issue but no longer protects against workflow developers making non-deterministic calls in that module. \ No newline at end of file diff --git a/pydantic_converter_v1/__init__.py b/pydantic_converter_v1/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/pydantic_converter/converter.py b/pydantic_converter_v1/converter.py similarity index 89% rename from pydantic_converter/converter.py rename to pydantic_converter_v1/converter.py index a3c1cee66..81997e81b 100644 --- a/pydantic_converter/converter.py +++ b/pydantic_converter_v1/converter.py @@ -42,9 +42,11 @@ class PydanticPayloadConverter(CompositePayloadConverter): def __init__(self) -> None: super().__init__( *( - c - if not isinstance(c, JSONPlainPayloadConverter) - else PydanticJSONPayloadConverter() + ( + c + if not isinstance(c, JSONPlainPayloadConverter) + else PydanticJSONPayloadConverter() + ) for c in DefaultPayloadConverter.default_encoding_payload_converters ) ) diff --git a/pydantic_converter_v1/starter.py b/pydantic_converter_v1/starter.py new file mode 100644 index 000000000..33b0ad280 --- /dev/null +++ b/pydantic_converter_v1/starter.py @@ -0,0 +1,46 @@ +import asyncio +import logging +from datetime import datetime +from ipaddress import IPv4Address + +from temporalio.client import Client +from temporalio.envconfig import ClientConfig + +from pydantic_converter_v1.converter import pydantic_data_converter +from pydantic_converter_v1.worker import MyPydanticModel, MyWorkflow + + +async def main(): + logging.basicConfig(level=logging.INFO) + + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + + # Connect client using the Pydantic converter + + client = await Client.connect( + **config, + data_converter=pydantic_data_converter, + ) + + # Run workflow + result = await client.execute_workflow( + MyWorkflow.run, + [ + MyPydanticModel( + some_ip=IPv4Address("127.0.0.1"), + some_date=datetime(2000, 1, 2, 3, 4, 5), + ), + MyPydanticModel( + some_ip=IPv4Address("127.0.0.2"), + some_date=datetime(2001, 2, 3, 4, 5, 6), + ), + ], + id="pydantic_converter-workflow-id", + task_queue="pydantic_converter-task-queue", + ) + logging.info("Got models from client: %s" % result) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/pydantic_converter_v1/worker.py b/pydantic_converter_v1/worker.py new file mode 100644 index 000000000..5ff65e1e2 --- /dev/null +++ b/pydantic_converter_v1/worker.py @@ -0,0 +1,104 @@ +import asyncio +import dataclasses +import logging +from datetime import datetime, timedelta +from ipaddress import IPv4Address +from typing import List + +from temporalio import activity, workflow +from temporalio.client import Client +from temporalio.envconfig import ClientConfig +from temporalio.worker import Worker +from temporalio.worker.workflow_sandbox import ( + SandboxedWorkflowRunner, + SandboxRestrictions, +) + +# We always want to pass through external modules to the sandbox that we know +# are safe for workflow use +with workflow.unsafe.imports_passed_through(): + from pydantic import BaseModel + + from pydantic_converter_v1.converter import pydantic_data_converter + + +class MyPydanticModel(BaseModel): + some_ip: IPv4Address + some_date: datetime + + +@activity.defn +async def my_activity(models: List[MyPydanticModel]) -> List[MyPydanticModel]: + activity.logger.info("Got models in activity: %s" % models) + return models + + +@workflow.defn +class MyWorkflow: + @workflow.run + async def run(self, models: List[MyPydanticModel]) -> List[MyPydanticModel]: + workflow.logger.info("Got models in workflow: %s" % models) + return await workflow.execute_activity( + my_activity, models, start_to_close_timeout=timedelta(minutes=1) + ) + + +# Due to known issues with Pydantic's use of issubclass and our inability to +# override the check in sandbox, Pydantic will think datetime is actually date +# in the sandbox. At the expense of protecting against datetime.now() use in +# workflows, we're going to remove datetime module restrictions. See sdk-python +# README's discussion of known sandbox issues for more details. +def new_sandbox_runner() -> SandboxedWorkflowRunner: + # TODO(cretz): Use with_child_unrestricted when https://github.com/temporalio/sdk-python/issues/254 + # is fixed and released + invalid_module_member_children = dict( + SandboxRestrictions.invalid_module_members_default.children + ) + del invalid_module_member_children["datetime"] + return SandboxedWorkflowRunner( + restrictions=dataclasses.replace( + SandboxRestrictions.default, + invalid_module_members=dataclasses.replace( + SandboxRestrictions.invalid_module_members_default, + children=invalid_module_member_children, + ), + ) + ) + + +interrupt_event = asyncio.Event() + + +async def main(): + logging.basicConfig(level=logging.INFO) + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + + # Connect client using the Pydantic converter + + client = await Client.connect( + **config, + data_converter=pydantic_data_converter, + ) + + # Run a worker for the workflow + async with Worker( + client, + task_queue="pydantic_converter-task-queue", + workflows=[MyWorkflow], + activities=[my_activity], + workflow_runner=new_sandbox_runner(), + ): + # Wait until interrupted + print("Worker started, ctrl+c to exit") + await interrupt_event.wait() + print("Shutting down") + + +if __name__ == "__main__": + loop = asyncio.new_event_loop() + try: + loop.run_until_complete(main()) + except KeyboardInterrupt: + interrupt_event.set() + loop.run_until_complete(loop.shutdown_asyncgens()) diff --git a/pyproject.toml b/pyproject.toml index 7a42f7756..4d8422610 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,69 +1,128 @@ -[tool.poetry] +[project] name = "temporalio-samples" version = "0.1a1" description = "Temporal.io Python SDK samples" -license = "MIT" -authors = ["Temporal Technologies Inc "] +authors = [{ name = "Temporal Technologies Inc", email = "sdk@temporal.io" }] +requires-python = ">=3.10" readme = "README.md" -homepage = "https://github.com/temporalio/samples-python" -repository = "https://github.com/temporalio/samples-python" -documentation = "https://docs.temporal.io/docs/python" -packages = [ - { include = "**/*.py", from = "." } -] +license = "MIT" +dependencies = ["temporalio>=1.31.0,<2", "protobuf>=5.29.6,<6"] -[tool.poetry.urls] +[project.urls] +Homepage = "https://github.com/temporalio/samples-python" +Repository = "https://github.com/temporalio/samples-python" +Documentation = "https://docs.temporal.io/docs/python" "Bug Tracker" = "https://github.com/temporalio/samples-python/issues" -[tool.poetry.dependencies] -python = "^3.8" -temporalio = "^1.5.0" - -[tool.poetry.dev-dependencies] -black = "^22.3.0" -isort = "^5.10.1" -mypy = "^0.961" -pytest = "^7.1.2" -pytest-asyncio = "^0.18.3" - -# All sample-specific dependencies are in optional groups below, named after the -# sample they apply to - -[tool.poetry.group.dsl] -optional = true -dependencies = { pyyaml = "^6.0.1", types-pyyaml = "^6.0.12", dacite = "^1.8.1" } - -[tool.poetry.group.encryption] -optional = true -dependencies = { cryptography = "^38.0.1", aiohttp = "^3.8.1" } - -[tool.poetry.group.gevent] -optional = true -dependencies = { gevent = { version = "^23.9.1", python = ">=3.8" } } +[dependency-groups] +dev = [ + "ruff>=0.5.0,<0.6", + "mypy>=1.4.1,<2", + "pytest>=7.1.2,<8", + "pytest-asyncio>=0.18.3,<0.19", + "fakeredis>=2,<3", + "frozenlist>=1.4.0,<2", + "pyright>=1.1.394", + "types-pyyaml>=6.0.12.20241230,<7", + "pytest-pretty>=1.3.0", + "poethepoet>=0.36.0", +] +bedrock = ["boto3>=1.34.92,<2"] +deepagents = [ + "deepagents>=0.6.12,<0.7 ; python_version >= '3.11'", + "langchain>=1.3.11,<2 ; python_version >= '3.11'", + "langchain-core>=1.4.8,<2 ; python_version >= '3.11'", + "langchain-anthropic>=1.4.7,<2 ; python_version >= '3.11'", + "temporalio[langsmith]>=1.31.0 ; python_version >= '3.11'", +] +dsl = ["pyyaml>=6.0.1,<7", "types-pyyaml>=6.0.12,<7", "dacite>=1.8.1,<2"] +encryption = ["cryptography>=38.0.1,<39", "aiohttp>=3.13.3,<4"] +external-storage = [ + "aioboto3>=15.1.0,<15.2", + "aiohttp>=3.13.3,<4", + "moto[server]>=4.0.0", + "types_aiobotocore_s3>=2.25.2", +] +external-storage-redis = ["redis>=5.0.0,<8"] +gevent = ["gevent>=25.4.2 ; python_version >= '3.8'"] +google-adk = ["temporalio[google-adk] >= 1.31.0", "google-adk>=2.2.0,<3"] +google-genai = [ + "mcp>=1.0.0", + "temporalio[google-genai,pydantic]>=1.31.0", +] +langfuse-tracing = [ + "openai>=1.4.0", + "temporalio[opentelemetry]>=1.30.0,<2", + # Langfuse's OTLP endpoint is HTTP-only (no gRPC), so use the http exporter. + "opentelemetry-exporter-otlp-proto-http>=1.30.0,<2", + "openinference-instrumentation-openai>=0.1.52", + "opentelemetry-instrumentation-openai-v2>=2.1b0", +] +langsmith-tracing = [ + "openai>=1.4.0", + "langsmith>=0.7.0", + "temporalio[pydantic,langsmith]>=1.31.0", +] +langgraph = [ + "langgraph>=1.1.3", + "langchain>=0.3.0", + "langchain-anthropic>=0.3.0", + "temporalio[langgraph,langsmith]>=1.31.0", +] +litellm = ["litellm>=1.85.0,<2"] +nexus = ["nexus-rpc>=1.1.0,<2"] +open-telemetry = [ + "temporalio[opentelemetry]", + "opentelemetry-exporter-otlp-proto-grpc", +] +openai-agents = [ + "openai-agents[litellm] >= 0.14.1", + "temporalio[openai-agents,opentelemetry] >= 1.31.0", + "requests>=2.32.0,<3", +] +pydantic-converter = ["pydantic>=2.10.6,<3"] +sentry = ["sentry-sdk>=2.13.0"] +strands-agents = [ + "strands-agents>=1.39.0", + "strands-agents-tools>=0.5.2", + "mcp>=1.0.0", + "boto3>=1.34.92,<2", + "temporalio[strands-agents,pydantic]>=1.31.0", +] +trio-async = ["trio>=0.28.0,<0.29", "trio-asyncio>=0.15.0,<0.16"] +cloud-export-to-parquet = [ + "pandas>=2.3.3,<3 ; python_version >= '3.10' and python_version < '4.0'", + "numpy>=1.26.0,<2 ; python_version >= '3.10' and python_version < '3.13'", + "boto3>=1.34.89,<2", + "pyarrow>=19.0.1", +] -[tool.poetry.group.open_telemetry] -optional = true -[tool.poetry.group.open_telemetry.dependencies] -temporalio = { version = "*", extras = ["opentelemetry"] } -opentelemetry-exporter-otlp-proto-grpc = "1.18.0" +[tool.uv] +constraint-dependencies = [ + # yarl 1.24.0 was published without an sdist and only has cp310 wheels, so it cannot + # install on the Python 3.14 CI jobs. + "yarl!=1.24.0", +] -[tool.poetry.group.pydantic] -optional = true -dependencies = { pydantic = "^1.10.4" } +[tool.setuptools.packages.find] +exclude = ["lambda_worker*"] -[tool.poetry.group.sentry] -optional = true -dependencies = { sentry-sdk = "^1.11.0" } +[build-system] +requires = ["setuptools>=77"] +build-backend = "setuptools.build_meta" [tool.poe.tasks] -format = [{cmd = "black ."}, {cmd = "isort ."}] -lint = [{cmd = "black --check ."}, {cmd = "isort --check-only ."}, {ref = "lint-types" }] -lint-types = "mypy --check-untyped-defs --namespace-packages ." -test = "pytest" - -[build-system] -requires = ["poetry-core>=1.0.0"] -build-backend = "poetry.core.masonry.api" +format = [ + { cmd = "uv run ruff check --select I --fix" }, + { cmd = "uv run ruff format" }, +] +lint = [ + { cmd = "uv run ruff check --select I" }, + { cmd = "uv run ruff format --check" }, + { ref = "lint-types" }, +] +lint-types = "uv run --all-groups --no-group cloud-export-to-parquet mypy --check-untyped-defs --namespace-packages ." +test = "uv run --all-groups --no-group cloud-export-to-parquet pytest" [tool.pytest.ini_options] asyncio_mode = "auto" @@ -71,13 +130,14 @@ log_cli = true log_cli_level = "INFO" log_cli_format = "%(asctime)s [%(levelname)8s] %(message)s (%(filename)s:%(lineno)s)" -[tool.isort] -profile = "black" -skip_gitignore = true +[tool.ruff] +target-version = "py310" +extend-exclude = ["lambda_worker"] [tool.mypy] ignore_missing_imports = true namespace_packages = true +exclude = ["lambda_worker/"] [[tool.mypy.overrides]] module = "aiohttp.*" diff --git a/replay/README.md b/replay/README.md index 3a67868a2..e9fc9ccf8 100644 --- a/replay/README.md +++ b/replay/README.md @@ -3,18 +3,18 @@ This sample shows you how you can verify changes to workflow code are compatible with existing workflow histories. -To run, first see [README.md](../README.md) for prerequisites. Then, run the following from this directory to start the +To run, first see [README.md](../README.md) for prerequisites. Then, run the following from the root directory to start the worker: - poetry run python worker.py + uv run replay/worker.py This will start the worker. Then, in another terminal, run the following to execute a workflow: - poetry run python starter.py + uv run replay/starter.py Next, run the replayer: - poetry run python replayer.py + uv run replay/replayer.py Which should produce some output like: diff --git a/replay/replayer.py b/replay/replayer.py index 49f163131..4787b33b9 100644 --- a/replay/replayer.py +++ b/replay/replayer.py @@ -1,6 +1,7 @@ import asyncio from temporalio.client import Client +from temporalio.envconfig import ClientConfig from temporalio.worker import Replayer from replay.worker import JustActivity, JustTimer, TimerThenActivity @@ -8,7 +9,9 @@ async def main(): # Connect client - client = await Client.connect("localhost:7233") + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) # Fetch the histories of the workflows to be replayed workflows = client.list_workflows('WorkflowId="replayer-workflow-id"') diff --git a/replay/starter.py b/replay/starter.py index daf070988..228e50c3b 100644 --- a/replay/starter.py +++ b/replay/starter.py @@ -1,13 +1,16 @@ import asyncio from temporalio.client import Client +from temporalio.envconfig import ClientConfig from replay.worker import JustActivity, JustTimer, TimerThenActivity async def main(): # Connect client - client = await Client.connect("localhost:7233") + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) # Run a few workflows # Importantly, normally we would *not* advise re-using the same workflow ID for all of these, diff --git a/replay/worker.py b/replay/worker.py index 3aebc099d..4ac57da28 100644 --- a/replay/worker.py +++ b/replay/worker.py @@ -5,6 +5,7 @@ from temporalio import activity, workflow from temporalio.client import Client +from temporalio.envconfig import ClientConfig from temporalio.worker import Worker @@ -71,7 +72,9 @@ async def main(): logging.basicConfig(level=logging.INFO) # Start client - client = await Client.connect("localhost:7233") + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) # Run a worker for the workflow async with Worker( diff --git a/resource_pool/README.md b/resource_pool/README.md new file mode 100644 index 000000000..5b746d7b5 --- /dev/null +++ b/resource_pool/README.md @@ -0,0 +1,48 @@ +# Resource Pool Sample + +This sample shows how to use a long-lived `ResourcePoolWorkflow` to allocate `resources` to `ResourceUserWorkflows`. +Each `ResourceUserWorkflow` runs several activities while it has ownership of a resource. Note that +`ResourcePoolWorkflow` is making resource allocation decisions based on in-memory state. + +Run the following from the root directory to start the worker: + + uv run resource_pool/worker.py + +This will start the worker. Then, in another terminal, run the following to execute several `ResourceUserWorkflows`. + + uv run resource_pool/starter.py + +You should see output indicating that the `ResourcePoolWorkflow` serialized access to each resource. + +You can query the set of current resource resource holders with: + + temporal workflow query --workflow-id resource_pool --name get_current_holders + +# Other approaches + +There are simpler ways to manage concurrent access to resources. Consider using resource-specific workers/task queues, +and limiting the number of activity slots on the workers. The golang SDK also [sessions](https://docs.temporal.io/develop/go/sessions) +that allow workflows to pin themselves to workers. + +The technique in this sample is capable of more complex resource allocation than the options above, but it doesn't scale +as well. Specifically, it can: +- Manage access to a set of resources that is decoupled from the set of workers and task queues +- Run arbitrary code to place workloads on resources as they become available + +# Caveats + +This sample uses true locking (not leasing!) to avoid complexity and scaling concerns associated with heartbeating via +signals. Locking carries a risk where failure to unlock permanently removing a resource from the pool. However, with +Temporal's durable execution guarantees, this can only happen if: + +- A ResourceUserWorkflows times out (prohibited in the sample code) +- An operator terminates a ResourceUserWorkflows. (Temporal recommends canceling workflows instead of terminating them whenever possible.) +- You shut down your workers and never restart them (unhandled, but irrelevant) + +If a leak were to happen, you could discover the identity of the leaker using the query above, then: + + temporal workflow signal --workflow-id resource_pool --name release_resource --input '{ "release_key": "" }' + +Performance: A single ResourcePoolWorkflow scales to tens, but not hundreds, of request/release events per second. It is +best suited for allocating resources to long-running workflows. Actual performance will depend on your temporal server's +persistence layer. \ No newline at end of file diff --git a/resource_pool/__init__.py b/resource_pool/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/resource_pool/pool_client/__init__.py b/resource_pool/pool_client/__init__.py new file mode 100644 index 000000000..b8471d8a7 --- /dev/null +++ b/resource_pool/pool_client/__init__.py @@ -0,0 +1,2 @@ +from .resource_pool_client import ResourcePoolClient +from .resource_pool_workflow import ResourcePoolWorkflow diff --git a/resource_pool/pool_client/resource_pool_client.py b/resource_pool/pool_client/resource_pool_client.py new file mode 100644 index 000000000..b7183afa6 --- /dev/null +++ b/resource_pool/pool_client/resource_pool_client.py @@ -0,0 +1,101 @@ +from contextlib import asynccontextmanager +from datetime import timedelta +from typing import AsyncGenerator, Optional + +from temporalio import workflow + +from resource_pool.pool_client.resource_pool_workflow import ResourcePoolWorkflow +from resource_pool.shared import ( + AcquiredResource, + AcquireRequest, + AcquireResponse, + DetachedResource, +) + + +# Use this class in workflow code that that needs to run on locked resources. +class ResourcePoolClient: + def __init__(self, pool_workflow_id: str) -> None: + self.pool_workflow_id = pool_workflow_id + self.acquired_resources: list[AcquiredResource] = [] + + signal_name = f"assign_resource_{self.pool_workflow_id}" + if workflow.get_signal_handler(signal_name) is None: + workflow.set_signal_handler(signal_name, self._handle_acquire_response) + else: + raise RuntimeError( + f"{signal_name} already registered - if you use multiple ResourcePoolClients within the " + f"same workflow, they must use different pool_workflow_ids" + ) + + def _handle_acquire_response(self, response: AcquireResponse) -> None: + self.acquired_resources.append( + AcquiredResource( + resource=response.resource, release_key=response.release_key + ) + ) + + async def _send_acquire_signal(self) -> None: + await workflow.get_external_workflow_handle_for( + ResourcePoolWorkflow.run, self.pool_workflow_id + ).signal("acquire_resource", AcquireRequest(workflow.info().workflow_id)) + + async def _send_release_signal(self, acquired_resource: AcquiredResource) -> None: + await workflow.get_external_workflow_handle_for( + ResourcePoolWorkflow.run, self.pool_workflow_id + ).signal( + "release_resource", + AcquireResponse( + resource=acquired_resource.resource, + release_key=acquired_resource.release_key, + ), + ) + + @asynccontextmanager + async def acquire_resource( + self, + *, + reattach: Optional[DetachedResource] = None, + max_wait_time: timedelta = timedelta(minutes=5), + ) -> AsyncGenerator[AcquiredResource, None]: + _warn_when_workflow_has_timeouts() + + if reattach is None: + await self._send_acquire_signal() + await workflow.wait_condition( + lambda: len(self.acquired_resources) > 0, timeout=max_wait_time + ) + resource = self.acquired_resources.pop(0) + else: + resource = AcquiredResource( + resource=reattach.resource, release_key=reattach.release_key + ) + + # Can't happen, but the typechecker doesn't know about workflow.wait_condition + if resource is None: + raise RuntimeError("resource was None when it can't be") + + # During the yield, the calling workflow owns the resource. Note that this is a lock, not a lease! Our + # finally block will release the resource if an activity fails. This is why we asserted the lack of + # workflow-level timeouts above - the finally block wouldn't run if there was a timeout. + try: + yield resource + finally: + if not resource.detached: + await self._send_release_signal(resource) + + +def _warn_when_workflow_has_timeouts() -> None: + def has_timeout(timeout: Optional[timedelta]) -> bool: + # After continue_as_new, timeouts are 0, even if they were None before continue_as_new (and were not set in the + # continue_as_new call). + return timeout is not None and timeout > timedelta(0) + + if has_timeout(workflow.info().run_timeout): + workflow.logger.warning( + f"ResourceLockingWorkflow cannot have a run_timeout (found {workflow.info().run_timeout}) - this will leak locks" + ) + if has_timeout(workflow.info().execution_timeout): + workflow.logger.warning( + f"ResourceLockingWorkflow cannot have an execution_timeout (found {workflow.info().execution_timeout}) - this will leak locks" + ) diff --git a/resource_pool/pool_client/resource_pool_workflow.py b/resource_pool/pool_client/resource_pool_workflow.py new file mode 100644 index 000000000..2321f82d6 --- /dev/null +++ b/resource_pool/pool_client/resource_pool_workflow.py @@ -0,0 +1,149 @@ +from dataclasses import dataclass +from typing import Optional + +from temporalio import workflow +from temporalio.exceptions import ApplicationError + +from resource_pool.shared import AcquireRequest, AcquireResponse + + +# Internal to this workflow, we'll associate randomly generated release signal names with each acquire request. +@dataclass +class InternalAcquireRequest(AcquireRequest): + release_signal: Optional[str] + + +@dataclass +class ResourcePoolWorkflowInput: + # Key is resource, value is current holder of the resource (None if not held) + resources: dict[str, Optional[InternalAcquireRequest]] + waiters: list[InternalAcquireRequest] + + +@workflow.defn +class ResourcePoolWorkflow: + @workflow.init + def __init__(self, input: ResourcePoolWorkflowInput) -> None: + self.resources = input.resources + self.waiters = input.waiters + self.release_key_to_resource: dict[str, str] = {} + + for resource, holder in self.resources.items(): + if holder is not None and holder.release_signal is not None: + self.release_key_to_resource[holder.release_signal] = resource + + @workflow.signal + async def add_resources(self, resources: list[str]) -> None: + for resource in resources: + if resource in self.resources: + workflow.logger.warning( + f"Ignoring attempt to add already-existing resource: {resource}" + ) + else: + self.resources[resource] = None + + @workflow.signal + async def acquire_resource(self, request: AcquireRequest) -> None: + self.waiters.append( + InternalAcquireRequest(workflow_id=request.workflow_id, release_signal=None) + ) + workflow.logger.info( + f"workflow_id={request.workflow_id} is waiting for a resource" + ) + + @workflow.signal + async def release_resource(self, acquire_response: AcquireResponse) -> None: + release_key = acquire_response.release_key + resource = self.release_key_to_resource.get(release_key) + if resource is None: + workflow.logger.warning(f"Ignoring unknown release_key: {release_key}") + return + + holder = self.resources[resource] + if holder is None: + workflow.logger.warning( + f"Ignoring request to release resource that is not held: {resource}" + ) + return + + # Remove the current holder + workflow.logger.info( + f"workflow_id={holder.workflow_id} released resource {resource}" + ) + self.resources[resource] = None + del self.release_key_to_resource[release_key] + + @workflow.query + def get_current_holders(self) -> dict[str, Optional[InternalAcquireRequest]]: + return self.resources + + async def assign_resource( + self, resource: str, internal_request: InternalAcquireRequest + ) -> None: + workflow.logger.info( + f"workflow_id={internal_request.workflow_id} acquired resource {resource}" + ) + + requester = workflow.get_external_workflow_handle(internal_request.workflow_id) + try: + release_signal = str(workflow.uuid4()) + await requester.signal( + f"assign_resource_{workflow.info().workflow_id}", + AcquireResponse(release_key=release_signal, resource=resource), + ) + + internal_request.release_signal = release_signal + self.resources[resource] = internal_request + self.release_key_to_resource[release_signal] = resource + except ApplicationError as e: + if e.type == "ExternalWorkflowExecutionNotFound": + workflow.logger.info( + f"Could not assign resource {resource} to {internal_request.workflow_id}: {e.message}" + ) + else: + raise e + + async def assign_next_resource(self) -> bool: + if len(self.waiters) == 0: + return False + + next_free_resource = self.get_free_resource() + if next_free_resource is None: + return False + + next_waiter = self.waiters.pop(0) + await self.assign_resource(next_free_resource, next_waiter) + return True + + def get_free_resource(self) -> Optional[str]: + return next( + (resource for resource, holder in self.resources.items() if holder is None), + None, + ) + + def can_assign_resource(self) -> bool: + return len(self.waiters) > 0 and self.get_free_resource() is not None + + def should_continue_as_new(self) -> bool: + return ( + workflow.info().is_continue_as_new_suggested() + and workflow.all_handlers_finished() + ) + + @workflow.run + async def run(self, _: ResourcePoolWorkflowInput) -> None: + while True: + await workflow.wait_condition( + lambda: self.can_assign_resource() or self.should_continue_as_new() + ) + + if await self.assign_next_resource(): + continue + + if self.should_continue_as_new(): + workflow.continue_as_new( + ResourcePoolWorkflowInput( + resources=self.resources, + waiters=self.waiters, + ) + ) diff --git a/resource_pool/resource_user_workflow.py b/resource_pool/resource_user_workflow.py new file mode 100644 index 000000000..80ee7fba4 --- /dev/null +++ b/resource_pool/resource_user_workflow.py @@ -0,0 +1,88 @@ +import asyncio +from dataclasses import dataclass, field +from datetime import timedelta +from typing import Optional + +from temporalio import activity, workflow + +from resource_pool.pool_client import ResourcePoolClient +from resource_pool.shared import DetachedResource + + +@dataclass +class UseResourceActivityInput: + resource: str + iteration: str + + +@activity.defn +async def use_resource(input: UseResourceActivityInput) -> None: + info = activity.info() + activity.logger.info( + f"{info.workflow_id} starts using {input.resource} the {input.iteration} time" + ) + await asyncio.sleep(3) + activity.logger.info( + f"{info.workflow_id} done using {input.resource} the {input.iteration} time" + ) + + +@dataclass +class ResourceUserWorkflowInput: + # The id of the resource pool workflow to request a resource from + resource_pool_workflow_id: str + + # If set, this workflow will fail after the "first" or "second" activity. + iteration_to_fail_after: Optional[str] + + # If True, this workflow will continue as new after the last activity. The next iteration will run more activities, + # but will not continue as new. + should_continue_as_new: bool + + # Used to transfer resource ownership between iterations during continue_as_new + already_acquired_resource: Optional[DetachedResource] = field(default=None) + + +class FailWorkflowException(Exception): + pass + + +# Wait this long for a resource before giving up +MAX_RESOURCE_WAIT_TIME = timedelta(minutes=5) + + +@workflow.defn(failure_exception_types=[FailWorkflowException]) +class ResourceUserWorkflow: + @workflow.run + async def run(self, input: ResourceUserWorkflowInput) -> None: + pool_client = ResourcePoolClient(input.resource_pool_workflow_id) + + async with pool_client.acquire_resource( + reattach=input.already_acquired_resource + ) as acquired_resource: + for iteration in ["first", "second"]: + await workflow.execute_activity( + use_resource, + UseResourceActivityInput(acquired_resource.resource, iteration), + start_to_close_timeout=timedelta(seconds=10), + ) + + if iteration == input.iteration_to_fail_after: + workflow.logger.info( + f"Failing after iteration {input.iteration_to_fail_after}" + ) + raise FailWorkflowException() + + # This workflow only continues as new so it can demonstrate how to pass acquired resources across + # iterations. Ordinarily, such a short workflow would not use continue as new. + if input.should_continue_as_new: + detached_resource = acquired_resource.detach() + + next_input = ResourceUserWorkflowInput( + resource_pool_workflow_id=input.resource_pool_workflow_id, + iteration_to_fail_after=input.iteration_to_fail_after, + should_continue_as_new=False, + already_acquired_resource=detached_resource, + ) + + workflow.continue_as_new(next_input) diff --git a/resource_pool/shared.py b/resource_pool/shared.py new file mode 100644 index 000000000..3930bb722 --- /dev/null +++ b/resource_pool/shared.py @@ -0,0 +1,31 @@ +from dataclasses import dataclass, field + +RESOURCE_POOL_WORKFLOW_ID = "resource_pool" + + +@dataclass +class AcquireRequest: + workflow_id: str + + +@dataclass +class AcquireResponse: + release_key: str + resource: str + + +@dataclass +class DetachedResource: + resource: str + release_key: str + + +@dataclass +class AcquiredResource: + resource: str + release_key: str + detached: bool = field(default=False) + + def detach(self) -> DetachedResource: + self.detached = True + return DetachedResource(resource=self.resource, release_key=self.release_key) diff --git a/resource_pool/starter.py b/resource_pool/starter.py new file mode 100644 index 000000000..2a50357a0 --- /dev/null +++ b/resource_pool/starter.py @@ -0,0 +1,69 @@ +import asyncio +from typing import Any + +from temporalio.client import Client, WorkflowFailureError, WorkflowHandle +from temporalio.common import WorkflowIDConflictPolicy +from temporalio.envconfig import ClientConfig + +from resource_pool.pool_client.resource_pool_workflow import ( + ResourcePoolWorkflow, + ResourcePoolWorkflowInput, +) +from resource_pool.resource_user_workflow import ( + ResourceUserWorkflow, + ResourceUserWorkflowInput, +) +from resource_pool.shared import RESOURCE_POOL_WORKFLOW_ID + + +async def main() -> None: + # Connect client + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) + + # Initialize the resource pool + resource_pool_handle = await client.start_workflow( + workflow=ResourcePoolWorkflow.run, + arg=ResourcePoolWorkflowInput( + resources={"resource_a": None, "resource_b": None}, + waiters=[], + ), + id=RESOURCE_POOL_WORKFLOW_ID, + task_queue="resource_pool-task-queue", + id_conflict_policy=WorkflowIDConflictPolicy.USE_EXISTING, + ) + + # Start the ResourceUserWorkflows + resource_user_handles: list[WorkflowHandle[Any, Any]] = [] + for i in range(0, 4): + input = ResourceUserWorkflowInput( + resource_pool_workflow_id=RESOURCE_POOL_WORKFLOW_ID, + iteration_to_fail_after=None, + should_continue_as_new=False, + ) + if i == 0: + input.should_continue_as_new = True + if i == 1: + input.iteration_to_fail_after = "first" + + handle = await client.start_workflow( + workflow=ResourceUserWorkflow.run, + arg=input, + id=f"resource-user-workflow-{i}", + task_queue="resource_pool-task-queue", + ) + resource_user_handles.append(handle) + + for handle in resource_user_handles: + try: + await handle.result() + except WorkflowFailureError: + pass + + # Clean up after ourselves. In the real world, the resource pool workflow would run forever. + await resource_pool_handle.terminate() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/resource_pool/worker.py b/resource_pool/worker.py new file mode 100644 index 000000000..253e5f8e3 --- /dev/null +++ b/resource_pool/worker.py @@ -0,0 +1,34 @@ +import asyncio +import logging + +from temporalio.client import Client +from temporalio.envconfig import ClientConfig +from temporalio.worker import Worker + +from resource_pool.pool_client.resource_pool_workflow import ResourcePoolWorkflow +from resource_pool.resource_user_workflow import ResourceUserWorkflow, use_resource + + +async def main() -> None: + logging.basicConfig(level=logging.INFO) + + # Start client + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) + + # Run a worker for the workflow + worker = Worker( + client, + task_queue="resource_pool-task-queue", + workflows=[ResourcePoolWorkflow, ResourceUserWorkflow], + activities=[ + use_resource, + ], + ) + + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/schedules/README.md b/schedules/README.md index 9bf40ca01..e5eed069f 100644 --- a/schedules/README.md +++ b/schedules/README.md @@ -2,20 +2,20 @@ These samples show how to schedule a Workflow Execution and control certain action. -To run, first see [README.md](../README.md) for prerequisites. Then, run the following from this directory to run the `schedules/` sample: +To run, first see [README.md](../README.md) for prerequisites. Then, run the following from the root directory to run the `schedules/` sample: - poetry run python run_worker.py - poetry run python start_schedule.py + uv run schedules/run_worker.py + uv run schedules/start_schedule.py -Replace `start_schedule.py` in the command with any other example filename to run it instead. +Replace `schedules/start_schedule.py` in the command with any other example filename to run it instead. - poetry run python backfill_schedule.py - poetry run python delete_schedule.py - poetry run python describe_schedule.py - poetry run python list_schedule.py - poetry run python pause_schedule.py - python run python trigger_schedule.py - poetry run python update_schedule.py + uv run schedules/backfill_schedule.py + uv run schedules/delete_schedule.py + uv run schedules/describe_schedule.py + uv run schedules/list_schedule.py + uv run schedules/pause_schedule.py + uv run schedules/trigger_schedule.py + uv run schedules/update_schedule.py - create: Creates a new Schedule. Newly created Schedules return a Schedule ID to be used in other Schedule commands. - backfill: Backfills the Schedule by going through the specified time periods as if they passed right now. diff --git a/schedules/backfill_schedule.py b/schedules/backfill_schedule.py index 656584091..708ad07f2 100644 --- a/schedules/backfill_schedule.py +++ b/schedules/backfill_schedule.py @@ -2,10 +2,14 @@ from datetime import datetime, timedelta from temporalio.client import Client, ScheduleBackfill, ScheduleOverlapPolicy +from temporalio.envconfig import ClientConfig async def main(): - client = await Client.connect("localhost:7233") + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) + handle = client.get_schedule_handle( "workflow-schedule-id", ) @@ -16,7 +20,7 @@ async def main(): end_at=now - timedelta(minutes=9), overlap=ScheduleOverlapPolicy.ALLOW_ALL, ), - ), + ) if __name__ == "__main__": diff --git a/schedules/delete_schedule.py b/schedules/delete_schedule.py index b62656360..d7b643942 100644 --- a/schedules/delete_schedule.py +++ b/schedules/delete_schedule.py @@ -1,10 +1,14 @@ import asyncio from temporalio.client import Client +from temporalio.envconfig import ClientConfig async def main(): - client = await Client.connect("localhost:7233") + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) + handle = client.get_schedule_handle( "workflow-schedule-id", ) diff --git a/schedules/describe_schedule.py b/schedules/describe_schedule.py index 22bb832d6..0db3fba5d 100644 --- a/schedules/describe_schedule.py +++ b/schedules/describe_schedule.py @@ -1,10 +1,14 @@ import asyncio from temporalio.client import Client +from temporalio.envconfig import ClientConfig async def main(): - client = await Client.connect("localhost:7233") + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) + handle = client.get_schedule_handle( "workflow-schedule-id", ) diff --git a/schedules/list_schedule.py b/schedules/list_schedule.py index a863aeee7..15c0fd6a9 100644 --- a/schedules/list_schedule.py +++ b/schedules/list_schedule.py @@ -1,10 +1,13 @@ import asyncio from temporalio.client import Client +from temporalio.envconfig import ClientConfig async def main() -> None: - client = await Client.connect("localhost:7233") + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) async for schedule in await client.list_schedules(): print(f"List Schedule Info: {schedule.info}.") diff --git a/schedules/pause_schedule.py b/schedules/pause_schedule.py index a6f8721c7..79a9ca030 100644 --- a/schedules/pause_schedule.py +++ b/schedules/pause_schedule.py @@ -1,10 +1,14 @@ import asyncio from temporalio.client import Client +from temporalio.envconfig import ClientConfig async def main(): - client = await Client.connect("localhost:7233") + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) + handle = client.get_schedule_handle( "workflow-schedule-id", ) diff --git a/schedules/run_worker.py b/schedules/run_worker.py index 5252b1f7f..00d14aaaf 100644 --- a/schedules/run_worker.py +++ b/schedules/run_worker.py @@ -1,13 +1,17 @@ import asyncio from temporalio.client import Client +from temporalio.envconfig import ClientConfig from temporalio.worker import Worker from your_activities import your_activity from your_workflows import YourSchedulesWorkflow async def main(): - client = await Client.connect("localhost:7233") + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) + worker = Worker( client, task_queue="schedules-task-queue", diff --git a/schedules/start_schedule.py b/schedules/start_schedule.py index 6089be952..ead6202be 100644 --- a/schedules/start_schedule.py +++ b/schedules/start_schedule.py @@ -9,11 +9,15 @@ ScheduleSpec, ScheduleState, ) +from temporalio.envconfig import ClientConfig from your_workflows import YourSchedulesWorkflow async def main(): - client = await Client.connect("localhost:7233") + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) + await client.create_schedule( "workflow-schedule-id", Schedule( diff --git a/schedules/trigger_schedule.py b/schedules/trigger_schedule.py index ca1f38f17..30939d321 100644 --- a/schedules/trigger_schedule.py +++ b/schedules/trigger_schedule.py @@ -1,10 +1,14 @@ import asyncio from temporalio.client import Client +from temporalio.envconfig import ClientConfig async def main(): - client = await Client.connect("localhost:7233") + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) + handle = client.get_schedule_handle( "workflow-schedule-id", ) diff --git a/schedules/update_schedule.py b/schedules/update_schedule.py index 979c23a86..709eeda35 100644 --- a/schedules/update_schedule.py +++ b/schedules/update_schedule.py @@ -6,10 +6,14 @@ ScheduleUpdate, ScheduleUpdateInput, ) +from temporalio.envconfig import ClientConfig async def main(): - client = await Client.connect("localhost:7233") + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) + handle = client.get_schedule_handle( "workflow-schedule-id", ) diff --git a/sentry/README.md b/sentry/README.md index 45977731b..0062f815f 100644 --- a/sentry/README.md +++ b/sentry/README.md @@ -1,19 +1,42 @@ # Sentry Sample -This sample shows how to configure [Sentry](https://sentry.io) to intercept and capture errors from the Temporal SDK. +This sample shows how to configure [Sentry](https://sentry.io) SDK (version 2) to intercept and capture errors from the Temporal SDK +for workflows and activities. The integration adds some useful context to the errors, such as the activity type, task queue, etc. + +Note: Sentry currently does not support Python 3.14, likewise this sample does not support Python 3.14. + +## Further details + +This is a small modification of the original example Sentry integration in this repo based on SDK v1. The integration +didn't work properly with Sentry SDK v2 due to some internal changes in the Sentry SDK that broke the worker sandbox. +Additionally, the v1 SDK has been deprecated and is only receiving security patches and will reach EOL some time in the future. +If you still need to use Sentry SDK v1, check the original example at this [commit](https://github.com/temporalio/samples-python/blob/090b96d750bafc10d4aad5ad506bb2439c413d5e/sentry). + +## Running the Sample For this sample, the optional `sentry` dependency group must be included. To include, run: - poetry install --with sentry + uv sync --no-default-groups --dev --group sentry + +> Note: this integration breaks when `gevent` is installed (e.g. by the gevent sample) so make sure to only install +> the `sentry` group and run the scripts below as described. To run, first see [README.md](../README.md) for prerequisites. Set `SENTRY_DSN` environment variable to the Sentry DSN. -Then, run the following from this directory to start the worker: +Then, run the following from the root directory to start the worker: - poetry run python worker.py + export SENTRY_DSN= # You'll need a Sentry account to test against + export ENVIRONMENT=dev + uv run sentry/worker.py This will start the worker. Then, in another terminal, run the following to execute the workflow: - poetry run python starter.py + uv run sentry/starter.py + +You should see the activity fail causing an error to be reported to Sentry. + +## Screenshot + +The screenshot below shows the extra tags and context included in the +Sentry error from the exception thrown in the activity. -The workflow should complete with the hello result. If you alter the workflow or the activity to raise an -`ApplicationError` instead, it should appear in Sentry. \ No newline at end of file +![Sentry screenshot](images/sentry.jpeg) diff --git a/sentry/activity.py b/sentry/activity.py new file mode 100644 index 000000000..148cd0d22 --- /dev/null +++ b/sentry/activity.py @@ -0,0 +1,25 @@ +from dataclasses import dataclass + +from temporalio import activity + + +@dataclass +class WorkingActivityInput: + message: str + + +@activity.defn +async def working_activity(input: WorkingActivityInput) -> str: + activity.logger.info("Running activity with parameter %s" % input) + return "Success" + + +@dataclass +class BrokenActivityInput: + message: str + + +@activity.defn +async def broken_activity(input: BrokenActivityInput) -> str: + activity.logger.info("Running activity with parameter %s" % input) + raise Exception("Activity failed!") diff --git a/sentry/images/sentry.jpeg b/sentry/images/sentry.jpeg new file mode 100644 index 000000000..0f62825bb Binary files /dev/null and b/sentry/images/sentry.jpeg differ diff --git a/sentry/interceptor.py b/sentry/interceptor.py index f1737ed23..d016acef4 100644 --- a/sentry/interceptor.py +++ b/sentry/interceptor.py @@ -1,5 +1,6 @@ +import logging from dataclasses import asdict, is_dataclass -from typing import Any, Optional, Type, Union +from typing import Any, Optional, Type from temporalio import activity, workflow from temporalio.worker import ( @@ -12,59 +13,65 @@ ) with workflow.unsafe.imports_passed_through(): - from sentry_sdk import Hub, capture_exception, set_context, set_tag + import sentry_sdk -def _set_common_workflow_tags(info: Union[workflow.Info, activity.Info]): - set_tag("temporal.workflow.type", info.workflow_type) - set_tag("temporal.workflow.id", info.workflow_id) +logger = logging.getLogger(__name__) class _SentryActivityInboundInterceptor(ActivityInboundInterceptor): async def execute_activity(self, input: ExecuteActivityInput) -> Any: # https://docs.sentry.io/platforms/python/troubleshooting/#addressing-concurrency-issues - with Hub(Hub.current): - set_tag("temporal.execution_type", "activity") - set_tag("module", input.fn.__module__ + "." + input.fn.__qualname__) - + with sentry_sdk.isolation_scope() as scope: + scope.set_tag("temporal.execution_type", "activity") + scope.set_tag("module", input.fn.__module__ + "." + input.fn.__qualname__) activity_info = activity.info() - _set_common_workflow_tags(activity_info) - set_tag("temporal.activity.id", activity_info.activity_id) - set_tag("temporal.activity.type", activity_info.activity_type) - set_tag("temporal.activity.task_queue", activity_info.task_queue) - set_tag("temporal.workflow.namespace", activity_info.workflow_namespace) - set_tag("temporal.workflow.run_id", activity_info.workflow_run_id) + scope.set_tag("temporal.workflow.type", activity_info.workflow_type) + scope.set_tag("temporal.workflow.id", activity_info.workflow_id) + scope.set_tag("temporal.activity.id", activity_info.activity_id) + scope.set_tag("temporal.activity.type", activity_info.activity_type) + scope.set_tag("temporal.activity.task_queue", activity_info.task_queue) + scope.set_tag( + "temporal.workflow.namespace", activity_info.workflow_namespace + ) + scope.set_tag("temporal.workflow.run_id", activity_info.workflow_run_id) try: return await super().execute_activity(input) except Exception as e: - if len(input.args) == 1 and is_dataclass(input.args[0]): - set_context("temporal.activity.input", asdict(input.args[0])) - set_context("temporal.activity.info", activity.info().__dict__) - capture_exception() + if len(input.args) == 1: + [arg] = input.args + if is_dataclass(arg) and not isinstance(arg, type): + scope.set_context("temporal.activity.input", asdict(arg)) + scope.set_context("temporal.activity.info", activity.info().__dict__) + scope.capture_exception() raise e class _SentryWorkflowInterceptor(WorkflowInboundInterceptor): async def execute_workflow(self, input: ExecuteWorkflowInput) -> Any: # https://docs.sentry.io/platforms/python/troubleshooting/#addressing-concurrency-issues - with Hub(Hub.current): - set_tag("temporal.execution_type", "workflow") - set_tag("module", input.run_fn.__module__ + "." + input.run_fn.__qualname__) + with sentry_sdk.isolation_scope() as scope: + scope.set_tag("temporal.execution_type", "workflow") + scope.set_tag( + "module", input.run_fn.__module__ + "." + input.run_fn.__qualname__ + ) workflow_info = workflow.info() - _set_common_workflow_tags(workflow_info) - set_tag("temporal.workflow.task_queue", workflow_info.task_queue) - set_tag("temporal.workflow.namespace", workflow_info.namespace) - set_tag("temporal.workflow.run_id", workflow_info.run_id) + scope.set_tag("temporal.workflow.type", workflow_info.workflow_type) + scope.set_tag("temporal.workflow.id", workflow_info.workflow_id) + scope.set_tag("temporal.workflow.task_queue", workflow_info.task_queue) + scope.set_tag("temporal.workflow.namespace", workflow_info.namespace) + scope.set_tag("temporal.workflow.run_id", workflow_info.run_id) try: return await super().execute_workflow(input) except Exception as e: - if len(input.args) == 1 and is_dataclass(input.args[0]): - set_context("temporal.workflow.input", asdict(input.args[0])) - set_context("temporal.workflow.info", workflow.info().__dict__) - + if len(input.args) == 1: + [arg] = input.args + if is_dataclass(arg) and not isinstance(arg, type): + scope.set_context("temporal.workflow.input", asdict(arg)) + scope.set_context("temporal.workflow.info", workflow.info().__dict__) if not workflow.unsafe.is_replaying(): with workflow.unsafe.sandbox_unrestricted(): - capture_exception() + scope.capture_exception() raise e @@ -74,9 +81,6 @@ class SentryInterceptor(Interceptor): def intercept_activity( self, next: ActivityInboundInterceptor ) -> ActivityInboundInterceptor: - """Implementation of - :py:meth:`temporalio.worker.Interceptor.intercept_activity`. - """ return _SentryActivityInboundInterceptor(super().intercept_activity(next)) def workflow_interceptor_class( diff --git a/sentry/starter.py b/sentry/starter.py index 9d0a0dc7c..aa3f6271e 100644 --- a/sentry/starter.py +++ b/sentry/starter.py @@ -1,23 +1,29 @@ import asyncio -import os from temporalio.client import Client +from temporalio.envconfig import ClientConfig -from sentry.worker import GreetingWorkflow +from sentry.workflow import SentryExampleWorkflow, SentryExampleWorkflowInput async def main(): + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + # Connect client - client = await Client.connect("localhost:7233") + client = await Client.connect(**config) # Run workflow - result = await client.execute_workflow( - GreetingWorkflow.run, - "World", - id="sentry-workflow-id", - task_queue="sentry-task-queue", - ) - print(f"Workflow result: {result}") + try: + result = await client.execute_workflow( + SentryExampleWorkflow.run, + SentryExampleWorkflowInput(option="broken"), + id="sentry-workflow-id", + task_queue="sentry-task-queue", + ) + print(f"Workflow result: {result}") + except Exception: + print("Workflow failed - check Sentry for details") if __name__ == "__main__": diff --git a/sentry/worker.py b/sentry/worker.py index 1db0826ba..1bcd153e1 100644 --- a/sentry/worker.py +++ b/sentry/worker.py @@ -1,64 +1,96 @@ import asyncio -import logging import os -from dataclasses import dataclass -from datetime import timedelta import sentry_sdk -from temporalio import activity, workflow +from sentry_sdk.integrations.asyncio import AsyncioIntegration +from sentry_sdk.types import Event, Hint from temporalio.client import Client +from temporalio.envconfig import ClientConfig from temporalio.worker import Worker +from temporalio.worker.workflow_sandbox import ( + SandboxedWorkflowRunner, + SandboxRestrictions, +) +from sentry.activity import broken_activity, working_activity from sentry.interceptor import SentryInterceptor +from sentry.workflow import SentryExampleWorkflow +interrupt_event = asyncio.Event() -@dataclass -class ComposeGreetingInput: - greeting: str - name: str +def before_send(event: Event, hint: Hint) -> Event | None: + # Filter out __ShutdownRequested events raised by the worker's internals + if str(hint.get("exc_info", [None])[0].__name__) == "_ShutdownRequested": + return None -@activity.defn -async def compose_greeting(input: ComposeGreetingInput) -> str: - activity.logger.info("Running activity with parameter %s" % input) - return f"{input.greeting}, {input.name}!" + return event -@workflow.defn -class GreetingWorkflow: - @workflow.run - async def run(self, name: str) -> str: - workflow.logger.info("Running workflow with parameter %s" % name) - return await workflow.execute_activity( - compose_greeting, - ComposeGreetingInput("Hello", name), - start_to_close_timeout=timedelta(seconds=10), +def initialise_sentry() -> None: + sentry_dsn = os.environ.get("SENTRY_DSN") + if not sentry_dsn: + print( + "SENTRY_DSN environment variable is not set. Sentry will not be initialized." ) + return + environment = os.environ.get("ENVIRONMENT") + sentry_sdk.init( + dsn=sentry_dsn, + environment=environment, + integrations=[ + AsyncioIntegration(), + ], + attach_stacktrace=True, + before_send=before_send, + ) + print(f"Sentry SDK initialized for environment: {environment!r}") -async def main(): - # Uncomment the line below to see logging - # logging.basicConfig(level=logging.INFO) +async def main(): # Initialize the Sentry SDK - sentry_sdk.init( - dsn=os.environ.get("SENTRY_DSN"), - ) + initialise_sentry() + + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") # Start client - client = await Client.connect("localhost:7233") + client = await Client.connect(**config) # Run a worker for the workflow - worker = Worker( + async with Worker( client, task_queue="sentry-task-queue", - workflows=[GreetingWorkflow], - activities=[compose_greeting], + workflows=[SentryExampleWorkflow], + activities=[broken_activity, working_activity], interceptors=[SentryInterceptor()], # Use SentryInterceptor for error reporting - ) - - await worker.run() + workflow_runner=SandboxedWorkflowRunner( + restrictions=SandboxRestrictions.default.with_passthrough_modules( + "sentry_sdk" + ) + ), + ): + # Wait until interrupted + print("Worker started, ctrl+c to exit") + await interrupt_event.wait() + print("Shutting down") if __name__ == "__main__": - asyncio.run(main()) + # Note: "Addressing Concurrency Issues" section in Sentry docs recommends using + # the AsyncioIntegration: "If you do concurrency with asyncio coroutines, make + # sure to use the AsyncioIntegration which will clone the correct scope in your Tasks" + # See https://docs.sentry.io/platforms/python/troubleshooting/ + # + # However, this captures all unhandled exceptions in the event loop. + # So handle shutdown gracefully to avoid CancelledError and KeyboardInterrupt + # exceptions being captured as errors. Sentry also captures the worker's + # _ShutdownRequested exception, which is probably not useful. We've filtered this + # out in Sentry's before_send function. + loop = asyncio.new_event_loop() + try: + loop.run_until_complete(main()) + except KeyboardInterrupt: + interrupt_event.set() + loop.run_until_complete(loop.shutdown_asyncgens()) diff --git a/sentry/workflow.py b/sentry/workflow.py new file mode 100644 index 000000000..4cb779eaf --- /dev/null +++ b/sentry/workflow.py @@ -0,0 +1,38 @@ +import typing +from dataclasses import dataclass +from datetime import timedelta + +from temporalio import workflow +from temporalio.common import RetryPolicy + +from sentry.activity import WorkingActivityInput, working_activity + +with workflow.unsafe.imports_passed_through(): + from sentry.activity import BrokenActivityInput, broken_activity + + +@dataclass +class SentryExampleWorkflowInput: + option: typing.Literal["working", "broken"] + + +@workflow.defn +class SentryExampleWorkflow: + @workflow.run + async def run(self, input: SentryExampleWorkflowInput) -> str: + workflow.logger.info("Running workflow with parameter %r" % input) + + if input.option == "working": + return await workflow.execute_activity( + working_activity, + WorkingActivityInput(message="Hello, Temporal!"), + start_to_close_timeout=timedelta(seconds=10), + retry_policy=RetryPolicy(maximum_attempts=1), + ) + + return await workflow.execute_activity( + broken_activity, + BrokenActivityInput(message="Hello, Temporal!"), + start_to_close_timeout=timedelta(seconds=10), + retry_policy=RetryPolicy(maximum_attempts=1), + ) diff --git a/sleep_for_days/README.md b/sleep_for_days/README.md new file mode 100644 index 000000000..5117fcdb2 --- /dev/null +++ b/sleep_for_days/README.md @@ -0,0 +1,18 @@ +# Sleep for Days + +This sample demonstrates how to create a Temporal workflow that runs forever, sending an email every 30 days. + +To run, first see the main [README.md](../README.md) for prerequisites. + +Then create two terminals. + +Run the worker in one terminal: + + uv run sleep_for_days/worker.py + +And execute the workflow in the other terminal: + + uv run sleep_for_days/starter.py + +This sample will run indefinitely until you send a signal to `complete`. See how to send a signal via Temporal CLI [here](https://docs.temporal.io/cli/workflow#signal). + diff --git a/sleep_for_days/__init__.py b/sleep_for_days/__init__.py new file mode 100644 index 000000000..04611d30b --- /dev/null +++ b/sleep_for_days/__init__.py @@ -0,0 +1 @@ +TASK_QUEUE = "sleep-for-days-task-queue" diff --git a/sleep_for_days/activities.py b/sleep_for_days/activities.py new file mode 100644 index 000000000..309720980 --- /dev/null +++ b/sleep_for_days/activities.py @@ -0,0 +1,18 @@ +from dataclasses import dataclass + +from temporalio import activity + + +@dataclass +class SendEmailInput: + email_msg: str + + +@activity.defn() +async def send_email(input: SendEmailInput) -> str: + """ + A stub Activity for sending an email. + """ + result = f"Email message: {input.email_msg}, sent" + activity.logger.info(result) + return result diff --git a/sleep_for_days/starter.py b/sleep_for_days/starter.py new file mode 100644 index 000000000..98dc083d4 --- /dev/null +++ b/sleep_for_days/starter.py @@ -0,0 +1,28 @@ +import asyncio +import uuid +from typing import Optional + +from temporalio.client import Client +from temporalio.envconfig import ClientConfig + +from sleep_for_days import TASK_QUEUE +from sleep_for_days.workflows import SleepForDaysWorkflow + + +async def main(client: Optional[Client] = None): + if not client: + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) + + wf_handle = await client.start_workflow( + SleepForDaysWorkflow.run, + id=f"sleep-for-days-workflow-id-{uuid.uuid4()}", + task_queue=TASK_QUEUE, + ) + # Wait for workflow completion (runs indefinitely until it receives a signal) + print(await wf_handle.result()) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/sleep_for_days/worker.py b/sleep_for_days/worker.py new file mode 100644 index 000000000..59799607a --- /dev/null +++ b/sleep_for_days/worker.py @@ -0,0 +1,30 @@ +import asyncio +import logging + +from temporalio.client import Client +from temporalio.envconfig import ClientConfig +from temporalio.worker import Worker + +from sleep_for_days import TASK_QUEUE +from sleep_for_days.activities import send_email +from sleep_for_days.workflows import SleepForDaysWorkflow + + +async def main(): + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) + + worker = Worker( + client, + task_queue=TASK_QUEUE, + workflows=[SleepForDaysWorkflow], + activities=[send_email], + ) + + await worker.run() + + +if __name__ == "__main__": + logging.basicConfig(level=logging.INFO) + asyncio.run(main()) diff --git a/sleep_for_days/workflows.py b/sleep_for_days/workflows.py new file mode 100644 index 000000000..eff0b273a --- /dev/null +++ b/sleep_for_days/workflows.py @@ -0,0 +1,37 @@ +import asyncio +from dataclasses import dataclass +from datetime import timedelta + +from temporalio import workflow + +with workflow.unsafe.imports_passed_through(): + from sleep_for_days.activities import SendEmailInput, send_email + + +@workflow.defn() +class SleepForDaysWorkflow: + def __init__(self) -> None: + self.is_complete = False + + @workflow.run + async def run(self) -> str: + while not self.is_complete: + await workflow.execute_activity( + send_email, + SendEmailInput("30 days until the next email"), + start_to_close_timeout=timedelta(seconds=10), + ) + await workflow.wait( + [ + asyncio.create_task(workflow.sleep(timedelta(days=30))), + asyncio.create_task( + workflow.wait_condition(lambda: self.is_complete) + ), + ], + return_when=asyncio.FIRST_COMPLETED, + ) + return "done!" + + @workflow.signal + def complete(self): + self.is_complete = True diff --git a/strands_plugin/README.md b/strands_plugin/README.md new file mode 100644 index 000000000..3fb4e2de7 --- /dev/null +++ b/strands_plugin/README.md @@ -0,0 +1,78 @@ +# Strands Agents Samples + +These samples demonstrate the [Temporal Strands plugin](https://github.com/temporalio/sdk-python/tree/main/temporalio/contrib/strands), which runs [Strands Agents](https://strandsagents.com/) inside Temporal Workflows. Model invocations, tool calls, and MCP tool calls all execute as Temporal Activities, so you get durable execution, Temporal-managed retries, and timeouts. + +## Samples + +| Sample | Description | +|--------|-------------| +| [hello_world](hello_world) | Minimal `TemporalAgent` invocation. Start here. | +| [tools](tools) | Three tool patterns side by side: in-workflow `@tool`, custom `@activity.defn` wrapped via `activity_as_tool`, and a `strands_tools` tool wrapped as a Temporal activity. | +| [human_in_the_loop](human_in_the_loop) | Pause a tool call on `BeforeToolCallEvent.interrupt()`, resume via Temporal signal. The canonical Strands HITL pattern. | +| [activity_interrupt](activity_interrupt) | Raise `InterruptException` from a Temporal activity to surface a HITL prompt across the activity boundary. Plugin-specific feature. | +| [hooks](hooks) | `HookProvider` with both an in-workflow callback and an `activity_as_hook` callback for I/O. | +| [mcp](mcp) | Connect to an MCP server (`FastMCP` echo) via `TemporalMCPClient`. | +| [structured_output](structured_output) | Pydantic-typed agent output via `structured_output_model`. | +| [streaming](streaming) | Forward model chunks to an external subscriber via `streaming_topic` + `WorkflowStream`. | +| [continue_as_new](continue_as_new) | Chat-style workflow that hands off `agent.messages` when history grows large. | + +## Prerequisites + +1. Install dependencies: + + ```bash + uv sync --group strands-agents + ``` + +2. Configure AWS credentials. The samples use the plugin's default `BedrockModel()`, which picks up the standard AWS SDK credential chain. Make sure the credentials grant access to a Bedrock model in your selected region (e.g., `us-west-2`). + + ```bash + export AWS_REGION=us-west-2 + # plus AWS_ACCESS_KEY_ID / AWS_SECRET_ACCESS_KEY or an SSO profile + ``` + + You can pick a specific model by passing it to `BedrockModel(model_id="...")` in each sample's worker. + +3. Start a local dev server with the [Temporal CLI](https://docs.temporal.io/cli): + + ```bash + temporal server start-dev + ``` + +## Running a Sample + +Each sample has two scripts. Start the Worker first, then the Workflow starter in a separate terminal: + +```bash +# Terminal 1: start the Worker +uv run strands_plugin//run_worker.py + +# Terminal 2: start the Workflow +uv run strands_plugin//run_workflow.py +``` + +For example, to run the tools sample: + +```bash +# Terminal 1 +uv run strands_plugin/tools/run_worker.py + +# Terminal 2 +uv run strands_plugin/tools/run_workflow.py +``` + +## Key Features Demonstrated + +- **Durable model invocation** — every model call runs in an `invoke_model` activity with configurable timeouts and retries. +- **Three ways to define tools** — pure Strands `@tool`, custom Temporal activities, and ecosystem `strands_tools` wrapped as activities. +- **Human-in-the-loop** — both hook-based (`BeforeToolCallEvent.interrupt()`) and tool-body (`raise InterruptException`) styles. +- **Hook system** — deterministic in-workflow callbacks plus I/O callbacks dispatched via `activity_as_hook`. +- **MCP integration** — connect to MCP servers at worker startup; tool calls dispatched through per-server activities. +- **Structured output** — Pydantic-typed agent results via the plugin's `pydantic_data_converter`. +- **Streaming** — forward model chunks live to external subscribers. +- **Long-lived chats** — hand off `agent.messages` via `continue-as-new` to stay under Temporal's history limit. + +## Related + +- [Temporal Strands plugin docs](https://github.com/temporalio/sdk-python/tree/main/temporalio/contrib/strands) +- [Strands Agents](https://strandsagents.com/) diff --git a/strands_plugin/__init__.py b/strands_plugin/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/strands_plugin/activity_interrupt/README.md b/strands_plugin/activity_interrupt/README.md new file mode 100644 index 000000000..bda67700a --- /dev/null +++ b/strands_plugin/activity_interrupt/README.md @@ -0,0 +1,34 @@ +# Activity Interrupt + +A `@activity.defn`-wrapped tool raises `InterruptException(Interrupt(...))` directly. The plugin's failure converter preserves the `Interrupt` payload across the activity boundary, so the agent stops with `stop_reason == "interrupt"` just like in the hook-based [human_in_the_loop](../human_in_the_loop) sample. + +When to reach for this style instead of a hook: + +- The decision to pause depends on data that's only visible inside the activity (a permissions service, a row in a database, etc.). +- You don't want to load that data into workflow context just to make the call. + +## What This Sample Demonstrates + +- Raising `InterruptException` from a Temporal activity tool +- The plugin's failure converter carrying `Interrupt` across the activity boundary +- Why `StrandsPlugin` must be attached to the **client** (not just the worker) + +## Running the Sample + +```bash +# Terminal 1 +uv run strands_plugin/activity_interrupt/run_worker.py + +# Terminal 2 +uv run strands_plugin/activity_interrupt/run_workflow.py +``` + +The starter requests deletion of a "protected" resource. The `delete_thing` activity raises an interrupt for protected names; the starter signals `"approve"` to release it. + +## Files + +| File | Description | +|------|-------------| +| `workflow.py` | `delete_thing` activity that raises `InterruptException`, plus the workflow that handles resumption | +| `run_worker.py` | `StrandsPlugin` on the client + worker, registers the activity | +| `run_workflow.py` | Starts the workflow and signals approval | diff --git a/strands_plugin/activity_interrupt/__init__.py b/strands_plugin/activity_interrupt/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/strands_plugin/activity_interrupt/run_worker.py b/strands_plugin/activity_interrupt/run_worker.py new file mode 100644 index 000000000..bfbf96003 --- /dev/null +++ b/strands_plugin/activity_interrupt/run_worker.py @@ -0,0 +1,37 @@ +"""Worker for the activity interrupt sample.""" + +# @@@SNIPSTART python-strands-activity-interrupt-worker +import asyncio +import os + +from temporalio.client import Client +from temporalio.contrib.strands import StrandsPlugin +from temporalio.worker import Worker + +from strands_plugin.activity_interrupt.workflow import ( + ActivityInterruptWorkflow, + delete_thing, +) + + +async def main() -> None: + plugin = StrandsPlugin() + # The plugin MUST be on the client so its failure converter is installed. + client = await Client.connect( + os.environ.get("TEMPORAL_ADDRESS", "localhost:7233"), + plugins=[plugin], + ) + + worker = Worker( + client, + task_queue="strands-activity-interrupt", + workflows=[ActivityInterruptWorkflow], + activities=[delete_thing], + ) + print("Worker started. Ctrl+C to exit.") + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) +# @@@SNIPEND diff --git a/strands_plugin/activity_interrupt/run_workflow.py b/strands_plugin/activity_interrupt/run_workflow.py new file mode 100644 index 000000000..7125c8237 --- /dev/null +++ b/strands_plugin/activity_interrupt/run_workflow.py @@ -0,0 +1,40 @@ +"""Start the activity interrupt workflow.""" + +import asyncio +import os + +from temporalio.client import Client +from temporalio.contrib.strands import StrandsPlugin + +from strands_plugin.activity_interrupt.workflow import ActivityInterruptWorkflow + + +async def main() -> None: + # The starter also goes through the plugin's failure converter so the + # Interrupt payload deserializes cleanly when the workflow result is read. + client = await Client.connect( + os.environ.get("TEMPORAL_ADDRESS", "localhost:7233"), + plugins=[StrandsPlugin()], + ) + + handle = await client.start_workflow( + ActivityInterruptWorkflow.run, + "Please delete the 'system' user.", + id="strands-activity-interrupt", + task_queue="strands-activity-interrupt", + ) + + reason = None + while reason is None: + await asyncio.sleep(0.5) + reason = await handle.query(ActivityInterruptWorkflow.pending_approval) + print(f"Approval requested: {reason}") + + await handle.signal(ActivityInterruptWorkflow.approve, "approve") + + result = await handle.result() + print(f"Result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/strands_plugin/activity_interrupt/workflow.py b/strands_plugin/activity_interrupt/workflow.py new file mode 100644 index 000000000..bf815630b --- /dev/null +++ b/strands_plugin/activity_interrupt/workflow.py @@ -0,0 +1,82 @@ +"""Activity interrupt: ``InterruptException`` raised from a Temporal activity. + +The plugin's failure converter preserves the ``Interrupt`` payload across the +activity boundary, so a Temporal activity can pause the agent for human input +the same way a hook can. + +For this to work, ``StrandsPlugin`` must be attached to the **client** (not +just the worker) so the failure converter is installed on the data converter. +The worker in this sample does exactly that. +""" + +from datetime import timedelta + +from strands.interrupt import Interrupt, InterruptException +from strands.types.interrupt import InterruptResponseContent +from temporalio import activity, workflow +from temporalio.contrib.strands import TemporalAgent +from temporalio.contrib.strands.workflow import activity_as_tool + +# Tracks names that have been approved out-of-band. In a real system, this +# would be a row in a policy database; the human reviewer flips a flag during +# the pause, and the activity's next attempt reads the new value and proceeds. +_APPROVED: set[str] = set() + + +# @@@SNIPSTART python-strands-activity-interrupt-activity +@activity.defn +async def delete_thing(name: str) -> str: + if name not in _APPROVED: + _APPROVED.add(name) + raise InterruptException( + Interrupt( + id=f"delete:{name}", + name="approval", + reason=f"approve delete of protected resource '{name}'?", + ) + ) + return f"deleted {name}" + + +# @@@SNIPEND + + +@workflow.defn +class ActivityInterruptWorkflow: + def __init__(self) -> None: + self.agent = TemporalAgent( + start_to_close_timeout=timedelta(seconds=60), + tools=[ + activity_as_tool( + delete_thing, + start_to_close_timeout=timedelta(seconds=30), + ), + ], + ) + self._approval: str | None = None + self._pending_reason: str | None = None + + @workflow.signal + def approve(self, response: str) -> None: + self._approval = response + + @workflow.query + def pending_approval(self) -> str | None: + return self._pending_reason + + @workflow.run + async def run(self, prompt: str) -> str: + result = await self.agent.invoke_async(prompt) + while result.stop_reason == "interrupt": + interrupts = list(result.interrupts or []) + self._pending_reason = interrupts[0].reason if interrupts else None + await workflow.wait_condition(lambda: self._approval is not None) + response = self._approval + self._approval = None + self._pending_reason = None + responses: list[InterruptResponseContent] = [ + {"interruptResponse": {"interruptId": i.id, "response": response}} + for i in interrupts + ] + result = await self.agent.invoke_async(responses) + return str(result) diff --git a/strands_plugin/continue_as_new/README.md b/strands_plugin/continue_as_new/README.md new file mode 100644 index 000000000..bffae4aee --- /dev/null +++ b/strands_plugin/continue_as_new/README.md @@ -0,0 +1,30 @@ +# Continue-as-new + +A chat-style workflow accumulates history with every turn and will eventually hit Temporal's per-workflow history limit. `workflow.info().is_continue_as_new_suggested()` flips `True` once the server decides history has grown large enough; this sample waits on it — alongside the `end_chat` signal — with `workflow.wait_condition(...)`, then hands off to a fresh run with `agent.messages` as input. + +## What This Sample Demonstrates + +- Driving a multi-turn chat with **updates**, so each caller gets the assistant's reply back from the same call +- Seeding a new `TemporalAgent` with prior `agent.messages` +- Using `workflow.info().is_continue_as_new_suggested()` + `workflow.continue_as_new(...)` to keep the workflow alive indefinitely +- Draining in-flight update handlers with `workflow.all_handlers_finished` before continue-as-new + +## Running the Sample + +```bash +# Terminal 1 +uv run strands_plugin/continue_as_new/run_worker.py + +# Terminal 2 +uv run strands_plugin/continue_as_new/run_workflow.py +``` + +The starter calls the `turn` update for each user message and prints the assistant's reply, then signals `end_chat`. In a real chatbot, a UI would drive the updates and the workflow would run indefinitely, continuing-as-new whenever history gets large. + +## Files + +| File | Description | +|------|-------------| +| `workflow.py` | `ChatInput`, `ChatWorkflow` with `turn` update, `end_chat` signal, and `messages` query | +| `run_worker.py` | Registers `StrandsPlugin`, starts the worker | +| `run_workflow.py` | Starts the chat, sends a few turns, ends it | diff --git a/strands_plugin/continue_as_new/__init__.py b/strands_plugin/continue_as_new/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/strands_plugin/continue_as_new/run_worker.py b/strands_plugin/continue_as_new/run_worker.py new file mode 100644 index 000000000..6cd092e4d --- /dev/null +++ b/strands_plugin/continue_as_new/run_worker.py @@ -0,0 +1,30 @@ +"""Worker for the chat continue-as-new sample.""" + +import asyncio +import os + +from temporalio.client import Client +from temporalio.contrib.strands import StrandsPlugin +from temporalio.worker import Worker + +from strands_plugin.continue_as_new.workflow import ChatWorkflow + + +async def main() -> None: + plugin = StrandsPlugin() + client = await Client.connect( + os.environ.get("TEMPORAL_ADDRESS", "localhost:7233"), + plugins=[plugin], + ) + + worker = Worker( + client, + task_queue="strands-chat", + workflows=[ChatWorkflow], + ) + print("Worker started. Ctrl+C to exit.") + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/strands_plugin/continue_as_new/run_workflow.py b/strands_plugin/continue_as_new/run_workflow.py new file mode 100644 index 000000000..2a98f4212 --- /dev/null +++ b/strands_plugin/continue_as_new/run_workflow.py @@ -0,0 +1,38 @@ +"""Start the chat workflow, send a few turns, then end it.""" + +import asyncio +import os + +from temporalio.client import Client +from temporalio.contrib.strands import StrandsPlugin + +from strands_plugin.continue_as_new.workflow import ChatInput, ChatWorkflow + + +async def main() -> None: + client = await Client.connect( + os.environ.get("TEMPORAL_ADDRESS", "localhost:7233"), + plugins=[StrandsPlugin()], + ) + + handle = await client.start_workflow( + ChatWorkflow.run, + ChatInput(), + id="strands-chat", + task_queue="strands-chat", + ) + + for prompt in [ + "Hi! What is durable execution?", + "Give me a one-sentence summary.", + ]: + reply = await handle.execute_update(ChatWorkflow.turn, prompt) + print(f"user: {prompt}") + print(f"assistant: {reply}\n") + + await handle.signal(ChatWorkflow.end_chat) + await handle.result() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/strands_plugin/continue_as_new/workflow.py b/strands_plugin/continue_as_new/workflow.py new file mode 100644 index 000000000..223a5c429 --- /dev/null +++ b/strands_plugin/continue_as_new/workflow.py @@ -0,0 +1,64 @@ +"""Chat-style workflow that continues-as-new before history grows too large. + +Each user turn arrives as a Temporal **update**, so the caller gets the +assistant's reply back from the same call. Once Temporal suggests +continue-as-new, the workflow drains any in-flight update handlers and hands +``agent.messages`` off to a fresh run. +""" + +# @@@SNIPSTART python-strands-continue-as-new-workflow +import asyncio +from dataclasses import dataclass, field +from datetime import timedelta + +from strands.types.content import Messages +from temporalio import workflow +from temporalio.contrib.strands import TemporalAgent + + +@dataclass +class ChatInput: + messages: Messages = field(default_factory=list) + + +@workflow.defn +class ChatWorkflow: + def __init__(self) -> None: + self._done = False + self._lock = asyncio.Lock() + self._agent: TemporalAgent | None = None + + @workflow.update + async def turn(self, prompt: str) -> str: + await workflow.wait_condition(lambda: self._agent is not None) + async with self._lock: + assert self._agent is not None + result = await self._agent.invoke_async(prompt) + return str(result).strip() + + @workflow.signal + def end_chat(self) -> None: + self._done = True + + @workflow.query + def messages(self) -> Messages: + return list(self._agent.messages) if self._agent else [] + + @workflow.run + async def run(self, input: ChatInput) -> None: + self._agent = TemporalAgent( + start_to_close_timeout=timedelta(seconds=60), + messages=list(input.messages), + ) + + await workflow.wait_condition( + lambda: self._done or workflow.info().is_continue_as_new_suggested() + ) + + await workflow.wait_condition(workflow.all_handlers_finished) + + if not self._done: + workflow.continue_as_new(ChatInput(messages=self._agent.messages)) + + +# @@@SNIPEND diff --git a/strands_plugin/hello_world/README.md b/strands_plugin/hello_world/README.md new file mode 100644 index 000000000..186b3580e --- /dev/null +++ b/strands_plugin/hello_world/README.md @@ -0,0 +1,29 @@ +# Hello World + +The simplest Strands + Temporal sample: one `TemporalAgent` invoked once. Every model call runs as an `invoke_model` Temporal activity, so it gets durable retries, timeouts, and crash recovery for free. + +## What This Sample Demonstrates + +- Wiring `StrandsPlugin` onto the client and worker +- Constructing a `TemporalAgent` with no explicit model (defaults to `BedrockModel()`) +- Invoking the agent from a `@workflow.defn` + +## Running the Sample + +Prerequisites: `uv sync --group strands-agents`, AWS credentials with Bedrock access, and a running Temporal dev server (`temporal server start-dev`). + +```bash +# Terminal 1 +uv run strands_plugin/hello_world/run_worker.py + +# Terminal 2 +uv run strands_plugin/hello_world/run_workflow.py +``` + +## Files + +| File | Description | +|------|-------------| +| `workflow.py` | `HelloWorldWorkflow` with a single `TemporalAgent` | +| `run_worker.py` | Registers `StrandsPlugin`, starts the worker | +| `run_workflow.py` | Executes the workflow and prints the result | diff --git a/strands_plugin/hello_world/__init__.py b/strands_plugin/hello_world/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/strands_plugin/hello_world/run_worker.py b/strands_plugin/hello_world/run_worker.py new file mode 100644 index 000000000..17b9ed9da --- /dev/null +++ b/strands_plugin/hello_world/run_worker.py @@ -0,0 +1,32 @@ +"""Worker for the hello world sample.""" + +# @@@SNIPSTART python-strands-hello-world-worker +import asyncio +import os + +from temporalio.client import Client +from temporalio.contrib.strands import StrandsPlugin +from temporalio.worker import Worker + +from strands_plugin.hello_world.workflow import HelloWorldWorkflow + + +async def main() -> None: + plugin = StrandsPlugin() + client = await Client.connect( + os.environ.get("TEMPORAL_ADDRESS", "localhost:7233"), + plugins=[plugin], + ) + + worker = Worker( + client, + task_queue="strands-hello-world", + workflows=[HelloWorldWorkflow], + ) + print("Worker started. Ctrl+C to exit.") + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) +# @@@SNIPEND diff --git a/strands_plugin/hello_world/run_workflow.py b/strands_plugin/hello_world/run_workflow.py new file mode 100644 index 000000000..ae26a0ea6 --- /dev/null +++ b/strands_plugin/hello_world/run_workflow.py @@ -0,0 +1,27 @@ +"""Start the hello world workflow.""" + +# @@@SNIPSTART python-strands-hello-world-run-workflow +import asyncio +import os + +from temporalio.client import Client + +from strands_plugin.hello_world.workflow import HelloWorldWorkflow + + +async def main() -> None: + client = await Client.connect(os.environ.get("TEMPORAL_ADDRESS", "localhost:7233")) + + result = await client.execute_workflow( + HelloWorldWorkflow.run, + "Write a haiku about durable execution.", + id="strands-hello-world", + task_queue="strands-hello-world", + ) + + print(f"Result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) +# @@@SNIPEND diff --git a/strands_plugin/hello_world/workflow.py b/strands_plugin/hello_world/workflow.py new file mode 100644 index 000000000..4ee98d9d0 --- /dev/null +++ b/strands_plugin/hello_world/workflow.py @@ -0,0 +1,21 @@ +"""Minimal Temporal + Strands workflow: one agent, one prompt.""" + +# @@@SNIPSTART python-strands-hello-world-workflow +from datetime import timedelta + +from temporalio import workflow +from temporalio.contrib.strands import TemporalAgent + + +@workflow.defn +class HelloWorldWorkflow: + def __init__(self) -> None: + self.agent = TemporalAgent(start_to_close_timeout=timedelta(seconds=60)) + + @workflow.run + async def run(self, prompt: str) -> str: + result = await self.agent.invoke_async(prompt) + return str(result) + + +# @@@SNIPEND diff --git a/strands_plugin/hooks/README.md b/strands_plugin/hooks/README.md new file mode 100644 index 000000000..dcdf44897 --- /dev/null +++ b/strands_plugin/hooks/README.md @@ -0,0 +1,34 @@ +# Hooks + +Strands' [hook system](https://strandsagents.com/) lets you subscribe callbacks to lifecycle events (`BeforeToolCallEvent`, `AfterToolCallEvent`, `BeforeModelCallEvent`, etc.). With the Temporal plugin, hook callbacks run in workflow context — so they must be deterministic — but you can dispatch I/O via `activity_as_hook`. + +This sample wires two callbacks to `AfterToolCallEvent`: + +1. An **in-workflow** callback that appends to per-workflow state. Pure data, deterministic across replay. +2. An **activity-backed** callback (`activity_as_hook(persist_tool_call, ...)`) that calls a Temporal activity for the actual audit write. The `activity_input=` selector pulls a serializable value out of the event. + +## What This Sample Demonstrates + +- Subscribing multiple callbacks to one hook event +- Mixing deterministic in-workflow callbacks with off-workflow activity callbacks +- The `activity_input=` selector for `activity_as_hook` + +## Running the Sample + +```bash +# Terminal 1 +uv run strands_plugin/hooks/run_worker.py + +# Terminal 2 +uv run strands_plugin/hooks/run_workflow.py +``` + +The Temporal UI will show one `invoke_model` activity per agent turn plus a `persist_tool_call` activity per tool call. + +## Files + +| File | Description | +|------|-------------| +| `workflow.py` | `echo` tool, `AuditHook` (in-workflow + activity-backed), `HooksWorkflow` | +| `run_worker.py` | Registers `StrandsPlugin` + `persist_tool_call`, starts worker | +| `run_workflow.py` | Executes the workflow and prints the list of fired events | diff --git a/strands_plugin/hooks/__init__.py b/strands_plugin/hooks/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/strands_plugin/hooks/run_worker.py b/strands_plugin/hooks/run_worker.py new file mode 100644 index 000000000..e03a1b9d1 --- /dev/null +++ b/strands_plugin/hooks/run_worker.py @@ -0,0 +1,31 @@ +"""Worker for the hooks sample.""" + +import asyncio +import os + +from temporalio.client import Client +from temporalio.contrib.strands import StrandsPlugin +from temporalio.worker import Worker + +from strands_plugin.hooks.workflow import HooksWorkflow, persist_tool_call + + +async def main() -> None: + plugin = StrandsPlugin() + client = await Client.connect( + os.environ.get("TEMPORAL_ADDRESS", "localhost:7233"), + plugins=[plugin], + ) + + worker = Worker( + client, + task_queue="strands-hooks", + workflows=[HooksWorkflow], + activities=[persist_tool_call], + ) + print("Worker started. Ctrl+C to exit.") + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/strands_plugin/hooks/run_workflow.py b/strands_plugin/hooks/run_workflow.py new file mode 100644 index 000000000..0959cf3ef --- /dev/null +++ b/strands_plugin/hooks/run_workflow.py @@ -0,0 +1,25 @@ +"""Start the hooks workflow.""" + +import asyncio +import os + +from temporalio.client import Client + +from strands_plugin.hooks.workflow import HooksWorkflow + + +async def main() -> None: + client = await Client.connect(os.environ.get("TEMPORAL_ADDRESS", "localhost:7233")) + + fired = await client.execute_workflow( + HooksWorkflow.run, + "Echo 'hello' once.", + id="strands-hooks", + task_queue="strands-hooks", + ) + + print(f"Tools that fired AfterToolCallEvent: {fired}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/strands_plugin/hooks/workflow.py b/strands_plugin/hooks/workflow.py new file mode 100644 index 000000000..2535cc249 --- /dev/null +++ b/strands_plugin/hooks/workflow.py @@ -0,0 +1,73 @@ +"""Two-hook audit example. + +``AuditHook`` subscribes two callbacks to ``AfterToolCallEvent``: + +* An in-workflow callback that mutates per-workflow state. It runs in workflow + context, so it must be deterministic — pure data manipulation only. +* An ``activity_as_hook`` callback that dispatches to a Temporal activity. Use + this for anything with I/O: writing to an audit log, calling out to a + metrics system, alerting, etc. +""" + +from datetime import timedelta + +from strands import tool +from strands.hooks import HookProvider, HookRegistry +from strands.hooks.events import AfterToolCallEvent +from temporalio import activity, workflow +from temporalio.contrib.strands import TemporalAgent +from temporalio.contrib.strands.workflow import activity_as_hook + + +# @@@SNIPSTART python-strands-hooks-activity +@activity.defn +async def persist_tool_call(tool_name: str) -> None: + # In production, write to a database / S3 / your audit pipeline. + activity.logger.info(f"audit: tool {tool_name} completed") + + +# @@@SNIPEND + + +@tool +def echo(text: str) -> str: + return text + + +# @@@SNIPSTART python-strands-hooks-provider +class AuditHook(HookProvider): + def __init__(self) -> None: + self.fired: list[str] = [] + + def register_hooks(self, registry: HookRegistry, **kwargs: object) -> None: + registry.add_callback(AfterToolCallEvent, self._record) + registry.add_callback( + AfterToolCallEvent, + activity_as_hook( + persist_tool_call, + activity_input=lambda event: event.tool_use["name"], + start_to_close_timeout=timedelta(seconds=15), + ), + ) + + def _record(self, event: AfterToolCallEvent) -> None: + self.fired.append(event.tool_use["name"]) + + +# @@@SNIPEND + + +@workflow.defn +class HooksWorkflow: + def __init__(self) -> None: + self.hook = AuditHook() + self.agent = TemporalAgent( + start_to_close_timeout=timedelta(seconds=60), + tools=[echo], + hooks=[self.hook], + ) + + @workflow.run + async def run(self, prompt: str) -> list[str]: + await self.agent.invoke_async(prompt) + return self.hook.fired diff --git a/strands_plugin/human_in_the_loop/README.md b/strands_plugin/human_in_the_loop/README.md new file mode 100644 index 000000000..84d7ca76e --- /dev/null +++ b/strands_plugin/human_in_the_loop/README.md @@ -0,0 +1,29 @@ +# Human-in-the-loop (Hook-based) + +The canonical Strands HITL pattern. A `BeforeToolCallEvent` hook gates a sensitive tool behind human approval by calling `event.interrupt(...)`. The agent stops with `stop_reason == "interrupt"`; the workflow waits on a Temporal signal for the response, then resumes with `InterruptResponseContent`. + +## What This Sample Demonstrates + +- Using `HookProvider` + `BeforeToolCallEvent` to interrupt before a tool runs +- Pairing Strands' interrupt machinery with Temporal signals + queries +- Resuming the agent with `[{"interruptResponse": ...}]` content + +## Running the Sample + +```bash +# Terminal 1 +uv run strands_plugin/human_in_the_loop/run_worker.py + +# Terminal 2 +uv run strands_plugin/human_in_the_loop/run_workflow.py +``` + +The starter script queries the workflow until an approval is pending, prints the reason, then signals `"approve"`. To exercise the denial path, change the signal to anything other than `"approve"`. + +## Files + +| File | Description | +|------|-------------| +| `workflow.py` | `delete_file` tool, `ApprovalHook`, `HumanInTheLoopWorkflow` | +| `run_worker.py` | Registers `StrandsPlugin`, starts the worker | +| `run_workflow.py` | Starts the workflow, polls for the approval, sends the signal | diff --git a/strands_plugin/human_in_the_loop/__init__.py b/strands_plugin/human_in_the_loop/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/strands_plugin/human_in_the_loop/run_worker.py b/strands_plugin/human_in_the_loop/run_worker.py new file mode 100644 index 000000000..13f4ee3a5 --- /dev/null +++ b/strands_plugin/human_in_the_loop/run_worker.py @@ -0,0 +1,30 @@ +"""Worker for the hook-based HITL sample.""" + +import asyncio +import os + +from temporalio.client import Client +from temporalio.contrib.strands import StrandsPlugin +from temporalio.worker import Worker + +from strands_plugin.human_in_the_loop.workflow import HumanInTheLoopWorkflow + + +async def main() -> None: + plugin = StrandsPlugin() + client = await Client.connect( + os.environ.get("TEMPORAL_ADDRESS", "localhost:7233"), + plugins=[plugin], + ) + + worker = Worker( + client, + task_queue="strands-human-in-the-loop", + workflows=[HumanInTheLoopWorkflow], + ) + print("Worker started. Ctrl+C to exit.") + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/strands_plugin/human_in_the_loop/run_workflow.py b/strands_plugin/human_in_the_loop/run_workflow.py new file mode 100644 index 000000000..b106d25c2 --- /dev/null +++ b/strands_plugin/human_in_the_loop/run_workflow.py @@ -0,0 +1,35 @@ +"""Start the hook-based HITL workflow and send an approval signal.""" + +import asyncio +import os + +from temporalio.client import Client + +from strands_plugin.human_in_the_loop.workflow import HumanInTheLoopWorkflow + + +async def main() -> None: + client = await Client.connect(os.environ.get("TEMPORAL_ADDRESS", "localhost:7233")) + + handle = await client.start_workflow( + HumanInTheLoopWorkflow.run, + "Please delete /tmp/sensitive.txt", + id="strands-human-in-the-loop", + task_queue="strands-human-in-the-loop", + ) + + # Poll until the agent reaches the approval interrupt. + reason = None + while reason is None: + await asyncio.sleep(0.5) + reason = await handle.query(HumanInTheLoopWorkflow.pending_approval) + print(f"Approval requested: {reason}") + + await handle.signal(HumanInTheLoopWorkflow.approve, "approve") + + result = await handle.result() + print(f"Result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/strands_plugin/human_in_the_loop/workflow.py b/strands_plugin/human_in_the_loop/workflow.py new file mode 100644 index 000000000..edeec98b0 --- /dev/null +++ b/strands_plugin/human_in_the_loop/workflow.py @@ -0,0 +1,80 @@ +"""Hook-based human-in-the-loop: pause on ``BeforeToolCallEvent.interrupt()``. + +A hook gates the ``delete_file`` tool behind human approval. The agent stops +with ``stop_reason == "interrupt"``; the workflow waits for a signal carrying +the approval response, then resumes the agent with the response. +""" + +from datetime import timedelta + +from strands import tool +from strands.hooks import HookProvider, HookRegistry +from strands.hooks.events import BeforeToolCallEvent +from strands.types.interrupt import InterruptResponseContent +from temporalio import workflow +from temporalio.contrib.strands import TemporalAgent + + +@tool +def delete_file(path: str) -> str: + return f"deleted {path}" + + +# @@@SNIPSTART python-strands-human-in-the-loop-hook +class ApprovalHook(HookProvider): + def register_hooks(self, registry: HookRegistry, **kwargs: object) -> None: + registry.add_callback(BeforeToolCallEvent, self._gate) + + def _gate(self, event: BeforeToolCallEvent) -> None: + if event.tool_use["name"] != "delete_file": + return + approval = event.interrupt( + "approval", + reason=f"approve delete of {event.tool_use['input']['path']}?", + ) + if approval != "approve": + event.cancel_tool = "denied" + + +# @@@SNIPEND + + +# @@@SNIPSTART python-strands-human-in-the-loop-workflow +@workflow.defn +class HumanInTheLoopWorkflow: + def __init__(self) -> None: + self.agent = TemporalAgent( + start_to_close_timeout=timedelta(seconds=60), + tools=[delete_file], + hooks=[ApprovalHook()], + ) + self._approval: str | None = None + self._pending_reason: str | None = None + + @workflow.signal + def approve(self, response: str) -> None: + self._approval = response + + @workflow.query + def pending_approval(self) -> str | None: + return self._pending_reason + + @workflow.run + async def run(self, prompt: str) -> str: + result = await self.agent.invoke_async(prompt) + while result.stop_reason == "interrupt": + interrupts = list(result.interrupts or []) + self._pending_reason = interrupts[0].reason if interrupts else None + await workflow.wait_condition(lambda: self._approval is not None) + response = self._approval + self._approval = None + self._pending_reason = None + responses: list[InterruptResponseContent] = [ + {"interruptResponse": {"interruptId": i.id, "response": response}} + for i in interrupts + ] + result = await self.agent.invoke_async(responses) + return str(result) + + +# @@@SNIPEND diff --git a/strands_plugin/mcp/README.md b/strands_plugin/mcp/README.md new file mode 100644 index 000000000..a8328536a --- /dev/null +++ b/strands_plugin/mcp/README.md @@ -0,0 +1,32 @@ +# MCP + +Connect a Strands agent to an [MCP](https://modelcontextprotocol.io/) server through `TemporalMCPClient`. The plugin opens the MCP session at worker startup, enumerates tools, and dispatches each tool call through a dedicated `-call-tool` Temporal activity. + +The included `echo_mcp_server.py` is a one-tool FastMCP server (returns the message it was given). Swap it for any real MCP server (filesystem, Postgres, GitHub, etc.) by changing the factory in `run_worker.py`. + +## What This Sample Demonstrates + +- Registering an MCP server with `StrandsPlugin(mcp_clients=...)` +- Referencing it from a workflow via `TemporalMCPClient(server="...")` +- Running MCP tool calls as durable Temporal activities + +## Running the Sample + +```bash +# Terminal 1 +uv run strands_plugin/mcp/run_worker.py + +# Terminal 2 +uv run strands_plugin/mcp/run_workflow.py +``` + +The worker spawns `echo_mcp_server.py` itself; you don't need to start it separately. The Temporal UI will show one `invoke_model` per agent turn plus `echo-call-tool` activities for each MCP tool invocation. + +## Files + +| File | Description | +|------|-------------| +| `echo_mcp_server.py` | FastMCP server exposing a single `echo` tool | +| `workflow.py` | `MCPWorkflow` with `TemporalMCPClient(server="echo")` | +| `run_worker.py` | Spawns the MCP server and starts the worker | +| `run_workflow.py` | Executes the workflow and prints the result | diff --git a/strands_plugin/mcp/__init__.py b/strands_plugin/mcp/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/strands_plugin/mcp/echo_mcp_server.py b/strands_plugin/mcp/echo_mcp_server.py new file mode 100644 index 000000000..4f3d35319 --- /dev/null +++ b/strands_plugin/mcp/echo_mcp_server.py @@ -0,0 +1,19 @@ +"""Tiny FastMCP server: exposes a single ``echo`` tool over stdio. + +The worker launches this script as a subprocess and connects to it via the +MCP stdio transport. Replace it with any real MCP server in production. +""" + +from mcp.server.fastmcp import FastMCP + +mcp = FastMCP("echo-server") + + +@mcp.tool() +def echo(message: str) -> str: + """Return the input message unchanged.""" + return message + + +if __name__ == "__main__": + mcp.run() diff --git a/strands_plugin/mcp/run_worker.py b/strands_plugin/mcp/run_worker.py new file mode 100644 index 000000000..772870e87 --- /dev/null +++ b/strands_plugin/mcp/run_worker.py @@ -0,0 +1,54 @@ +"""Worker for the MCP sample. + +The worker launches the ``echo_mcp_server.py`` script as a subprocess at +startup. The plugin opens a stdio MCP session, enumerates tools once, and +caches the schema for the worker's lifetime. +""" + +# @@@SNIPSTART python-strands-mcp-worker +import asyncio +import os +import sys +from pathlib import Path + +from mcp import StdioServerParameters, stdio_client +from strands.tools.mcp.mcp_client import MCPClient +from temporalio.client import Client +from temporalio.contrib.strands import StrandsPlugin +from temporalio.worker import Worker + +from strands_plugin.mcp.workflow import MCPWorkflow + +ECHO_SERVER = Path(__file__).parent / "echo_mcp_server.py" + + +def _make_echo_client() -> MCPClient: + return MCPClient( + lambda: stdio_client( + StdioServerParameters( + command=sys.executable, + args=[str(ECHO_SERVER)], + ) + ) + ) + + +async def main() -> None: + plugin = StrandsPlugin(mcp_clients={"echo": _make_echo_client}) + client = await Client.connect( + os.environ.get("TEMPORAL_ADDRESS", "localhost:7233"), + plugins=[plugin], + ) + + worker = Worker( + client, + task_queue="strands-mcp", + workflows=[MCPWorkflow], + ) + print("Worker started. Ctrl+C to exit.") + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) +# @@@SNIPEND diff --git a/strands_plugin/mcp/run_workflow.py b/strands_plugin/mcp/run_workflow.py new file mode 100644 index 000000000..7b9a6765a --- /dev/null +++ b/strands_plugin/mcp/run_workflow.py @@ -0,0 +1,25 @@ +"""Start the MCP workflow.""" + +import asyncio +import os + +from temporalio.client import Client + +from strands_plugin.mcp.workflow import MCPWorkflow + + +async def main() -> None: + client = await Client.connect(os.environ.get("TEMPORAL_ADDRESS", "localhost:7233")) + + result = await client.execute_workflow( + MCPWorkflow.run, + "Use the echo tool to echo the message 'hello from MCP'.", + id="strands-mcp", + task_queue="strands-mcp", + ) + + print(f"Result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/strands_plugin/mcp/workflow.py b/strands_plugin/mcp/workflow.py new file mode 100644 index 000000000..d83689c6a --- /dev/null +++ b/strands_plugin/mcp/workflow.py @@ -0,0 +1,34 @@ +"""Workflow that uses an MCP server through ``TemporalMCPClient``. + +The plugin connects to each registered MCP server at worker startup and +caches the tool manifest. ``TemporalMCPClient`` on the workflow side is a +pure handle that references the server by name and carries the activity +options for each tool call. +""" + +# @@@SNIPSTART python-strands-mcp-workflow +from datetime import timedelta + +from temporalio import workflow +from temporalio.contrib.strands import TemporalAgent, TemporalMCPClient + + +@workflow.defn +class MCPWorkflow: + def __init__(self) -> None: + echo = TemporalMCPClient( + server="echo", + start_to_close_timeout=timedelta(seconds=30), + ) + self.agent = TemporalAgent( + start_to_close_timeout=timedelta(seconds=60), + tools=[echo], + ) + + @workflow.run + async def run(self, prompt: str) -> str: + result = await self.agent.invoke_async(prompt) + return str(result) + + +# @@@SNIPEND diff --git a/strands_plugin/streaming/README.md b/strands_plugin/streaming/README.md new file mode 100644 index 000000000..598dde942 --- /dev/null +++ b/strands_plugin/streaming/README.md @@ -0,0 +1,29 @@ +# Streaming + +Strands' model stream produces a sequence of `StreamEvent`s (token deltas, tool-use chunks, message-stop, etc.). With `TemporalAgent(streaming_topic="events")`, the model activity publishes each chunk onto a workflow-hosted `WorkflowStream`; external subscribers read it via `WorkflowStreamClient`. Chunks are batched on `streaming_batch_interval` (default 100ms) to keep activity overhead low. + +## What This Sample Demonstrates + +- Hosting a `WorkflowStream` on a workflow +- `TemporalAgent(streaming_topic=...)` publishing model events +- An external subscriber reading `StreamEvent`s in real time via `WorkflowStreamClient` + +## Running the Sample + +```bash +# Terminal 1 +uv run strands_plugin/streaming/run_worker.py + +# Terminal 2 +uv run strands_plugin/streaming/run_workflow.py +``` + +The starter prints text deltas as they arrive, then the final workflow result. + +## Files + +| File | Description | +|------|-------------| +| `workflow.py` | `StreamingWorkflow` hosting `WorkflowStream` + `TemporalAgent(streaming_topic="events")` | +| `run_worker.py` | Registers `StrandsPlugin`, starts the worker | +| `run_workflow.py` | Starts the workflow and subscribes to the `events` topic | diff --git a/strands_plugin/streaming/__init__.py b/strands_plugin/streaming/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/strands_plugin/streaming/run_worker.py b/strands_plugin/streaming/run_worker.py new file mode 100644 index 000000000..d009de1e4 --- /dev/null +++ b/strands_plugin/streaming/run_worker.py @@ -0,0 +1,30 @@ +"""Worker for the streaming sample.""" + +import asyncio +import os + +from temporalio.client import Client +from temporalio.contrib.strands import StrandsPlugin +from temporalio.worker import Worker + +from strands_plugin.streaming.workflow import StreamingWorkflow + + +async def main() -> None: + plugin = StrandsPlugin() + client = await Client.connect( + os.environ.get("TEMPORAL_ADDRESS", "localhost:7233"), + plugins=[plugin], + ) + + worker = Worker( + client, + task_queue="strands-streaming", + workflows=[StreamingWorkflow], + ) + print("Worker started. Ctrl+C to exit.") + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/strands_plugin/streaming/run_workflow.py b/strands_plugin/streaming/run_workflow.py new file mode 100644 index 000000000..9a78b464f --- /dev/null +++ b/strands_plugin/streaming/run_workflow.py @@ -0,0 +1,51 @@ +"""Start the streaming workflow and consume model events live.""" + +# @@@SNIPSTART python-strands-streaming-client +import asyncio +import os +from datetime import timedelta + +from strands.types.streaming import StreamEvent +from temporalio.client import Client +from temporalio.contrib.workflow_streams import WorkflowStreamClient + +from strands_plugin.streaming.workflow import StreamingWorkflow + + +async def main() -> None: + client = await Client.connect(os.environ.get("TEMPORAL_ADDRESS", "localhost:7233")) + workflow_id = "strands-streaming" + + handle = await client.start_workflow( + StreamingWorkflow.run, + "Count from 1 to 5, one number per sentence.", + id=workflow_id, + task_queue="strands-streaming", + ) + + async def consume() -> None: + stream = WorkflowStreamClient.create(client, workflow_id) + async for item in stream.subscribe( + ["events"], + from_offset=0, + result_type=StreamEvent, + poll_cooldown=timedelta(milliseconds=50), + ): + event: StreamEvent = item.data + if "contentBlockDelta" in event: + delta = event["contentBlockDelta"].get("delta", {}) + if "text" in delta: + print(delta["text"], end="", flush=True) + elif "messageStop" in event: + print() + return + + consume_task = asyncio.create_task(consume()) + result = await handle.result() + await asyncio.wait_for(consume_task, timeout=10.0) + print(f"Final result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) +# @@@SNIPEND diff --git a/strands_plugin/streaming/workflow.py b/strands_plugin/streaming/workflow.py new file mode 100644 index 000000000..416f6e6e2 --- /dev/null +++ b/strands_plugin/streaming/workflow.py @@ -0,0 +1,32 @@ +"""Workflow that streams model chunks to an external subscriber. + +``TemporalAgent(streaming_topic="events")`` publishes each ``StreamEvent`` from +inside the model activity onto a workflow-hosted ``WorkflowStream``. +Subscribers connect via ``WorkflowStreamClient`` and read the topic in real +time. Chunks are batched on the ``streaming_batch_interval`` (default 100ms). +""" + +# @@@SNIPSTART python-strands-streaming-workflow +from datetime import timedelta + +from temporalio import workflow +from temporalio.contrib.strands import TemporalAgent +from temporalio.contrib.workflow_streams import WorkflowStream + + +@workflow.defn +class StreamingWorkflow: + def __init__(self) -> None: + self.stream = WorkflowStream() + self.agent = TemporalAgent( + start_to_close_timeout=timedelta(seconds=60), + streaming_topic="events", + ) + + @workflow.run + async def run(self, prompt: str) -> str: + result = await self.agent.invoke_async(prompt) + return str(result) + + +# @@@SNIPEND diff --git a/strands_plugin/structured_output/README.md b/strands_plugin/structured_output/README.md new file mode 100644 index 000000000..e913f187a --- /dev/null +++ b/strands_plugin/structured_output/README.md @@ -0,0 +1,27 @@ +# Structured Output + +`TemporalAgent` accepts a `structured_output_model=` Pydantic class and returns the agent's response coerced into that model. The plugin installs Temporal's `pydantic_data_converter` by default, so the typed value serializes cleanly across the activity and workflow boundary. + +## What This Sample Demonstrates + +- `TemporalAgent(structured_output_model=PersonInfo, ...)` +- Pulling `result.structured_output` out of the agent result +- The plugin's auto-configured pydantic data converter — no extra setup needed to ship Pydantic types in workflow inputs/outputs + +## Running the Sample + +```bash +# Terminal 1 +uv run strands_plugin/structured_output/run_worker.py + +# Terminal 2 +uv run strands_plugin/structured_output/run_workflow.py +``` + +## Files + +| File | Description | +|------|-------------| +| `workflow.py` | `PersonInfo` model and `StructuredOutputWorkflow` | +| `run_worker.py` | Registers `StrandsPlugin`, starts the worker | +| `run_workflow.py` | Executes the workflow and prints the typed result | diff --git a/strands_plugin/structured_output/__init__.py b/strands_plugin/structured_output/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/strands_plugin/structured_output/run_worker.py b/strands_plugin/structured_output/run_worker.py new file mode 100644 index 000000000..032526f5d --- /dev/null +++ b/strands_plugin/structured_output/run_worker.py @@ -0,0 +1,30 @@ +"""Worker for the structured output sample.""" + +import asyncio +import os + +from temporalio.client import Client +from temporalio.contrib.strands import StrandsPlugin +from temporalio.worker import Worker + +from strands_plugin.structured_output.workflow import StructuredOutputWorkflow + + +async def main() -> None: + plugin = StrandsPlugin() + client = await Client.connect( + os.environ.get("TEMPORAL_ADDRESS", "localhost:7233"), + plugins=[plugin], + ) + + worker = Worker( + client, + task_queue="strands-structured-output", + workflows=[StructuredOutputWorkflow], + ) + print("Worker started. Ctrl+C to exit.") + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/strands_plugin/structured_output/run_workflow.py b/strands_plugin/structured_output/run_workflow.py new file mode 100644 index 000000000..9116ae6a9 --- /dev/null +++ b/strands_plugin/structured_output/run_workflow.py @@ -0,0 +1,30 @@ +"""Start the structured output workflow.""" + +import asyncio +import os + +from temporalio.client import Client +from temporalio.contrib.strands import StrandsPlugin + +from strands_plugin.structured_output.workflow import StructuredOutputWorkflow + + +async def main() -> None: + # Plugin on the client so the pydantic data converter is installed. + client = await Client.connect( + os.environ.get("TEMPORAL_ADDRESS", "localhost:7233"), + plugins=[StrandsPlugin()], + ) + + person = await client.execute_workflow( + StructuredOutputWorkflow.run, + "John Smith is a 30 year-old software engineer.", + id="strands-structured-output", + task_queue="strands-structured-output", + ) + + print(f"name={person.name} age={person.age} occupation={person.occupation}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/strands_plugin/structured_output/workflow.py b/strands_plugin/structured_output/workflow.py new file mode 100644 index 000000000..3ee6a7a60 --- /dev/null +++ b/strands_plugin/structured_output/workflow.py @@ -0,0 +1,38 @@ +"""Structured output: agent returns a typed Pydantic model. + +``TemporalAgent(structured_output_model=PersonInfo)`` makes Strands coerce the +model's final response into an instance of ``PersonInfo``. The plugin installs +``pydantic_data_converter`` by default, so the typed value flows back across +the activity/workflow boundary without extra wiring. +""" + +# @@@SNIPSTART python-strands-structured-output-workflow +from datetime import timedelta + +from pydantic import BaseModel, Field +from temporalio import workflow +from temporalio.contrib.strands import TemporalAgent + + +class PersonInfo(BaseModel): + name: str = Field(description="Name of the person") + age: int = Field(description="Age of the person") + occupation: str = Field(description="Occupation of the person") + + +@workflow.defn +class StructuredOutputWorkflow: + def __init__(self) -> None: + self.agent = TemporalAgent( + start_to_close_timeout=timedelta(seconds=60), + structured_output_model=PersonInfo, + ) + + @workflow.run + async def run(self, prompt: str) -> PersonInfo: + result = await self.agent.invoke_async(prompt) + assert isinstance(result.structured_output, PersonInfo) + return result.structured_output + + +# @@@SNIPEND diff --git a/strands_plugin/tools/README.md b/strands_plugin/tools/README.md new file mode 100644 index 000000000..13fc32d84 --- /dev/null +++ b/strands_plugin/tools/README.md @@ -0,0 +1,35 @@ +# Tools + +Three Strands tool patterns wired into one `TemporalAgent`: + +| Pattern | When to use it | +|---------|----------------| +| `@tool` from `strands` | Pure, deterministic logic with no I/O. Runs in workflow context. | +| `@activity.defn` wrapped via `activity_as_tool` | Anything with I/O, non-determinism, or significant runtime — gets durable retries and timeouts. | +| `strands_tools.` wrapped in an `@activity.defn` | Reuse Strands ecosystem tools (`environment`, `http_request`, `python_repl`, …) while keeping workflow code deterministic. | + +A single prompt exercises all three. The resulting Temporal history shows an `invoke_model` for each model turn, plus `fetch_weather` and `environment` activity calls; the `letter_counter` call runs in-workflow and doesn't show up as an activity. + +## What This Sample Demonstrates + +- Three coexisting tool surfaces on one agent +- `activity_as_tool` (from `temporalio.contrib.strands.workflow`) carrying per-tool activity options (timeouts) +- Wrapping `strands_tools` tools so runtime host access happens in an activity + +## Running the Sample + +```bash +# Terminal 1 +uv run strands_plugin/tools/run_worker.py + +# Terminal 2 +uv run strands_plugin/tools/run_workflow.py +``` + +## Files + +| File | Description | +|------|-------------| +| `workflow.py` | The three tools, the `TemporalAgent`, and `ToolsWorkflow` | +| `run_worker.py` | Registers `StrandsPlugin` + the two activities, starts the worker | +| `run_workflow.py` | Executes the workflow and prints the result | diff --git a/strands_plugin/tools/__init__.py b/strands_plugin/tools/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/strands_plugin/tools/run_worker.py b/strands_plugin/tools/run_worker.py new file mode 100644 index 000000000..bffa83e31 --- /dev/null +++ b/strands_plugin/tools/run_worker.py @@ -0,0 +1,37 @@ +"""Worker for the tools sample.""" + +# @@@SNIPSTART python-strands-tools-worker +import asyncio +import os + +from temporalio.client import Client +from temporalio.contrib.strands import StrandsPlugin +from temporalio.worker import Worker + +from strands_plugin.tools.workflow import ( + ToolsWorkflow, + environment_activity, + fetch_weather, +) + + +async def main() -> None: + plugin = StrandsPlugin() + client = await Client.connect( + os.environ.get("TEMPORAL_ADDRESS", "localhost:7233"), + plugins=[plugin], + ) + + worker = Worker( + client, + task_queue="strands-tools", + workflows=[ToolsWorkflow], + activities=[fetch_weather, environment_activity], + ) + print("Worker started. Ctrl+C to exit.") + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) +# @@@SNIPEND diff --git a/strands_plugin/tools/run_workflow.py b/strands_plugin/tools/run_workflow.py new file mode 100644 index 000000000..04256c737 --- /dev/null +++ b/strands_plugin/tools/run_workflow.py @@ -0,0 +1,30 @@ +"""Start the tools workflow.""" + +import asyncio +import os + +from temporalio.client import Client + +from strands_plugin.tools.workflow import ToolsWorkflow + + +async def main() -> None: + client = await Client.connect(os.environ.get("TEMPORAL_ADDRESS", "localhost:7233")) + + result = await client.execute_workflow( + ToolsWorkflow.run, + ( + "Please do three things:\n" + "1. Count the letter R's in 'strawberry'.\n" + "2. Fetch the weather in San Francisco.\n" + "3. Validate that the PATH environment variable exists." + ), + id="strands-tools", + task_queue="strands-tools", + ) + + print(f"Result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/strands_plugin/tools/workflow.py b/strands_plugin/tools/workflow.py new file mode 100644 index 000000000..34aecd3cb --- /dev/null +++ b/strands_plugin/tools/workflow.py @@ -0,0 +1,101 @@ +"""Three tool patterns wired into one Strands agent. + +1. ``@tool letter_counter`` — pure Strands tool, runs deterministically in + workflow context. +2. ``@activity.defn fetch_weather`` — custom Temporal activity wrapped with + ``activity_as_tool``; suitable for I/O and non-deterministic work. +3. ``environment`` (from ``strands_tools``) — third-party Strands tool wrapped + in a thin ``@activity.defn`` so its runtime host access runs in an activity. +""" + +from datetime import timedelta +from typing import Any, cast + +from strands import tool +from strands.types.tools import ToolUse +from strands_tools import environment # type: ignore[import-untyped] +from temporalio import activity, workflow +from temporalio.contrib.strands import TemporalAgent +from temporalio.contrib.strands.workflow import activity_as_tool + + +@tool +def letter_counter(word: str, letter: str) -> int: + """Count how many times ``letter`` appears in ``word`` (case-insensitive).""" + return word.lower().count(letter.lower()) + + +# @@@SNIPSTART python-strands-tools-activity +@activity.defn +async def fetch_weather(city: str) -> dict: + """Stub weather lookup — replace with a real HTTP call in production.""" + return { + "city": city, + "temperature_f": 72, + "conditions": "sunny", + } + + +# @@@SNIPEND + + +@activity.defn(name="environment") +async def environment_activity( + action: str, + name: str | None = None, + value: str | None = None, + prefix: str | None = None, + masked: bool | None = None, +) -> dict: + """Run ``strands_tools.environment`` inside an activity. + + Environment variables are runtime host state. Wrapping this tool in an + activity keeps that non-deterministic access out of workflow replay. + """ + tool_input: dict[str, Any] = {"action": action} + if name is not None: + tool_input["name"] = name + if value is not None: + tool_input["value"] = value + if prefix is not None: + tool_input["prefix"] = prefix + if masked is not None: + tool_input["masked"] = masked + + tool_use = cast( + ToolUse, + { + "toolUseId": "environment", + "name": "environment", + "input": tool_input, + }, + ) + return environment.environment(tool_use) + + +# @@@SNIPSTART python-strands-tools-workflow +@workflow.defn +class ToolsWorkflow: + def __init__(self) -> None: + self.agent = TemporalAgent( + start_to_close_timeout=timedelta(seconds=60), + tools=[ + letter_counter, + activity_as_tool( + fetch_weather, + start_to_close_timeout=timedelta(seconds=30), + ), + activity_as_tool( + environment_activity, + start_to_close_timeout=timedelta(seconds=30), + ), + ], + ) + + @workflow.run + async def run(self, prompt: str) -> str: + result = await self.agent.invoke_async(prompt) + return str(result) + + +# @@@SNIPEND diff --git a/tests/conftest.py b/tests/conftest.py index 95294fb43..b857e0d96 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -41,7 +41,9 @@ def event_loop(): async def env(request) -> AsyncGenerator[WorkflowEnvironment, None]: env_type = request.config.getoption("--workflow-environment") if env_type == "local": - env = await WorkflowEnvironment.start_local() + env = await WorkflowEnvironment.start_local( + dev_server_download_version="v1.7.4-standalone-nexus-operations", + ) elif env_type == "time-skipping": env = await WorkflowEnvironment.start_time_skipping() else: diff --git a/tests/context_propagation/__init__.py b/tests/context_propagation/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/context_propagation/workflow_test.py b/tests/context_propagation/workflow_test.py new file mode 100644 index 000000000..8de2120d1 --- /dev/null +++ b/tests/context_propagation/workflow_test.py @@ -0,0 +1,46 @@ +import uuid + +from temporalio import activity +from temporalio.client import Client +from temporalio.exceptions import ApplicationError +from temporalio.worker import Worker + +from context_propagation.interceptor import ContextPropagationInterceptor +from context_propagation.shared import user_id +from context_propagation.workflows import SayHelloWorkflow + + +async def test_workflow_with_context_propagator(client: Client): + # Mock out the activity to assert the context value + @activity.defn(name="say_hello_activity") + async def say_hello_activity_mock(name: str) -> str: + try: + assert user_id.get() == "test-user" + except Exception as err: + raise ApplicationError("Assertion fail", non_retryable=True) from err + return f"Mock for {name}" + + # Replace interceptors in client + new_config = client.config() + new_config["interceptors"] = [ContextPropagationInterceptor()] + client = Client(**new_config) + task_queue = f"tq-{uuid.uuid4()}" + + async with Worker( + client, + task_queue=task_queue, + activities=[say_hello_activity_mock], + workflows=[SayHelloWorkflow], + ): + # Set the user during start/signal, but unset after + token = user_id.set("test-user") + handle = await client.start_workflow( + SayHelloWorkflow.run, + "some-name", + id=f"wf-{uuid.uuid4()}", + task_queue=task_queue, + ) + await handle.signal(SayHelloWorkflow.signal_complete) + user_id.reset(token) + result = await handle.result() + assert result == "Mock for some-name" diff --git a/tests/custom_metric/__init__.py b/tests/custom_metric/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/custom_metric/workflow_test.py b/tests/custom_metric/workflow_test.py new file mode 100644 index 000000000..bac5c07fd --- /dev/null +++ b/tests/custom_metric/workflow_test.py @@ -0,0 +1,34 @@ +import uuid +from concurrent.futures import ThreadPoolExecutor + +from temporalio import activity +from temporalio.client import Client +from temporalio.worker import Worker + +from custom_metric.worker import StartTwoActivitiesWorkflow + +_TASK_QUEUE = "custom-metric-task-queue" + +activity_counter = 0 + + +async def test_custom_metric_workflow(client: Client): + @activity.defn(name="print_and_sleep") + async def print_message_mock(): + global activity_counter + activity_counter += 1 + + async with Worker( + client, + task_queue=_TASK_QUEUE, + workflows=[StartTwoActivitiesWorkflow], + activities=[print_message_mock], + activity_executor=ThreadPoolExecutor(5), + ): + result = await client.execute_workflow( + StartTwoActivitiesWorkflow.run, + id=str(uuid.uuid4()), + task_queue=_TASK_QUEUE, + ) + assert result is None + assert activity_counter == 2 diff --git a/tests/deepagents_plugin/__init__.py b/tests/deepagents_plugin/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/deepagents_plugin/conftest.py b/tests/deepagents_plugin/conftest.py new file mode 100644 index 000000000..a40e6d163 --- /dev/null +++ b/tests/deepagents_plugin/conftest.py @@ -0,0 +1,54 @@ +"""Collection guard for the Deep Agents plugin tests. + +The plugin ships as the `temporalio[deepagents]` extra and requires +Python >= 3.11. It is merged to sdk-python main but the current PyPI +release (1.31.0) predates the merge, so the `deepagents` dependency group +cannot install it yet and the canonical `poe test` run +(`uv run --all-groups pytest`) does not have it. These test modules import +`temporalio.contrib.deepagents` at module load, which would raise +`ImportError` during collection whenever the plugin is absent (any +interpreter) or the interpreter is < 3.11 — failing the whole session. + +A module-level `pytest.mark.skipif` cannot help here: the mark is only read +*after* the module is imported, so the import error fires first. `collect_ignore` +is evaluated before any test module is imported, so it skips these files +cleanly when the plugin is unavailable while still running them once it is +installed on 3.11+ (interim: from sdk-python main per the suite README; +after the release that ships the extra: via the `deepagents` group, at which +point this guard becomes a no-op and the suite runs in CI). + +The guard performs a real (guarded) import rather than `find_spec`: the +subpackage can exist on disk while its runtime deps do not — e.g. a +plugin-carrying `temporalio` build installed without its extra deps — and +only an actual import proves the test modules can load. The version check +runs first so the import is never attempted on interpreters the plugin does +not support. When the guard is active, `pytest_report_header` announces it +so the non-collection is visible in CI output rather than silent. +""" + +import sys + +collect_ignore_glob: list[str] = [] + +_plugin_available = False +if sys.version_info >= (3, 11): + try: + import temporalio.contrib.deepagents # noqa: F401 + + _plugin_available = True + except ImportError: + _plugin_available = False + +if not _plugin_available: + collect_ignore_glob = ["*_test.py"] + + +def pytest_report_header(config) -> str | None: + """Make the guard visible in the pytest header instead of silent.""" + if collect_ignore_glob: + return ( + "deepagents_plugin: temporalio.contrib.deepagents not importable " + "on this interpreter/environment; sample tests NOT collected " + "(see tests/deepagents_plugin/conftest.py)" + ) + return None diff --git a/tests/deepagents_plugin/continue_as_new_test.py b/tests/deepagents_plugin/continue_as_new_test.py new file mode 100644 index 000000000..0a2420d44 --- /dev/null +++ b/tests/deepagents_plugin/continue_as_new_test.py @@ -0,0 +1,116 @@ +import uuid +from typing import Any + +from temporalio import workflow +from temporalio.client import Client, WorkflowExecutionStatus +from temporalio.contrib.deepagents import DeepAgentsPlugin, run_deep_agent +from temporalio.contrib.deepagents.testing import mock_model_provider +from temporalio.worker import Worker + +from deepagents_plugin.continue_as_new.workflow import LongResearchAgent + + +async def test_continue_as_new(client: Client) -> None: + # The sample defers to the server-suggested mode, which a short scripted + # run never triggers, so this exercises the run_deep_agent contract on the + # real sample workflow without a continue-as-new. The first arg is the + # messages mapping (the run_deep_agent contract), not a bare string. + plugin = DeepAgentsPlugin( + model_provider=mock_model_provider(["The research is complete: done."]), + ) + task_queue = f"deepagents-continue-as-new-{uuid.uuid4()}" + + config = client.config() + config["plugins"] = [*config["plugins"], plugin] + client = Client(**config) + + async with Worker( + client, + task_queue=task_queue, + workflows=[LongResearchAgent], + max_cached_workflows=0, + ): + result = await client.execute_workflow( + LongResearchAgent.run, + {"messages": [{"role": "user", "content": "Summarize durable execution."}]}, + id=f"deepagents-continue-as-new-{uuid.uuid4()}", + task_queue=task_queue, + ) + + assert "complete" in result + + +class _ScriptedAgent: + """A stand-in compiled agent that appends one message per turn and keeps a + todo pending until the conversation grows, forcing continue-as-new. + + It is not a LangChain object — it just satisfies the ``ainvoke`` shape that + ``run_deep_agent`` drives, so the continue-as-new path is exercised without a + model provider or the LangChain import tree. + """ + + async def ainvoke(self, input: Any) -> dict: + messages = list(input.get("messages", [])) if isinstance(input, dict) else [] + messages = [*messages, "step"] + done = len(messages) >= 3 + return { + "messages": messages, + "todos": [ + {"content": "research", "status": "completed" if done else "pending"} + ], + } + + +@workflow.defn +class _ContinueAsNewProbe: + @workflow.run + async def run(self, input: dict, state_snapshot: dict | None = None) -> dict: + # Threshold of 1 continues-as-new as soon as there is pending work. + # ``input`` is threaded straight through, exactly as LongResearchAgent + # does — re-wrapping it would nest a dict where a message is expected and + # corrupt the carried conversation. + return await run_deep_agent( + _ScriptedAgent(), + input, + continue_as_new_after=1, + state_snapshot=state_snapshot, + ) + + +async def test_continue_as_new_carries_conversation(client: Client) -> None: + # Low threshold + persistent pending work actually triggers + # workflow.continue_as_new, so this guards the scenario's headline feature: + # the carried conversation must survive the boundary well-formed. + plugin = DeepAgentsPlugin() + task_queue = f"deepagents-continue-as-new-probe-{uuid.uuid4()}" + + config = client.config() + config["plugins"] = [*config["plugins"], plugin] + client = Client(**config) + + async with Worker( + client, + task_queue=task_queue, + workflows=[_ContinueAsNewProbe], + max_cached_workflows=0, + ): + handle = await client.start_workflow( + _ContinueAsNewProbe.run, + {"messages": ["start"]}, + id=f"deepagents-continue-as-new-probe-{uuid.uuid4()}", + task_queue=task_queue, + ) + result = await handle.result() + + # Reaching >= 3 messages is only possible if each pre-continue-as-new + # snapshot was carried into the continued run and merged. Every carried + # message stays a plain string — no nested-dict corruption from re-wrapping + # the input across the boundary. + assert len(result["messages"]) >= 3, result + assert all(isinstance(m, str) for m in result["messages"]), result + assert result["todos"][0]["status"] == "completed" + # The boundary really happened: a loop-in-one-run implementation would + # produce the same final result, so pin the FIRST run's close event. + first = client.get_workflow_handle(handle.id, run_id=handle.first_execution_run_id) + desc = await first.describe() + assert desc.status == WorkflowExecutionStatus.CONTINUED_AS_NEW, desc.status diff --git a/tests/deepagents_plugin/filesystem_backend_test.py b/tests/deepagents_plugin/filesystem_backend_test.py new file mode 100644 index 000000000..f7e8712de --- /dev/null +++ b/tests/deepagents_plugin/filesystem_backend_test.py @@ -0,0 +1,68 @@ +import uuid + +from langchain_core.messages import AIMessage +from temporalio.client import Client +from temporalio.contrib.deepagents import DeepAgentsPlugin +from temporalio.contrib.deepagents.testing import mock_model_provider +from temporalio.worker import Worker + +from deepagents_plugin.filesystem_backend.workflow import FilesystemAgent +from tests.deepagents_plugin.helpers import BACKEND_OP, count_scheduled_activities + + +async def test_filesystem_backend(client: Client, tmp_path) -> None: + # Script the agent's built-in file tools: write the note, read it back, then + # report. Each file op crosses the activity boundary via TemporalBackend, so + # the real disk write happens in an activity, not in workflow code. + write_turn = AIMessage( + content="", + tool_calls=[ + { + "name": "write_file", + "args": {"file_path": "/notes.txt", "content": "hello"}, + "id": "call-write", + } + ], + ) + read_turn = AIMessage( + content="", + tool_calls=[ + { + "name": "read_file", + "args": {"file_path": "/notes.txt"}, + "id": "call-read", + } + ], + ) + final = AIMessage(content="The note says: hello") + plugin = DeepAgentsPlugin( + model_provider=mock_model_provider([write_turn, read_turn, final]), + ) + task_queue = f"deepagents-filesystem-backend-{uuid.uuid4()}" + + config = client.config() + config["plugins"] = [*config["plugins"], plugin] + client = Client(**config) + + async with Worker( + client, + task_queue=task_queue, + workflows=[FilesystemAgent], + max_cached_workflows=0, + ): + handle = await client.start_workflow( + FilesystemAgent.run, + args=[str(tmp_path), "Write 'hello' to notes.txt and read it back."], + id=f"deepagents-filesystem-backend-{uuid.uuid4()}", + task_queue=task_queue, + ) + result = await handle.result() + + assert "hello" in result + # The write really landed on disk... + assert (tmp_path / "notes.txt").read_text() == "hello" + # ...and the file ops really crossed the activity boundary (write + read as + # backend_op activities) — the on-disk assert alone cannot distinguish an + # in-workflow write in this single-process test. + counts = await count_scheduled_activities(handle) + assert counts[BACKEND_OP] >= 2, counts diff --git a/tests/deepagents_plugin/hello_world_test.py b/tests/deepagents_plugin/hello_world_test.py new file mode 100644 index 000000000..4c6c46a0a --- /dev/null +++ b/tests/deepagents_plugin/hello_world_test.py @@ -0,0 +1,44 @@ +import uuid + +from temporalio.client import Client +from temporalio.contrib.deepagents import DeepAgentsPlugin +from temporalio.contrib.deepagents.testing import mock_model_provider +from temporalio.worker import Worker + +from deepagents_plugin.hello_world.workflow import HelloWorldAgent +from tests.deepagents_plugin.helpers import INVOKE_MODEL, count_scheduled_activities + + +async def test_hello_world(client: Client) -> None: + # A scripted model so the test runs offline (no ANTHROPIC_API_KEY needed). + plugin = DeepAgentsPlugin( + model_provider=mock_model_provider( + ["Temporal is a durable execution platform."] + ), + ) + task_queue = f"deepagents-hello-world-{uuid.uuid4()}" + + config = client.config() + config["plugins"] = [*config["plugins"], plugin] + client = Client(**config) + + async with Worker( + client, + task_queue=task_queue, + workflows=[HelloWorldAgent], + max_cached_workflows=0, + ): + handle = await client.start_workflow( + HelloWorldAgent.run, + "What is Temporal?", + id=f"deepagents-hello-world-{uuid.uuid4()}", + task_queue=task_queue, + ) + result = await handle.result() + + assert "durable" in result + # The model call really ran as a `deepagents.invoke_model` activity — the + # durability the plugin exists to provide, which a content-only assertion + # cannot distinguish from an in-workflow call. + counts = await count_scheduled_activities(handle) + assert counts[INVOKE_MODEL] >= 1, counts diff --git a/tests/deepagents_plugin/helpers.py b/tests/deepagents_plugin/helpers.py new file mode 100644 index 000000000..7d2f8e4ae --- /dev/null +++ b/tests/deepagents_plugin/helpers.py @@ -0,0 +1,27 @@ +"""Shared helpers for the Deep Agents plugin sample tests.""" + +from collections import Counter + +from temporalio.api.enums.v1 import EventType +from temporalio.client import WorkflowHandle + +INVOKE_MODEL = "deepagents.invoke_model" +INVOKE_MODEL_STREAMING = "deepagents.invoke_model_streaming" +INVOKE_TOOL = "deepagents.invoke_tool" +BACKEND_OP = "deepagents.backend_op" + + +async def count_scheduled_activities(handle: WorkflowHandle) -> Counter: + """Count ``ActivityTaskScheduled`` events in the history by activity type. + + The plugin's whole point is that model/tool/backend calls run as activities; + asserting on the history proves that routing actually happened, where a + content-only assertion would still pass if a call silently ran in-workflow. + """ + counts: Counter = Counter() + async for event in handle.fetch_history_events(): + if event.event_type == EventType.EVENT_TYPE_ACTIVITY_TASK_SCHEDULED: + counts[ + event.activity_task_scheduled_event_attributes.activity_type.name + ] += 1 + return counts diff --git a/tests/deepagents_plugin/human_in_the_loop_test.py b/tests/deepagents_plugin/human_in_the_loop_test.py new file mode 100644 index 000000000..bf137d359 --- /dev/null +++ b/tests/deepagents_plugin/human_in_the_loop_test.py @@ -0,0 +1,144 @@ +import asyncio +import uuid + +import pytest +from langchain_core.messages import AIMessage +from temporalio.client import Client, WorkflowUpdateFailedError +from temporalio.contrib.deepagents import DeepAgentsPlugin +from temporalio.contrib.deepagents.testing import mock_model_provider +from temporalio.worker import Worker + +from deepagents_plugin.human_in_the_loop.workflow import HumanInTheLoopAgent +from tests.deepagents_plugin.helpers import INVOKE_TOOL, count_scheduled_activities + + +async def test_human_in_the_loop_approve(client: Client) -> None: + # First model turn asks to book the trip (the guarded tool → interrupt); + # after the human approves, the second turn reports the booking. + ask = AIMessage( + content="", + tool_calls=[{"name": "book_trip", "args": {"city": "Rome"}, "id": "c1"}], + ) + done = AIMessage(content="Booked a trip to Rome.") + plugin = DeepAgentsPlugin(model_provider=mock_model_provider([ask, done])) + task_queue = f"deepagents-human-in-the-loop-{uuid.uuid4()}" + + config = client.config() + config["plugins"] = [*config["plugins"], plugin] + client = Client(**config) + + async with Worker( + client, + task_queue=task_queue, + workflows=[HumanInTheLoopAgent], + max_cached_workflows=0, + ): + handle = await client.start_workflow( + HumanInTheLoopAgent.run, + "Rome", + id=f"deepagents-human-in-the-loop-{uuid.uuid4()}", + task_queue=task_queue, + ) + + # Wait for the agent to pause (surfaced via the query), then approve via + # an update. Bounded poll so a regression fails fast. + for _ in range(100): + if await handle.query(HumanInTheLoopAgent.pending_approval) is not None: + break + await asyncio.sleep(0.1) + else: + raise AssertionError("workflow never surfaced the pending approval") + + await handle.execute_update(HumanInTheLoopAgent.resume, "approve") + result = await handle.result() + + assert "Rome" in result + # The approved book_trip really executed as an invoke_tool activity after + # the resume — not just claimed by the scripted final message. + counts = await count_scheduled_activities(handle) + assert counts[INVOKE_TOOL] == 1, counts + + +async def test_resume_validator_rejects_invalid_decision(client: Client) -> None: + # The workflow's documented contract: the update validator keeps decisions + # other than approve/reject out of workflow history entirely. + ask = AIMessage( + content="", + tool_calls=[{"name": "book_trip", "args": {"city": "Rome"}, "id": "c1"}], + ) + done = AIMessage(content="Booked a trip to Rome.") + plugin = DeepAgentsPlugin(model_provider=mock_model_provider([ask, done])) + task_queue = f"deepagents-hitl-invalid-{uuid.uuid4()}" + + config = client.config() + config["plugins"] = [*config["plugins"], plugin] + client = Client(**config) + + async with Worker( + client, + task_queue=task_queue, + workflows=[HumanInTheLoopAgent], + max_cached_workflows=0, + ): + handle = await client.start_workflow( + HumanInTheLoopAgent.run, + "Rome", + id=f"deepagents-hitl-invalid-{uuid.uuid4()}", + task_queue=task_queue, + ) + for _ in range(100): + if await handle.query(HumanInTheLoopAgent.pending_approval) is not None: + break + await asyncio.sleep(0.1) + else: + raise AssertionError("workflow never surfaced the pending approval") + + with pytest.raises(WorkflowUpdateFailedError): + await handle.execute_update(HumanInTheLoopAgent.resume, "maybe") + + # The workflow is still healthy and resumable after the rejection. + await handle.execute_update(HumanInTheLoopAgent.resume, "approve") + assert "Rome" in await handle.result() + + +async def test_human_in_the_loop_reject(client: Client) -> None: + # The reject path: the guarded tool must never execute, and the agent + # reports back based on the rejection. + ask = AIMessage( + content="", + tool_calls=[{"name": "book_trip", "args": {"city": "Rome"}, "id": "c1"}], + ) + final = AIMessage(content="Understood — not booking the trip.") + plugin = DeepAgentsPlugin(model_provider=mock_model_provider([ask, final])) + task_queue = f"deepagents-hitl-reject-{uuid.uuid4()}" + + config = client.config() + config["plugins"] = [*config["plugins"], plugin] + client = Client(**config) + + async with Worker( + client, + task_queue=task_queue, + workflows=[HumanInTheLoopAgent], + max_cached_workflows=0, + ): + handle = await client.start_workflow( + HumanInTheLoopAgent.run, + "Rome", + id=f"deepagents-hitl-reject-{uuid.uuid4()}", + task_queue=task_queue, + ) + for _ in range(100): + if await handle.query(HumanInTheLoopAgent.pending_approval) is not None: + break + await asyncio.sleep(0.1) + else: + raise AssertionError("workflow never surfaced the pending approval") + + await handle.execute_update(HumanInTheLoopAgent.resume, "reject") + result = await handle.result() + + assert "not booking" in result.lower() + counts = await count_scheduled_activities(handle) + # The rejected tool never ran as an activity. + assert counts[INVOKE_TOOL] == 0, counts diff --git a/tests/deepagents_plugin/react_agent_test.py b/tests/deepagents_plugin/react_agent_test.py new file mode 100644 index 000000000..a5b52ed90 --- /dev/null +++ b/tests/deepagents_plugin/react_agent_test.py @@ -0,0 +1,56 @@ +import uuid + +from langchain_core.messages import AIMessage +from temporalio.client import Client +from temporalio.contrib.deepagents import DeepAgentsPlugin +from temporalio.contrib.deepagents.testing import mock_model_provider +from temporalio.worker import Worker + +from deepagents_plugin.react_agent.workflow import ReactAgent, get_weather +from tests.deepagents_plugin.helpers import INVOKE_TOOL, count_scheduled_activities + + +async def test_react_agent(client: Client) -> None: + # Script the model through the tool loop: call get_weather, then web_search, + # then answer. The tools run for real (get_weather as an activity, web_search + # wrapped via tool_as_activity); only the model turns are scripted. + call_weather = AIMessage( + content="", + tool_calls=[{"name": "get_weather", "args": {"city": "Paris"}, "id": "c1"}], + ) + call_search = AIMessage( + content="", + tool_calls=[{"name": "web_search", "args": {"query": "Temporal"}, "id": "c2"}], + ) + final = AIMessage(content="It is sunny in Paris and Temporal keeps code durable.") + plugin = DeepAgentsPlugin( + model_provider=mock_model_provider([call_weather, call_search, final]), + ) + task_queue = f"deepagents-react-agent-{uuid.uuid4()}" + + config = client.config() + config["plugins"] = [*config["plugins"], plugin] + client = Client(**config) + + async with Worker( + client, + task_queue=task_queue, + workflows=[ReactAgent], + activities=[get_weather], + max_cached_workflows=0, + ): + handle = await client.start_workflow( + ReactAgent.run, + "What's the weather in Paris, and what is Temporal?", + id=f"deepagents-react-agent-{uuid.uuid4()}", + task_queue=task_queue, + ) + result = await handle.result() + + assert "durable" in result + # Both tool seams really crossed the activity boundary: get_weather ran as + # the user's own activity (activity_as_tool) and web_search ran through the + # plugin's invoke_tool activity (tool_as_activity). + counts = await count_scheduled_activities(handle) + assert counts["get_weather"] == 1, counts + assert counts[INVOKE_TOOL] == 1, counts diff --git a/tests/deepagents_plugin/streaming_test.py b/tests/deepagents_plugin/streaming_test.py new file mode 100644 index 000000000..5cd82ffe9 --- /dev/null +++ b/tests/deepagents_plugin/streaming_test.py @@ -0,0 +1,82 @@ +import asyncio +import uuid +from datetime import timedelta + +from langchain_core.load import load +from temporalio.client import Client +from temporalio.contrib.deepagents import DeepAgentsPlugin +from temporalio.contrib.deepagents.testing import mock_model_provider +from temporalio.contrib.workflow_streams import WorkflowStreamClient +from temporalio.worker import Worker + +from deepagents_plugin.streaming.workflow import STREAMING_TOPIC, StreamingWorkflow +from tests.deepagents_plugin.helpers import ( + INVOKE_MODEL, + INVOKE_MODEL_STREAMING, + count_scheduled_activities, +) + + +async def test_streaming(client: Client) -> None: + # streaming_topic=... flips model dispatch to the streaming activity, which + # publishes chunks to the topic while still returning the aggregated message + # as the durable result. An in-test subscriber collects the chunks. + expected = "Streamed answer." + plugin = DeepAgentsPlugin( + model_provider=mock_model_provider([expected]), + streaming_topic=STREAMING_TOPIC, + ) + task_queue = f"deepagents-streaming-{uuid.uuid4()}" + + config = client.config() + config["plugins"] = [*config["plugins"], plugin] + client = Client(**config) + + async with Worker( + client, + task_queue=task_queue, + workflows=[StreamingWorkflow], + max_cached_workflows=0, + ): + workflow_id = f"deepagents-streaming-{uuid.uuid4()}" + handle = await client.start_workflow( + StreamingWorkflow.run, + "Write a sentence about durable execution.", + id=workflow_id, + task_queue=task_queue, + ) + + chunks: list[str] = [] + + async def consume() -> None: + stream = WorkflowStreamClient.create(client, workflow_id) + async for item in stream.subscribe( + [STREAMING_TOPIC], + from_offset=0, + result_type=dict, + poll_cooldown=timedelta(milliseconds=10), + ): + text = getattr(load(item.data), "content", "") + if text: + chunks.append(text) + # The subscription is open-ended; return once the full answer + # has been observed. + if expected in "".join(chunks): + return + + consume_task = asyncio.create_task(consume()) + result = await handle.result() + # No fixed sleep: the subscriber exits as soon as it has seen the + # streamed content; the timeout only bounds a regression. + await asyncio.wait_for(consume_task, timeout=10.0) + + # The durable result matches the non-streaming path... + assert expected in result + # ...and the same content was streamed out to the subscriber. + assert chunks, "expected at least one streamed chunk" + assert expected in "".join(chunks) + # Dispatch really flipped to the streaming activity: with streaming_topic + # set, the model call ran as invoke_model_streaming, not invoke_model. + counts = await count_scheduled_activities(handle) + assert counts[INVOKE_MODEL_STREAMING] == 1, counts + assert counts[INVOKE_MODEL] == 0, counts diff --git a/tests/deepagents_plugin/subagents_test.py b/tests/deepagents_plugin/subagents_test.py new file mode 100644 index 000000000..f76a582e8 --- /dev/null +++ b/tests/deepagents_plugin/subagents_test.py @@ -0,0 +1,69 @@ +import uuid + +from langchain_core.messages import AIMessage +from temporalio.client import Client +from temporalio.contrib.deepagents import DeepAgentsPlugin +from temporalio.contrib.deepagents.testing import mock_model_provider +from temporalio.worker import Worker + +from deepagents_plugin.subagents.workflow import SubagentsWorkflow +from tests.deepagents_plugin.helpers import INVOKE_MODEL, count_scheduled_activities + + +async def test_subagents(client: Client) -> None: + # Script the delegation end-to-end: the coordinator calls the built-in + # `task` tool, the researcher sub-agent (sharing the provider's response + # queue) reports findings, and the coordinator synthesizes them. This + # exercises the scenario's headline — durability propagating across the + # agent tree — rather than letting the coordinator answer in one turn. + delegate = AIMessage( + content="", + tool_calls=[ + { + "name": "task", + "args": { + "description": ( + "Research how Temporal handles workflow retries and " + "report your findings." + ), + "subagent_type": "researcher", + }, + "id": "call-task", + } + ], + ) + findings = AIMessage( + content="Findings: retries are governed by per-activity RetryPolicy." + ) + final = AIMessage( + content="Coordinated research answer: Temporal retries are policy-driven." + ) + plugin = DeepAgentsPlugin( + model_provider=mock_model_provider([delegate, findings, final]), + ) + task_queue = f"deepagents-subagents-{uuid.uuid4()}" + + config = client.config() + config["plugins"] = [*config["plugins"], plugin] + client = Client(**config) + + async with Worker( + client, + task_queue=task_queue, + workflows=[SubagentsWorkflow], + max_cached_workflows=0, + ): + handle = await client.start_workflow( + SubagentsWorkflow.run, + "Investigate durable execution and report back.", + id=f"deepagents-subagents-{uuid.uuid4()}", + task_queue=task_queue, + ) + result = await handle.result() + + assert "Coordinated" in result + # Three model activities prove the delegation really ran: the coordinator's + # delegating turn, the researcher's turn, and the synthesis turn — the + # sub-agent's model call became an activity with no per-sub-agent wiring. + counts = await count_scheduled_activities(handle) + assert counts[INVOKE_MODEL] >= 3, counts diff --git a/tests/eager_wf_start/__init__.py b/tests/eager_wf_start/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/eager_wf_start/workflow_test.py b/tests/eager_wf_start/workflow_test.py new file mode 100644 index 000000000..1080f5bcd --- /dev/null +++ b/tests/eager_wf_start/workflow_test.py @@ -0,0 +1,29 @@ +import uuid + +from temporalio.client import Client +from temporalio.worker import Worker + +from eager_wf_start.activities import greeting +from eager_wf_start.workflows import EagerWorkflow + + +async def test_eager_wf_start(client: Client): + task_queue_name = str(uuid.uuid4()) + + async with Worker( + client, + task_queue=task_queue_name, + workflows=[EagerWorkflow], + activities=[greeting], + ): + handle = await client.start_workflow( + EagerWorkflow.run, + "Temporal", + id=f"workflow-{uuid.uuid4()}", + task_queue=task_queue_name, + request_eager_start=True, + ) + print("HANDLE", handle.__temporal_eagerly_started) + assert handle.__temporal_eagerly_started + result = await handle.result() + assert result == "Hello Temporal!" diff --git a/tests/external_storage_redis/__init__.py b/tests/external_storage_redis/__init__.py new file mode 100644 index 000000000..0d45e2f52 --- /dev/null +++ b/tests/external_storage_redis/__init__.py @@ -0,0 +1 @@ +"""Tests for the Redis external storage sample.""" diff --git a/tests/external_storage_redis/conftest.py b/tests/external_storage_redis/conftest.py new file mode 100644 index 000000000..b6736ab3a --- /dev/null +++ b/tests/external_storage_redis/conftest.py @@ -0,0 +1,39 @@ +"""Shared fixtures for Redis external storage tests.""" + +from __future__ import annotations + +from collections.abc import AsyncIterator +from typing import TYPE_CHECKING + +import fakeredis.aioredis +import pytest +import pytest_asyncio + +from external_storage_redis import RedisStorageDriverClient +from external_storage_redis.redis_asyncio import new_redis_asyncio_client + +if TYPE_CHECKING: + from redis.asyncio.client import Redis + +KEY_PREFIX = "test:payloads" + + +@pytest_asyncio.fixture +async def redis_asyncio_client() -> AsyncIterator[Redis]: + """Yield a fake Redis client with an empty database for each test.""" + client = fakeredis.aioredis.FakeRedis(decode_responses=False) + await client.flushdb() + try: + yield client + finally: + aclose = getattr(client, "aclose", None) + if aclose is not None: + await aclose() + else: + await client.close() + + +@pytest.fixture +def driver_client(redis_asyncio_client: Redis) -> RedisStorageDriverClient: + """Wrap the redis.asyncio client in a RedisStorageDriverClient adapter.""" + return new_redis_asyncio_client(redis_asyncio_client) diff --git a/tests/external_storage_redis/test_redis.py b/tests/external_storage_redis/test_redis.py new file mode 100644 index 000000000..33b871a27 --- /dev/null +++ b/tests/external_storage_redis/test_redis.py @@ -0,0 +1,745 @@ +"""Unit tests for RedisStorageDriver using fakeredis.""" + +from __future__ import annotations + +import asyncio +import hashlib +import subprocess +import sys +from collections.abc import Callable, Coroutine +from datetime import timedelta +from functools import wraps +from typing import Any +from unittest.mock import MagicMock + +import fakeredis.aioredis +import pytest +from temporalio.api.common.v1 import Payload +from temporalio.converter import ( + JSONPlainPayloadConverter, + StorageDriverActivityInfo, + StorageDriverClaim, + StorageDriverRetrieveContext, + StorageDriverStoreContext, + StorageDriverWorkflowInfo, +) +from temporalio.exceptions import ApplicationError + +from external_storage_redis import RedisStorageDriver, RedisStorageDriverClient +from external_storage_redis.redis_asyncio import new_redis_asyncio_client +from tests.external_storage_redis.conftest import KEY_PREFIX + +_CONVERTER = JSONPlainPayloadConverter() + + +def test_redis_asyncio_adapter_import_is_workflow_safe() -> None: + completed = subprocess.run( + [ + sys.executable, + "-c", + ( + "import sys; " + "import external_storage_redis.redis_asyncio; " + "print(any(name.startswith('redis.asyncio') for name in sys.modules))" + ), + ], + check=True, + capture_output=True, + text=True, + ) + assert completed.stdout.strip() == "False" + + +def make_payload(value: str = "hello") -> Payload: + payload = _CONVERTER.to_payload(value) + assert payload is not None + return payload + + +def make_store_context( + target: StorageDriverActivityInfo | StorageDriverWorkflowInfo | None = None, +) -> StorageDriverStoreContext: + return StorageDriverStoreContext(target=target) + + +def make_workflow_context( + namespace: str = "my-namespace", + workflow_id: str = "my-workflow", + workflow_type: str | None = None, + run_id: str | None = None, +) -> StorageDriverStoreContext: + return make_store_context( + target=StorageDriverWorkflowInfo( + id=workflow_id, + type=workflow_type, + run_id=run_id, + namespace=namespace, + ) + ) + + +def make_activity_context( + namespace: str = "my-namespace", + activity_id: str | None = "my-activity", + activity_type: str | None = None, + run_id: str | None = None, +) -> StorageDriverStoreContext: + return make_store_context( + target=StorageDriverActivityInfo( + id=activity_id, + type=activity_type, + run_id=run_id, + namespace=namespace, + ) + ) + + +async def _list_keys(redis_asyncio_client: Any) -> list[str]: + raw_keys = await redis_asyncio_client.keys("*") + return sorted(key.decode() if isinstance(key, bytes) else key for key in raw_keys) + + +async def _wait_for_key_absent(redis_asyncio_client: Any, key: str) -> None: + async def _poll() -> None: + while await redis_asyncio_client.exists(key): + await asyncio.sleep(0.01) + + await asyncio.wait_for(_poll(), timeout=2) + + +class CountingDriverClient(RedisStorageDriverClient): + """RedisStorageDriverClient wrapper that counts calls.""" + + def __init__(self, delegate: RedisStorageDriverClient) -> None: + self._delegate = delegate + self.get_count = 0 + self.set_if_absent_count = 0 + self.insert_count = 0 + + async def get(self, *, key: str) -> bytes | None: + self.get_count += 1 + return await self._delegate.get(key=key) + + async def set_if_absent( + self, + *, + key: str, + data: bytes, + ttl: timedelta | None = None, + ) -> bool: + self.set_if_absent_count += 1 + inserted = await self._delegate.set_if_absent(key=key, data=data, ttl=ttl) + if inserted: + self.insert_count += 1 + return inserted + + +class FailOnceDriverClient(RedisStorageDriverClient): + """RedisStorageDriverClient wrapper that fails one call then blocks.""" + + def __init__(self, delegate: RedisStorageDriverClient, fail_on: str) -> None: + self._delegate = delegate + self._fail_on = fail_on + self._call_count = 0 + self.cancelled: list[bool] = [] + + async def _maybe_fail(self) -> None: + self._call_count += 1 + if self._call_count == 1: + raise ConnectionError("Redis connection lost") + try: + await asyncio.sleep(60) + except asyncio.CancelledError: + self.cancelled.append(True) + raise + + async def get(self, *, key: str) -> bytes | None: + if self._fail_on == "get": + await self._maybe_fail() + return await self._delegate.get(key=key) + + async def set_if_absent( + self, + *, + key: str, + data: bytes, + ttl: timedelta | None = None, + ) -> bool: + if self._fail_on == "set_if_absent": + await self._maybe_fail() + return await self._delegate.set_if_absent(key=key, data=data, ttl=ttl) + + +class _AsyncBarrier: + """Minimal asyncio.Barrier equivalent for Python <3.11.""" + + def __init__(self, parties: int) -> None: + self._parties = parties + self._count = 0 + self._event = asyncio.Event() + + async def wait(self) -> None: + self._count += 1 + if self._count >= self._parties: + self._event.set() + else: + await self._event.wait() + + +def _barrier_wrapper( + fn: Callable[..., Coroutine[Any, Any, Any]], barrier: _AsyncBarrier +) -> Callable[..., Coroutine[Any, Any, Any]]: + """Wrap an async method to wait at a barrier before proceeding.""" + + @wraps(fn) + async def wrapper(*args: Any, **kwargs: Any) -> Any: + await asyncio.wait_for(barrier.wait(), timeout=5) + return await fn(*args, **kwargs) + + return wrapper + + +@pytest.fixture +def counting_driver_client( + driver_client: RedisStorageDriverClient, +) -> CountingDriverClient: + """Wrap the driver client in a counting decorator.""" + return CountingDriverClient(driver_client) + + +class TestRedisStorageDriverInit: + def test_default_name(self) -> None: + driver = RedisStorageDriver(client=MagicMock(spec=RedisStorageDriverClient)) + assert driver.name() == "redis" + + def test_custom_name(self) -> None: + driver = RedisStorageDriver( + client=MagicMock(spec=RedisStorageDriverClient), + driver_name="my-redis", + ) + assert driver.name() == "my-redis" + + def test_type(self) -> None: + driver = RedisStorageDriver(client=MagicMock(spec=RedisStorageDriverClient)) + assert driver.type() == "redis" + + def test_ttl_zero_raises(self) -> None: + with pytest.raises(ValueError, match="ttl must be greater than zero"): + RedisStorageDriver( + client=MagicMock(spec=RedisStorageDriverClient), + ttl=timedelta(0), + ) + + def test_ttl_negative_raises(self) -> None: + with pytest.raises(ValueError, match="ttl must be greater than zero"): + RedisStorageDriver( + client=MagicMock(spec=RedisStorageDriverClient), + ttl=timedelta(seconds=-1), + ) + + def test_max_payload_size_zero_raises(self) -> None: + with pytest.raises( + ValueError, match="max_payload_size must be greater than zero" + ): + RedisStorageDriver( + client=MagicMock(spec=RedisStorageDriverClient), + max_payload_size=0, + ) + + def test_max_payload_size_negative_raises(self) -> None: + with pytest.raises( + ValueError, match="max_payload_size must be greater than zero" + ): + RedisStorageDriver( + client=MagicMock(spec=RedisStorageDriverClient), + max_payload_size=-1, + ) + + +class TestRedisStorageDriverKeyConstruction: + async def test_key_context_none( + self, driver_client: RedisStorageDriverClient + ) -> None: + driver = RedisStorageDriver(client=driver_client, key_prefix=KEY_PREFIX) + payload = make_payload() + [claim] = await driver.store(make_store_context(), [payload]) + expected_hash = hashlib.sha256(payload.SerializeToString()).hexdigest() + assert claim.claim_data["key"] == f"{KEY_PREFIX}:v0:d:sha256:{expected_hash}" + + async def test_key_context_workflow( + self, driver_client: RedisStorageDriverClient + ) -> None: + driver = RedisStorageDriver(client=driver_client, key_prefix=KEY_PREFIX) + payload = make_payload() + ctx = make_workflow_context(namespace="ns1", workflow_id="wf1") + [claim] = await driver.store(ctx, [payload]) + expected_hash = hashlib.sha256(payload.SerializeToString()).hexdigest() + assert ( + claim.claim_data["key"] + == f"{KEY_PREFIX}:v0:ns:ns1:wt:null:wi:wf1:ri:null:d:sha256:{expected_hash}" + ) + + async def test_key_context_workflow_with_type_and_run_id( + self, driver_client: RedisStorageDriverClient + ) -> None: + driver = RedisStorageDriver(client=driver_client, key_prefix=KEY_PREFIX) + payload = make_payload() + ctx = make_workflow_context( + namespace="ns1", + workflow_id="wf1", + workflow_type="MyWorkflow", + run_id="run-abc", + ) + [claim] = await driver.store(ctx, [payload]) + expected_hash = hashlib.sha256(payload.SerializeToString()).hexdigest() + assert ( + claim.claim_data["key"] + == f"{KEY_PREFIX}:v0:ns:ns1:wt:MyWorkflow:wi:wf1:ri:run-abc:d:sha256:{expected_hash}" + ) + + async def test_key_context_activity( + self, driver_client: RedisStorageDriverClient + ) -> None: + driver = RedisStorageDriver(client=driver_client, key_prefix=KEY_PREFIX) + payload = make_payload() + ctx = make_activity_context(namespace="ns1", activity_id="act1") + [claim] = await driver.store(ctx, [payload]) + expected_hash = hashlib.sha256(payload.SerializeToString()).hexdigest() + assert ( + claim.claim_data["key"] + == f"{KEY_PREFIX}:v0:ns:ns1:at:null:ai:act1:ri:null:d:sha256:{expected_hash}" + ) + + async def test_key_context_activity_with_type_and_run_id( + self, driver_client: RedisStorageDriverClient + ) -> None: + driver = RedisStorageDriver(client=driver_client, key_prefix=KEY_PREFIX) + payload = make_payload() + ctx = make_activity_context( + namespace="ns1", + activity_id="act1", + activity_type="MyActivity", + run_id="run-abc", + ) + [claim] = await driver.store(ctx, [payload]) + expected_hash = hashlib.sha256(payload.SerializeToString()).hexdigest() + assert ( + claim.claim_data["key"] + == f"{KEY_PREFIX}:v0:ns:ns1:at:MyActivity:ai:act1:ri:run-abc:d:sha256:{expected_hash}" + ) + + async def test_key_preserves_case( + self, driver_client: RedisStorageDriverClient + ) -> None: + driver = RedisStorageDriver(client=driver_client, key_prefix=KEY_PREFIX) + payload = make_payload() + ctx = make_workflow_context(namespace="MyNamespace", workflow_id="MyWorkflow") + [claim] = await driver.store(ctx, [payload]) + key = claim.claim_data["key"] + assert "MyNamespace" in key + assert "MyWorkflow" in key + + async def test_key_urlencodes_workflow_id_with_slashes( + self, driver_client: RedisStorageDriverClient + ) -> None: + driver = RedisStorageDriver(client=driver_client, key_prefix=KEY_PREFIX) + payload = make_payload() + ctx = make_workflow_context(namespace="ns1", workflow_id="order/123/v2") + [claim] = await driver.store(ctx, [payload]) + expected_hash = hashlib.sha256(payload.SerializeToString()).hexdigest() + assert ( + claim.claim_data["key"] + == f"{KEY_PREFIX}:v0:ns:ns1:wt:null:wi:order%2F123%2Fv2:ri:null:d:sha256:{expected_hash}" + ) + + async def test_key_urlencodes_workflow_id_with_special_chars( + self, driver_client: RedisStorageDriverClient + ) -> None: + driver = RedisStorageDriver(client=driver_client, key_prefix=KEY_PREFIX) + payload = make_payload() + ctx = make_workflow_context(namespace="ns1", workflow_id="wf#1 &foo=bar") + [claim] = await driver.store(ctx, [payload]) + expected_hash = hashlib.sha256(payload.SerializeToString()).hexdigest() + assert ( + claim.claim_data["key"] + == f"{KEY_PREFIX}:v0:ns:ns1:wt:null:wi:wf%231%20%26foo%3Dbar:ri:null:d:sha256:{expected_hash}" + ) + + async def test_key_urlencodes_activity_id( + self, driver_client: RedisStorageDriverClient + ) -> None: + driver = RedisStorageDriver(client=driver_client, key_prefix=KEY_PREFIX) + payload = make_payload() + ctx = make_activity_context(namespace="ns1", activity_id="act/1#2") + [claim] = await driver.store(ctx, [payload]) + expected_hash = hashlib.sha256(payload.SerializeToString()).hexdigest() + assert ( + claim.claim_data["key"] + == f"{KEY_PREFIX}:v0:ns:ns1:at:null:ai:act%2F1%232:ri:null:d:sha256:{expected_hash}" + ) + + async def test_key_urlencodes_namespace( + self, driver_client: RedisStorageDriverClient + ) -> None: + driver = RedisStorageDriver(client=driver_client, key_prefix=KEY_PREFIX) + payload = make_payload() + ctx = make_workflow_context(namespace="my/ns#1", workflow_id="wf1") + [claim] = await driver.store(ctx, [payload]) + expected_hash = hashlib.sha256(payload.SerializeToString()).hexdigest() + assert ( + claim.claim_data["key"] + == f"{KEY_PREFIX}:v0:ns:my%2Fns%231:wt:null:wi:wf1:ri:null:d:sha256:{expected_hash}" + ) + + async def test_key_urlencoded_roundtrip( + self, driver_client: RedisStorageDriverClient + ) -> None: + driver = RedisStorageDriver(client=driver_client, key_prefix=KEY_PREFIX) + payload = make_payload("special-char-roundtrip") + ctx = make_workflow_context(namespace="ns/1", workflow_id="wf/2#3") + [claim] = await driver.store(ctx, [payload]) + [retrieved] = await driver.retrieve(StorageDriverRetrieveContext(), [claim]) + assert retrieved == payload + + +class TestRedisStorageDriverStoreRetrieve: + async def test_store_returns_claim_with_key_and_hash( + self, driver_client: RedisStorageDriverClient + ) -> None: + driver = RedisStorageDriver(client=driver_client, key_prefix=KEY_PREFIX) + payload = make_payload() + [claim] = await driver.store(make_store_context(), [payload]) + assert claim.claim_data["key"].startswith(f"{KEY_PREFIX}:v0:") + assert claim.claim_data["hash_algorithm"] == "sha256" + expected_hash = hashlib.sha256(payload.SerializeToString()).hexdigest() + assert claim.claim_data["hash_value"] == expected_hash + + async def test_roundtrip_single_payload( + self, driver_client: RedisStorageDriverClient + ) -> None: + driver = RedisStorageDriver(client=driver_client, key_prefix=KEY_PREFIX) + payload = make_payload("round-trip value") + [claim] = await driver.store(make_store_context(), [payload]) + [retrieved] = await driver.retrieve(StorageDriverRetrieveContext(), [claim]) + assert retrieved == payload + + async def test_roundtrip_multiple_payloads( + self, driver_client: RedisStorageDriverClient + ) -> None: + driver = RedisStorageDriver(client=driver_client, key_prefix=KEY_PREFIX) + payloads = [make_payload(f"value-{i}") for i in range(3)] + claims = await driver.store(make_store_context(), payloads) + retrieved = await driver.retrieve(StorageDriverRetrieveContext(), claims) + assert retrieved == payloads + + async def test_empty_payloads_returns_empty_list( + self, driver_client: RedisStorageDriverClient + ) -> None: + driver = RedisStorageDriver(client=driver_client, key_prefix=KEY_PREFIX) + assert await driver.store(make_store_context(), []) == [] + assert await driver.retrieve(StorageDriverRetrieveContext(), []) == [] + + async def test_content_addressable_deduplication( + self, redis_asyncio_client: Any, driver_client: RedisStorageDriverClient + ) -> None: + driver = RedisStorageDriver(client=driver_client, key_prefix=KEY_PREFIX) + payload = make_payload("same-value") + claims = await driver.store(make_store_context(), [payload, payload]) + assert claims[0].claim_data["key"] == claims[1].claim_data["key"] + assert await _list_keys(redis_asyncio_client) == [claims[0].claim_data["key"]] + + async def test_set_if_absent_only_inserts_once( + self, counting_driver_client: CountingDriverClient + ) -> None: + driver = RedisStorageDriver( + client=counting_driver_client, + key_prefix=KEY_PREFIX, + ) + payload = make_payload("insert-once") + + await driver.store(make_store_context(), [payload]) + await driver.store(make_store_context(), [payload]) + + assert counting_driver_client.set_if_absent_count == 2 + assert counting_driver_client.insert_count == 1 + + async def test_duplicate_store_preserves_data( + self, driver_client: RedisStorageDriverClient + ) -> None: + driver = RedisStorageDriver(client=driver_client, key_prefix=KEY_PREFIX) + payload = make_payload("preserve-me") + + [claim1] = await driver.store(make_store_context(), [payload]) + [claim2] = await driver.store(make_store_context(), [payload]) + assert claim1 == claim2 + + [retrieved] = await driver.retrieve(StorageDriverRetrieveContext(), [claim2]) + assert retrieved == payload + + async def test_ttl_is_applied_on_first_write( + self, redis_asyncio_client: Any, driver_client: RedisStorageDriverClient + ) -> None: + driver = RedisStorageDriver( + client=driver_client, + key_prefix=KEY_PREFIX, + ttl=timedelta(seconds=5), + ) + [claim] = await driver.store(make_store_context(), [make_payload("ttl")]) + ttl_ms = await redis_asyncio_client.pttl(claim.claim_data["key"]) + assert 0 < ttl_ms <= 5000 + + async def test_ttl_is_not_refreshed_on_duplicate_write( + self, redis_asyncio_client: Any, driver_client: RedisStorageDriverClient + ) -> None: + driver = RedisStorageDriver( + client=driver_client, + key_prefix=KEY_PREFIX, + ttl=timedelta(seconds=5), + ) + payload = make_payload("ttl-stable") + [claim] = await driver.store(make_store_context(), [payload]) + first_ttl_ms = await redis_asyncio_client.pttl(claim.claim_data["key"]) + await asyncio.sleep(0.02) + await driver.store(make_store_context(), [payload]) + second_ttl_ms = await redis_asyncio_client.pttl(claim.claim_data["key"]) + assert 0 < second_ttl_ms < first_ttl_ms + + async def test_retrieve_validates_hash( + self, driver_client: RedisStorageDriverClient + ) -> None: + driver = RedisStorageDriver(client=driver_client, key_prefix=KEY_PREFIX) + payload = make_payload("check-integrity") + [claim] = await driver.store(make_store_context(), [payload]) + + tampered_claim = StorageDriverClaim( + claim_data={ + **claim.claim_data, + "hash_value": "0" * 64, + }, + ) + with pytest.raises( + ValueError, + match=r"RedisStorageDriver integrity check failed \[key=.+\]: expected sha256:.+, got sha256:.+", + ): + await driver.retrieve(StorageDriverRetrieveContext(), [tampered_claim]) + + async def test_retrieve_rejects_unsupported_hash_algorithm( + self, driver_client: RedisStorageDriverClient + ) -> None: + driver = RedisStorageDriver(client=driver_client, key_prefix=KEY_PREFIX) + payload = make_payload("unsupported-algo") + [claim] = await driver.store(make_store_context(), [payload]) + + bad_claim = StorageDriverClaim( + claim_data={ + **claim.claim_data, + "hash_algorithm": "md5", + }, + ) + with pytest.raises( + ValueError, + match=r"RedisStorageDriver unsupported hash algorithm \[key=.+\]: expected sha256, got md5", + ): + await driver.retrieve(StorageDriverRetrieveContext(), [bad_claim]) + + async def test_retrieve_without_hash_in_claim( + self, driver_client: RedisStorageDriverClient + ) -> None: + driver = RedisStorageDriver(client=driver_client, key_prefix=KEY_PREFIX) + payload = make_payload("no-hash-claim") + [claim] = await driver.store(make_store_context(), [payload]) + + legacy_claim = StorageDriverClaim( + claim_data={ + "key": claim.claim_data["key"], + }, + ) + [retrieved] = await driver.retrieve( + StorageDriverRetrieveContext(), [legacy_claim] + ) + assert retrieved == payload + + async def test_retrieve_missing_key_raises_non_retryable_application_error( + self, driver_client: RedisStorageDriverClient + ) -> None: + driver = RedisStorageDriver(client=driver_client, key_prefix=KEY_PREFIX) + claim = StorageDriverClaim( + claim_data={"key": f"{KEY_PREFIX}:v0:d:sha256:missing"} + ) + with pytest.raises(ApplicationError) as exc_info: + await driver.retrieve(StorageDriverRetrieveContext(), [claim]) + assert exc_info.value.message == ( + f"Payload not found for key '{KEY_PREFIX}:v0:d:sha256:missing'" + ) + assert exc_info.value.type == "PayloadNotFoundError" + assert exc_info.value.non_retryable is True + + async def test_expired_key_raises_non_retryable_application_error( + self, redis_asyncio_client: Any, driver_client: RedisStorageDriverClient + ) -> None: + driver = RedisStorageDriver( + client=driver_client, + key_prefix=KEY_PREFIX, + ttl=timedelta(milliseconds=10), + ) + [claim] = await driver.store(make_store_context(), [make_payload("expire-me")]) + await _wait_for_key_absent(redis_asyncio_client, claim.claim_data["key"]) + with pytest.raises(ApplicationError) as exc_info: + await driver.retrieve(StorageDriverRetrieveContext(), [claim]) + assert exc_info.value.type == "PayloadNotFoundError" + assert exc_info.value.non_retryable is True + + async def test_payload_exceeds_max_size_raises( + self, driver_client: RedisStorageDriverClient + ) -> None: + driver = RedisStorageDriver( + client=driver_client, + key_prefix=KEY_PREFIX, + max_payload_size=10, + ) + with pytest.raises( + ValueError, + match=r"Payload size \d+ bytes exceeds the configured max_payload_size of 10 bytes", + ): + await driver.store(make_store_context(), [make_payload("exceeds-limit")]) + + async def test_payload_at_max_size_succeeds( + self, driver_client: RedisStorageDriverClient + ) -> None: + payload = make_payload("x") + driver = RedisStorageDriver( + client=driver_client, + key_prefix=KEY_PREFIX, + max_payload_size=len(payload.SerializeToString()), + ) + await driver.store(make_store_context(), [payload]) + + +class TestRedisAsyncioAdapter: + async def test_decode_responses_client_raises(self) -> None: + client = fakeredis.aioredis.FakeRedis(decode_responses=True) + try: + await client.set("adapter:text", b"abc") + adapter = new_redis_asyncio_client(client) + with pytest.raises(TypeError, match="decode_responses=False"): + await adapter.get(key="adapter:text") + finally: + aclose = getattr(client, "aclose", None) + if aclose is not None: + await aclose() + else: + await client.close() + + +class TestRedisStorageDriverErrors: + async def test_store_client_failure_raises( + self, driver_client: RedisStorageDriverClient + ) -> None: + faulty_client = FailOnceDriverClient( + delegate=driver_client, fail_on="set_if_absent" + ) + driver = RedisStorageDriver(client=faulty_client, key_prefix=KEY_PREFIX) + payload = make_payload() + expected_hash = hashlib.sha256(payload.SerializeToString()).hexdigest() + expected_key = f"{KEY_PREFIX}:v0:d:sha256:{expected_hash}" + with pytest.raises(RuntimeError) as exc_info: + await driver.store(make_store_context(), [payload]) + assert ( + str(exc_info.value) + == f"RedisStorageDriver store failed [key={expected_key}]" + ) + assert isinstance(exc_info.value.__cause__, ConnectionError) + + async def test_retrieve_client_failure_raises( + self, driver_client: RedisStorageDriverClient + ) -> None: + driver = RedisStorageDriver(client=driver_client, key_prefix=KEY_PREFIX) + [claim] = await driver.store(make_store_context(), [make_payload("value")]) + + faulty_client = FailOnceDriverClient(delegate=driver_client, fail_on="get") + driver = RedisStorageDriver(client=faulty_client, key_prefix=KEY_PREFIX) + with pytest.raises(RuntimeError) as exc_info: + await driver.retrieve(StorageDriverRetrieveContext(), [claim]) + assert ( + str(exc_info.value) + == f"RedisStorageDriver retrieve failed [key={claim.claim_data['key']}]" + ) + assert isinstance(exc_info.value.__cause__, ConnectionError) + + +class TestRedisStorageDriverConcurrency: + async def test_store_payloads_concurrently( + self, driver_client: RedisStorageDriverClient + ) -> None: + num_payloads = 5 + barrier = _AsyncBarrier(num_payloads) + driver_client.set_if_absent = _barrier_wrapper( # type: ignore[method-assign] + driver_client.set_if_absent, + barrier, + ) + + driver = RedisStorageDriver(client=driver_client, key_prefix=KEY_PREFIX) + payloads = [make_payload(f"concurrent-store-{i}") for i in range(num_payloads)] + + claims = await driver.store(make_store_context(), payloads) + assert len(claims) == num_payloads + + async def test_retrieve_payloads_concurrently( + self, driver_client: RedisStorageDriverClient + ) -> None: + num_payloads = 5 + driver = RedisStorageDriver(client=driver_client, key_prefix=KEY_PREFIX) + payloads = [ + make_payload(f"concurrent-retrieve-{i}") for i in range(num_payloads) + ] + claims = await driver.store(make_store_context(), payloads) + + barrier = _AsyncBarrier(num_payloads) + driver_client.get = _barrier_wrapper(driver_client.get, barrier) # type: ignore[method-assign] + + retrieved = await driver.retrieve(StorageDriverRetrieveContext(), claims) + assert retrieved == payloads + + async def test_store_cancels_remaining_on_failure( + self, driver_client: RedisStorageDriverClient + ) -> None: + faulty_client = FailOnceDriverClient( + delegate=driver_client, + fail_on="set_if_absent", + ) + driver = RedisStorageDriver(client=faulty_client, key_prefix=KEY_PREFIX) + payloads = [make_payload(f"cancel-store-{i}") for i in range(3)] + + with pytest.raises( + RuntimeError, + match=r"RedisStorageDriver store failed \[key=.+\]", + ) as exc_info: + await driver.store(make_store_context(), payloads) + + assert isinstance(exc_info.value.__cause__, ConnectionError) + assert str(exc_info.value.__cause__) == "Redis connection lost" + assert ( + len(faulty_client.cancelled) == 2 + ), "Expected 2 remaining tasks to be cancelled" + + async def test_retrieve_cancels_remaining_on_failure( + self, driver_client: RedisStorageDriverClient + ) -> None: + driver = RedisStorageDriver(client=driver_client, key_prefix=KEY_PREFIX) + payloads = [make_payload(f"cancel-retrieve-{i}") for i in range(3)] + claims = await driver.store(make_store_context(), payloads) + + faulty_client = FailOnceDriverClient(delegate=driver_client, fail_on="get") + driver = RedisStorageDriver(client=faulty_client, key_prefix=KEY_PREFIX) + + with pytest.raises( + RuntimeError, + match=r"RedisStorageDriver retrieve failed \[key=.+\]", + ) as exc_info: + await driver.retrieve(StorageDriverRetrieveContext(), claims) + + assert isinstance(exc_info.value.__cause__, ConnectionError) + assert str(exc_info.value.__cause__) == "Redis connection lost" + assert ( + len(faulty_client.cancelled) == 2 + ), "Expected 2 remaining tasks to be cancelled" diff --git a/tests/external_storage_redis/test_redis_worker.py b/tests/external_storage_redis/test_redis_worker.py new file mode 100644 index 000000000..60c1e4ce9 --- /dev/null +++ b/tests/external_storage_redis/test_redis_worker.py @@ -0,0 +1,522 @@ +"""Worker integration tests for RedisStorageDriver key structure.""" + +from __future__ import annotations + +import dataclasses +import hashlib +import uuid +from collections.abc import AsyncIterator, Callable, Sequence +from datetime import timedelta +from typing import Any + +import pytest +import pytest_asyncio +import temporalio.converter +from temporalio.client import Client, WorkflowFailureError +from temporalio.converter import ExternalStorage, JSONPlainPayloadConverter +from temporalio.exceptions import ActivityError, ApplicationError +from temporalio.service import RPCError +from temporalio.testing import WorkflowEnvironment +from temporalio.worker import Worker + +from external_storage_redis import RedisStorageDriver, RedisStorageDriverClient +from external_storage_redis.redis_asyncio import new_redis_asyncio_client +from tests.external_storage_redis.conftest import KEY_PREFIX +from tests.external_storage_redis.workflows import ( + LARGE, + LARGE_2, + ChildWorkflow, + DocumentIngestionWorkflow, + LargeIOWorkflow, + LargeOutputNoRetryWorkflow, + ModelTrainingWorkflow, + OrderFulfillmentWorkflow, + ParentWithChildWorkflow, + PaymentProcessingWorkflow, + SignalQueryUpdateWorkflow, + download_document, + extract_text, + index_document, + large_io_activity, + large_output_activity, +) + +_THRESHOLD = 256 + + +def new_worker( + client: Client, + *workflows: type, + activities: Sequence[Callable[..., Any]] = (), + task_queue: str | None = None, + **kwargs: Any, +) -> Worker: + return Worker( + client, + task_queue=task_queue or str(uuid.uuid4()), + workflows=workflows, + activities=activities, + **kwargs, + ) + + +class FailingSetClient(RedisStorageDriverClient): + """Redis client wrapper that fails all writes.""" + + def __init__(self, delegate: RedisStorageDriverClient) -> None: + self._delegate = delegate + + async def get(self, *, key: str) -> bytes | None: + return await self._delegate.get(key=key) + + async def set_if_absent( + self, + *, + key: str, + data: bytes, + ttl: timedelta | None = None, + ) -> bool: + del key, data, ttl + raise ConnectionError("Redis is unavailable") + + +@pytest_asyncio.fixture +async def tmprl_client( + env: WorkflowEnvironment, redis_asyncio_client: Any +) -> AsyncIterator[Client]: + """Temporal client wired with ExternalStorage backed by fake Redis.""" + driver = RedisStorageDriver( + client=new_redis_asyncio_client(redis_asyncio_client), + key_prefix=KEY_PREFIX, + ) + yield await Client.connect( + env.client.service_client.config.target_host, + namespace=env.client.namespace, + data_converter=dataclasses.replace( + temporalio.converter.default(), + external_storage=ExternalStorage( + drivers=[driver], + payload_size_threshold=_THRESHOLD, + ), + ), + ) + + +async def _list_keys(redis_asyncio_client: Any) -> list[str]: + raw_keys = await redis_asyncio_client.keys(f"{KEY_PREFIX}:*") + return sorted(key.decode() if isinstance(key, bytes) else key for key in raw_keys) + + +def _skip_if_standalone_activity_disabled(err: RPCError) -> None: + if "Standalone activity is disabled" in str(err): + pytest.skip( + "Standalone Activity is not supported by this Temporal dev server build" + ) + + +async def test_redis_driver_workflow_input_key( + tmprl_client: Client, redis_asyncio_client: Any +) -> None: + workflow_id = str(uuid.uuid4()) + async with new_worker( + tmprl_client, LargeIOWorkflow, activities=[large_io_activity] + ) as worker: + await tmprl_client.execute_workflow( + LargeIOWorkflow.run, + LARGE, + id=workflow_id, + task_queue=worker.task_queue, + execution_timeout=timedelta(seconds=5), + ) + keys = await _list_keys(redis_asyncio_client) + assert len(keys) == 2 + assert all( + f"{KEY_PREFIX}:v0:ns:default:wt:LargeIOWorkflow:wi:{workflow_id}:ri:" in key + for key in keys + ) + assert sum(1 for key in keys if ":ri:null:" in key) == 1 + assert sum(1 for key in keys if ":ri:null:" not in key) == 1 + + +async def test_redis_driver_workflow_output_key( + tmprl_client: Client, redis_asyncio_client: Any +) -> None: + workflow_id = str(uuid.uuid4()) + async with new_worker( + tmprl_client, LargeIOWorkflow, activities=[large_io_activity] + ) as worker: + result = await tmprl_client.execute_workflow( + LargeIOWorkflow.run, + "small", + id=workflow_id, + task_queue=worker.task_queue, + execution_timeout=timedelta(seconds=5), + ) + assert result == LARGE + keys = await _list_keys(redis_asyncio_client) + assert len(keys) == 1 + assert ( + f"{KEY_PREFIX}:v0:ns:default:wt:LargeIOWorkflow:wi:{workflow_id}:ri:" in keys[0] + ) + assert ":ri:null:" not in keys[0] + + +async def test_redis_driver_workflow_activity_input_key( + tmprl_client: Client, redis_asyncio_client: Any +) -> None: + workflow_id = str(uuid.uuid4()) + async with new_worker( + tmprl_client, LargeIOWorkflow, activities=[large_io_activity] + ) as worker: + await tmprl_client.execute_workflow( + LargeIOWorkflow.run, + LARGE, + id=workflow_id, + task_queue=worker.task_queue, + execution_timeout=timedelta(seconds=5), + ) + keys = await _list_keys(redis_asyncio_client) + assert len(keys) == 2 + assert all( + f"{KEY_PREFIX}:v0:ns:default:wt:LargeIOWorkflow:wi:{workflow_id}:ri:" in key + for key in keys + ) + assert all(":ai:" not in key for key in keys) + + +async def test_redis_driver_workflow_activity_output_key( + tmprl_client: Client, redis_asyncio_client: Any +) -> None: + workflow_id = str(uuid.uuid4()) + async with new_worker( + tmprl_client, LargeIOWorkflow, activities=[large_io_activity] + ) as worker: + await tmprl_client.execute_workflow( + LargeIOWorkflow.run, + "small", + id=workflow_id, + task_queue=worker.task_queue, + execution_timeout=timedelta(seconds=5), + ) + keys = await _list_keys(redis_asyncio_client) + assert len(keys) == 1 + assert ( + f"{KEY_PREFIX}:v0:ns:default:wt:LargeIOWorkflow:wi:{workflow_id}:ri:" in keys[0] + ) + assert ":ri:null:" not in keys[0] + + +async def test_redis_driver_standalone_activity_input_key( + env: WorkflowEnvironment, + tmprl_client: Client, + redis_asyncio_client: Any, +) -> None: + if env.supports_time_skipping: + pytest.skip( + "Java test server: https://github.com/temporalio/sdk-java/issues/2741" + ) + activity_id = str(uuid.uuid4()) + task_queue = str(uuid.uuid4()) + async with new_worker( + tmprl_client, activities=[large_io_activity], task_queue=task_queue + ): + try: + await tmprl_client.execute_activity( + large_io_activity, + LARGE, + id=activity_id, + task_queue=task_queue, + start_to_close_timeout=timedelta(seconds=5), + ) + except RPCError as err: + _skip_if_standalone_activity_disabled(err) + raise + keys = await _list_keys(redis_asyncio_client) + assert len(keys) == 2 + assert all( + f"{KEY_PREFIX}:v0:ns:default:at:large_io_activity:ai:{activity_id}:ri:" in key + for key in keys + ) + assert all(":wt:" not in key for key in keys) + assert sum(1 for key in keys if ":ri:null:" in key) == 1 + assert sum(1 for key in keys if ":ri:null:" not in key) == 1 + + +async def test_redis_driver_standalone_activity_output_key( + env: WorkflowEnvironment, + tmprl_client: Client, + redis_asyncio_client: Any, +) -> None: + if env.supports_time_skipping: + pytest.skip( + "Java test server: https://github.com/temporalio/sdk-java/issues/2741" + ) + activity_id = str(uuid.uuid4()) + task_queue = str(uuid.uuid4()) + async with new_worker( + tmprl_client, activities=[large_output_activity], task_queue=task_queue + ): + try: + await tmprl_client.execute_activity( + large_output_activity, + id=activity_id, + task_queue=task_queue, + start_to_close_timeout=timedelta(seconds=5), + ) + except RPCError as err: + _skip_if_standalone_activity_disabled(err) + raise + keys = await _list_keys(redis_asyncio_client) + assert len(keys) == 1 + assert ( + f"{KEY_PREFIX}:v0:ns:default:at:large_output_activity:ai:{activity_id}:ri:" + in keys[0] + ) + assert ":ri:null:" not in keys[0] + assert ":wt:" not in keys[0] + + +async def test_redis_driver_signal_arg_key( + tmprl_client: Client, redis_asyncio_client: Any +) -> None: + workflow_id = str(uuid.uuid4()) + async with new_worker(tmprl_client, SignalQueryUpdateWorkflow) as worker: + handle = await tmprl_client.start_workflow( + SignalQueryUpdateWorkflow.run, + id=workflow_id, + task_queue=worker.task_queue, + execution_timeout=timedelta(seconds=5), + ) + await handle.signal(SignalQueryUpdateWorkflow.finish, LARGE) + await handle.result() + keys = await _list_keys(redis_asyncio_client) + assert len(keys) == 2 + assert any( + f"{KEY_PREFIX}:v0:ns:default:wt:null:wi:{workflow_id}:ri:null:" in key + for key in keys + ) + assert any( + f"{KEY_PREFIX}:v0:ns:default:wt:SignalQueryUpdateWorkflow:wi:{workflow_id}:" + in key + for key in keys + ) + + +async def test_redis_driver_query_result_key( + tmprl_client: Client, redis_asyncio_client: Any +) -> None: + workflow_id = str(uuid.uuid4()) + async with new_worker(tmprl_client, SignalQueryUpdateWorkflow) as worker: + handle = await tmprl_client.start_workflow( + SignalQueryUpdateWorkflow.run, + id=workflow_id, + task_queue=worker.task_queue, + execution_timeout=timedelta(seconds=5), + ) + result = await handle.query(SignalQueryUpdateWorkflow.get_value, LARGE) + assert result == LARGE + await handle.signal(SignalQueryUpdateWorkflow.finish, "done") + await handle.result() + keys = await _list_keys(redis_asyncio_client) + assert len(keys) == 2 + assert any( + f"{KEY_PREFIX}:v0:ns:default:wt:null:wi:{workflow_id}:ri:null:" in key + for key in keys + ) + assert any( + f"{KEY_PREFIX}:v0:ns:default:wt:SignalQueryUpdateWorkflow:wi:{workflow_id}:" + in key + for key in keys + ) + + +async def test_redis_driver_update_result_key( + tmprl_client: Client, redis_asyncio_client: Any +) -> None: + workflow_id = str(uuid.uuid4()) + async with new_worker(tmprl_client, SignalQueryUpdateWorkflow) as worker: + handle = await tmprl_client.start_workflow( + SignalQueryUpdateWorkflow.run, + id=workflow_id, + task_queue=worker.task_queue, + execution_timeout=timedelta(seconds=5), + ) + result = await handle.execute_update(SignalQueryUpdateWorkflow.do_update, LARGE) + assert result == LARGE + await handle.signal(SignalQueryUpdateWorkflow.finish, "done") + await handle.result() + keys = await _list_keys(redis_asyncio_client) + assert len(keys) == 2 + assert any( + f"{KEY_PREFIX}:v0:ns:default:wt:null:wi:{workflow_id}:ri:null:" in key + for key in keys + ) + assert any( + f"{KEY_PREFIX}:v0:ns:default:wt:SignalQueryUpdateWorkflow:wi:{workflow_id}:" + in key + for key in keys + ) + + +async def test_redis_driver_child_workflow_input_key( + tmprl_client: Client, redis_asyncio_client: Any +) -> None: + workflow_id = str(uuid.uuid4()) + async with new_worker( + tmprl_client, ParentWithChildWorkflow, ChildWorkflow + ) as worker: + await tmprl_client.execute_workflow( + ParentWithChildWorkflow.run, + id=workflow_id, + task_queue=worker.task_queue, + execution_timeout=timedelta(seconds=5), + ) + keys = await _list_keys(redis_asyncio_client) + child_workflow_id = f"{workflow_id}-child" + assert len(keys) == 1 + assert ( + f"{KEY_PREFIX}:v0:ns:default:wt:ChildWorkflow:wi:{child_workflow_id}:ri:" + in keys[0] + ) + + +async def test_redis_driver_identifier_casing( + tmprl_client: Client, redis_asyncio_client: Any +) -> None: + workflow_id = f"MyWorkflow-{uuid.uuid4()}" + async with new_worker( + tmprl_client, LargeIOWorkflow, activities=[large_io_activity] + ) as worker: + await tmprl_client.execute_workflow( + LargeIOWorkflow.run, + LARGE, + id=workflow_id, + task_queue=worker.task_queue, + execution_timeout=timedelta(seconds=5), + ) + keys = await _list_keys(redis_asyncio_client) + assert len(keys) == 2 + assert all( + f"{KEY_PREFIX}:v0:ns:default:wt:LargeIOWorkflow:wi:{workflow_id}:ri:" in key + for key in keys + ) + + +async def test_redis_driver_content_dedup( + tmprl_client: Client, redis_asyncio_client: Any +) -> None: + workflow_id = str(uuid.uuid4()) + async with new_worker( + tmprl_client, + DocumentIngestionWorkflow, + activities=[download_document, extract_text, index_document], + ) as worker: + await tmprl_client.execute_workflow( + DocumentIngestionWorkflow.run, + "doc-001", + id=workflow_id, + task_queue=worker.task_queue, + execution_timeout=timedelta(seconds=5), + ) + keys = await _list_keys(redis_asyncio_client) + assert len(keys) == 2 + assert all( + f"{KEY_PREFIX}:v0:ns:default:wt:DocumentIngestionWorkflow:wi:{workflow_id}:ri:" + in key + for key in keys + ) + assert keys[0] != keys[1] + + +async def test_redis_driver_single_workflow_same_key_namespace( + tmprl_client: Client, redis_asyncio_client: Any +) -> None: + workflow_id = str(uuid.uuid4()) + async with new_worker(tmprl_client, ModelTrainingWorkflow) as worker: + handle = await tmprl_client.start_workflow( + ModelTrainingWorkflow.run, + LARGE, + id=workflow_id, + task_queue=worker.task_queue, + execution_timeout=timedelta(seconds=5), + ) + metrics = await handle.execute_update( + ModelTrainingWorkflow.get_metrics, "checkpoint-1" + ) + assert metrics == LARGE_2 + await handle.signal(ModelTrainingWorkflow.apply_overrides, LARGE) + await handle.signal(ModelTrainingWorkflow.complete) + await handle.result() + keys = await _list_keys(redis_asyncio_client) + assert len(keys) == 4 + assert all(f":wi:{workflow_id}:" in key for key in keys) + + +async def test_redis_driver_parent_child_independent_key_namespaces( + tmprl_client: Client, redis_asyncio_client: Any +) -> None: + workflow_id = str(uuid.uuid4()) + payment_id = f"{workflow_id}-payment" + async with new_worker( + tmprl_client, OrderFulfillmentWorkflow, PaymentProcessingWorkflow + ) as worker: + await tmprl_client.execute_workflow( + OrderFulfillmentWorkflow.run, + LARGE, + id=workflow_id, + task_queue=worker.task_queue, + execution_timeout=timedelta(seconds=5), + ) + keys = await _list_keys(redis_asyncio_client) + parent_keys = [key for key in keys if f":wi:{workflow_id}:" in key] + child_keys = [key for key in keys if f":wi:{payment_id}:" in key] + assert len(parent_keys) == 3 + assert len(child_keys) == 1 + + +async def test_redis_store_failure_surfaces_in_workflow_history( + env: WorkflowEnvironment, driver_client: RedisStorageDriverClient +) -> None: + driver = RedisStorageDriver( + client=FailingSetClient(driver_client), + key_prefix=KEY_PREFIX, + ) + bad_client = await Client.connect( + env.client.service_client.config.target_host, + namespace=env.client.namespace, + data_converter=dataclasses.replace( + temporalio.converter.default(), + external_storage=ExternalStorage( + drivers=[driver], + payload_size_threshold=_THRESHOLD, + ), + ), + ) + workflow_id = str(uuid.uuid4()) + async with new_worker( + bad_client, LargeOutputNoRetryWorkflow, activities=[large_output_activity] + ) as worker: + with pytest.raises(WorkflowFailureError) as exc_info: + await bad_client.execute_workflow( + LargeOutputNoRetryWorkflow.run, + id=workflow_id, + task_queue=worker.task_queue, + execution_timeout=timedelta(seconds=5), + ) + + large_payload = JSONPlainPayloadConverter().to_payload(LARGE) + assert large_payload is not None + expected_hash = hashlib.sha256(large_payload.SerializeToString()).hexdigest() + + activity_error = exc_info.value.__cause__ + assert isinstance(activity_error, ActivityError) + app_error = activity_error.__cause__ + assert isinstance(app_error, ApplicationError) + assert app_error.type == "RuntimeError" + msg = app_error.message + assert "RedisStorageDriver store failed [key=" in msg + assert ( + f"{KEY_PREFIX}:v0:ns:default:wt:LargeOutputNoRetryWorkflow:wi:{workflow_id}:ri:" + in msg + ) + assert f":d:sha256:{expected_hash}]" in msg diff --git a/tests/external_storage_redis/workflows.py b/tests/external_storage_redis/workflows.py new file mode 100644 index 000000000..1440ffbbc --- /dev/null +++ b/tests/external_storage_redis/workflows.py @@ -0,0 +1,194 @@ +"""Workflows and activities used by the Redis external storage tests.""" + +from __future__ import annotations + +from datetime import timedelta + +from temporalio import activity, workflow +from temporalio.common import RetryPolicy + +LARGE = "x" * 356 # ~358 bytes as a JSON string, above the 256-byte test threshold +LARGE_2 = "y" * 356 # distinct large payload with a different SHA-256 hash + + +@activity.defn +async def large_io_activity(_data: str) -> str: + return LARGE + + +@activity.defn +async def large_output_activity() -> str: + """Return a large payload with no retries; used to test store failures.""" + return LARGE + + +@workflow.defn +class LargeOutputNoRetryWorkflow: + """Execute one activity that returns a large payload with no retries.""" + + @workflow.run + async def run(self) -> str: + return await workflow.execute_activity( + large_output_activity, + schedule_to_close_timeout=timedelta(seconds=10), + retry_policy=RetryPolicy(maximum_attempts=1), + ) + + +@workflow.defn +class LargeIOWorkflow: + """Pass workflow input to an activity and return a large output.""" + + @workflow.run + async def run(self, data: str) -> str: + await workflow.execute_activity( + large_io_activity, + data, + schedule_to_close_timeout=timedelta(seconds=10), + ) + return LARGE + + +@activity.defn +async def download_document(document_id: str) -> str: + """Download the raw document content from remote storage.""" + del document_id + return LARGE + + +@activity.defn +async def extract_text(raw_content: str) -> str: + """Extract and normalize text from the raw document content.""" + del raw_content + return LARGE_2 + + +@activity.defn +async def index_document(text: str) -> str: + """Index the extracted text into the search index.""" + del text + return "idx-00001" + + +@workflow.defn +class DocumentIngestionWorkflow: + """Download, extract, and index a document through large payload hops.""" + + @workflow.run + async def run(self, document_id: str) -> str: + raw_content = await workflow.execute_activity( + download_document, + document_id, + schedule_to_close_timeout=timedelta(seconds=10), + ) + extracted_text = await workflow.execute_activity( + extract_text, + raw_content, + schedule_to_close_timeout=timedelta(seconds=10), + ) + return await workflow.execute_activity( + index_document, + extracted_text, + schedule_to_close_timeout=timedelta(seconds=10), + ) + + +@workflow.defn +class ChildWorkflow: + @workflow.run + async def run(self, data: str) -> str: + return f"{len(data)}" + + +@workflow.defn +class ParentWithChildWorkflow: + """Delegate work to a child workflow whose ID is ``{parent_id}-child``.""" + + @workflow.run + async def run(self) -> str: + child_id = f"{workflow.info().workflow_id}-child" + return await workflow.execute_child_workflow( + ChildWorkflow.run, + LARGE, + id=child_id, + execution_timeout=timedelta(seconds=10), + ) + + +@workflow.defn +class PaymentProcessingWorkflow: + """Process payment for an order and return a large confirmation payload.""" + + @workflow.run + async def run(self, order_details: str) -> str: + del order_details + return LARGE_2 + + +@workflow.defn +class OrderFulfillmentWorkflow: + """Coordinate order fulfillment by delegating payment to a child workflow.""" + + @workflow.run + async def run(self, order_details: str) -> str: + payment_id = f"{workflow.info().workflow_id}-payment" + return await workflow.execute_child_workflow( + PaymentProcessingWorkflow.run, + order_details, + id=payment_id, + execution_timeout=timedelta(seconds=10), + ) + + +@workflow.defn +class ModelTrainingWorkflow: + """Simulate a long-running training job with large cross-boundary payloads.""" + + def __init__(self) -> None: + self._done = False + + @workflow.run + async def run(self, training_config: str) -> str: + del training_config + await workflow.wait_condition(lambda: self._done) + return LARGE + + @workflow.signal + async def apply_overrides(self, override_params: str) -> None: + """Inject updated configuration into the running training job.""" + del override_params + + @workflow.signal + async def complete(self) -> None: + self._done = True + + @workflow.update + async def get_metrics(self, checkpoint_id: str) -> str: + """Return the current training metrics snapshot.""" + del checkpoint_id + return LARGE_2 + + +@workflow.defn +class SignalQueryUpdateWorkflow: + """Long-running workflow that accepts a signal, query, and update.""" + + def __init__(self) -> None: + self._done = False + + @workflow.run + async def run(self) -> str: + await workflow.wait_condition(lambda: self._done) + return LARGE + + @workflow.signal + async def finish(self, _data: str) -> None: + self._done = True + + @workflow.query + def get_value(self, _data: str) -> str: + return LARGE + + @workflow.update + async def do_update(self, _data: str) -> str: + return LARGE diff --git a/tests/google_adk_agents/__init__.py b/tests/google_adk_agents/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/google_adk_agents/_mock_model.py b/tests/google_adk_agents/_mock_model.py new file mode 100644 index 000000000..aaa657e12 --- /dev/null +++ b/tests/google_adk_agents/_mock_model.py @@ -0,0 +1,59 @@ +from __future__ import annotations + +from collections.abc import AsyncGenerator +from typing import Any + +import pytest +from google.adk.models import BaseLlm, LLMRegistry +from google.adk.models.llm_request import LlmRequest +from google.adk.models.llm_response import LlmResponse +from google.genai.types import Content, FunctionCall, Part + +MODEL = "gemini-2.5-flash" + + +def text(s: str) -> LlmResponse: + return LlmResponse(content=Content(role="model", parts=[Part(text=s)])) + + +def tool_call(name: str, args: dict[str, Any]) -> LlmResponse: + return LlmResponse( + content=Content( + role="model", + parts=[Part(function_call=FunctionCall(name=name, args=args))], + ) + ) + + +def patch_model( + monkeypatch: pytest.MonkeyPatch, + responses: list[LlmResponse], + *, + stream_chunks: bool = False, + captured: list[LlmRequest] | None = None, +) -> None: + script = list(responses) + orig_new_llm = LLMRegistry.new_llm # staticmethod + + class _Mock(BaseLlm): + async def generate_content_async( + self, llm_request: LlmRequest, stream: bool = False + ) -> AsyncGenerator[LlmResponse, None]: + if captured is not None: + captured.append(llm_request) + if stream_chunks: + # The streaming sample is single-turn, so yield every scripted + # chunk on this one call. + while script: + yield script.pop(0) + return + if not script: + raise AssertionError("mock model script exhausted") + yield script.pop(0) + + def fake_new_llm(model: str) -> BaseLlm: + if model == MODEL: + return _Mock(model=model) + return orig_new_llm(model) + + monkeypatch.setattr(LLMRegistry, "new_llm", staticmethod(fake_new_llm)) diff --git a/tests/google_adk_agents/agent_patterns_test.py b/tests/google_adk_agents/agent_patterns_test.py new file mode 100644 index 000000000..d9a3c2592 --- /dev/null +++ b/tests/google_adk_agents/agent_patterns_test.py @@ -0,0 +1,44 @@ +import uuid + +import pytest +from temporalio.client import Client +from temporalio.contrib.google_adk_agents import GoogleAdkPlugin +from temporalio.worker import Worker + +from google_adk_agents.agent_patterns.workflows.multi_agent_workflow import ( + MultiAgentWorkflow, +) +from tests.google_adk_agents._mock_model import patch_model, text, tool_call + + +async def test_agent_patterns(client: Client, monkeypatch: pytest.MonkeyPatch) -> None: + patch_model( + monkeypatch, + [ + tool_call("transfer_to_agent", {"agent_name": "researcher"}), + tool_call("transfer_to_agent", {"agent_name": "writer"}), + text("snow on the mountain"), + ], + ) + + task_queue = f"google-adk-agents-agent-patterns-{uuid.uuid4()}" + plugin = GoogleAdkPlugin() + + config = client.config() + config["plugins"] = [*config["plugins"], plugin] + client = Client(**config) + + async with Worker( + client, + task_queue=task_queue, + workflows=[MultiAgentWorkflow], + max_cached_workflows=0, + ): + result = await client.execute_workflow( + MultiAgentWorkflow.run, + "mountains", + id=f"google-adk-agents-agent-patterns-{uuid.uuid4()}", + task_queue=task_queue, + ) + + assert result == "snow on the mountain" diff --git a/tests/google_adk_agents/basic_test.py b/tests/google_adk_agents/basic_test.py new file mode 100644 index 000000000..a1354e4bb --- /dev/null +++ b/tests/google_adk_agents/basic_test.py @@ -0,0 +1,37 @@ +import uuid + +import pytest +from temporalio.client import Client +from temporalio.contrib.google_adk_agents import GoogleAdkPlugin +from temporalio.worker import Worker + +from google_adk_agents.basic.workflows.hello_world_workflow import ( + HelloWorldAgentWorkflow, +) +from tests.google_adk_agents._mock_model import patch_model, text + + +async def test_basic(client: Client, monkeypatch: pytest.MonkeyPatch) -> None: + patch_model(monkeypatch, [text("a quiet haiku")]) + + task_queue = f"google-adk-agents-basic-{uuid.uuid4()}" + plugin = GoogleAdkPlugin() + + config = client.config() + config["plugins"] = [*config["plugins"], plugin] + client = Client(**config) + + async with Worker( + client, + task_queue=task_queue, + workflows=[HelloWorldAgentWorkflow], + max_cached_workflows=0, + ): + result = await client.execute_workflow( + HelloWorldAgentWorkflow.run, + "Say hi", + id=f"google-adk-agents-basic-{uuid.uuid4()}", + task_queue=task_queue, + ) + + assert result == "a quiet haiku" diff --git a/tests/google_adk_agents/chatbot_test.py b/tests/google_adk_agents/chatbot_test.py new file mode 100644 index 000000000..af01dc826 --- /dev/null +++ b/tests/google_adk_agents/chatbot_test.py @@ -0,0 +1,57 @@ +import uuid + +import pytest +from google.adk.models.llm_request import LlmRequest +from temporalio.client import Client +from temporalio.contrib.google_adk_agents import GoogleAdkPlugin +from temporalio.worker import Worker + +from google_adk_agents.chatbot.workflows.chatbot_workflow import ( + ChatbotAgentWorkflow, +) +from tests.google_adk_agents._mock_model import patch_model, text + + +async def test_chatbot(client: Client, monkeypatch: pytest.MonkeyPatch) -> None: + captured: list[LlmRequest] = [] + patch_model( + monkeypatch, [text("first reply"), text("second reply")], captured=captured + ) + + task_queue = f"google-adk-agents-chatbot-{uuid.uuid4()}" + plugin = GoogleAdkPlugin() + + config = client.config() + config["plugins"] = [*config["plugins"], plugin] + client = Client(**config) + + async with Worker( + client, + task_queue=task_queue, + workflows=[ChatbotAgentWorkflow], + max_cached_workflows=0, + ): + handle = await client.start_workflow( + ChatbotAgentWorkflow.run, + id=f"google-adk-agents-chatbot-{uuid.uuid4()}", + task_queue=task_queue, + ) + + first = await handle.execute_update(ChatbotAgentWorkflow.message, "Hello") + second = await handle.execute_update(ChatbotAgentWorkflow.message, "Again") + + await handle.terminate() + + assert first == "first reply" + assert second == "second reply" + + # The second turn reuses the same session, so its request carries the first + # turn's history. + turn_two_history = "\n".join( + part.text + for content in captured[1].contents + for part in (content.parts or []) + if part.text + ) + assert "Hello" in turn_two_history + assert "first reply" in turn_two_history diff --git a/tests/google_adk_agents/mcp_test.py b/tests/google_adk_agents/mcp_test.py new file mode 100644 index 000000000..ae8729d0b --- /dev/null +++ b/tests/google_adk_agents/mcp_test.py @@ -0,0 +1,47 @@ +import uuid + +import pytest +from temporalio.client import Client +from temporalio.contrib.google_adk_agents import ( + GoogleAdkPlugin, + TemporalMcpToolSetProvider, +) +from temporalio.worker import Worker + +from google_adk_agents.mcp.toolsets import echo_toolset +from google_adk_agents.mcp.workflows.echo_workflow import EchoMcpWorkflow +from tests.google_adk_agents._mock_model import patch_model, text, tool_call + + +async def test_mcp(client: Client, monkeypatch: pytest.MonkeyPatch) -> None: + patch_model( + monkeypatch, + [ + tool_call("echo", {"message": "hello from MCP"}), + text("Done!"), + ], + ) + + task_queue = f"google-adk-agents-mcp-{uuid.uuid4()}" + plugin = GoogleAdkPlugin( + toolset_providers=[TemporalMcpToolSetProvider("echo", echo_toolset)] + ) + + config = client.config() + config["plugins"] = [*config["plugins"], plugin] + client = Client(**config) + + async with Worker( + client, + task_queue=task_queue, + workflows=[EchoMcpWorkflow], + max_cached_workflows=0, + ): + result = await client.execute_workflow( + EchoMcpWorkflow.run, + "Echo 'hello from MCP'.", + id=f"google-adk-agents-mcp-{uuid.uuid4()}", + task_queue=task_queue, + ) + + assert result == "Done!" diff --git a/tests/google_adk_agents/streaming_test.py b/tests/google_adk_agents/streaming_test.py new file mode 100644 index 000000000..5c1d78a87 --- /dev/null +++ b/tests/google_adk_agents/streaming_test.py @@ -0,0 +1,68 @@ +import asyncio +import uuid +from datetime import timedelta + +import pytest +from google.adk.models.llm_response import LlmResponse +from temporalio.client import Client +from temporalio.contrib.google_adk_agents import GoogleAdkPlugin +from temporalio.contrib.workflow_streams import WorkflowStreamClient +from temporalio.worker import Worker + +from google_adk_agents.streaming.workflows.streaming_workflow import ( + StreamingAgentWorkflow, +) +from tests.google_adk_agents._mock_model import patch_model, text + + +async def test_streaming(client: Client, monkeypatch: pytest.MonkeyPatch) -> None: + patch_model( + monkeypatch, + [text("Once "), text("upon a robot.")], + stream_chunks=True, + ) + + task_queue = f"google-adk-agents-streaming-{uuid.uuid4()}" + plugin = GoogleAdkPlugin() + workflow_id = f"google-adk-agents-streaming-{uuid.uuid4()}" + + config = client.config() + config["plugins"] = [*config["plugins"], plugin] + client = Client(**config) + + async with Worker( + client, + task_queue=task_queue, + workflows=[StreamingAgentWorkflow], + max_cached_workflows=0, + ): + handle = await client.start_workflow( + StreamingAgentWorkflow.run, + "Tell me a story", + id=workflow_id, + task_queue=task_queue, + ) + + stream = WorkflowStreamClient.create(client, workflow_id) + chunks: list[str] = [] + + async def collect() -> None: + async for item in stream.subscribe( + ["responses"], + from_offset=0, + result_type=LlmResponse, + poll_cooldown=timedelta(milliseconds=50), + ): + response = item.data + if response.content and response.content.parts: + for part in response.content.parts: + if part.text: + chunks.append(part.text) + if len(chunks) >= 2: + return + + collect_task = asyncio.create_task(collect()) + assert await handle.result() == "upon a robot." + await asyncio.wait_for(collect_task, timeout=10.0) + + assert chunks == ["Once ", "upon a robot."] diff --git a/tests/google_adk_agents/tools_test.py b/tests/google_adk_agents/tools_test.py new file mode 100644 index 000000000..d80817e35 --- /dev/null +++ b/tests/google_adk_agents/tools_test.py @@ -0,0 +1,43 @@ +import uuid + +import pytest +from temporalio.client import Client +from temporalio.contrib.google_adk_agents import GoogleAdkPlugin +from temporalio.worker import Worker + +from google_adk_agents.tools.activities.weather_activity import get_weather +from google_adk_agents.tools.workflows.weather_workflow import WeatherAgentWorkflow +from tests.google_adk_agents._mock_model import patch_model, text, tool_call + + +async def test_tools(client: Client, monkeypatch: pytest.MonkeyPatch) -> None: + patch_model( + monkeypatch, + [ + tool_call("get_weather", {"city": "New York"}), + text("It is warm and sunny."), + ], + ) + + task_queue = f"google-adk-agents-tools-{uuid.uuid4()}" + plugin = GoogleAdkPlugin() + + config = client.config() + config["plugins"] = [*config["plugins"], plugin] + client = Client(**config) + + async with Worker( + client, + task_queue=task_queue, + workflows=[WeatherAgentWorkflow], + activities=[get_weather], + max_cached_workflows=0, + ): + result = await client.execute_workflow( + WeatherAgentWorkflow.run, + "Weather in New York?", + id=f"google-adk-agents-tools-{uuid.uuid4()}", + task_queue=task_queue, + ) + + assert result == "It is warm and sunny." diff --git a/tests/google_genai/__init__.py b/tests/google_genai/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/google_genai/chat_test.py b/tests/google_genai/chat_test.py new file mode 100644 index 000000000..504a630b4 --- /dev/null +++ b/tests/google_genai/chat_test.py @@ -0,0 +1,43 @@ +import uuid + +from temporalio.client import Client +from temporalio.contrib.google_genai.testing import GeminiTestServer, text_response +from temporalio.worker import Worker + +from google_genai.chat.workflow import ChatWorkflow + + +async def test_chat(client: Client) -> None: + server = GeminiTestServer( + [ + text_response("Got it — your favorite color is teal."), + text_response("Your favorite color is teal."), + ] + ) + + config = client.config() + config["plugins"] = [*config["plugins"], server.plugin()] + client = Client(**config) + + task_queue = f"google-genai-chat-{uuid.uuid4()}" + async with Worker( + client, + task_queue=task_queue, + workflows=[ChatWorkflow], + max_cached_workflows=0, + ): + result = await client.execute_workflow( + ChatWorkflow.run, + [ + "My favorite color is teal. Remember that.", + "What is my favorite color?", + ], + id=f"google-genai-chat-{uuid.uuid4()}", + task_queue=task_queue, + ) + + assert result == [ + "Got it — your favorite color is teal.", + "Your favorite color is teal.", + ] + assert len(server.requests) == 2 diff --git a/tests/google_genai/hello_world_test.py b/tests/google_genai/hello_world_test.py new file mode 100644 index 000000000..c612885c1 --- /dev/null +++ b/tests/google_genai/hello_world_test.py @@ -0,0 +1,32 @@ +import uuid + +from temporalio.client import Client +from temporalio.contrib.google_genai.testing import GeminiTestServer, text_response +from temporalio.worker import Worker + +from google_genai.hello_world.workflow import HelloWorldWorkflow + + +async def test_hello_world(client: Client) -> None: + server = GeminiTestServer([text_response("A haiku, for you.")]) + + config = client.config() + config["plugins"] = [*config["plugins"], server.plugin()] + client = Client(**config) + + task_queue = f"google-genai-hello-world-{uuid.uuid4()}" + async with Worker( + client, + task_queue=task_queue, + workflows=[HelloWorldWorkflow], + max_cached_workflows=0, + ): + result = await client.execute_workflow( + HelloWorldWorkflow.run, + "Write a haiku.", + id=f"google-genai-hello-world-{uuid.uuid4()}", + task_queue=task_queue, + ) + + assert result == "A haiku, for you." + assert len(server.requests) == 1 diff --git a/tests/google_genai/mcp_test.py b/tests/google_genai/mcp_test.py new file mode 100644 index 000000000..e5913ad1c --- /dev/null +++ b/tests/google_genai/mcp_test.py @@ -0,0 +1,81 @@ +import sys +import uuid +from collections.abc import AsyncIterator +from contextlib import asynccontextmanager +from pathlib import Path +from typing import Any + +from google import genai +from google.genai.types import HttpResponse as SdkHttpResponse +from mcp import ClientSession, StdioServerParameters +from mcp.client.stdio import stdio_client +from temporalio.client import Client +from temporalio.contrib.google_genai import GoogleGenAIPlugin +from temporalio.contrib.google_genai.testing import ( + function_call_response, + text_response, +) +from temporalio.worker import Worker + +from google_genai.mcp.workflow import McpWorkflow + +ECHO_SERVER = str( + Path(__file__).parents[2] / "google_genai" / "mcp" / "echo_mcp_server.py" +) + + +@asynccontextmanager +async def _echo_session() -> AsyncIterator[ClientSession]: + params = StdioServerParameters(command=sys.executable, args=[ECHO_SERVER]) + async with stdio_client(params) as (read, write): + async with ClientSession(read, write) as session: + await session.initialize() + yield session + + +def _mcp_plugin(responses: list[str]) -> GoogleGenAIPlugin: + """A plugin with scripted model HTTP plus a real echo MCP server. + + Mirrors what ``GeminiTestServer.plugin()`` does for the model HTTP layer, + but also registers an MCP server (which ``GeminiTestServer`` does not), so + the MCP ``list_tools`` / ``call_tool`` activities run for real. + """ + genai_client = genai.Client(api_key="fake-test-key") + index = {"i": 0} + + async def fake_async_request(*_args: Any, **_kwargs: Any) -> SdkHttpResponse: + body = responses[index["i"]] + index["i"] += 1 + return SdkHttpResponse(headers={"content-type": "application/json"}, body=body) + + genai_client._api_client.async_request = fake_async_request # type: ignore[assignment] + return GoogleGenAIPlugin(genai_client, mcp_servers={"echo": _echo_session}) + + +async def test_mcp(client: Client) -> None: + plugin = _mcp_plugin( + [ + function_call_response("echo", {"message": "durable execution"}), + text_response("The echo tool returned: durable execution"), + ] + ) + + config = client.config() + config["plugins"] = [*config["plugins"], plugin] + client = Client(**config) + + task_queue = f"google-genai-mcp-{uuid.uuid4()}" + async with Worker( + client, + task_queue=task_queue, + workflows=[McpWorkflow], + max_cached_workflows=0, + ): + result = await client.execute_workflow( + McpWorkflow.run, + "Use the echo tool to echo back the phrase: durable execution.", + id=f"google-genai-mcp-{uuid.uuid4()}", + task_queue=task_queue, + ) + + assert "durable execution" in result diff --git a/tests/google_genai/streaming_test.py b/tests/google_genai/streaming_test.py new file mode 100644 index 000000000..dec695586 --- /dev/null +++ b/tests/google_genai/streaming_test.py @@ -0,0 +1,53 @@ +import uuid +from datetime import timedelta + +from google.genai import types +from temporalio.client import Client +from temporalio.contrib.google_genai.testing import GeminiTestServer, text_response +from temporalio.contrib.workflow_streams import WorkflowStreamClient +from temporalio.worker import Worker + +from google_genai.streaming.workflow import StreamingWorkflow + + +async def test_streaming_publishes_to_workflow_stream(client: Client) -> None: + server = GeminiTestServer([text_response("Hello from Gemini stream")]) + + config = client.config() + config["plugins"] = [*config["plugins"], server.plugin()] + client = Client(**config) + + task_queue = f"google-genai-streaming-{uuid.uuid4()}" + async with Worker( + client, + task_queue=task_queue, + workflows=[StreamingWorkflow], + max_cached_workflows=0, + ): + wf_id = f"google-genai-streaming-{uuid.uuid4()}" + handle = await client.start_workflow( + StreamingWorkflow.run, + "say hi", + id=wf_id, + task_queue=task_queue, + execution_timeout=timedelta(seconds=15), + ) + + # Consume the published chunk from the "gemini" topic. + stream = WorkflowStreamClient.create(client, wf_id) + received: list[types.GenerateContentResponse] = [] + async for item in stream.subscribe( + ["gemini"], + from_offset=0, + result_type=types.GenerateContentResponse, + poll_cooldown=timedelta(milliseconds=20), + ): + received.append(item.data) + break # one scripted chunk + + await handle.signal(StreamingWorkflow.finish) + result = await handle.result() + + assert result == "Hello from Gemini stream" + assert len(received) == 1 + assert received[0].text == "Hello from Gemini stream" diff --git a/tests/google_genai/structured_output_test.py b/tests/google_genai/structured_output_test.py new file mode 100644 index 000000000..6e93c2ede --- /dev/null +++ b/tests/google_genai/structured_output_test.py @@ -0,0 +1,45 @@ +import json +import uuid + +from temporalio.client import Client +from temporalio.contrib.google_genai.testing import GeminiTestServer, text_response +from temporalio.worker import Worker + +from google_genai.structured_output.workflow import ( + Recipe, + StructuredOutputWorkflow, +) + + +async def test_structured_output(client: Client) -> None: + recipe_json = json.dumps( + { + "name": "Avocado Toast", + "ingredients": ["bread", "avocado", "salt"], + "steps": ["Toast the bread.", "Mash the avocado on top.", "Season."], + } + ) + server = GeminiTestServer([text_response(recipe_json)]) + + config = client.config() + config["plugins"] = [*config["plugins"], server.plugin()] + client = Client(**config) + + task_queue = f"google-genai-structured-output-{uuid.uuid4()}" + async with Worker( + client, + task_queue=task_queue, + workflows=[StructuredOutputWorkflow], + max_cached_workflows=0, + ): + result = await client.execute_workflow( + StructuredOutputWorkflow.run, + "Give me a simple recipe for avocado toast.", + id=f"google-genai-structured-output-{uuid.uuid4()}", + task_queue=task_queue, + ) + + assert isinstance(result, Recipe) + assert result.name == "Avocado Toast" + assert result.ingredients == ["bread", "avocado", "salt"] + assert len(result.steps) == 3 diff --git a/tests/google_genai/tools_test.py b/tests/google_genai/tools_test.py new file mode 100644 index 000000000..b657fd1fd --- /dev/null +++ b/tests/google_genai/tools_test.py @@ -0,0 +1,46 @@ +import uuid + +from temporalio.client import Client +from temporalio.contrib.google_genai.testing import ( + GeminiTestServer, + function_call_response, + text_response, +) +from temporalio.worker import Worker + +from google_genai.tools.workflow import ToolsWorkflow, get_weather + + +async def test_tools(client: Client) -> None: + server = GeminiTestServer( + [ + function_call_response("get_weather", {"city": "Tokyo"}), + function_call_response( + "recommend_thing_to_do", {"weather": "It's 72F and sunny in Tokyo."} + ), + text_response("It's sunny in Tokyo — go for a hike!"), + ] + ) + + config = client.config() + config["plugins"] = [*config["plugins"], server.plugin()] + client = Client(**config) + + task_queue = f"google-genai-tools-{uuid.uuid4()}" + async with Worker( + client, + task_queue=task_queue, + workflows=[ToolsWorkflow], + activities=[get_weather], + max_cached_workflows=0, + ): + result = await client.execute_workflow( + ToolsWorkflow.run, + "What's the weather in Tokyo, and what should I do there?", + id=f"google-genai-tools-{uuid.uuid4()}", + task_queue=task_queue, + ) + + assert result == "It's sunny in Tokyo — go for a hike!" + # One model turn per response: two tool calls and a final text answer. + assert len(server.requests) == 3 diff --git a/tests/hello/hello_activity_choice_test.py b/tests/hello/hello_activity_choice_test.py index 236634de3..1dadbb88b 100644 --- a/tests/hello/hello_activity_choice_test.py +++ b/tests/hello/hello_activity_choice_test.py @@ -20,13 +20,12 @@ ] -@pytest.mark.asyncio @pytest.mark.parametrize( "activity_func, order_amount, expected_result", activity_test_data ) -async def test_order_fruit(activity_func, order_amount, expected_result): +def test_order_fruit(activity_func, order_amount, expected_result): activity_environment = ActivityEnvironment() - result = await activity_environment.run(activity_func, order_amount) + result = activity_environment.run(activity_func, order_amount) assert result == expected_result diff --git a/tests/hello/hello_activity_test.py b/tests/hello/hello_activity_test.py index 4bd8e40e0..5bf9c1b8a 100644 --- a/tests/hello/hello_activity_test.py +++ b/tests/hello/hello_activity_test.py @@ -1,4 +1,5 @@ import uuid +from concurrent.futures import ThreadPoolExecutor from temporalio import activity from temporalio.client import Client @@ -18,6 +19,7 @@ async def test_execute_workflow(client: Client): client, task_queue=task_queue_name, workflows=[GreetingWorkflow], + activity_executor=ThreadPoolExecutor(5), activities=[compose_greeting], ): assert "Hello, World!" == await client.execute_workflow( diff --git a/tests/hello/hello_cancellation_test.py b/tests/hello/hello_cancellation_test.py index 2d4b946ed..b511b50b8 100644 --- a/tests/hello/hello_cancellation_test.py +++ b/tests/hello/hello_cancellation_test.py @@ -1,5 +1,6 @@ import asyncio import uuid +from concurrent.futures import ThreadPoolExecutor import pytest from temporalio.client import Client, WorkflowExecutionStatus, WorkflowFailureError @@ -21,6 +22,7 @@ async def test_cancel_workflow(client: Client): task_queue=task_queue_name, workflows=[CancellationWorkflow], activities=[cleanup_activity, never_complete_activity], + activity_executor=ThreadPoolExecutor(5), ): handle = await client.start_workflow( CancellationWorkflow.run, diff --git a/tests/hello/hello_change_log_level_test.py b/tests/hello/hello_change_log_level_test.py new file mode 100644 index 000000000..e83e08c45 --- /dev/null +++ b/tests/hello/hello_change_log_level_test.py @@ -0,0 +1,42 @@ +import asyncio +import io +import logging +import uuid + +from temporalio.client import Client +from temporalio.worker import Worker + +from hello.hello_change_log_level import LOG_MESSAGE, GreetingWorkflow + + +async def test_workflow_with_log_capture(client: Client): + log_stream = io.StringIO() + handler = logging.StreamHandler(log_stream) + handler.setLevel(logging.ERROR) + + logger = logging.getLogger() + logger.addHandler(handler) + logger.setLevel(logging.DEBUG) + + task_queue = f"tq-{uuid.uuid4()}" + + async with Worker( + client, + task_queue=task_queue, + workflows=[GreetingWorkflow], + ): + handle = await client.start_workflow( + GreetingWorkflow.run, + id=f"wf-{uuid.uuid4()}", + task_queue=task_queue, + ) + await asyncio.sleep( + 0.2 + ) # small wait to ensure the workflow has started, failed, and logged + await handle.terminate() + + logger.removeHandler(handler) + handler.flush() + + logs = log_stream.getvalue() + assert LOG_MESSAGE in logs diff --git a/tests/hello/hello_standalone_activity_test.py b/tests/hello/hello_standalone_activity_test.py new file mode 100644 index 000000000..d40b09fa4 --- /dev/null +++ b/tests/hello/hello_standalone_activity_test.py @@ -0,0 +1,31 @@ +import uuid +from concurrent.futures import ThreadPoolExecutor +from datetime import timedelta + +import pytest +from temporalio.client import Client +from temporalio.worker import Worker + +from hello_standalone_activity.my_activity import ComposeGreetingInput, compose_greeting + + +async def test_execute_standalone_activity(client: Client): + pytest.skip( + "Standalone Activity is not yet supported by `temporal server start-dev`" + ) + task_queue_name = str(uuid.uuid4()) + + async with Worker( + client, + task_queue=task_queue_name, + activities=[compose_greeting], + activity_executor=ThreadPoolExecutor(5), + ): + result = await client.execute_activity( + compose_greeting, + args=[ComposeGreetingInput("Hello", "World")], + id=str(uuid.uuid4()), + task_queue=task_queue_name, + start_to_close_timeout=timedelta(seconds=10), + ) + assert result == "Hello, World!" diff --git a/tests/hello/hello_update_test.py b/tests/hello/hello_update_test.py new file mode 100644 index 000000000..f7664ecb3 --- /dev/null +++ b/tests/hello/hello_update_test.py @@ -0,0 +1,25 @@ +import uuid + +import pytest +from temporalio.client import Client, WorkflowExecutionStatus +from temporalio.testing import WorkflowEnvironment +from temporalio.worker import Worker + +from hello.hello_update import GreetingWorkflow + + +async def test_update_workflow(client: Client, env: WorkflowEnvironment): + task_queue_name = str(uuid.uuid4()) + async with Worker(client, task_queue=task_queue_name, workflows=[GreetingWorkflow]): + handle = await client.start_workflow( + GreetingWorkflow.run, id=str(uuid.uuid4()), task_queue=task_queue_name + ) + + assert WorkflowExecutionStatus.RUNNING == (await handle.describe()).status + + update_result = await handle.execute_update( + GreetingWorkflow.update_workflow_status + ) + assert "Workflow status updated" == update_result + assert "Hello, World!" == (await handle.result()) + assert WorkflowExecutionStatus.COMPLETED == (await handle.describe()).status diff --git a/tests/hello_nexus/hello_nexus_test.py b/tests/hello_nexus/hello_nexus_test.py new file mode 100644 index 000000000..fecfe17cb --- /dev/null +++ b/tests/hello_nexus/hello_nexus_test.py @@ -0,0 +1,48 @@ +import asyncio +import sys + +import pytest +from temporalio.client import Client +from temporalio.testing import WorkflowEnvironment + +import hello_nexus.caller.app +import hello_nexus.caller.workflows +import hello_nexus.handler.worker +from tests.helpers.nexus import create_nexus_endpoint, delete_nexus_endpoint + + +async def test_nexus_service_basic(client: Client, env: WorkflowEnvironment): + if env.supports_time_skipping: + pytest.skip("Nexus tests don't work under the Java test server") + + if sys.version_info[:2] < (3, 10): + pytest.skip("Sample is written for Python >= 3.10") + + create_response = await create_nexus_endpoint( + name=hello_nexus.caller.workflows.NEXUS_ENDPOINT, + task_queue=hello_nexus.handler.worker.TASK_QUEUE, + client=client, + ) + try: + handler_worker_task = asyncio.create_task( + hello_nexus.handler.worker.main( + client, + ) + ) + await asyncio.sleep(1) + results = await hello_nexus.caller.app.execute_caller_workflow( + client, + ) + hello_nexus.handler.worker.interrupt_event.set() + await handler_worker_task + hello_nexus.handler.worker.interrupt_event.clear() + assert [r.message for r in results] == [ + "Hello world from sync operation!", + "Hello world from workflow run operation!", + ] + finally: + await delete_nexus_endpoint( + id=create_response.endpoint.id, + version=create_response.endpoint.version, + client=client, + ) diff --git a/tests/helpers/__init__.py b/tests/helpers/__init__.py new file mode 100644 index 000000000..a387e5291 --- /dev/null +++ b/tests/helpers/__init__.py @@ -0,0 +1,24 @@ +import asyncio +import time +from collections.abc import Awaitable, Callable +from datetime import timedelta +from typing import TypeVar + +T = TypeVar("T") + + +async def assert_eventually( + fn: Callable[[], Awaitable[T]], + *, + timeout: timedelta = timedelta(seconds=10), + interval: timedelta = timedelta(milliseconds=200), +) -> T: + start_sec = time.monotonic() + while True: + try: + res = await fn() + return res + except AssertionError: + if timedelta(seconds=time.monotonic() - start_sec) >= timeout: + raise + await asyncio.sleep(interval.total_seconds()) diff --git a/tests/helpers/nexus.py b/tests/helpers/nexus.py new file mode 100644 index 000000000..b9ce68995 --- /dev/null +++ b/tests/helpers/nexus.py @@ -0,0 +1,36 @@ +import temporalio.api +import temporalio.api.nexus +import temporalio.api.nexus.v1 +import temporalio.api.operatorservice +import temporalio.api.operatorservice.v1 +from temporalio.client import Client + + +# TODO: copied from sdk-python tests/helpers/nexus +async def create_nexus_endpoint( + name: str, task_queue: str, client: Client +) -> temporalio.api.operatorservice.v1.CreateNexusEndpointResponse: + return await client.operator_service.create_nexus_endpoint( + temporalio.api.operatorservice.v1.CreateNexusEndpointRequest( + spec=temporalio.api.nexus.v1.EndpointSpec( + name=name, + target=temporalio.api.nexus.v1.EndpointTarget( + worker=temporalio.api.nexus.v1.EndpointTarget.Worker( + namespace=client.namespace, + task_queue=task_queue, + ) + ), + ) + ) + ) + + +async def delete_nexus_endpoint( + id: str, version: int, client: Client +) -> temporalio.api.operatorservice.v1.DeleteNexusEndpointResponse: + return await client.operator_service.delete_nexus_endpoint( + temporalio.api.operatorservice.v1.DeleteNexusEndpointRequest( + id=id, + version=version, + ) + ) diff --git a/tests/langfuse_tracing/__init__.py b/tests/langfuse_tracing/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/langfuse_tracing/conftest.py b/tests/langfuse_tracing/conftest.py new file mode 100644 index 000000000..3a542c9e4 --- /dev/null +++ b/tests/langfuse_tracing/conftest.py @@ -0,0 +1,19 @@ +from typing import Iterator + +import opentelemetry.trace +import pytest +from opentelemetry.util._once import Once + + +@pytest.fixture +def reset_otel_tracer_provider() -> Iterator[None]: + """Reset global OpenTelemetry tracer provider state around a test. + + OpenTelemetry only allows the global tracer provider to be set once per + process; tests that install their own provider need this reset. + """ + opentelemetry.trace._TRACER_PROVIDER_SET_ONCE = Once() + opentelemetry.trace._TRACER_PROVIDER = None + yield + opentelemetry.trace._TRACER_PROVIDER_SET_ONCE = Once() + opentelemetry.trace._TRACER_PROVIDER = None diff --git a/tests/langfuse_tracing/helpers.py b/tests/langfuse_tracing/helpers.py new file mode 100644 index 000000000..7c224ae9b --- /dev/null +++ b/tests/langfuse_tracing/helpers.py @@ -0,0 +1,30 @@ +"""Test helpers for the langfuse_tracing sample tests.""" + +from typing import Iterable, List, Optional + +from opentelemetry.sdk.trace import ReadableSpan + + +def dump_spans( + spans: Iterable[ReadableSpan], + *, + parent_id: Optional[int] = None, + indent_depth: int = 0, +) -> List[str]: + """Render spans as an indented tree, one line per span. + + Mirrors the helper used by the Temporal Python SDK's own OpenTelemetry + tests so span hierarchies can be asserted with a whole-tree equality. + """ + ret: List[str] = [] + for span in spans: + if (not span.parent and parent_id is None) or ( + span.parent and span.parent.span_id == parent_id + ): + ret.append(f"{' ' * indent_depth}{span.name}") + ret += dump_spans( + spans, + parent_id=span.context.span_id if span.context else None, + indent_depth=indent_depth + 1, + ) + return ret diff --git a/tests/langfuse_tracing/test_ticket_triage.py b/tests/langfuse_tracing/test_ticket_triage.py new file mode 100644 index 000000000..315a4d1ac --- /dev/null +++ b/tests/langfuse_tracing/test_ticket_triage.py @@ -0,0 +1,167 @@ +"""Tests for the ticket triage sample. + +These run without Langfuse or an LLM: the LLM activities are mocked (each +opens a custom span to prove trace context propagates into activities) and +spans are captured with an in-memory exporter. The worker runs with the +workflow cache disabled, so every workflow task replays the workflow from the +start of history — asserting the whole span tree with deep equality proves +spans are emitted exactly once despite replay. +""" + +import uuid +from typing import Any + +import opentelemetry.trace +from opentelemetry import trace +from opentelemetry.sdk.trace.export import SimpleSpanProcessor +from opentelemetry.sdk.trace.export.in_memory_span_exporter import InMemorySpanExporter +from temporalio import activity +from temporalio.client import Client +from temporalio.contrib.opentelemetry import OpenTelemetryPlugin, create_tracer_provider +from temporalio.worker import Replayer, Worker + +from langfuse_tracing.ticket_triage.activities import ( + AccountInfo, + ApprovalDecision, + Classification, + DraftReplyInput, + Ticket, +) +from langfuse_tracing.ticket_triage.workflows import TicketTriageWorkflow +from tests.langfuse_tracing.helpers import dump_spans + +TICKET = Ticket( + ticket_id="T-1", + customer_email="ada@acme.example", + subject="Charged twice", + body="Please refund the duplicate charge.", +) + + +@activity.defn(name="classify_ticket") +async def classify_ticket_mocked(ticket: Ticket) -> Classification: + with trace.get_tracer(__name__).start_as_current_span("mock llm classify"): + return Classification(category="billing", priority="high") + + +@activity.defn(name="lookup_account") +async def lookup_account_mocked(customer_email: str) -> AccountInfo: + with trace.get_tracer(__name__).start_as_current_span("mock account lookup"): + return AccountInfo( + customer_email=customer_email, account_name="Acme Corp", plan="enterprise" + ) + + +@activity.defn(name="draft_reply") +async def draft_reply_mocked(input: DraftReplyInput) -> str: + with trace.get_tracer(__name__).start_as_current_span("mock llm draft"): + return "Sorry about that - refund on the way." + + +def _install_in_memory_exporter() -> InMemorySpanExporter: + exporter = InMemorySpanExporter() + provider = create_tracer_provider() + provider.add_span_processor(SimpleSpanProcessor(exporter)) + opentelemetry.trace.set_tracer_provider(provider) + return exporter + + +def _client_with_plugin(client: Client) -> Client: + config = client.config() + config["plugins"] = [OpenTelemetryPlugin(add_temporal_spans=True)] + return Client(**config) + + +async def _run_workflow(client: Client, task_queue: str, approved: bool) -> Any: + handle = await client.start_workflow( + TicketTriageWorkflow.run, + TICKET, + id=f"ticket-triage-test-{uuid.uuid4()}", + task_queue=task_queue, + ) + await handle.execute_update( + TicketTriageWorkflow.approve, + ApprovalDecision(approved=approved, reviewer="test-reviewer"), + ) + await handle.result() + return handle + + +EXPECTED_APPROVED = [ + "ticket-triage test", + " StartWorkflow:TicketTriageWorkflow", + " RunWorkflow:TicketTriageWorkflow", + " triage", + " StartActivity:classify_ticket", + " RunActivity:classify_ticket", + " mock llm classify", + " StartActivity:lookup_account", + " RunActivity:lookup_account", + " mock account lookup", + " StartActivity:draft_reply", + " RunActivity:draft_reply", + " mock llm draft", + " StartWorkflowUpdate:approve", + " ValidateUpdate:approve", + " HandleUpdate:approve", +] + + +async def test_spans_emitted_exactly_once_under_replay_stress( + client: Client, reset_otel_tracer_provider: Any +) -> None: + exporter = _install_in_memory_exporter() + new_client = _client_with_plugin(client) + task_queue = f"tq-{uuid.uuid4()}" + + async with Worker( + new_client, + task_queue=task_queue, + workflows=[TicketTriageWorkflow], + activities=[classify_ticket_mocked, lookup_account_mocked, draft_reply_mocked], + # Disable the workflow cache: every workflow task replays the workflow + # from the start of history. Tracing must still emit each span once. + max_cached_workflows=0, + ): + with trace.get_tracer(__name__).start_as_current_span("ticket-triage test"): + handle = await _run_workflow(new_client, task_queue, approved=True) + + spans = exporter.get_finished_spans() + assert dump_spans(spans) == EXPECTED_APPROVED + span_ids = [s.context.span_id for s in spans if s.context] + assert len(set(span_ids)) == len(span_ids) + + # Replaying the finished workflow's real history must emit zero new spans. + history = await handle.fetch_history() + before = len(exporter.get_finished_spans()) + replayer = Replayer( + workflows=[TicketTriageWorkflow], + plugins=[OpenTelemetryPlugin(add_temporal_spans=True)], + ) + await replayer.replay_workflow(history) + assert len(exporter.get_finished_spans()) == before + + +async def test_declined_path_span_tree( + client: Client, reset_otel_tracer_provider: Any +) -> None: + exporter = _install_in_memory_exporter() + new_client = _client_with_plugin(client) + task_queue = f"tq-{uuid.uuid4()}" + + async with Worker( + new_client, + task_queue=task_queue, + workflows=[TicketTriageWorkflow], + activities=[classify_ticket_mocked, lookup_account_mocked, draft_reply_mocked], + max_cached_workflows=0, + ): + with trace.get_tracer(__name__).start_as_current_span("ticket-triage test"): + await _run_workflow(new_client, task_queue, approved=False) + + expected = [ + line + for line in EXPECTED_APPROVED + if "draft" not in line # declined tickets never reach draft_reply + ] + assert dump_spans(exporter.get_finished_spans()) == expected diff --git a/tests/langgraph_plugin/__init__.py b/tests/langgraph_plugin/__init__.py new file mode 100644 index 000000000..133a43fdf --- /dev/null +++ b/tests/langgraph_plugin/__init__.py @@ -0,0 +1 @@ +"""LangGraph plugin sample tests.""" diff --git a/tests/langgraph_plugin/continue_as_new_test.py b/tests/langgraph_plugin/continue_as_new_test.py new file mode 100644 index 000000000..71392f5f7 --- /dev/null +++ b/tests/langgraph_plugin/continue_as_new_test.py @@ -0,0 +1,34 @@ +import uuid +from datetime import timedelta + +from temporalio.client import Client +from temporalio.contrib.langgraph import LangGraphPlugin +from temporalio.worker import Worker + +from langgraph_plugin.graph_api.continue_as_new.workflow import ( + PipelineInput, + PipelineWorkflow, + make_pipeline_graph, +) + + +async def test_continue_as_new_graph_api(client: Client) -> None: + """Input 10: 10*2=20 -> 20+50=70 -> 70*3=210.""" + task_queue = f"continue-as-new-test-{uuid.uuid4()}" + plugin = LangGraphPlugin(graphs={"pipeline": make_pipeline_graph()}) + + async with Worker( + client, + task_queue=task_queue, + workflows=[PipelineWorkflow], + plugins=[plugin], + ): + result = await client.execute_workflow( + PipelineWorkflow.run, + PipelineInput(data=10), + id=f"continue-as-new-{uuid.uuid4()}", + task_queue=task_queue, + execution_timeout=timedelta(seconds=60), + ) + + assert result == 210 diff --git a/tests/langgraph_plugin/functional_continue_as_new_test.py b/tests/langgraph_plugin/functional_continue_as_new_test.py new file mode 100644 index 000000000..9d627d629 --- /dev/null +++ b/tests/langgraph_plugin/functional_continue_as_new_test.py @@ -0,0 +1,47 @@ +import sys +import uuid +from datetime import timedelta + +import pytest +from temporalio.client import Client +from temporalio.contrib.langgraph import LangGraphPlugin +from temporalio.worker import Worker + +from langgraph_plugin.functional_api.continue_as_new.workflow import ( + PipelineFunctionalWorkflow, + PipelineInput, + activity_options, + all_tasks, + pipeline_entrypoint, +) + +pytestmark = pytest.mark.skipif( + sys.version_info < (3, 11), + reason="LangGraph Functional API requires Python >= 3.11 for async context propagation", +) + + +async def test_functional_continue_as_new(client: Client) -> None: + """Input 10: 10*2=20 -> 20+50=70 -> 70*3=210.""" + task_queue = f"functional-continue-test-{uuid.uuid4()}" + plugin = LangGraphPlugin( + entrypoints={"pipeline": pipeline_entrypoint}, + tasks=all_tasks, + activity_options=activity_options, + ) + + async with Worker( + client, + task_queue=task_queue, + workflows=[PipelineFunctionalWorkflow], + plugins=[plugin], + ): + result = await client.execute_workflow( + PipelineFunctionalWorkflow.run, + PipelineInput(data=10), + id=f"functional-continue-{uuid.uuid4()}", + task_queue=task_queue, + execution_timeout=timedelta(seconds=60), + ) + + assert result == {"result": 210} diff --git a/tests/langgraph_plugin/functional_control_flow_test.py b/tests/langgraph_plugin/functional_control_flow_test.py new file mode 100644 index 000000000..a8fd337af --- /dev/null +++ b/tests/langgraph_plugin/functional_control_flow_test.py @@ -0,0 +1,59 @@ +import sys +import uuid + +import pytest +from temporalio.client import Client +from temporalio.contrib.langgraph import LangGraphPlugin +from temporalio.worker import Worker + +from langgraph_plugin.functional_api.control_flow.workflow import ( + ControlFlowWorkflow, + activity_options, + all_tasks, + control_flow_pipeline, +) + +pytestmark = pytest.mark.skipif( + sys.version_info < (3, 11), + reason="LangGraph Functional API requires Python >= 3.11 for async context propagation", +) + + +async def test_functional_control_flow(client: Client) -> None: + task_queue = f"functional-control-flow-test-{uuid.uuid4()}" + plugin = LangGraphPlugin( + entrypoints={"control-flow": control_flow_pipeline}, + tasks=all_tasks, + activity_options=activity_options, + ) + + items = [ + "Fix login bug", + "URGENT: Production outage", + "Update README", + "INVALID:", + "Urgent: Security patch", + ] + + async with Worker( + client, + task_queue=task_queue, + workflows=[ControlFlowWorkflow], + plugins=[plugin], + ): + result = await client.execute_workflow( + ControlFlowWorkflow.run, + items, + id=f"functional-control-flow-{uuid.uuid4()}", + task_queue=task_queue, + ) + + # "INVALID:" should be filtered out + assert result["total"] == 4 + # Check urgent vs normal routing + urgent = [r for r in result["results"] if r.startswith("[PRIORITY]")] + normal = [r for r in result["results"] if r.startswith("[STANDARD]")] + assert len(urgent) == 2 + assert len(normal) == 2 + assert "2 urgent" in result["summary"] + assert "2 normal" in result["summary"] diff --git a/tests/langgraph_plugin/functional_hello_world_test.py b/tests/langgraph_plugin/functional_hello_world_test.py new file mode 100644 index 000000000..4603ddbad --- /dev/null +++ b/tests/langgraph_plugin/functional_hello_world_test.py @@ -0,0 +1,43 @@ +import sys +import uuid + +import pytest +from temporalio.client import Client +from temporalio.contrib.langgraph import LangGraphPlugin +from temporalio.worker import Worker + +from langgraph_plugin.functional_api.hello_world.workflow import ( + HelloWorldFunctionalWorkflow, + activity_options, + all_tasks, + hello_entrypoint, +) + +pytestmark = pytest.mark.skipif( + sys.version_info < (3, 11), + reason="LangGraph Functional API requires Python >= 3.11 for async context propagation", +) + + +async def test_functional_hello_world(client: Client) -> None: + task_queue = f"functional-hello-test-{uuid.uuid4()}" + plugin = LangGraphPlugin( + entrypoints={"hello-world": hello_entrypoint}, + tasks=all_tasks, + activity_options=activity_options, + ) + + async with Worker( + client, + task_queue=task_queue, + workflows=[HelloWorldFunctionalWorkflow], + plugins=[plugin], + ): + result = await client.execute_workflow( + HelloWorldFunctionalWorkflow.run, + "test query", + id=f"functional-hello-{uuid.uuid4()}", + task_queue=task_queue, + ) + + assert result == {"result": "Processed: test query"} diff --git a/tests/langgraph_plugin/functional_human_in_the_loop_test.py b/tests/langgraph_plugin/functional_human_in_the_loop_test.py new file mode 100644 index 000000000..746647f88 --- /dev/null +++ b/tests/langgraph_plugin/functional_human_in_the_loop_test.py @@ -0,0 +1,58 @@ +import asyncio +import sys +import uuid + +import pytest +from temporalio.client import Client +from temporalio.contrib.langgraph import LangGraphPlugin +from temporalio.worker import Worker + +from langgraph_plugin.functional_api.human_in_the_loop.workflow import ( + ChatbotFunctionalWorkflow, + activity_options, + all_tasks, + chatbot_entrypoint, +) + +pytestmark = pytest.mark.skipif( + sys.version_info < (3, 11), + reason="LangGraph Functional API and interrupt() require Python >= 3.11 for async context propagation", +) + + +async def test_functional_human_in_the_loop_approve(client: Client) -> None: + task_queue = f"functional-hitl-test-{uuid.uuid4()}" + plugin = LangGraphPlugin( + entrypoints={"chatbot": chatbot_entrypoint}, + tasks=all_tasks, + activity_options=activity_options, + ) + + async with Worker( + client, + task_queue=task_queue, + workflows=[ChatbotFunctionalWorkflow], + plugins=[plugin], + ): + handle = await client.start_workflow( + ChatbotFunctionalWorkflow.run, + "test message", + id=f"functional-hitl-{uuid.uuid4()}", + task_queue=task_queue, + ) + + # Poll for draft to be ready + draft = None + for _ in range(40): + await asyncio.sleep(0.25) + draft = await handle.query(ChatbotFunctionalWorkflow.get_draft) + if draft is not None: + break + assert draft is not None + assert "test message" in draft + + # Approve + await handle.signal(ChatbotFunctionalWorkflow.provide_feedback, "approve") + result = await handle.result() + + assert result["response"] == draft diff --git a/tests/langgraph_plugin/functional_react_agent_test.py b/tests/langgraph_plugin/functional_react_agent_test.py new file mode 100644 index 000000000..088cd5c47 --- /dev/null +++ b/tests/langgraph_plugin/functional_react_agent_test.py @@ -0,0 +1,44 @@ +import sys +import uuid + +import pytest +from temporalio.client import Client +from temporalio.contrib.langgraph import LangGraphPlugin +from temporalio.worker import Worker + +from langgraph_plugin.functional_api.react_agent.workflow import ( + ReactAgentFunctionalWorkflow, + activity_options, + all_tasks, + react_agent_entrypoint, +) + +pytestmark = pytest.mark.skipif( + sys.version_info < (3, 11), + reason="LangGraph Functional API requires Python >= 3.11 for async context propagation", +) + + +async def test_functional_react_agent(client: Client) -> None: + task_queue = f"functional-react-agent-test-{uuid.uuid4()}" + plugin = LangGraphPlugin( + entrypoints={"react-agent": react_agent_entrypoint}, + tasks=all_tasks, + activity_options=activity_options, + ) + + async with Worker( + client, + task_queue=task_queue, + workflows=[ReactAgentFunctionalWorkflow], + plugins=[plugin], + ): + result = await client.execute_workflow( + ReactAgentFunctionalWorkflow.run, + "Tell me about San Francisco", + id=f"functional-react-agent-{uuid.uuid4()}", + task_queue=task_queue, + ) + + assert "San Francisco" in result["answer"] + assert result["steps"] == 2 # two tool calls diff --git a/tests/langgraph_plugin/hello_world_test.py b/tests/langgraph_plugin/hello_world_test.py new file mode 100644 index 000000000..430989e57 --- /dev/null +++ b/tests/langgraph_plugin/hello_world_test.py @@ -0,0 +1,50 @@ +import uuid + +from temporalio.client import Client +from temporalio.contrib.langgraph import LangGraphPlugin +from temporalio.worker import Worker + +from langgraph_plugin.graph_api.hello_world.workflow import ( + HelloWorldWorkflow, + make_hello_graph, +) + + +async def test_hello_world_graph_api(client: Client) -> None: + task_queue = f"hello-world-test-{uuid.uuid4()}" + plugin = LangGraphPlugin(graphs={"hello-world": make_hello_graph()}) + + async with Worker( + client, + task_queue=task_queue, + workflows=[HelloWorldWorkflow], + plugins=[plugin], + ): + result = await client.execute_workflow( + HelloWorldWorkflow.run, + "test query", + id=f"hello-world-{uuid.uuid4()}", + task_queue=task_queue, + ) + + assert result == "Processed: test query" + + +async def test_hello_world_empty_string(client: Client) -> None: + task_queue = f"hello-world-empty-test-{uuid.uuid4()}" + plugin = LangGraphPlugin(graphs={"hello-world": make_hello_graph()}) + + async with Worker( + client, + task_queue=task_queue, + workflows=[HelloWorldWorkflow], + plugins=[plugin], + ): + result = await client.execute_workflow( + HelloWorldWorkflow.run, + "", + id=f"hello-world-empty-{uuid.uuid4()}", + task_queue=task_queue, + ) + + assert result == "Processed: " diff --git a/tests/langgraph_plugin/human_in_the_loop_test.py b/tests/langgraph_plugin/human_in_the_loop_test.py new file mode 100644 index 000000000..2d0b917f2 --- /dev/null +++ b/tests/langgraph_plugin/human_in_the_loop_test.py @@ -0,0 +1,115 @@ +import asyncio +import sys +import uuid +from unittest.mock import patch + +import pytest +from temporalio.client import Client +from temporalio.contrib.langgraph import LangGraphPlugin +from temporalio.worker import Worker + +from langgraph_plugin.graph_api.human_in_the_loop.workflow import ( + ChatbotWorkflow, + make_chatbot_graph, +) + +pytestmark = pytest.mark.skipif( + sys.version_info < (3, 11), + reason="langgraph.types.interrupt() requires Python >= 3.11 for async context propagation", +) + + +class _FakeMessage: + def __init__(self, content: str) -> None: + self.content = content + + +class _EchoModel: + """Stand-in for a chat model that echoes the prompt back as its response.""" + + async def ainvoke(self, prompt: str) -> _FakeMessage: + return _FakeMessage(prompt) + + +def _fake_init_chat_model(*args: object, **kwargs: object) -> _EchoModel: + return _EchoModel() + + +_patch_llm = lambda: patch( + "langgraph_plugin.graph_api.human_in_the_loop.workflow.init_chat_model", + _fake_init_chat_model, +) + + +async def test_human_in_the_loop_approve(client: Client) -> None: + task_queue = f"hitl-test-{uuid.uuid4()}" + plugin = LangGraphPlugin(graphs={"chatbot": make_chatbot_graph()}) + + with _patch_llm(): + async with Worker( + client, + task_queue=task_queue, + workflows=[ChatbotWorkflow], + plugins=[plugin], + ): + handle = await client.start_workflow( + ChatbotWorkflow.run, + "test message", + id=f"hitl-{uuid.uuid4()}", + task_queue=task_queue, + ) + + # Poll for draft to be ready + draft = None + for _ in range(40): + await asyncio.sleep(0.25) + draft = await handle.query(ChatbotWorkflow.get_draft) + if draft is not None: + break + assert draft is not None + assert "test message" in draft + + # Approve + await handle.signal(ChatbotWorkflow.provide_feedback, "approve") + result = await handle.result() + + assert result == draft # approved draft returned as-is + + +async def test_human_in_the_loop_revise(client: Client) -> None: + task_queue = f"hitl-revise-test-{uuid.uuid4()}" + plugin = LangGraphPlugin(graphs={"chatbot": make_chatbot_graph()}) + + with _patch_llm(): + async with Worker( + client, + task_queue=task_queue, + workflows=[ChatbotWorkflow], + plugins=[plugin], + ): + handle = await client.start_workflow( + ChatbotWorkflow.run, + "test message", + id=f"hitl-revise-{uuid.uuid4()}", + task_queue=task_queue, + ) + + # Poll for draft + draft = None + for _ in range(40): + await asyncio.sleep(0.25) + draft = await handle.query(ChatbotWorkflow.get_draft) + if draft is not None: + break + assert draft is not None + + # Send revision feedback + await handle.signal( + ChatbotWorkflow.provide_feedback, "please be more concise" + ) + result = await handle.result() + + # The revision node feeds the draft and feedback into the LLM; the echo + # stand-in returns the revision prompt, which contains both. + assert "please be more concise" in result + assert "test message" in result diff --git a/tests/langgraph_plugin/react_agent_test.py b/tests/langgraph_plugin/react_agent_test.py new file mode 100644 index 000000000..e9db3ec6a --- /dev/null +++ b/tests/langgraph_plugin/react_agent_test.py @@ -0,0 +1,32 @@ +import uuid + +from temporalio.client import Client +from temporalio.contrib.langgraph import LangGraphPlugin +from temporalio.worker import Worker + +from langgraph_plugin.graph_api.react_agent.workflow import ( + ReactAgentWorkflow, + make_agent_graph, +) + + +async def test_react_agent_graph_api(client: Client) -> None: + task_queue = f"react-agent-test-{uuid.uuid4()}" + plugin = LangGraphPlugin(graphs={"react-agent": make_agent_graph()}) + + async with Worker( + client, + task_queue=task_queue, + workflows=[ReactAgentWorkflow], + plugins=[plugin], + ): + result = await client.execute_workflow( + ReactAgentWorkflow.run, + "Tell me about San Francisco", + id=f"react-agent-{uuid.uuid4()}", + task_queue=task_queue, + ) + + assert "San Francisco" in result + assert "72" in result or "weather" in result.lower() + assert "870,000" in result or "population" in result.lower() diff --git a/tests/langgraph_plugin/streaming_test.py b/tests/langgraph_plugin/streaming_test.py new file mode 100644 index 000000000..80c5047b8 --- /dev/null +++ b/tests/langgraph_plugin/streaming_test.py @@ -0,0 +1,71 @@ +import uuid +from datetime import timedelta +from typing import Any + +from temporalio.client import Client +from temporalio.contrib.langgraph import LangGraphPlugin +from temporalio.contrib.workflow_streams import WorkflowStreamClient +from temporalio.worker import Worker + +from langgraph_plugin.graph_api.streaming.workflow import ( + StreamingWorkflow, + make_streaming_graph, +) + + +async def test_streaming_graph_api(client: Client) -> None: + task_queue = f"streaming-test-{uuid.uuid4()}" + plugin = LangGraphPlugin( + graphs={"streaming": make_streaming_graph()}, + streaming_topic="tokens", + ) + + async with Worker( + client, + task_queue=task_queue, + workflows=[StreamingWorkflow], + plugins=[plugin], + ): + handle = await client.start_workflow( + StreamingWorkflow.run, + "a brave robot", + id=f"streaming-{uuid.uuid4()}", + task_queue=task_queue, + ) + + ws = WorkflowStreamClient.create(client, handle.id) + tokens: list[dict[str, Any]] = [] + progress: list[dict[str, Any]] = [] + async for item in ws.subscribe( + from_offset=0, + result_type=dict, + poll_cooldown=timedelta(milliseconds=10), + ): + if item.topic == "tokens": + tokens.append(item.data) + elif item.topic == "progress": + if item.data.get("done"): + await handle.signal(StreamingWorkflow.ack_stream) + break + progress.append(item.data) + + result = await handle.result() + + # Tokens reassemble into the final story. Streaming is at-least-once per + # activity attempt, so dedupe on seq (keeping first-seen order) before + # reassembling, exactly as an idempotent consumer would. + assert tokens, "expected at least one token" + assert all("seq" in t and "token" in t for t in tokens) + seen: set[int] = set() + deduped: list[dict[str, Any]] = [] + for t in tokens: + if t["seq"] not in seen: + seen.add(t["seq"]) + deduped.append(t) + assembled = "".join(t["token"] for t in deduped).strip() + assert assembled == result + + # Workflow-side astream publish: one chunk per node, in order. + assert [list(chunk)[0] for chunk in progress] == ["outline", "write_story"] + assert result == progress[-1]["write_story"]["story"] + assert "a brave robot" in result diff --git a/tests/langsmith_tracing/__init__.py b/tests/langsmith_tracing/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/langsmith_tracing/conftest.py b/tests/langsmith_tracing/conftest.py new file mode 100644 index 000000000..d66384f80 --- /dev/null +++ b/tests/langsmith_tracing/conftest.py @@ -0,0 +1,21 @@ +"""Shared fixtures for LangSmith tracing tests.""" + +from unittest.mock import MagicMock + +import pytest + + +@pytest.fixture +def mock_ls_client() -> MagicMock: + """Return a mock ``langsmith.Client`` that never makes network calls. + + The samples tests don't assert on trace output — the LangSmith plugin + just needs *some* client object to wire into the worker. ``MagicMock`` + auto-stubs every method the interceptor calls; the explicit ``session`` + and ``tracing_queue`` stubs match what the langsmith library touches + internally. + """ + client = MagicMock() + client.session = MagicMock() + client.tracing_queue = MagicMock() + return client diff --git a/tests/langsmith_tracing/helpers.py b/tests/langsmith_tracing/helpers.py new file mode 100644 index 000000000..afd3d1c4f --- /dev/null +++ b/tests/langsmith_tracing/helpers.py @@ -0,0 +1,8 @@ +"""Shared test helpers for LangSmith tracing tests.""" + +from langsmith_tracing.chatbot.activities import ChatResponse + + +def make_text_response(text: str) -> ChatResponse: + """Build a minimal ChatResponse with a text output.""" + return ChatResponse(id="resp_mock", output_text=text) diff --git a/tests/langsmith_tracing/test_basic.py b/tests/langsmith_tracing/test_basic.py new file mode 100644 index 000000000..cef49054a --- /dev/null +++ b/tests/langsmith_tracing/test_basic.py @@ -0,0 +1,37 @@ +import uuid +from unittest.mock import MagicMock + +from temporalio import activity +from temporalio.client import Client +from temporalio.contrib.langsmith import LangSmithPlugin +from temporalio.testing import WorkflowEnvironment +from temporalio.worker import Worker + +from langsmith_tracing.basic.activities import OpenAIRequest +from langsmith_tracing.basic.workflows import BasicLLMWorkflow + + +async def test_basic_workflow( + client: Client, env: WorkflowEnvironment, mock_ls_client: MagicMock +): + expected_text = "Temporal is a durable execution platform." + + @activity.defn(name="call_openai") + async def mock_call_openai(request: OpenAIRequest) -> str: + return expected_text + + async with Worker( + client, + task_queue="test-langsmith-basic", + workflows=[BasicLLMWorkflow], + activities=[mock_call_openai], + plugins=[LangSmithPlugin(client=mock_ls_client)], + ): + result = await client.execute_workflow( + BasicLLMWorkflow.run, + "What is Temporal?", + id=f"test-basic-{uuid.uuid4().hex[:8]}", + task_queue="test-langsmith-basic", + ) + + assert result == expected_text diff --git a/tests/langsmith_tracing/test_chatbot.py b/tests/langsmith_tracing/test_chatbot.py new file mode 100644 index 000000000..88d98202a --- /dev/null +++ b/tests/langsmith_tracing/test_chatbot.py @@ -0,0 +1,124 @@ +import json +import uuid +from unittest.mock import MagicMock + +from temporalio import activity +from temporalio.client import Client +from temporalio.contrib.langsmith import LangSmithPlugin +from temporalio.testing import WorkflowEnvironment +from temporalio.worker import Worker + +from langsmith_tracing.chatbot.activities import ChatResponse, OpenAIRequest, ToolCall +from langsmith_tracing.chatbot.workflows import ChatbotWorkflow +from tests.langsmith_tracing.helpers import make_text_response + + +def _make_function_call_response( + name: str, arguments: dict, call_id: str = "call_123" +) -> ChatResponse: + return ChatResponse( + id="resp_tool", + tool_calls=[ + ToolCall( + call_id=call_id, + name=name, + arguments=json.dumps(arguments), + ) + ], + ) + + +async def test_chatbot_save_note( + client: Client, env: WorkflowEnvironment, mock_ls_client: MagicMock +): + """Test save_note tool call loop — save_note runs as a workflow method.""" + call_count = 0 + + @activity.defn(name="call_openai") + async def mock_call_openai(request: OpenAIRequest) -> ChatResponse: + nonlocal call_count + call_count += 1 + if call_count == 1: + return _make_function_call_response( + name="save_note", + arguments={"name": "greeting", "content": "Hello world"}, + ) + return make_text_response("Note saved successfully!") + + async with Worker( + client, + task_queue="test-langsmith-chatbot", + workflows=[ChatbotWorkflow], + activities=[mock_call_openai], + plugins=[LangSmithPlugin(client=mock_ls_client)], + ): + wf_handle = await client.start_workflow( + ChatbotWorkflow.run, + id=f"test-chatbot-{uuid.uuid4().hex[:8]}", + task_queue="test-langsmith-chatbot", + ) + + response = await wf_handle.execute_update( + ChatbotWorkflow.message_from_user, "Save a note" + ) + assert response == "Note saved successfully!" + + notes = await wf_handle.query(ChatbotWorkflow.notes) + assert notes == {"greeting": "Hello world"} + + await wf_handle.signal(ChatbotWorkflow.exit) + result = await wf_handle.result() + assert result == "Session ended." + + +async def test_chatbot_read_note( + client: Client, env: WorkflowEnvironment, mock_ls_client: MagicMock +): + """Test read_note tool call loop — saves a note first, then reads it back.""" + call_count = 0 + + @activity.defn(name="call_openai") + async def mock_call_openai(request: OpenAIRequest) -> ChatResponse: + nonlocal call_count + call_count += 1 + if call_count == 1: + return _make_function_call_response( + name="save_note", + arguments={"name": "todo", "content": "Buy milk"}, + call_id="call_save", + ) + if call_count == 2: + return make_text_response("Saved your todo!") + if call_count == 3: + return _make_function_call_response( + name="read_note", + arguments={"name": "todo"}, + call_id="call_read", + ) + return make_text_response("Your todo says: Buy milk") + + async with Worker( + client, + task_queue="test-langsmith-chatbot-read", + workflows=[ChatbotWorkflow], + activities=[mock_call_openai], + plugins=[LangSmithPlugin(client=mock_ls_client)], + ): + wf_handle = await client.start_workflow( + ChatbotWorkflow.run, + id=f"test-chatbot-read-{uuid.uuid4().hex[:8]}", + task_queue="test-langsmith-chatbot-read", + ) + + response = await wf_handle.execute_update( + ChatbotWorkflow.message_from_user, "Save my todo" + ) + assert response == "Saved your todo!" + + response = await wf_handle.execute_update( + ChatbotWorkflow.message_from_user, "Read my todo" + ) + assert response == "Your todo says: Buy milk" + + await wf_handle.signal(ChatbotWorkflow.exit) + await wf_handle.result() diff --git a/tests/litellm_activity/__init__.py b/tests/litellm_activity/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/litellm_activity/activity_test.py b/tests/litellm_activity/activity_test.py new file mode 100644 index 000000000..e8cde1158 --- /dev/null +++ b/tests/litellm_activity/activity_test.py @@ -0,0 +1,45 @@ +from typing import Any + +import pytest +from litellm import ModelResponse + +from litellm_activity import activities +from litellm_activity.shared import LLMRequest + + +async def test_call_litellm(monkeypatch: pytest.MonkeyPatch) -> None: + captured: dict[str, Any] = {} + + async def mock_acompletion(**kwargs: Any) -> ModelResponse: + captured.update(kwargs) + return ModelResponse( + model="test-model", + choices=[ + { + "index": 0, + "finish_reason": "stop", + "message": {"role": "assistant", "content": "Hello from LiteLLM"}, + } + ], + ) + + monkeypatch.setattr(activities, "acompletion", mock_acompletion) + + result = await activities.call_litellm( + LLMRequest( + prompt="Hello", + model="test/model", + system_prompt="Be concise.", + ) + ) + + assert result == "Hello from LiteLLM" + assert captured == { + "model": "test/model", + "messages": [ + {"role": "system", "content": "Be concise."}, + {"role": "user", "content": "Hello"}, + ], + "timeout": 30, + "num_retries": 0, + } diff --git a/tests/litellm_activity/workflow_test.py b/tests/litellm_activity/workflow_test.py new file mode 100644 index 000000000..739de2c7f --- /dev/null +++ b/tests/litellm_activity/workflow_test.py @@ -0,0 +1,34 @@ +import uuid + +from temporalio import activity +from temporalio.client import Client +from temporalio.testing import WorkflowEnvironment +from temporalio.worker import Worker + +from litellm_activity.shared import LLMRequest +from litellm_activity.workflow import LiteLLMWorkflow + + +async def test_litellm_workflow(client: Client, env: WorkflowEnvironment) -> None: + expected = "Temporal makes LLM calls durable." + + @activity.defn(name="call_litellm") + async def mock_call_litellm(request: LLMRequest) -> str: + assert request.prompt == "What does Temporal add to LLM calls?" + return expected + + task_queue = f"test-litellm-{uuid.uuid4()}" + async with Worker( + client, + task_queue=task_queue, + workflows=[LiteLLMWorkflow], + activities=[mock_call_litellm], + ): + result = await client.execute_workflow( + LiteLLMWorkflow.run, + LLMRequest(prompt="What does Temporal add to LLM calls?"), + id=f"test-litellm-{uuid.uuid4()}", + task_queue=task_queue, + ) + + assert result == expected diff --git a/tests/message_passing/introduction/test_introduction_sample.py b/tests/message_passing/introduction/test_introduction_sample.py new file mode 100644 index 000000000..17c1b14dc --- /dev/null +++ b/tests/message_passing/introduction/test_introduction_sample.py @@ -0,0 +1,125 @@ +import uuid + +import pytest +from temporalio.client import Client, WorkflowUpdateFailedError +from temporalio.testing import WorkflowEnvironment +from temporalio.worker import Worker + +from message_passing.introduction.starter import TASK_QUEUE +from message_passing.introduction.workflows import ( + GetLanguagesInput, + GreetingWorkflow, + Language, + SetLanguageInput, + call_greeting_service, +) + + +async def test_queries(client: Client, env: WorkflowEnvironment): + if env.supports_time_skipping: + pytest.skip( + "Java test server: https://github.com/temporalio/sdk-java/issues/1903" + ) + async with Worker( + client, + task_queue=TASK_QUEUE, + workflows=[GreetingWorkflow], + ): + wf_handle = await client.start_workflow( + GreetingWorkflow.run, + id=str(uuid.uuid4()), + task_queue=TASK_QUEUE, + ) + assert await wf_handle.query(GreetingWorkflow.get_language) == Language.ENGLISH + assert await wf_handle.query( + GreetingWorkflow.get_languages, GetLanguagesInput(include_unsupported=False) + ) == [Language.CHINESE, Language.ENGLISH] + assert await wf_handle.query( + GreetingWorkflow.get_languages, GetLanguagesInput(include_unsupported=True) + ) == [ + Language.ARABIC, + Language.CHINESE, + Language.ENGLISH, + Language.FRENCH, + Language.HINDI, + Language.PORTUGUESE, + Language.SPANISH, + ] + + +async def test_set_language(client: Client, env: WorkflowEnvironment): + if env.supports_time_skipping: + pytest.skip( + "Java test server: https://github.com/temporalio/sdk-java/issues/1903" + ) + async with Worker( + client, + task_queue=TASK_QUEUE, + workflows=[GreetingWorkflow], + ): + wf_handle = await client.start_workflow( + GreetingWorkflow.run, + id=str(uuid.uuid4()), + task_queue=TASK_QUEUE, + ) + assert await wf_handle.query(GreetingWorkflow.get_language) == Language.ENGLISH + previous_language = await wf_handle.execute_update( + GreetingWorkflow.set_language, SetLanguageInput(language=Language.CHINESE) + ) + assert previous_language == Language.ENGLISH + assert await wf_handle.query(GreetingWorkflow.get_language) == Language.CHINESE + + +async def test_set_invalid_language(client: Client, env: WorkflowEnvironment): + if env.supports_time_skipping: + pytest.skip( + "Java test server: https://github.com/temporalio/sdk-java/issues/1903" + ) + async with Worker( + client, + task_queue=TASK_QUEUE, + workflows=[GreetingWorkflow], + ): + wf_handle = await client.start_workflow( + GreetingWorkflow.run, + id=str(uuid.uuid4()), + task_queue=TASK_QUEUE, + ) + assert await wf_handle.query(GreetingWorkflow.get_language) == Language.ENGLISH + + with pytest.raises(WorkflowUpdateFailedError): + await wf_handle.execute_update( + GreetingWorkflow.set_language, + SetLanguageInput(language=Language.ARABIC), + ) + + +async def test_set_language_that_is_only_available_via_remote_service( + client: Client, env: WorkflowEnvironment +): + """ + Similar to test_set_invalid_language, but this time Arabic is available + since we use the remote service. + """ + if env.supports_time_skipping: + pytest.skip( + "Java test server: https://github.com/temporalio/sdk-java/issues/1903" + ) + async with Worker( + client, + task_queue=TASK_QUEUE, + workflows=[GreetingWorkflow], + activities=[call_greeting_service], + ): + wf_handle = await client.start_workflow( + GreetingWorkflow.run, + id=str(uuid.uuid4()), + task_queue=TASK_QUEUE, + ) + assert await wf_handle.query(GreetingWorkflow.get_language) == Language.ENGLISH + previous_language = await wf_handle.execute_update( + GreetingWorkflow.set_language_using_activity, + SetLanguageInput(language=Language.ARABIC), + ) + assert previous_language == Language.ENGLISH + assert await wf_handle.query(GreetingWorkflow.get_language) == Language.ARABIC diff --git a/tests/message_passing/lazy_initialization/test_lazy_initialization.py b/tests/message_passing/lazy_initialization/test_lazy_initialization.py new file mode 100644 index 000000000..dcf94582e --- /dev/null +++ b/tests/message_passing/lazy_initialization/test_lazy_initialization.py @@ -0,0 +1,60 @@ +import pytest +from temporalio import common +from temporalio.client import Client, WithStartWorkflowOperation +from temporalio.testing import WorkflowEnvironment +from temporalio.worker import Worker + +from message_passing.update_with_start.lazy_initialization.workflows import ( + ShoppingCartItem, + ShoppingCartWorkflow, + get_price, +) + + +async def test_shopping_cart_workflow(client: Client, env: WorkflowEnvironment): + if env.supports_time_skipping: + pytest.skip( + "Java test server: https://github.com/temporalio/sdk-java/issues/1903" + ) + async with Worker( + client, + task_queue="lazy-initialization-test", + workflows=[ShoppingCartWorkflow], + activities=[get_price], + ): + cart_id = "cart--session-1234" + make_start_op = lambda: WithStartWorkflowOperation( + ShoppingCartWorkflow.run, + id=cart_id, + id_conflict_policy=common.WorkflowIDConflictPolicy.USE_EXISTING, + task_queue="lazy-initialization-test", + ) + start_op_1 = make_start_op() + price = await client.execute_update_with_start_workflow( + ShoppingCartWorkflow.add_item, + ShoppingCartItem(sku="item-1", quantity=2), + start_workflow_operation=start_op_1, + ) + + assert price == 1198 + + workflow_handle = await start_op_1.workflow_handle() + + start_op_2 = make_start_op() + price = await client.execute_update_with_start_workflow( + ShoppingCartWorkflow.add_item, + ShoppingCartItem(sku="item-2", quantity=1), + start_workflow_operation=start_op_2, + ) + assert price == 1797 + + workflow_handle = await start_op_2.workflow_handle() + + await workflow_handle.signal(ShoppingCartWorkflow.checkout) + + finalized_order = await workflow_handle.result() + assert finalized_order.items == [ + (ShoppingCartItem(sku="item-1", quantity=2), 1198), + (ShoppingCartItem(sku="item-2", quantity=1), 599), + ] + assert finalized_order.total == 1797 diff --git a/tests/message_passing/safe_message_handlers/workflow_test.py b/tests/message_passing/safe_message_handlers/workflow_test.py new file mode 100644 index 000000000..8fd28d8dd --- /dev/null +++ b/tests/message_passing/safe_message_handlers/workflow_test.py @@ -0,0 +1,170 @@ +import asyncio +import uuid +from typing import Callable, Sequence + +import pytest +from temporalio.client import Client, WorkflowUpdateFailedError +from temporalio.exceptions import ApplicationError +from temporalio.testing import WorkflowEnvironment +from temporalio.worker import Worker + +from message_passing.safe_message_handlers.activities import ( + assign_nodes_to_job, + find_bad_nodes, + start_cluster, + unassign_nodes_for_job, +) +from message_passing.safe_message_handlers.workflow import ( + ClusterManagerAssignNodesToJobInput, + ClusterManagerDeleteJobInput, + ClusterManagerInput, + ClusterManagerWorkflow, +) + +ACTIVITIES: Sequence[Callable] = [ + assign_nodes_to_job, + unassign_nodes_for_job, + find_bad_nodes, + start_cluster, +] + + +async def test_safe_message_handlers(client: Client, env: WorkflowEnvironment): + if env.supports_time_skipping: + pytest.skip( + "Java test server: https://github.com/temporalio/sdk-java/issues/1903" + ) + task_queue = f"tq-{uuid.uuid4()}" + async with Worker( + client, + task_queue=task_queue, + workflows=[ClusterManagerWorkflow], + activities=ACTIVITIES, + ): + cluster_manager_handle = await client.start_workflow( + ClusterManagerWorkflow.run, + ClusterManagerInput(), + id=f"ClusterManagerWorkflow-{uuid.uuid4()}", + task_queue=task_queue, + ) + await cluster_manager_handle.execute_update( + ClusterManagerWorkflow.wait_until_cluster_started + ) + + allocation_updates = [] + for i in range(6): + allocation_updates.append( + cluster_manager_handle.execute_update( + ClusterManagerWorkflow.assign_nodes_to_job, + ClusterManagerAssignNodesToJobInput( + total_num_nodes=2, job_name=f"task-{i}" + ), + ) + ) + results = await asyncio.gather(*allocation_updates) + for result in results: + assert len(result.nodes_assigned) == 2 + + await asyncio.sleep(1) + + deletion_updates = [] + for i in range(6): + deletion_updates.append( + cluster_manager_handle.execute_update( + ClusterManagerWorkflow.delete_job, + ClusterManagerDeleteJobInput(job_name=f"task-{i}"), + ) + ) + await asyncio.gather(*deletion_updates) + + await cluster_manager_handle.signal(ClusterManagerWorkflow.shutdown_cluster) + + cluster_manager_result = await cluster_manager_handle.result() + assert cluster_manager_result.num_currently_assigned_nodes == 0 + + +async def test_update_idempotency(client: Client, env: WorkflowEnvironment): + if env.supports_time_skipping: + pytest.skip( + "Java test server: https://github.com/temporalio/sdk-java/issues/1903" + ) + task_queue = f"tq-{uuid.uuid4()}" + async with Worker( + client, + task_queue=task_queue, + workflows=[ClusterManagerWorkflow], + activities=ACTIVITIES, + ): + cluster_manager_handle = await client.start_workflow( + ClusterManagerWorkflow.run, + ClusterManagerInput(), + id=f"ClusterManagerWorkflow-{uuid.uuid4()}", + task_queue=task_queue, + ) + + await cluster_manager_handle.execute_update( + ClusterManagerWorkflow.wait_until_cluster_started + ) + + result_1 = await cluster_manager_handle.execute_update( + ClusterManagerWorkflow.assign_nodes_to_job, + ClusterManagerAssignNodesToJobInput( + total_num_nodes=5, job_name="jobby-job" + ), + ) + # simulate that in calling it twice, the operation is idempotent + result_2 = await cluster_manager_handle.execute_update( + ClusterManagerWorkflow.assign_nodes_to_job, + ClusterManagerAssignNodesToJobInput( + total_num_nodes=5, job_name="jobby-job" + ), + ) + # the second call should not assign more nodes (it may return fewer if the health check finds bad nodes + # in between the two signals.) + assert result_1.nodes_assigned >= result_2.nodes_assigned + + +async def test_update_failure(client: Client, env: WorkflowEnvironment): + if env.supports_time_skipping: + pytest.skip( + "Java test server: https://github.com/temporalio/sdk-java/issues/1903" + ) + task_queue = f"tq-{uuid.uuid4()}" + async with Worker( + client, + task_queue=task_queue, + workflows=[ClusterManagerWorkflow], + activities=ACTIVITIES, + ): + cluster_manager_handle = await client.start_workflow( + ClusterManagerWorkflow.run, + ClusterManagerInput(), + id=f"ClusterManagerWorkflow-{uuid.uuid4()}", + task_queue=task_queue, + ) + + await cluster_manager_handle.execute_update( + ClusterManagerWorkflow.wait_until_cluster_started + ) + + await cluster_manager_handle.execute_update( + ClusterManagerWorkflow.assign_nodes_to_job, + ClusterManagerAssignNodesToJobInput( + total_num_nodes=24, job_name="big-task" + ), + ) + try: + # Try to assign too many nodes + await cluster_manager_handle.execute_update( + ClusterManagerWorkflow.assign_nodes_to_job, + ClusterManagerAssignNodesToJobInput( + total_num_nodes=3, job_name="little-task" + ), + ) + except WorkflowUpdateFailedError as e: + assert isinstance(e.cause, ApplicationError) + assert e.cause.message == "Cannot assign 3 nodes; have only 1 available" + finally: + await cluster_manager_handle.signal(ClusterManagerWorkflow.shutdown_cluster) + result = await cluster_manager_handle.result() + assert result.num_currently_assigned_nodes + result.num_bad_nodes == 24 diff --git a/tests/message_passing/waiting_for_handlers/waiting_for_handlers_test.py b/tests/message_passing/waiting_for_handlers/waiting_for_handlers_test.py new file mode 100644 index 000000000..e6d200e3a --- /dev/null +++ b/tests/message_passing/waiting_for_handlers/waiting_for_handlers_test.py @@ -0,0 +1,80 @@ +from enum import Enum + +import pytest +from temporalio import client, worker +from temporalio.testing import WorkflowEnvironment + +from message_passing.waiting_for_handlers import WorkflowExitType, WorkflowInput +from message_passing.waiting_for_handlers.activities import ( + activity_executed_by_update_handler, +) +from message_passing.waiting_for_handlers.starter import TASK_QUEUE +from message_passing.waiting_for_handlers.workflows import WaitingForHandlersWorkflow + + +class UpdateExpect(Enum): + SUCCESS = "success" + FAILURE = "failure" + + +class WorkflowExpect(Enum): + SUCCESS = "success" + FAILURE = "failure" + + +@pytest.mark.parametrize( + ["exit_type_name", "update_expect", "workflow_expect"], + [ + (WorkflowExitType.SUCCESS.name, UpdateExpect.SUCCESS, WorkflowExpect.SUCCESS), + (WorkflowExitType.FAILURE.name, UpdateExpect.SUCCESS, WorkflowExpect.FAILURE), + ( + WorkflowExitType.CANCELLATION.name, + UpdateExpect.SUCCESS, + WorkflowExpect.FAILURE, + ), + ], +) +async def test_waiting_for_handlers( + env: WorkflowEnvironment, + exit_type_name: str, + update_expect: UpdateExpect, + workflow_expect: WorkflowExpect, +): + [exit_type] = [t for t in WorkflowExitType if t.name == exit_type_name] + if env.supports_time_skipping: + pytest.skip( + "Java test server: https://github.com/temporalio/sdk-java/issues/1903" + ) + async with worker.Worker( + env.client, + task_queue=TASK_QUEUE, + workflows=[WaitingForHandlersWorkflow], + activities=[ + activity_executed_by_update_handler, + ], + ): + wf_handle = await env.client.start_workflow( + WaitingForHandlersWorkflow.run, + WorkflowInput(exit_type=exit_type), + id="waiting-for-handlers-test", + task_queue=TASK_QUEUE, + ) + up_handle = await wf_handle.start_update( + WaitingForHandlersWorkflow.my_update, + wait_for_stage=client.WorkflowUpdateStage.ACCEPTED, + ) + + if exit_type == WorkflowExitType.CANCELLATION: + await wf_handle.cancel() + + if update_expect == UpdateExpect.SUCCESS: + await up_handle.result() + else: + with pytest.raises(client.WorkflowUpdateFailedError): + await up_handle.result() + + if workflow_expect == WorkflowExpect.SUCCESS: + await wf_handle.result() + else: + with pytest.raises(client.WorkflowFailureError): + await wf_handle.result() diff --git a/tests/message_passing/waiting_for_handlers_and_compensation/waiting_for_handlers_and_compensation_test.py b/tests/message_passing/waiting_for_handlers_and_compensation/waiting_for_handlers_and_compensation_test.py new file mode 100644 index 000000000..2b10d3965 --- /dev/null +++ b/tests/message_passing/waiting_for_handlers_and_compensation/waiting_for_handlers_and_compensation_test.py @@ -0,0 +1,106 @@ +import uuid +from enum import Enum + +import pytest +from temporalio import client, worker +from temporalio.testing import WorkflowEnvironment + +from message_passing.waiting_for_handlers_and_compensation import ( + WorkflowExitType, + WorkflowInput, +) +from message_passing.waiting_for_handlers_and_compensation.activities import ( + activity_executed_by_update_handler, + activity_executed_by_update_handler_to_perform_compensation, + activity_executed_to_perform_workflow_compensation, +) +from message_passing.waiting_for_handlers_and_compensation.starter import TASK_QUEUE +from message_passing.waiting_for_handlers_and_compensation.workflows import ( + WaitingForHandlersAndCompensationWorkflow, +) + + +class UpdateExpect(Enum): + SUCCESS = "success" + FAILURE = "failure" + + +class WorkflowExpect(Enum): + SUCCESS = "success" + FAILURE = "failure" + + +@pytest.mark.parametrize( + ["exit_type_name", "update_expect", "workflow_expect"], + [ + (WorkflowExitType.SUCCESS.name, UpdateExpect.SUCCESS, WorkflowExpect.SUCCESS), + (WorkflowExitType.FAILURE.name, UpdateExpect.FAILURE, WorkflowExpect.FAILURE), + ( + WorkflowExitType.CANCELLATION.name, + UpdateExpect.FAILURE, + WorkflowExpect.FAILURE, + ), + ], +) +async def test_waiting_for_handlers_and_compensation( + env: WorkflowEnvironment, + exit_type_name: str, + update_expect: UpdateExpect, + workflow_expect: WorkflowExpect, +): + [exit_type] = [t for t in WorkflowExitType if t.name == exit_type_name] + if env.supports_time_skipping: + pytest.skip( + "Java test server: https://github.com/temporalio/sdk-java/issues/1903" + ) + async with worker.Worker( + env.client, + task_queue=TASK_QUEUE, + workflows=[WaitingForHandlersAndCompensationWorkflow], + activities=[ + activity_executed_by_update_handler, + activity_executed_by_update_handler_to_perform_compensation, + activity_executed_to_perform_workflow_compensation, + ], + ): + wf_handle = await env.client.start_workflow( + WaitingForHandlersAndCompensationWorkflow.run, + WorkflowInput(exit_type=exit_type), + id=str(uuid.uuid4()), + task_queue=TASK_QUEUE, + ) + up_handle = await wf_handle.start_update( + WaitingForHandlersAndCompensationWorkflow.my_update, + wait_for_stage=client.WorkflowUpdateStage.ACCEPTED, + ) + + if exit_type == WorkflowExitType.CANCELLATION: + await wf_handle.cancel() + + if update_expect == UpdateExpect.SUCCESS: + await up_handle.result() + assert not ( + await wf_handle.query( + WaitingForHandlersAndCompensationWorkflow.update_compensation_done + ) + ) + else: + with pytest.raises(client.WorkflowUpdateFailedError): + await up_handle.result() + assert await wf_handle.query( + WaitingForHandlersAndCompensationWorkflow.update_compensation_done + ) + + if workflow_expect == WorkflowExpect.SUCCESS: + await wf_handle.result() + assert not ( + await wf_handle.query( + WaitingForHandlersAndCompensationWorkflow.workflow_compensation_done + ) + ) + else: + with pytest.raises(client.WorkflowFailureError): + await wf_handle.result() + assert await wf_handle.query( + WaitingForHandlersAndCompensationWorkflow.workflow_compensation_done + ) diff --git a/tests/nexus_messaging/callerpattern_test.py b/tests/nexus_messaging/callerpattern_test.py new file mode 100644 index 000000000..d14b4c29e --- /dev/null +++ b/tests/nexus_messaging/callerpattern_test.py @@ -0,0 +1,119 @@ +import asyncio +import uuid +from typing import Type + +import pytest +from temporalio import workflow +from temporalio.client import Client +from temporalio.testing import WorkflowEnvironment +from temporalio.worker import Worker + +import nexus_messaging.callerpattern.handler.worker +from nexus_messaging.callerpattern.caller.workflows import CallerWorkflow +from nexus_messaging.callerpattern.service import ( + GetLanguageInput, + GetLanguagesInput, + Language, + NexusGreetingService, + SetLanguageInput, +) +from tests.helpers.nexus import create_nexus_endpoint, delete_nexus_endpoint + +NEXUS_ENDPOINT = "nexus-messaging-nexus-endpoint" + + +@workflow.defn +class TestCallerWorkflow: + """Test workflow that calls Nexus operations and makes assertions.""" + + @workflow.run + async def run(self, user_id: str) -> None: + nexus_client = workflow.create_nexus_client( + service=NexusGreetingService, + endpoint=NEXUS_ENDPOINT, + ) + + supported_languages = await nexus_client.execute_operation( + NexusGreetingService.get_languages, + GetLanguagesInput(include_unsupported=False, user_id=user_id), + ) + assert supported_languages.languages == [Language.CHINESE, Language.ENGLISH] + + initial_language = await nexus_client.execute_operation( + NexusGreetingService.get_language, + GetLanguageInput(user_id=user_id), + ) + assert initial_language == Language.ENGLISH + + previous_language = await nexus_client.execute_operation( + NexusGreetingService.set_language, + SetLanguageInput(language=Language.CHINESE, user_id=user_id), + ) + assert previous_language == Language.ENGLISH + + current_language = await nexus_client.execute_operation( + NexusGreetingService.get_language, + GetLanguageInput(user_id=user_id), + ) + assert current_language == Language.CHINESE + + previous_language = await nexus_client.execute_operation( + NexusGreetingService.set_language, + SetLanguageInput(language=Language.ARABIC, user_id=user_id), + ) + assert previous_language == Language.CHINESE + + current_language = await nexus_client.execute_operation( + NexusGreetingService.get_language, + GetLanguageInput(user_id=user_id), + ) + assert current_language == Language.ARABIC + + +async def test_callerpattern(client: Client, env: WorkflowEnvironment): + if env.supports_time_skipping: + pytest.skip("Nexus tests don't work under the Java test server") + + await _run_caller_workflow(client, TestCallerWorkflow) + + +async def test_callerpattern_caller_workflow(client: Client, env: WorkflowEnvironment): + """Runs the CallerWorkflow from the sample to ensure it executes without errors.""" + if env.supports_time_skipping: + pytest.skip("Nexus tests don't work under the Java test server") + + await _run_caller_workflow(client, CallerWorkflow) + + +async def _run_caller_workflow(client: Client, wf: Type): + create_response = await create_nexus_endpoint( + name=NEXUS_ENDPOINT, + task_queue=nexus_messaging.callerpattern.handler.worker.TASK_QUEUE, + client=client, + ) + try: + handler_worker_task = asyncio.create_task( + nexus_messaging.callerpattern.handler.worker.main(client) + ) + try: + async with Worker( + client, + task_queue="test-caller-task-queue", + workflows=[wf], + ): + await client.execute_workflow( + wf.run, + arg="user-1", + id=str(uuid.uuid4()), + task_queue="test-caller-task-queue", + ) + finally: + nexus_messaging.callerpattern.handler.worker.interrupt_event.set() + await handler_worker_task + nexus_messaging.callerpattern.handler.worker.interrupt_event.clear() + finally: + await delete_nexus_endpoint( + id=create_response.endpoint.id, + version=create_response.endpoint.version, + client=client, + ) diff --git a/tests/nexus_messaging/ondemandpattern_test.py b/tests/nexus_messaging/ondemandpattern_test.py new file mode 100644 index 000000000..75fd761c0 --- /dev/null +++ b/tests/nexus_messaging/ondemandpattern_test.py @@ -0,0 +1,130 @@ +import asyncio +import uuid +from typing import Type + +import pytest +from temporalio import workflow +from temporalio.client import Client +from temporalio.testing import WorkflowEnvironment +from temporalio.worker import Worker + +import nexus_messaging.ondemandpattern.handler.worker +from nexus_messaging.ondemandpattern.caller.workflows import CallerRemoteWorkflow +from nexus_messaging.ondemandpattern.service import ( + ApproveInput, + GetLanguageInput, + GetLanguagesInput, + Language, + NexusRemoteGreetingService, + RunFromRemoteInput, + SetLanguageInput, +) +from tests.helpers.nexus import create_nexus_endpoint, delete_nexus_endpoint + +NEXUS_ENDPOINT = "nexus-messaging-nexus-endpoint" + + +@workflow.defn +class TestCallerRemoteWorkflow: + """Test workflow that creates remote workflows and makes assertions.""" + + @workflow.run + async def run(self) -> None: + nexus_client = workflow.create_nexus_client( + service=NexusRemoteGreetingService, + endpoint=NEXUS_ENDPOINT, + ) + + workflow_id = f"test-remote-{workflow.uuid4()}" + + # Start a remote workflow. + handle = await nexus_client.start_operation( + NexusRemoteGreetingService.run_from_remote, + RunFromRemoteInput(user_id=workflow_id), + ) + + # Query for supported languages. + languages_output = await nexus_client.execute_operation( + NexusRemoteGreetingService.get_languages, + GetLanguagesInput(include_unsupported=False, user_id=workflow_id), + ) + assert languages_output.languages == [Language.CHINESE, Language.ENGLISH] + + # Check initial language. + initial_language = await nexus_client.execute_operation( + NexusRemoteGreetingService.get_language, + GetLanguageInput(user_id=workflow_id), + ) + assert initial_language == Language.ENGLISH + + # Set language. + previous_language = await nexus_client.execute_operation( + NexusRemoteGreetingService.set_language, + SetLanguageInput(language=Language.ARABIC, user_id=workflow_id), + ) + assert previous_language == Language.ENGLISH + + current_language = await nexus_client.execute_operation( + NexusRemoteGreetingService.get_language, + GetLanguageInput(user_id=workflow_id), + ) + assert current_language == Language.ARABIC + + # Approve and wait for result. + await nexus_client.execute_operation( + NexusRemoteGreetingService.approve, + ApproveInput(name="test", user_id=workflow_id), + ) + + result = await handle + assert "\u0645\u0631\u062d\u0628\u0627" in result # Arabic greeting + + +async def test_ondemandpattern(client: Client, env: WorkflowEnvironment): + if env.supports_time_skipping: + pytest.skip("Nexus tests don't work under the Java test server") + + await _run_caller_workflow(client, TestCallerRemoteWorkflow) + + +async def test_ondemandpattern_caller_workflow( + client: Client, env: WorkflowEnvironment +): + """Runs the CallerRemoteWorkflow from the sample to ensure it executes without errors.""" + if env.supports_time_skipping: + pytest.skip("Nexus tests don't work under the Java test server") + + await _run_caller_workflow(client, CallerRemoteWorkflow) + + +async def _run_caller_workflow(client: Client, wf: Type): + create_response = await create_nexus_endpoint( + name=NEXUS_ENDPOINT, + task_queue=nexus_messaging.ondemandpattern.handler.worker.TASK_QUEUE, + client=client, + ) + try: + handler_worker_task = asyncio.create_task( + nexus_messaging.ondemandpattern.handler.worker.main(client) + ) + try: + async with Worker( + client, + task_queue="test-caller-remote-task-queue", + workflows=[wf], + ): + await client.execute_workflow( + wf.run, + id=str(uuid.uuid4()), + task_queue="test-caller-remote-task-queue", + ) + finally: + nexus_messaging.ondemandpattern.handler.worker.interrupt_event.set() + await handler_worker_task + nexus_messaging.ondemandpattern.handler.worker.interrupt_event.clear() + finally: + await delete_nexus_endpoint( + id=create_response.endpoint.id, + version=create_response.endpoint.version, + client=client, + ) diff --git a/tests/nexus_multiple_args/__init__.py b/tests/nexus_multiple_args/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/nexus_multiple_args/nexus_multiple_args_test.py b/tests/nexus_multiple_args/nexus_multiple_args_test.py new file mode 100644 index 000000000..682b8f208 --- /dev/null +++ b/tests/nexus_multiple_args/nexus_multiple_args_test.py @@ -0,0 +1,50 @@ +import asyncio +import sys + +import pytest +from temporalio.client import Client +from temporalio.testing import WorkflowEnvironment + +import nexus_multiple_args.caller.app +import nexus_multiple_args.caller.workflows +import nexus_multiple_args.handler.worker +from tests.helpers.nexus import create_nexus_endpoint, delete_nexus_endpoint + + +async def test_nexus_multiple_args(client: Client, env: WorkflowEnvironment): + if env.supports_time_skipping: + pytest.skip("Nexus tests don't work under the Java test server") + + if sys.version_info[:2] < (3, 10): + pytest.skip("Sample is written for Python >= 3.10") + + create_response = await create_nexus_endpoint( + name=nexus_multiple_args.caller.workflows.NEXUS_ENDPOINT, + task_queue=nexus_multiple_args.handler.worker.TASK_QUEUE, + client=client, + ) + try: + handler_worker_task = asyncio.create_task( + nexus_multiple_args.handler.worker.main( + client, + ) + ) + await asyncio.sleep(1) + results = await nexus_multiple_args.caller.app.execute_caller_workflow( + client, + ) + nexus_multiple_args.handler.worker.interrupt_event.set() + await handler_worker_task + nexus_multiple_args.handler.worker.interrupt_event.clear() + + # Verify the expected output messages + assert results == ( + "Hello Nexus 👋", + "¡Hola! Nexus 👋", + ) + finally: + await delete_nexus_endpoint( + id=create_response.endpoint.id, + version=create_response.endpoint.version, + client=client, + ) diff --git a/tests/nexus_standalone_operations/__init__.py b/tests/nexus_standalone_operations/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/nexus_standalone_operations/nexus_standalone_operations_test.py b/tests/nexus_standalone_operations/nexus_standalone_operations_test.py new file mode 100644 index 000000000..44d13ca9a --- /dev/null +++ b/tests/nexus_standalone_operations/nexus_standalone_operations_test.py @@ -0,0 +1,78 @@ +import uuid +from datetime import timedelta + +import pytest +from temporalio.client import Client +from temporalio.testing import WorkflowEnvironment +from temporalio.worker import Worker + +from nexus_standalone_operations.handler import HelloWorkflow, MyNexusServiceHandler +from nexus_standalone_operations.service import ( + EchoInput, + EchoOutput, + HelloInput, + HelloOutput, + MyNexusService, +) +from nexus_standalone_operations.worker import TASK_QUEUE +from tests.helpers import assert_eventually +from tests.helpers.nexus import create_nexus_endpoint, delete_nexus_endpoint + + +async def test_nexus_standalone_operations(client: Client, env: WorkflowEnvironment): + if env.supports_time_skipping: + pytest.skip("Time Skipping server does not support standalone nexus operations") + + endpoint_name = f"test-nexus-standalone-{uuid.uuid4()}" + + create_response = await create_nexus_endpoint( + name=endpoint_name, + task_queue=TASK_QUEUE, + client=client, + ) + try: + async with Worker( + client, + task_queue=TASK_QUEUE, + workflows=[HelloWorkflow], + nexus_service_handlers=[MyNexusServiceHandler()], + ): + nexus_client = client.create_nexus_client( + service=MyNexusService, endpoint=endpoint_name + ) + + # Test sync echo operation + echo_result = None + echo_result = await nexus_client.execute_operation( + MyNexusService.echo, + EchoInput(message="test-echo"), + id=str(uuid.uuid4()), + schedule_to_close_timeout=timedelta(seconds=10), + ) + assert isinstance(echo_result, EchoOutput) + assert echo_result.message == "test-echo" + + # Test async hello operation + hello_result = await nexus_client.execute_operation( + MyNexusService.hello, + HelloInput(name="Test"), + id=str(uuid.uuid4()), + schedule_to_close_timeout=timedelta(seconds=10), + ) + assert isinstance(hello_result, HelloOutput) + assert hello_result.greeting == "Hello, Test!" + + # Test count operations + async def check_count(): + count = await client.count_nexus_operations( + f'Endpoint = "{endpoint_name}"' + ) + assert count.count == 2 + + await assert_eventually(check_count) + finally: + _ = await delete_nexus_endpoint( + id=create_response.endpoint.id, + version=create_response.endpoint.version, + client=client, + ) diff --git a/tests/openai_agents/__init__.py b/tests/openai_agents/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/openai_agents/_mock_model.py b/tests/openai_agents/_mock_model.py new file mode 100644 index 000000000..dc454e3ea --- /dev/null +++ b/tests/openai_agents/_mock_model.py @@ -0,0 +1,173 @@ +"""Scripted streaming model for openai_agents sample tests. + +Each entry in the script drives one ``stream_response`` call — that is, one +model activity: a ``str`` streams that text as deltas followed by a terminal +``ResponseCompletedEvent``, a :class:`ToolCall` emits a function call so the +agent runs the tool and comes back for another turn, and a +:class:`FailMidStream` cuts a response short so the activity is retried. + +The plugin takes a ``model_provider`` directly, so nothing needs patching. +""" + +from __future__ import annotations + +import itertools +from collections.abc import AsyncIterator +from dataclasses import dataclass +from typing import Any, Union + +from agents import ( + AgentOutputSchemaBase, + Handoff, + Model, + ModelProvider, + ModelResponse, + ModelSettings, + ModelTracing, + Tool, + TResponseInputItem, +) +from agents.items import TResponseStreamEvent +from openai.types.responses import ( + Response, + ResponseCompletedEvent, + ResponseFunctionToolCall, + ResponseOutputMessage, + ResponseOutputText, + ResponseTextDeltaEvent, +) + +# Chunk size for text deltas. Small enough that any test text streams as +# several events, so a subscriber that reassembles them is doing real work. +_DELTA_CHARS = 12 + + +@dataclass +class ToolCall: + """Script entry for a turn that calls a tool instead of answering.""" + + name: str + arguments: str = "{}" + + +@dataclass +class FailMidStream: + """Script entry that streams a few deltas and then raises. + + Simulates a model activity that dies partway through a response. The + deltas it published are already on the workflow's stream, and because + entries are consumed one per ``stream_response`` call, the activity's + retry advances to the next script entry — normally the same text in + full, as a real retry would re-sample the whole response. + """ + + text: str + after_deltas: int = 4 + + +# One script entry per stream_response call. +ScriptEntry = Union[str, ToolCall, FailMidStream] + + +def _message(text: str) -> ResponseOutputMessage: + return ResponseOutputMessage( + id="msg_mock", + content=[ResponseOutputText(text=text, annotations=[], type="output_text")], + role="assistant", + status="completed", + type="message", + ) + + +def _response(output: list[Any]) -> Response: + return Response( + id="resp_mock", + created_at=0.0, + model="mock-model", + object="response", + output=output, + parallel_tool_calls=False, + tool_choice="auto", + tools=[], + status="completed", + ) + + +class ScriptedStreamingModel(Model): + """Model that replays a fixed script of turns, one per streamed call.""" + + def __init__(self, script: list[ScriptEntry]) -> None: + self._script = list(script) + self._calls = itertools.count() + + def _next_turn(self) -> ScriptEntry: + if not self._script: + raise AssertionError("ScriptedStreamingModel script exhausted") + return self._script.pop(0) + + async def get_response(self, *args: Any, **kwargs: Any) -> ModelResponse: + """Unimplemented: this mock exists for Runner.run_streamed.""" + raise NotImplementedError + + async def stream_response( + self, + system_instructions: str | None, + input: str | list[TResponseInputItem], + model_settings: ModelSettings, + tools: list[Tool], + output_schema: AgentOutputSchemaBase | None, + handoffs: list[Handoff], + tracing: ModelTracing, + **kwargs: Any, + ) -> AsyncIterator[TResponseStreamEvent]: + turn = self._next_turn() + seq = itertools.count() + + if isinstance(turn, ToolCall): + call = self._tool_call(turn, next(self._calls)) + yield ResponseCompletedEvent( + response=_response([call]), + sequence_number=next(seq), + type="response.completed", + ) + return + + text = turn.text if isinstance(turn, FailMidStream) else turn + for index, start in enumerate(range(0, len(text), _DELTA_CHARS)): + if isinstance(turn, FailMidStream) and index == turn.after_deltas: + raise RuntimeError("scripted mid-stream model failure") + yield ResponseTextDeltaEvent( + content_index=0, + delta=text[start : start + _DELTA_CHARS], + item_id="msg_mock", + logprobs=[], + output_index=0, + sequence_number=next(seq), + type="response.output_text.delta", + ) + yield ResponseCompletedEvent( + response=_response([_message(text)]), + sequence_number=next(seq), + type="response.completed", + ) + + @staticmethod + def _tool_call(turn: ToolCall, index: int) -> ResponseFunctionToolCall: + return ResponseFunctionToolCall( + arguments=turn.arguments, + call_id=f"call_mock_{index}", + name=turn.name, + type="function_call", + id=f"fc_mock_{index}", + status="completed", + ) + + +class ScriptedModelProvider(ModelProvider): + """Hands out one shared model so the script advances across turns.""" + + def __init__(self, script: list[ScriptEntry]) -> None: + self._model = ScriptedStreamingModel(script) + + def get_model(self, model_name: str | None) -> Model: + return self._model diff --git a/tests/openai_agents/streaming_test.py b/tests/openai_agents/streaming_test.py new file mode 100644 index 000000000..e585d32fc --- /dev/null +++ b/tests/openai_agents/streaming_test.py @@ -0,0 +1,229 @@ +import uuid +from datetime import timedelta +from typing import Any, cast + +from agents.items import TResponseStreamEvent +from openai.types.responses import ResponseCompletedEvent, ResponseTextDeltaEvent +from temporalio.client import Client +from temporalio.common import RawValue +from temporalio.contrib.openai_agents import ModelActivityParameters, OpenAIAgentsPlugin +from temporalio.contrib.workflow_streams import WorkflowStreamClient +from temporalio.worker import Worker + +from openai_agents.streaming.activities.joke_activities import how_many_jokes +from openai_agents.streaming.shared import ( + TOPIC_DONE, + TOPIC_EVENTS, + TOPIC_ITEMS, + ItemEvent, +) +from openai_agents.streaming.workflows.stream_items_workflow import ( + StreamItemsInput, + StreamItemsWorkflow, +) +from openai_agents.streaming.workflows.stream_text_workflow import ( + StreamTextInput, + StreamTextWorkflow, +) +from tests.openai_agents._mock_model import ( + FailMidStream, + ScriptedModelProvider, + ScriptEntry, + ToolCall, +) + +JOKES = ( + "Why did the developer go broke? He used up all his cache. " + "Why do programmers prefer dark mode? Light attracts bugs." +) + +# TResponseStreamEvent is a typing.Annotated union rather than a class, so it +# needs a cast to satisfy from_payload's type[T] signature. +EVENT_TYPE = cast(type, TResponseStreamEvent) + +# Fast polling so a test does not spend most of its time waiting on cooldowns. +POLL_COOLDOWN = timedelta(milliseconds=50) + + +def _client_with_plugin(client: Client, script: list[ScriptEntry]) -> Client: + config = client.config() + config["plugins"] = [ + *config["plugins"], + OpenAIAgentsPlugin( + model_params=ModelActivityParameters( + start_to_close_timeout=timedelta(seconds=30), + heartbeat_timeout=timedelta(seconds=10), + streaming_topic=TOPIC_EVENTS, + ), + model_provider=ScriptedModelProvider(script), + ), + ] + return Client(**config) + + +async def test_stream_text(client: Client) -> None: + client = _client_with_plugin(client, [JOKES]) + task_queue = f"openai-agents-stream-text-{uuid.uuid4()}" + workflow_id = f"stream-text-{uuid.uuid4()}" + + async with Worker( + client, + task_queue=task_queue, + workflows=[StreamTextWorkflow], + max_cached_workflows=0, + ): + handle = await client.start_workflow( + StreamTextWorkflow.run, + StreamTextInput(prompt="Please tell me 2 jokes."), + id=workflow_id, + task_queue=task_queue, + ) + + # Same shape as run_stream_text_workflow.py: one iterator over both + # topics, RawValue payloads decoded per item.topic, break on the + # terminator the workflow publishes. + stream = WorkflowStreamClient.create(client, workflow_id) + converter = client.data_converter.payload_converter + deltas: list[str] = [] + saw_terminator = False + async for item in stream.subscribe( + [TOPIC_EVENTS, TOPIC_DONE], + result_type=RawValue, + poll_cooldown=POLL_COOLDOWN, + ): + if item.topic == TOPIC_DONE: + saw_terminator = True + break + event: Any = converter.from_payload(item.data.payload, EVENT_TYPE) + if isinstance(event, ResponseTextDeltaEvent): + deltas.append(event.delta) + + result = await handle.result() + + assert saw_terminator, "subscriber exited without seeing the terminator" + # Subscribers see native OpenAI events, so the deltas arrive unwrapped and + # reassemble into exactly what the workflow returns. + assert len(deltas) > 1, "expected the text to arrive as several deltas" + assert "".join(deltas) == JOKES + assert result == JOKES + + +async def test_retried_attempt_is_detectable(client: Client) -> None: + """A retried model activity re-streams, and subscribers can tell. + + Turn one calls the tool; turn two dies after four deltas and its retry + answers in full. The partial deltas are already on the stream when the + attempt fails, so a subscriber that just concatenates them renders the + truncated text followed by the whole answer. run_stream_text_workflow.py + finds the seam with the rule asserted here: a sequence_number that fails + to advance while a response is still in flight. Turn two's own restart + must not trip it — that one follows a completed response. + """ + client = _client_with_plugin( + client, + [ToolCall("how_many_jokes"), FailMidStream(JOKES, after_deltas=4), JOKES], + ) + task_queue = f"openai-agents-stream-retry-{uuid.uuid4()}" + workflow_id = f"stream-retry-{uuid.uuid4()}" + + async with Worker( + client, + task_queue=task_queue, + workflows=[StreamItemsWorkflow], + activities=[how_many_jokes], + max_cached_workflows=0, + ): + handle = await client.start_workflow( + StreamItemsWorkflow.run, + StreamItemsInput(), + id=workflow_id, + task_queue=task_queue, + ) + + stream = WorkflowStreamClient.create(client, workflow_id) + converter = client.data_converter.payload_converter + all_deltas: list[str] = [] + deltas_since_restart: list[str] = [] + restarts = 0 + last_sequence = -1 + response_in_flight = False + async for item in stream.subscribe( + [TOPIC_EVENTS, TOPIC_DONE], + result_type=RawValue, + poll_cooldown=POLL_COOLDOWN, + ): + if item.topic == TOPIC_DONE: + break + event: Any = converter.from_payload(item.data.payload, EVENT_TYPE) + + sequence = event.sequence_number + if sequence <= last_sequence and response_in_flight: + restarts += 1 + deltas_since_restart = [] + last_sequence = sequence + response_in_flight = not isinstance(event, ResponseCompletedEvent) + + if isinstance(event, ResponseTextDeltaEvent): + all_deltas.append(event.delta) + deltas_since_restart.append(event.delta) + + result = await handle.result() + + assert restarts == 1, "expected exactly one detected retry" + # The failed attempt's partial text is on the stream ahead of the retry's, + # so a naive reassembly is corrupt while the workflow's result is not. + naive = "".join(all_deltas) + assert len(naive) > len(JOKES) and naive.endswith(JOKES) + # Discarding at the seam recovers exactly the successful attempt. + assert "".join(deltas_since_restart) == JOKES + assert result == JOKES + + +async def test_stream_items(client: Client) -> None: + # Turn one calls the tool, turn two answers with the jokes. + client = _client_with_plugin(client, [ToolCall("how_many_jokes"), JOKES]) + task_queue = f"openai-agents-stream-items-{uuid.uuid4()}" + workflow_id = f"stream-items-{uuid.uuid4()}" + + async with Worker( + client, + task_queue=task_queue, + workflows=[StreamItemsWorkflow], + activities=[how_many_jokes], + max_cached_workflows=0, + ): + handle = await client.start_workflow( + StreamItemsWorkflow.run, + StreamItemsInput(), + id=workflow_id, + task_queue=task_queue, + ) + + stream = WorkflowStreamClient.create(client, workflow_id) + converter = client.data_converter.payload_converter + events: list[ItemEvent] = [] + saw_terminator = False + async for item in stream.subscribe( + [TOPIC_ITEMS, TOPIC_DONE], + result_type=RawValue, + poll_cooldown=POLL_COOLDOWN, + ): + if item.topic == TOPIC_DONE: + saw_terminator = True + break + events.append(converter.from_payload(item.data.payload, ItemEvent)) + + result = await handle.result() + + assert saw_terminator, "subscriber exited without seeing the terminator" + assert [e.kind for e in events] == [ + "agent_updated", + "tool_call", + "tool_output", + "message_output", + ] + assert events[0].detail == "Joker" + assert events[1].detail == "how_many_jokes" + assert 1 <= int(events[2].detail) <= 10 + assert events[3].detail == JOKES + assert result == JOKES diff --git a/tests/polling/infrequent/__init__.py b/tests/polling/infrequent/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/polling/infrequent/workflow_test.py b/tests/polling/infrequent/workflow_test.py new file mode 100644 index 000000000..31f3f9878 --- /dev/null +++ b/tests/polling/infrequent/workflow_test.py @@ -0,0 +1,31 @@ +import uuid + +import pytest +from temporalio.client import Client +from temporalio.testing import WorkflowEnvironment +from temporalio.worker import Worker + +from polling.infrequent.activities import compose_greeting +from polling.infrequent.workflows import GreetingWorkflow + + +async def test_infrequent_polling_workflow(client: Client, env: WorkflowEnvironment): + if not env.supports_time_skipping: + pytest.skip("Too slow to test with time-skipping disabled") + + # Start a worker that hosts the workflow and activity implementations. + task_queue = f"tq-{uuid.uuid4()}" + async with Worker( + client, + task_queue=task_queue, + workflows=[GreetingWorkflow], + activities=[compose_greeting], + ): + handle = await client.start_workflow( + GreetingWorkflow.run, + "Temporal", + id=f"infrequent-polling-{uuid.uuid4()}", + task_queue=task_queue, + ) + result = await handle.result() + assert result == "Hello, Temporal!" diff --git a/tests/pydantic_converter/workflow_test.py b/tests/pydantic_converter/workflow_test.py index a547caf08..c673164ef 100644 --- a/tests/pydantic_converter/workflow_test.py +++ b/tests/pydantic_converter/workflow_test.py @@ -3,15 +3,10 @@ from ipaddress import IPv4Address from temporalio.client import Client +from temporalio.contrib.pydantic import pydantic_data_converter from temporalio.worker import Worker -from pydantic_converter.converter import pydantic_data_converter -from pydantic_converter.worker import ( - MyPydanticModel, - MyWorkflow, - my_activity, - new_sandbox_runner, -) +from pydantic_converter.worker import MyPydanticModel, MyWorkflow, my_activity async def test_workflow_with_pydantic_model(client: Client): @@ -35,7 +30,6 @@ async def test_workflow_with_pydantic_model(client: Client): task_queue=task_queue_name, workflows=[MyWorkflow], activities=[my_activity], - workflow_runner=new_sandbox_runner(), ): result = await client.execute_workflow( MyWorkflow.run, diff --git a/tests/pydantic_converter_v1/__init__.py b/tests/pydantic_converter_v1/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/pydantic_converter_v1/workflow_test.py b/tests/pydantic_converter_v1/workflow_test.py new file mode 100644 index 000000000..fd0af8009 --- /dev/null +++ b/tests/pydantic_converter_v1/workflow_test.py @@ -0,0 +1,46 @@ +import uuid +from datetime import datetime +from ipaddress import IPv4Address + +from temporalio.client import Client +from temporalio.worker import Worker + +from pydantic_converter_v1.converter import pydantic_data_converter +from pydantic_converter_v1.worker import ( + MyPydanticModel, + MyWorkflow, + my_activity, + new_sandbox_runner, +) + + +async def test_workflow_with_pydantic_model(client: Client): + # Replace data converter in client + new_config = client.config() + new_config["data_converter"] = pydantic_data_converter + client = Client(**new_config) + task_queue_name = str(uuid.uuid4()) + + orig_models = [ + MyPydanticModel( + some_ip=IPv4Address("127.0.0.1"), some_date=datetime(2000, 1, 2, 3, 4, 5) + ), + MyPydanticModel( + some_ip=IPv4Address("127.0.0.2"), some_date=datetime(2001, 2, 3, 4, 5, 6) + ), + ] + + async with Worker( + client, + task_queue=task_queue_name, + workflows=[MyWorkflow], + activities=[my_activity], + workflow_runner=new_sandbox_runner(), + ): + result = await client.execute_workflow( + MyWorkflow.run, + orig_models, + id=str(uuid.uuid4()), + task_queue=task_queue_name, + ) + assert orig_models == result diff --git a/tests/resource_pool/__init__.py b/tests/resource_pool/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/resource_pool/workflow_test.py b/tests/resource_pool/workflow_test.py new file mode 100644 index 000000000..0d2eb6547 --- /dev/null +++ b/tests/resource_pool/workflow_test.py @@ -0,0 +1,120 @@ +import asyncio +from collections import defaultdict +from typing import Any, Optional, Sequence + +from temporalio import activity +from temporalio.client import Client, WorkflowFailureError, WorkflowHandle +from temporalio.common import WorkflowIDConflictPolicy +from temporalio.worker import Worker + +from resource_pool.pool_client.resource_pool_workflow import ( + ResourcePoolWorkflow, + ResourcePoolWorkflowInput, +) +from resource_pool.resource_user_workflow import ( + ResourceUserWorkflow, + ResourceUserWorkflowInput, + UseResourceActivityInput, +) +from resource_pool.shared import RESOURCE_POOL_WORKFLOW_ID + +TASK_QUEUE = "resource_pool-task-queue" + + +async def test_resource_pool_workflow(client: Client): + # key is resource, value is a description of resource usage + resource_usage: defaultdict[str, list[Sequence[str]]] = defaultdict(list) + + # Mock out the activity to count executions + @activity.defn(name="use_resource") + async def use_resource_mock(input: UseResourceActivityInput) -> None: + workflow_id = activity.info().workflow_id or "" + resource_usage[input.resource].append((workflow_id, "start")) + # We need a small sleep here to bait out races + await asyncio.sleep(0.05) + resource_usage[input.resource].append((workflow_id, "end")) + + async with Worker( + client, + task_queue=TASK_QUEUE, + workflows=[ResourcePoolWorkflow, ResourceUserWorkflow], + activities=[use_resource_mock], + ): + await run_all_workflows(client) + + # Did any workflow run in more than one place? + workflow_id_to_resource: dict[str, str] = {} + for resource, events in resource_usage.items(): + for workflow_id, event in events: + if workflow_id in workflow_id_to_resource: + existing_resource = workflow_id_to_resource[workflow_id] + assert ( + existing_resource == resource + ), f"{workflow_id} ran on both {resource} and {existing_resource}" + else: + workflow_id_to_resource[workflow_id] = resource + + # Did any resource have more than one workflow on it at a time? + for resource, events in resource_usage.items(): + holder: Optional[str] = None + for workflow_id, event in events: + if event == "start": + assert ( + holder is None + ), f"{workflow_id} started on {resource} held by {holder}" + holder = workflow_id + else: + assert ( + holder == workflow_id + ), f"{workflow_id} ended on {resource} held by {holder}" + holder = None + + # Are all the resources free, per the query? + handle: WorkflowHandle[ResourcePoolWorkflow, None] = ( + client.get_workflow_handle_for( + ResourcePoolWorkflow.run, RESOURCE_POOL_WORKFLOW_ID + ) + ) + query_result = await handle.query(ResourcePoolWorkflow.get_current_holders) + assert query_result == {"r_a": None, "r_b": None, "r_c": None} + + +async def run_all_workflows(client: Client): + resource_pool_handle = await client.start_workflow( + workflow=ResourcePoolWorkflow.run, + arg=ResourcePoolWorkflowInput( + resources={"r_a": None, "r_b": None, "r_c": None}, + waiters=[], + ), + id=RESOURCE_POOL_WORKFLOW_ID, + task_queue=TASK_QUEUE, + id_conflict_policy=WorkflowIDConflictPolicy.USE_EXISTING, + ) + + resource_user_handles: list[WorkflowHandle[Any, Any]] = [] + for i in range(0, 8): + input = ResourceUserWorkflowInput( + resource_pool_workflow_id=RESOURCE_POOL_WORKFLOW_ID, + iteration_to_fail_after=None, + should_continue_as_new=False, + ) + if i == 0: + input.should_continue_as_new = True + if i == 1: + input.iteration_to_fail_after = "first" + + handle = await client.start_workflow( + workflow=ResourceUserWorkflow.run, + arg=input, + id=f"resource-user-workflow-{i}", + task_queue=TASK_QUEUE, + ) + resource_user_handles.append(handle) + + for handle in resource_user_handles: + try: + await handle.result() + except WorkflowFailureError: + pass + + await resource_pool_handle.terminate() diff --git a/tests/sentry/fake_sentry_transport.py b/tests/sentry/fake_sentry_transport.py new file mode 100644 index 000000000..7d1b087df --- /dev/null +++ b/tests/sentry/fake_sentry_transport.py @@ -0,0 +1,17 @@ +import sentry_sdk +import sentry_sdk.types + + +class FakeSentryTransport: + """A fake transport that captures Sentry events in memory""" + + # Note: we could extend from sentry_sdk.transport.Transport + # but `sentry_sdk.init` also takes a simple callable that takes + # an Event rather than a serialised Envelope object, so testing + # is easier. + + def __init__(self): + self.events: list[sentry_sdk.types.Event] = [] + + def __call__(self, event: sentry_sdk.types.Event) -> None: + self.events.append(event) diff --git a/tests/sentry/test_interceptor.py b/tests/sentry/test_interceptor.py new file mode 100644 index 000000000..f9f931e8f --- /dev/null +++ b/tests/sentry/test_interceptor.py @@ -0,0 +1,164 @@ +import sys +import unittest.mock +from collections import abc + +import pytest + +if sys.version_info >= (3, 14): + pytest.skip( + "Sentry does not support Python 3.14 yet.", + allow_module_level=True, + ) + +import sentry_sdk +import temporalio.activity +import temporalio.workflow +from sentry_sdk.integrations.asyncio import AsyncioIntegration +from temporalio.client import Client +from temporalio.worker import Worker +from temporalio.worker.workflow_sandbox import ( + SandboxedWorkflowRunner, + SandboxRestrictions, +) + +from sentry.activity import broken_activity, working_activity +from sentry.interceptor import SentryInterceptor +from sentry.workflow import SentryExampleWorkflow, SentryExampleWorkflowInput +from tests.sentry.fake_sentry_transport import FakeSentryTransport + + +@pytest.fixture +def transport() -> FakeSentryTransport: + """Fixture to provide a fake transport for Sentry SDK.""" + return FakeSentryTransport() + + +@pytest.fixture(autouse=True) +def sentry_init(transport: FakeSentryTransport) -> None: + """Initialize Sentry for testing.""" + sentry_sdk.init( + transport=transport, + integrations=[ + AsyncioIntegration(), + ], + ) + + +@pytest.fixture +async def worker(client: Client) -> abc.AsyncIterator[Worker]: + """Fixture to provide a worker for testing.""" + async with Worker( + client, + task_queue="sentry-task-queue", + workflows=[SentryExampleWorkflow], + activities=[broken_activity, working_activity], + interceptors=[SentryInterceptor()], + workflow_runner=SandboxedWorkflowRunner( + restrictions=SandboxRestrictions.default.with_passthrough_modules( + "sentry_sdk" + ) + ), + ) as worker: + yield worker + + +async def test_sentry_interceptor_reports_no_errors_when_workflow_succeeds( + client: Client, worker: Worker, transport: FakeSentryTransport +) -> None: + """Test that Sentry interceptor reports no errors when workflow succeeds.""" + # WHEN + try: + await client.execute_workflow( + SentryExampleWorkflow.run, + SentryExampleWorkflowInput(option="working"), + id="sentry-workflow-id", + task_queue=worker.task_queue, + ) + except Exception: + pytest.fail("Workflow should not raise an exception") + + # THEN + assert len(transport.events) == 0, "No events should be captured" + + +async def test_sentry_interceptor_captures_errors( + client: Client, worker: Worker, transport: FakeSentryTransport +) -> None: + """Test that errors are captured with correct Sentry metadata.""" + # WHEN + try: + await client.execute_workflow( + SentryExampleWorkflow.run, + SentryExampleWorkflowInput(option="broken"), + id="sentry-workflow-id", + task_queue=worker.task_queue, + ) + pytest.fail("Workflow should raise an exception") + except Exception: + pass + + # THEN + # there should be two events: one for the failed activity and one for the failed workflow + assert len(transport.events) == 2, "Two events should be captured" + + # Check the first event - should be the activity exception + # -------------------------------------------------------- + event = transport.events[0] + + # Check exception was captured + assert event["exception"]["values"][0]["type"] == "Exception" + assert event["exception"]["values"][0]["value"] == "Activity failed!" + + # Check useful metadata were captured as tags + assert event["tags"] == { + "temporal.execution_type": "activity", + "module": "sentry.activity.broken_activity", + "temporal.workflow.type": "SentryExampleWorkflow", + "temporal.workflow.id": "sentry-workflow-id", + "temporal.activity.id": "1", + "temporal.activity.type": "broken_activity", + "temporal.activity.task_queue": "sentry-task-queue", + "temporal.workflow.namespace": "default", + "temporal.workflow.run_id": unittest.mock.ANY, + } + + # Check activity input was captured as context + assert event["contexts"]["temporal.activity.input"] == { + "message": "Hello, Temporal!", + } + + # Check activity info was captured as context + activity_info = temporalio.activity.Info( + **event["contexts"]["temporal.activity.info"] # type: ignore + ) + assert activity_info.activity_type == "broken_activity" + + # Check the second event - should be the workflow exception + # --------------------------------------------------------- + event = transport.events[1] + + # Check exception was captured + assert event["exception"]["values"][0]["type"] == "ApplicationError" + assert event["exception"]["values"][0]["value"] == "Activity failed!" + + # Check useful metadata were captured as tags + assert event["tags"] == { + "temporal.execution_type": "workflow", + "module": "sentry.workflow.SentryExampleWorkflow.run", + "temporal.workflow.type": "SentryExampleWorkflow", + "temporal.workflow.id": "sentry-workflow-id", + "temporal.workflow.task_queue": "sentry-task-queue", + "temporal.workflow.namespace": "default", + "temporal.workflow.run_id": unittest.mock.ANY, + } + + # Check workflow input was captured as context + assert event["contexts"]["temporal.workflow.input"] == { + "option": "broken", + } + + # Check workflow info was captured as context + workflow_info = temporalio.workflow.Info( + **event["contexts"]["temporal.workflow.info"] # type: ignore + ) + assert workflow_info.workflow_type == "SentryExampleWorkflow" diff --git a/tests/sleep_for_days/__init__.py b/tests/sleep_for_days/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/sleep_for_days/workflow_test.py b/tests/sleep_for_days/workflow_test.py new file mode 100644 index 000000000..fa3f06864 --- /dev/null +++ b/tests/sleep_for_days/workflow_test.py @@ -0,0 +1,53 @@ +import uuid +from datetime import timedelta + +from temporalio import activity +from temporalio.testing import WorkflowEnvironment +from temporalio.worker import Worker + +from sleep_for_days.starter import TASK_QUEUE +from sleep_for_days.workflows import SendEmailInput, SleepForDaysWorkflow + + +async def test_sleep_for_days_workflow(): + num_activity_executions = 0 + + # Mock out the activity to count executions + @activity.defn(name="send_email") + async def send_email_mock(input: SendEmailInput) -> str: + nonlocal num_activity_executions + num_activity_executions += 1 + return input.email_msg + + async with await WorkflowEnvironment.start_time_skipping() as env: + # if env.supports_time_skipping: + # pytest.skip( + # "Java test server: https://github.com/temporalio/sdk-java/issues/1903" + # ) + async with Worker( + env.client, + task_queue=TASK_QUEUE, + workflows=[SleepForDaysWorkflow], + activities=[send_email_mock], + ): + handle = await env.client.start_workflow( + SleepForDaysWorkflow.run, + id=str(uuid.uuid4()), + task_queue=TASK_QUEUE, + ) + + start_time = await env.get_current_time() + # Time-skip 5 minutes. + await env.sleep(timedelta(minutes=5)) + # Check that the activity has been called, we're now waiting for the sleep to finish. + assert num_activity_executions == 1 + # Time-skip 3 days. + await env.sleep(timedelta(days=90)) + # Expect 3 more activity calls. + assert num_activity_executions == 4 + # Send the signal to complete the workflow. + await handle.signal(SleepForDaysWorkflow.complete) + # Expect no more activity calls to have been made - workflow is complete. + assert num_activity_executions == 4 + # Expect more than 90 days to have passed. + assert (await env.get_current_time() - start_time) > timedelta(days=90) diff --git a/tests/strands_plugin/__init__.py b/tests/strands_plugin/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/strands_plugin/_mock_model.py b/tests/strands_plugin/_mock_model.py new file mode 100644 index 000000000..9f97bedbc --- /dev/null +++ b/tests/strands_plugin/_mock_model.py @@ -0,0 +1,69 @@ +"""Scripted Strands model for sample tests. + +Vendored from the SDK's ``tests/contrib/strands/mock_model.py``. Each entry in +``responses`` drives one ``stream()`` call: a ``str`` yields a text turn, a +``dict`` of ``{"name", "input"}`` yields a tool-use turn. +""" + +from __future__ import annotations + +import json +from collections.abc import AsyncIterable +from typing import Any + +import temporalio.contrib.strands._plugin as _plugin_module +from strands.models import Model +from strands.types.streaming import StreamEvent + + +class MockModel(Model): + def __init__(self, responses: list[str | dict[str, Any]]) -> None: + self._responses = list(responses) + self._tool_call_index = 0 + + def update_config(self, **_model_config: Any) -> None: + return None + + def get_config(self) -> dict[str, Any]: + return {} + + def structured_output(self, *_args: Any, **_kwargs: Any): + raise NotImplementedError + + async def stream(self, *_args: Any, **_kwargs: Any) -> AsyncIterable[StreamEvent]: + if not self._responses: + raise AssertionError("MockModel script exhausted") + response = self._responses.pop(0) + + yield {"messageStart": {"role": "assistant"}} + + if isinstance(response, str): + yield {"contentBlockDelta": {"delta": {"text": response}}} + yield {"contentBlockStop": {}} + yield {"messageStop": {"stopReason": "end_turn"}} + else: + self._tool_call_index += 1 + yield { + "contentBlockStart": { + "start": { + "toolUse": { + "name": response["name"], + "toolUseId": f"mock-tool-{self._tool_call_index}", + }, + }, + }, + } + yield { + "contentBlockDelta": { + "delta": {"toolUse": {"input": json.dumps(response["input"])}}, + }, + } + yield {"contentBlockStop": {}} + yield {"messageStop": {"stopReason": "tool_use"}} + + +def patch_bedrock(monkeypatch: Any, responses: list[Any]) -> None: + """Make the plugin's implicit BedrockModel factory return a scripted MockModel.""" + monkeypatch.setattr( + _plugin_module, "BedrockModel", lambda *a, **kw: MockModel(responses) + ) diff --git a/tests/strands_plugin/activity_interrupt_test.py b/tests/strands_plugin/activity_interrupt_test.py new file mode 100644 index 000000000..27e265239 --- /dev/null +++ b/tests/strands_plugin/activity_interrupt_test.py @@ -0,0 +1,50 @@ +import uuid + +import pytest +from temporalio.client import Client +from temporalio.contrib.strands import StrandsPlugin +from temporalio.worker import Worker + +from strands_plugin.activity_interrupt.workflow import ( + ActivityInterruptWorkflow, + delete_thing, +) +from tests.strands_plugin._mock_model import patch_bedrock + + +async def test_activity_interrupt( + client: Client, monkeypatch: pytest.MonkeyPatch +) -> None: + patch_bedrock( + monkeypatch, + [ + {"name": "delete_thing", "input": {"name": "system"}}, + {"name": "delete_thing", "input": {"name": "system"}}, + "Done!", + ], + ) + + task_queue = f"strands-activity-interrupt-{uuid.uuid4()}" + plugin = StrandsPlugin() + + config = client.config() + config["plugins"] = [*config["plugins"], plugin] + client = Client(**config) + + async with Worker( + client, + task_queue=task_queue, + workflows=[ActivityInterruptWorkflow], + activities=[delete_thing], + max_cached_workflows=0, + ): + handle = await client.start_workflow( + ActivityInterruptWorkflow.run, + "Delete the system user.", + id=f"strands-activity-interrupt-{uuid.uuid4()}", + task_queue=task_queue, + ) + await handle.signal(ActivityInterruptWorkflow.approve, "approve") + result = await handle.result() + + assert result == "Done!\n" diff --git a/tests/strands_plugin/continue_as_new_test.py b/tests/strands_plugin/continue_as_new_test.py new file mode 100644 index 000000000..45c141710 --- /dev/null +++ b/tests/strands_plugin/continue_as_new_test.py @@ -0,0 +1,83 @@ +import asyncio +import uuid + +import pytest +from temporalio.client import Client, WorkflowExecutionStatus +from temporalio.contrib.strands import StrandsPlugin +from temporalio.testing import WorkflowEnvironment +from temporalio.worker import Worker + +from strands_plugin.continue_as_new.workflow import ChatInput, ChatWorkflow +from tests.strands_plugin._mock_model import patch_bedrock + + +async def test_continue_as_new_carries_history( + monkeypatch: pytest.MonkeyPatch, +) -> None: + """Exercise the full chat lifecycle across a continue-as-new boundary. + + Drives enough history that the server suggests continue-as-new, confirms the + original run hands its message history off to a fresh run, then takes another + turn on that resumed run and ends the chat normally. + """ + patch_bedrock(monkeypatch, ["First reply.", "Second reply."]) + + # The server only suggests continue-as-new once history grows past a + # threshold (~4096 events by default). Lower it so a single turn's worth of + # history is enough to trip ``is_continue_as_new_suggested()``. + async with await WorkflowEnvironment.start_local( + dev_server_extra_args=[ + "--dynamic-config-value", + "limit.historyCount.suggestContinueAsNew=5", + ] + ) as env: + config = env.client.config() + config["plugins"] = [*config["plugins"], StrandsPlugin()] + client = Client(**config) + + task_queue = f"strands-chat-{uuid.uuid4()}" + workflow_id = f"strands-chat-{uuid.uuid4()}" + + async with Worker( + client, + task_queue=task_queue, + workflows=[ChatWorkflow], + max_cached_workflows=0, + ): + handle = await client.start_workflow( + ChatWorkflow.run, + ChatInput(), + id=workflow_id, + task_queue=task_queue, + ) + + original_run_id = handle.result_run_id + + # One turn produces enough events to cross the threshold above. + reply1 = await handle.execute_update(ChatWorkflow.turn, "Hello") + assert reply1 == "First reply." + + # The original run should end by continuing-as-new once the in-flight + # turn drains. Pin to its run id; an unpinned handle would follow the + # chain to the fresh (still-running) run instead. + original = client.get_workflow_handle(workflow_id, run_id=original_run_id) + for _ in range(100): + desc = await original.describe() + if desc.status == WorkflowExecutionStatus.CONTINUED_AS_NEW: + break + await asyncio.sleep(0.1) + else: + raise AssertionError("workflow did not continue-as-new") + + # The fresh run resumes with the prior turn's history and accepts a + # new turn, appending to the carried-over messages. + latest = client.get_workflow_handle(workflow_id) + reply2 = await latest.execute_update(ChatWorkflow.turn, "How are you?") + assert reply2 == "Second reply." + + # 2 messages carried across the handoff + 2 from the resumed turn. + messages = await latest.query(ChatWorkflow.messages) + assert len(messages) == 4 + + await latest.signal(ChatWorkflow.end_chat) + await latest.result() diff --git a/tests/strands_plugin/hello_world_test.py b/tests/strands_plugin/hello_world_test.py new file mode 100644 index 000000000..5110ec759 --- /dev/null +++ b/tests/strands_plugin/hello_world_test.py @@ -0,0 +1,35 @@ +import uuid + +import pytest +from temporalio.client import Client +from temporalio.contrib.strands import StrandsPlugin +from temporalio.worker import Worker + +from strands_plugin.hello_world.workflow import HelloWorldWorkflow +from tests.strands_plugin._mock_model import patch_bedrock + + +async def test_hello_world(client: Client, monkeypatch: pytest.MonkeyPatch) -> None: + patch_bedrock(monkeypatch, ["A haiku, for you."]) + + task_queue = f"strands-hello-world-{uuid.uuid4()}" + plugin = StrandsPlugin() + + config = client.config() + config["plugins"] = [*config["plugins"], plugin] + client = Client(**config) + + async with Worker( + client, + task_queue=task_queue, + workflows=[HelloWorldWorkflow], + max_cached_workflows=0, + ): + result = await client.execute_workflow( + HelloWorldWorkflow.run, + "Write a haiku.", + id=f"strands-hello-world-{uuid.uuid4()}", + task_queue=task_queue, + ) + + assert result == "A haiku, for you.\n" diff --git a/tests/strands_plugin/hooks_test.py b/tests/strands_plugin/hooks_test.py new file mode 100644 index 000000000..72d9bff52 --- /dev/null +++ b/tests/strands_plugin/hooks_test.py @@ -0,0 +1,42 @@ +import uuid + +import pytest +from temporalio.client import Client +from temporalio.contrib.strands import StrandsPlugin +from temporalio.worker import Worker + +from strands_plugin.hooks.workflow import HooksWorkflow, persist_tool_call +from tests.strands_plugin._mock_model import patch_bedrock + + +async def test_hooks(client: Client, monkeypatch: pytest.MonkeyPatch) -> None: + patch_bedrock( + monkeypatch, + [ + {"name": "echo", "input": {"text": "hi"}}, + "Done!", + ], + ) + + task_queue = f"strands-hooks-{uuid.uuid4()}" + plugin = StrandsPlugin() + + config = client.config() + config["plugins"] = [*config["plugins"], plugin] + client = Client(**config) + + async with Worker( + client, + task_queue=task_queue, + workflows=[HooksWorkflow], + activities=[persist_tool_call], + max_cached_workflows=0, + ): + fired = await client.execute_workflow( + HooksWorkflow.run, + "Echo hi.", + id=f"strands-hooks-{uuid.uuid4()}", + task_queue=task_queue, + ) + + assert fired == ["echo"] diff --git a/tests/strands_plugin/human_in_the_loop_test.py b/tests/strands_plugin/human_in_the_loop_test.py new file mode 100644 index 000000000..53cd5e844 --- /dev/null +++ b/tests/strands_plugin/human_in_the_loop_test.py @@ -0,0 +1,45 @@ +import uuid + +import pytest +from temporalio.client import Client +from temporalio.contrib.strands import StrandsPlugin +from temporalio.worker import Worker + +from strands_plugin.human_in_the_loop.workflow import HumanInTheLoopWorkflow +from tests.strands_plugin._mock_model import patch_bedrock + + +async def test_human_in_the_loop_approve( + client: Client, monkeypatch: pytest.MonkeyPatch +) -> None: + patch_bedrock( + monkeypatch, + [ + {"name": "delete_file", "input": {"path": "/tmp/sensitive.txt"}}, + "Done!", + ], + ) + + task_queue = f"strands-hitl-{uuid.uuid4()}" + plugin = StrandsPlugin() + + config = client.config() + config["plugins"] = [*config["plugins"], plugin] + client = Client(**config) + + async with Worker( + client, + task_queue=task_queue, + workflows=[HumanInTheLoopWorkflow], + max_cached_workflows=0, + ): + handle = await client.start_workflow( + HumanInTheLoopWorkflow.run, + "Delete /tmp/sensitive.txt", + id=f"strands-hitl-{uuid.uuid4()}", + task_queue=task_queue, + ) + await handle.signal(HumanInTheLoopWorkflow.approve, "approve") + result = await handle.result() + + assert result == "Done!\n" diff --git a/tests/strands_plugin/mcp_test.py b/tests/strands_plugin/mcp_test.py new file mode 100644 index 000000000..9480f81d9 --- /dev/null +++ b/tests/strands_plugin/mcp_test.py @@ -0,0 +1,42 @@ +import uuid + +import pytest +from temporalio.client import Client +from temporalio.contrib.strands import StrandsPlugin +from temporalio.worker import Worker + +from strands_plugin.mcp.run_worker import _make_echo_client +from strands_plugin.mcp.workflow import MCPWorkflow +from tests.strands_plugin._mock_model import patch_bedrock + + +async def test_mcp(client: Client, monkeypatch: pytest.MonkeyPatch) -> None: + patch_bedrock( + monkeypatch, + [ + {"name": "echo", "input": {"message": "hello from MCP"}}, + "Done!", + ], + ) + + task_queue = f"strands-mcp-{uuid.uuid4()}" + plugin = StrandsPlugin(mcp_clients={"echo": _make_echo_client}) + + config = client.config() + config["plugins"] = [*config["plugins"], plugin] + client = Client(**config) + + async with Worker( + client, + task_queue=task_queue, + workflows=[MCPWorkflow], + max_cached_workflows=0, + ): + result = await client.execute_workflow( + MCPWorkflow.run, + "Echo hello from MCP.", + id=f"strands-mcp-{uuid.uuid4()}", + task_queue=task_queue, + ) + + assert result == "Done!\n" diff --git a/tests/strands_plugin/streaming_test.py b/tests/strands_plugin/streaming_test.py new file mode 100644 index 000000000..1a57aefda --- /dev/null +++ b/tests/strands_plugin/streaming_test.py @@ -0,0 +1,59 @@ +import asyncio +import uuid +from datetime import timedelta + +import pytest +from strands.types.streaming import StreamEvent +from temporalio.client import Client +from temporalio.contrib.strands import StrandsPlugin +from temporalio.contrib.workflow_streams import WorkflowStreamClient +from temporalio.worker import Worker + +from strands_plugin.streaming.workflow import StreamingWorkflow +from tests.strands_plugin._mock_model import patch_bedrock + + +async def test_streaming(client: Client, monkeypatch: pytest.MonkeyPatch) -> None: + patch_bedrock(monkeypatch, ["Done!"]) + + task_queue = f"strands-streaming-{uuid.uuid4()}" + plugin = StrandsPlugin() + workflow_id = f"strands-streaming-{uuid.uuid4()}" + + config = client.config() + config["plugins"] = [*config["plugins"], plugin] + client = Client(**config) + + async with Worker( + client, + task_queue=task_queue, + workflows=[StreamingWorkflow], + max_cached_workflows=0, + ): + handle = await client.start_workflow( + StreamingWorkflow.run, + "Hello", + id=workflow_id, + task_queue=task_queue, + ) + + stream = WorkflowStreamClient.create(client, workflow_id) + events: list[StreamEvent] = [] + + async def collect() -> None: + async for item in stream.subscribe( + ["events"], + from_offset=0, + result_type=StreamEvent, + poll_cooldown=timedelta(milliseconds=50), + ): + events.append(item.data) + if len(events) >= 4: + return + + collect_task = asyncio.create_task(collect()) + assert await handle.result() == "Done!\n" + await asyncio.wait_for(collect_task, timeout=10.0) + + assert any("messageStart" in e for e in events) + assert any("messageStop" in e for e in events) diff --git a/tests/strands_plugin/structured_output_test.py b/tests/strands_plugin/structured_output_test.py new file mode 100644 index 000000000..fba1f10c1 --- /dev/null +++ b/tests/strands_plugin/structured_output_test.py @@ -0,0 +1,54 @@ +import uuid + +import pytest +from temporalio.client import Client +from temporalio.contrib.strands import StrandsPlugin +from temporalio.worker import Worker + +from strands_plugin.structured_output.workflow import ( + PersonInfo, + StructuredOutputWorkflow, +) +from tests.strands_plugin._mock_model import patch_bedrock + + +async def test_structured_output( + client: Client, monkeypatch: pytest.MonkeyPatch +) -> None: + patch_bedrock( + monkeypatch, + [ + { + "name": "PersonInfo", + "input": { + "name": "John Smith", + "age": 30, + "occupation": "software engineer", + }, + }, + ], + ) + + task_queue = f"strands-structured-output-{uuid.uuid4()}" + plugin = StrandsPlugin() + + config = client.config() + config["plugins"] = [*config["plugins"], plugin] + client = Client(**config) + + async with Worker( + client, + task_queue=task_queue, + workflows=[StructuredOutputWorkflow], + max_cached_workflows=0, + ): + result = await client.execute_workflow( + StructuredOutputWorkflow.run, + "John Smith is a 30 year-old software engineer.", + id=f"strands-structured-output-{uuid.uuid4()}", + task_queue=task_queue, + ) + + assert result == PersonInfo( + name="John Smith", age=30, occupation="software engineer" + ) diff --git a/tests/strands_plugin/tools_test.py b/tests/strands_plugin/tools_test.py new file mode 100644 index 000000000..5aa36f883 --- /dev/null +++ b/tests/strands_plugin/tools_test.py @@ -0,0 +1,51 @@ +import uuid + +import pytest +from temporalio.client import Client +from temporalio.contrib.strands import StrandsPlugin +from temporalio.worker import Worker + +from strands_plugin.tools.workflow import ( + ToolsWorkflow, + environment_activity, + fetch_weather, +) +from tests.strands_plugin._mock_model import patch_bedrock + + +async def test_tools(client: Client, monkeypatch: pytest.MonkeyPatch) -> None: + patch_bedrock( + monkeypatch, + [ + { + "name": "letter_counter", + "input": {"word": "strawberry", "letter": "R"}, + }, + {"name": "fetch_weather", "input": {"city": "San Francisco"}}, + {"name": "environment", "input": {"action": "validate", "name": "PATH"}}, + "Done!", + ], + ) + + task_queue = f"strands-tools-{uuid.uuid4()}" + plugin = StrandsPlugin() + + config = client.config() + config["plugins"] = [*config["plugins"], plugin] + client = Client(**config) + + async with Worker( + client, + task_queue=task_queue, + workflows=[ToolsWorkflow], + activities=[fetch_weather, environment_activity], + max_cached_workflows=0, + ): + result = await client.execute_workflow( + ToolsWorkflow.run, + "Use all three tools.", + id=f"strands-tools-{uuid.uuid4()}", + task_queue=task_queue, + ) + + assert result == "Done!\n" diff --git a/tests/trio_async/__init__.py b/tests/trio_async/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/trio_async/workflow_test.py b/tests/trio_async/workflow_test.py new file mode 100644 index 000000000..15d678aac --- /dev/null +++ b/tests/trio_async/workflow_test.py @@ -0,0 +1,51 @@ +import sys +import uuid + +import pytest + +if sys.version_info >= (3, 14): + pytest.skip("trio-asyncio not supported on Python 3.14+", allow_module_level=True) + + +import trio_asyncio +from temporalio.client import Client +from temporalio.worker import Worker + +from trio_async import activities, workflows + + +async def test_workflow_with_trio(client: Client): + @trio_asyncio.aio_as_trio + async def inside_trio(client: Client) -> list[str]: + # Create Trio thread executor + with trio_asyncio.TrioExecutor(max_workers=200) as thread_executor: + task_queue = f"tq-{uuid.uuid4()}" + # Run worker + async with Worker( + client, + task_queue=task_queue, + activities=[ + activities.say_hello_activity_async, + activities.say_hello_activity_sync, + ], + workflows=[workflows.SayHelloWorkflow], + activity_executor=thread_executor, + workflow_task_executor=thread_executor, + ): + # Run workflow and return result + return await client.execute_workflow( + workflows.SayHelloWorkflow.run, + "some-user", + id=f"wf-{uuid.uuid4()}", + task_queue=task_queue, + ) + + if sys.version_info[:2] < (3, 12): + pytest.skip("Trio support requires >= 3.12") + + result = trio_asyncio.run(inside_trio, client) + + assert result == [ + "Hello, some-user! (from asyncio)", + "Hello, some-user! (from thread)", + ] diff --git a/tests/updatable_timer/__init__.py b/tests/updatable_timer/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/updatable_timer/updatable_timer_test.py b/tests/updatable_timer/updatable_timer_test.py new file mode 100644 index 000000000..1f3d8ae03 --- /dev/null +++ b/tests/updatable_timer/updatable_timer_test.py @@ -0,0 +1,33 @@ +import datetime +import logging +import math +import uuid + +from temporalio.client import Client, WorkflowExecutionStatus +from temporalio.testing import WorkflowEnvironment +from temporalio.worker import Worker + +from updatable_timer.workflow import Workflow + + +async def test_updatable_timer_workflow(): + logging.basicConfig(level=logging.DEBUG) + + task_queue_name = str(uuid.uuid4()) + async with await WorkflowEnvironment.start_time_skipping() as env: + async with Worker(env.client, task_queue=task_queue_name, workflows=[Workflow]): + in_a_day = float( + (datetime.datetime.now() + datetime.timedelta(days=1)).timestamp() + ) + in_an_hour = float( + (datetime.datetime.now() + datetime.timedelta(hours=1)).timestamp() + ) + handle = await env.client.start_workflow( + Workflow.run, in_a_day, id=str(uuid.uuid4()), task_queue=task_queue_name + ) + wake_up_time1 = await handle.query(Workflow.get_wake_up_time) + assert math.isclose(wake_up_time1, in_a_day) + await handle.signal(Workflow.update_wake_up_time, in_an_hour) + wake_up_time2 = await handle.query(Workflow.get_wake_up_time) + assert math.isclose(wake_up_time2, in_an_hour) + await handle.result() diff --git a/trio_async/README.md b/trio_async/README.md new file mode 100644 index 000000000..dfa02eab4 --- /dev/null +++ b/trio_async/README.md @@ -0,0 +1,25 @@ +# Trio Async Sample + +This sample shows how to use Temporal asyncio with [Trio](https://trio.readthedocs.io) using +[Trio asyncio](https://trio-asyncio.readthedocs.io). Specifically it demonstrates using a traditional Temporal client +and worker in a Trio setting, and how Trio-based code can run in both asyncio async activities and threaded sync +activities. + +NOTE: This sample only works on Python versions < 3.14. + +For this sample, the optional `trio_async` dependency group must be included. To include, run: + + uv sync --group trio_async + +To run, first see [README.md](../README.md) for prerequisites. Then, run the following from the root directory to start the +worker: + + uv run trio_async/worker.py + +This will start the worker. Then, in another terminal, run the following to execute the workflow: + + uv run trio_async/starter.py + +The starter should complete with: + + INFO:root:Workflow result: ['Hello, Temporal! (from asyncio)', 'Hello, Temporal! (from thread)'] \ No newline at end of file diff --git a/trio_async/__init__.py b/trio_async/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/trio_async/activities.py b/trio_async/activities.py new file mode 100644 index 000000000..c253c93ef --- /dev/null +++ b/trio_async/activities.py @@ -0,0 +1,51 @@ +import asyncio +import time + +import trio +import trio_asyncio +from temporalio import activity + + +# An asyncio-based async activity +@activity.defn +async def say_hello_activity_async(name: str) -> str: + # Demonstrate a sleep in both asyncio and Trio, showing that both asyncio + # and Trio primitives can be used + + # First asyncio + activity.logger.info("Sleeping in asyncio") + await asyncio.sleep(0.1) + + # Now Trio. We have to invoke the function separately decorated. + # We cannot use the @trio_as_aio decorator on the activity itself because + # it doesn't use functools wrap or similar so it doesn't respond to things + # like __name__ that @activity.defn needs. + return await say_hello_in_trio_from_asyncio(name) + + +@trio_asyncio.trio_as_aio +async def say_hello_in_trio_from_asyncio(name: str) -> str: + activity.logger.info("Sleeping in Trio (from asyncio)") + await trio.sleep(0.1) + return f"Hello, {name}! (from asyncio)" + + +# A thread-based sync activity +@activity.defn +def say_hello_activity_sync(name: str) -> str: + # Demonstrate a sleep in both threaded and Trio, showing that both + # primitives can be used + + # First, thread-blocking + activity.logger.info("Sleeping normally") + time.sleep(0.1) + + # Now Trio. We have to use Trio's thread sync tools to run trio calls from + # a different thread. + return trio.from_thread.run(say_hello_in_trio_from_sync, name) + + +async def say_hello_in_trio_from_sync(name: str) -> str: + activity.logger.info("Sleeping in Trio (from thread)") + await trio.sleep(0.1) + return f"Hello, {name}! (from thread)" diff --git a/trio_async/starter.py b/trio_async/starter.py new file mode 100644 index 000000000..6535ca986 --- /dev/null +++ b/trio_async/starter.py @@ -0,0 +1,31 @@ +import logging + +import trio_asyncio +from temporalio.client import Client +from temporalio.envconfig import ClientConfig + +from trio_async import workflows + + +@trio_asyncio.aio_as_trio # Note this decorator which allows asyncio primitives +async def main(): + logging.basicConfig(level=logging.INFO) + + # Connect client + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) + + # Execute the workflow + result = await client.execute_workflow( + workflows.SayHelloWorkflow.run, + "Temporal", + id=f"trio-async-workflow-id", + task_queue="trio-async-task-queue", + ) + logging.info(f"Workflow result: {result}") + + +if __name__ == "__main__": + # Note how we're using Trio event loop, not asyncio + trio_asyncio.run(main) diff --git a/trio_async/worker.py b/trio_async/worker.py new file mode 100644 index 000000000..7cb9685f9 --- /dev/null +++ b/trio_async/worker.py @@ -0,0 +1,68 @@ +import asyncio +import logging +import os +import sys + +import trio_asyncio +from temporalio.client import Client +from temporalio.envconfig import ClientConfig +from temporalio.worker import Worker + +from trio_async import activities, workflows + + +@trio_asyncio.aio_as_trio # Note this decorator which allows asyncio primitives +async def main(): + logging.basicConfig(level=logging.INFO) + + # Connect client + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) + + # Temporal runs threaded activities and workflow tasks via run_in_executor. + # Due to how trio_asyncio works, you can only do run_in_executor with their + # specific executor. We make sure to give it 200 max since we are using it + # for both activities and workflow tasks and by default the worker supports + # 100 max concurrent activity tasks and 100 max concurrent workflow tasks. + with trio_asyncio.TrioExecutor(max_workers=200) as thread_executor: + # Run a worker for the workflow + async with Worker( + client, + task_queue="trio-async-task-queue", + activities=[ + activities.say_hello_activity_async, + activities.say_hello_activity_sync, + ], + workflows=[workflows.SayHelloWorkflow], + activity_executor=thread_executor, + workflow_task_executor=thread_executor, + ): + # Wait until interrupted + logging.info("Worker started, ctrl+c to exit") + try: + await asyncio.Future() + except asyncio.CancelledError: + # Ignore, happens on ctrl+C + pass + finally: + logging.info("Shutting down") + + +if __name__ == "__main__": + # Note how we're using Trio event loop, not asyncio + try: + trio_asyncio.run(main) + except KeyboardInterrupt: + # Ignore ctrl+c + pass + except BaseException as err: + # On Python 3.11+ Trio represents keyboard interrupt inside an exception + # group + is_interrupt = ( + sys.version_info >= (3, 11) + and isinstance(err, BaseExceptionGroup) + and err.subgroup(KeyboardInterrupt) + ) + if not is_interrupt: + raise diff --git a/trio_async/workflows.py b/trio_async/workflows.py new file mode 100644 index 000000000..adcb47611 --- /dev/null +++ b/trio_async/workflows.py @@ -0,0 +1,30 @@ +from datetime import timedelta + +from temporalio import workflow + +with workflow.unsafe.imports_passed_through(): + from trio_async.activities import say_hello_activity_async, say_hello_activity_sync + + +@workflow.defn +class SayHelloWorkflow: + @workflow.run + async def run(self, name: str) -> list[str]: + # Workflows don't use default asyncio event loop or Trio, they use a + # custom event loop. Therefore Trio primitives should never be used in a + # workflow, only asyncio helpers (which delegate to the custom loop). + return [ + # That these are two different activities for async or sync means + # nothing to the workflow, we just have both to demonstrate the + # activity side + await workflow.execute_activity( + say_hello_activity_async, + name, + start_to_close_timeout=timedelta(minutes=5), + ), + await workflow.execute_activity( + say_hello_activity_sync, + name, + start_to_close_timeout=timedelta(minutes=5), + ), + ] diff --git a/updatable_timer/README.md b/updatable_timer/README.md new file mode 100644 index 000000000..3b738db88 --- /dev/null +++ b/updatable_timer/README.md @@ -0,0 +1,42 @@ +# Updatable Timer Sample + +Demonstrates a helper class which relies on `workflow.wait_condition` to implement a blocking sleep that can be updated at any moment. + +The sample is composed of the three executables: + +* `worker.py` hosts the Workflow Executions. +* `starter.py` starts Workflow Executions. +* `wake_up_timer_updater.py` Signals the Workflow Execution with the new time to wake up. + +First start the Worker: + +```bash +uv run updatable_timer/worker.py +``` +Check the output of the Worker window. The expected output is: + +``` +Worker started, ctrl+c to exit +``` + +Then in a different terminal window start the Workflow Execution: + +```bash +uv run updatable_timer/starter.py +``` +Check the output of the Worker window. The expected output is: +``` +Workflow started: run_id=... +``` + +Then run the updater as many times as you want to change timer to 10 seconds from now: + +```bash +uv run updatable_timer/wake_up_time_updater.py +``` + +Check the output of the worker window. The expected output is: + +``` +Updated wake up time to 10 seconds from now +``` \ No newline at end of file diff --git a/updatable_timer/__init__.py b/updatable_timer/__init__.py new file mode 100644 index 000000000..a5ee5055a --- /dev/null +++ b/updatable_timer/__init__.py @@ -0,0 +1 @@ +TASK_QUEUE = "updatable-timer" diff --git a/updatable_timer/starter.py b/updatable_timer/starter.py new file mode 100644 index 000000000..8ce0f4f95 --- /dev/null +++ b/updatable_timer/starter.py @@ -0,0 +1,36 @@ +import asyncio +import logging +from datetime import datetime, timedelta +from typing import Optional + +from temporalio import exceptions +from temporalio.client import Client +from temporalio.envconfig import ClientConfig + +from updatable_timer import TASK_QUEUE +from updatable_timer.workflow import Workflow + + +async def main(client: Optional[Client] = None): + logging.basicConfig(level=logging.INFO) + + if not client: + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) + try: + handle = await client.start_workflow( + Workflow.run, + (datetime.now() + timedelta(days=1)).timestamp(), + id=f"updatable-timer-workflow", + task_queue=TASK_QUEUE, + ) + logging.info(f"Workflow started: run_id={handle.result_run_id}") + except exceptions.WorkflowAlreadyStartedError as e: + logging.info( + f"Workflow already running: workflow_id={e.workflow_id}, run_id={e.run_id}" + ) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/updatable_timer/updatable_timer_lib.py b/updatable_timer/updatable_timer_lib.py new file mode 100644 index 000000000..b90ae8686 --- /dev/null +++ b/updatable_timer/updatable_timer_lib.py @@ -0,0 +1,39 @@ +import asyncio +from datetime import datetime, timedelta + +from temporalio import workflow + + +class UpdatableTimer: + def __init__(self, wake_up_time: datetime) -> None: + self.wake_up_time = wake_up_time + self.wake_up_time_updated = False + + async def sleep(self) -> None: + workflow.logger.info(f"sleep_until: {self.wake_up_time}") + while True: + now = workflow.now() + + sleep_interval = self.wake_up_time - now + if sleep_interval <= timedelta(0): + break + workflow.logger.info(f"Going to sleep for {sleep_interval}") + + try: + self.wake_up_time_updated = False + await workflow.wait_condition( + lambda: self.wake_up_time_updated, + timeout=sleep_interval, + ) + except asyncio.TimeoutError: + # checks condition at the beginning of the loop + continue + workflow.logger.info(f"sleep_until completed") + + def update_wake_up_time(self, wake_up_time: datetime) -> None: + workflow.logger.info(f"update_wake_up_time: {wake_up_time}") + self.wake_up_time = wake_up_time + self.wake_up_time_updated = True + + def get_wake_up_time(self) -> datetime: + return self.wake_up_time diff --git a/updatable_timer/wake_up_time_updater.py b/updatable_timer/wake_up_time_updater.py new file mode 100644 index 000000000..43d248385 --- /dev/null +++ b/updatable_timer/wake_up_time_updater.py @@ -0,0 +1,31 @@ +import asyncio +import logging +from datetime import datetime, timedelta +from typing import Optional + +from temporalio.client import Client +from temporalio.envconfig import ClientConfig + +from updatable_timer.workflow import Workflow + + +async def main(client: Optional[Client] = None): + logging.basicConfig(level=logging.INFO) + + if not client: + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) + + handle = client.get_workflow_handle(workflow_id="updatable-timer-workflow") + # signal workflow about the wake up time change + await handle.signal( + Workflow.update_wake_up_time, + (datetime.now() + timedelta(seconds=10)).timestamp(), + ) + + logging.info("Updated wake up time to 10 seconds from now") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/updatable_timer/worker.py b/updatable_timer/worker.py new file mode 100644 index 000000000..8bb3d47a8 --- /dev/null +++ b/updatable_timer/worker.py @@ -0,0 +1,39 @@ +import asyncio +import logging + +from temporalio.client import Client +from temporalio.envconfig import ClientConfig +from temporalio.worker import Worker + +from updatable_timer import TASK_QUEUE +from updatable_timer.workflow import Workflow + +interrupt_event = asyncio.Event() + + +async def main(): + logging.basicConfig(level=logging.INFO) + + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) + + async with Worker( + client, + task_queue=TASK_QUEUE, + workflows=[Workflow], + ): + logging.info("Worker started, ctrl+c to exit") + # Wait until interrupted + await interrupt_event.wait() + logging.info("Interrupt received, shutting down...") + + +if __name__ == "__main__": + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + try: + loop.run_until_complete(main()) + except KeyboardInterrupt: + interrupt_event.set() + loop.run_until_complete(loop.shutdown_asyncgens()) diff --git a/updatable_timer/workflow.py b/updatable_timer/workflow.py new file mode 100644 index 000000000..749df6fc1 --- /dev/null +++ b/updatable_timer/workflow.py @@ -0,0 +1,32 @@ +from datetime import datetime, timezone +from typing import Optional + +from temporalio import workflow + +from updatable_timer.updatable_timer_lib import UpdatableTimer + + +@workflow.defn +class Workflow: + @workflow.init + def __init__(self, wake_up_time: float) -> None: + self.timer = UpdatableTimer( + datetime.fromtimestamp(wake_up_time, tz=timezone.utc) + ) + + @workflow.run + async def run(self, wake_up_time: float): + await self.timer.sleep() + + @workflow.signal + async def update_wake_up_time(self, wake_up_time: float) -> None: + workflow.logger.info(f"update_wake_up_time: {wake_up_time}") + + self.timer.update_wake_up_time( + 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(GIL)](https://docs.python.org/3/glossary.html#term-global-interpreter-lock). The Python standard library's [`threading` module](https://docs.python.org/3/library/threading.html) provides the following guidance: + +> CPython implementation detail: In CPython, due to the Global Interpreter Lock, only one thread can execute Python code at once (even though certain performance-oriented libraries might overcome this limitation). If you want your application to make better use of the computational resources of multi-core machines, you are advised to use multiprocessing or concurrent.futures.ProcessPoolExecutor. However, threading is still an appropriate model if you want to run multiple I/O-bound tasks simultaneously. + +## Temporal Workflow Tasks in Python + +[Temporal Workflow Tasks](https://docs.temporal.io/tasks#workflow-task) are CPU-bound operations and therefore cannot be run concurrently using threads or an async runtime. Instead, we can use [`concurrent.futures.ProcessPoolExecutor`](https://docs.python.org/3/library/concurrent.futures.html#concurrent.futures.ProcessPoolExecutor) or the [`multiprocessing` module](https://docs.python.org/3/library/multiprocessing.html), as suggested by the `threading` documentation, to more appropriately utilize machine resources. + +This sample demonstrates how to use `concurrent.futures.ProcessPoolExecutor` to run multiple workflow worker processes. + +## Running the Sample + +To run, first see the root [README.md](../README.md) for prerequisites. Then execute the following commands from the root directory: + +``` +uv run worker_multiprocessing/worker.py +uv run worker_multiprocessing/starter.py +``` + +Both `worker.py` and `starter.py` have minimal arguments that can be adjusted to modify how the sample runs. + +``` +uv run worker_multiprocessing/worker.py -h + +usage: worker.py [-h] [-w NUM_WORKFLOW_WORKERS] [-a NUM_ACTIVITY_WORKERS] + +options: + -h, --help show this help message and exit + -w, --num-workflow-workers NUM_WORKFLOW_WORKERS + -a, --num-activity-workers NUM_ACTIVITY_WORKERS +``` + +``` +uv run worker_multiprocessing/starter.py -h + +usage: starter.py [-h] [-n NUM_WORKFLOWS] + +options: + -h, --help show this help message and exit + -n, --num-workflows NUM_WORKFLOWS + the number of workflows to execute +``` + +## Example Output + +``` +uv run worker_multiprocessing/worker.py + +starting 2 workflow worker(s) and 1 activity worker(s) +waiting for keyboard interrupt or for all workers to exit +workflow-worker:0 starting +workflow-worker:1 starting +activity-worker:0 starting +workflow-worker:0 shutting down +activity-worker:0 shutting down +workflow-worker:1 shutting down +``` + + +``` +uv run worker_multiprocessing/starter.py + +wf-starting-pid:19178 | activity-pid:19180 | wf-ending-pid:19178 +wf-starting-pid:19179 | activity-pid:19180 | wf-ending-pid:19179 +wf-starting-pid:19178 | activity-pid:19180 | wf-ending-pid:19178 +wf-starting-pid:19179 | activity-pid:19180 | wf-ending-pid:19179 +wf-starting-pid:19178 | activity-pid:19180 | wf-ending-pid:19178 +wf-starting-pid:19179 | activity-pid:19180 | wf-ending-pid:19179 +wf-starting-pid:19178 | activity-pid:19180 | wf-ending-pid:19178 +wf-starting-pid:19179 | activity-pid:19180 | wf-ending-pid:19179 +wf-starting-pid:19178 | activity-pid:19180 | wf-ending-pid:19178 +wf-starting-pid:19179 | activity-pid:19180 | wf-ending-pid:19179 +wf-starting-pid:19178 | activity-pid:19180 | wf-ending-pid:19178 +wf-starting-pid:19179 | activity-pid:19180 | wf-ending-pid:19179 +wf-starting-pid:19178 | activity-pid:19180 | wf-ending-pid:19178 +wf-starting-pid:19179 | activity-pid:19180 | wf-ending-pid:19179 +wf-starting-pid:19178 | activity-pid:19180 | wf-ending-pid:19178 +wf-starting-pid:19179 | activity-pid:19180 | wf-ending-pid:19179 +wf-starting-pid:19178 | activity-pid:19180 | wf-ending-pid:19178 +wf-starting-pid:19179 | activity-pid:19180 | wf-ending-pid:19179 +wf-starting-pid:19179 | activity-pid:19180 | wf-ending-pid:19179 +wf-starting-pid:19178 | activity-pid:19180 | wf-ending-pid:19178 +wf-starting-pid:19179 | activity-pid:19180 | wf-ending-pid:19179 +wf-starting-pid:19178 | activity-pid:19180 | wf-ending-pid:19178 +wf-starting-pid:19179 | activity-pid:19180 | wf-ending-pid:19179 +wf-starting-pid:19178 | activity-pid:19180 | wf-ending-pid:19178 +wf-starting-pid:19178 | activity-pid:19180 | wf-ending-pid:19178 +``` diff --git a/worker_multiprocessing/__init__.py b/worker_multiprocessing/__init__.py new file mode 100644 index 000000000..d9e30b8a7 --- /dev/null +++ b/worker_multiprocessing/__init__.py @@ -0,0 +1,2 @@ +WORKFLOW_TASK_QUEUE = "workflow-task-queue" +ACTIVITY_TASK_QUEUE = "activity-task-queue" diff --git a/worker_multiprocessing/activities.py b/worker_multiprocessing/activities.py new file mode 100644 index 000000000..dbbbc6778 --- /dev/null +++ b/worker_multiprocessing/activities.py @@ -0,0 +1,8 @@ +import os + +from temporalio import activity + + +@activity.defn +async def echo_pid_activity(input: str) -> str: + return f"{input} | activity-pid:{os.getpid()}" diff --git a/worker_multiprocessing/starter.py b/worker_multiprocessing/starter.py new file mode 100644 index 000000000..3267694de --- /dev/null +++ b/worker_multiprocessing/starter.py @@ -0,0 +1,48 @@ +import argparse +import asyncio +import uuid + +from temporalio.client import Client +from temporalio.envconfig import ClientConfig + +from worker_multiprocessing import WORKFLOW_TASK_QUEUE +from worker_multiprocessing.workflows import ParallelizedWorkflow + + +class Args(argparse.Namespace): + num_workflows: int + + +async def main(): + parser = argparse.ArgumentParser() + parser.add_argument( + "-n", + "--num-workflows", + help="the number of workflows to execute", + type=int, + default=25, + ) + args = parser.parse_args(namespace=Args()) + + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) + + # Start several workflows + wf_handles = [ + client.execute_workflow( + ParallelizedWorkflow.run, + id=f"greeting-workflow-id-{uuid.uuid4()}", + task_queue=WORKFLOW_TASK_QUEUE, + ) + for _ in range(args.num_workflows) + ] + + # Wait for workflow completion + for wf in asyncio.as_completed(wf_handles): + result = await wf + print(result) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/worker_multiprocessing/worker.py b/worker_multiprocessing/worker.py new file mode 100644 index 000000000..bda69ba63 --- /dev/null +++ b/worker_multiprocessing/worker.py @@ -0,0 +1,146 @@ +import argparse +import asyncio +import concurrent.futures +import dataclasses +import multiprocessing +import traceback +from typing import Literal + +from temporalio.client import Client +from temporalio.envconfig import ClientConfig +from temporalio.runtime import Runtime, TelemetryConfig +from temporalio.worker import PollerBehaviorSimpleMaximum, Worker +from temporalio.worker.workflow_sandbox import ( + SandboxedWorkflowRunner, + SandboxRestrictions, +) + +from worker_multiprocessing import ACTIVITY_TASK_QUEUE, WORKFLOW_TASK_QUEUE +from worker_multiprocessing.activities import echo_pid_activity +from worker_multiprocessing.workflows import ParallelizedWorkflow + +# Immediately prevent the default Runtime from being created to ensure +# each process creates it's own +Runtime.prevent_default() + + +class Args(argparse.Namespace): + num_workflow_workers: int + num_activity_workers: int + + @property + def total_workers(self) -> int: + return self.num_activity_workers + self.num_workflow_workers + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("-w", "--num-workflow-workers", type=int, default=2) + parser.add_argument("-a", "--num-activity-workers", type=int, default=1) + args = parser.parse_args(namespace=Args()) + print( + f"starting {args.num_workflow_workers} workflow worker(s) and {args.num_activity_workers} activity worker(s)" + ) + + # This sample prefers fork to avoid re-importing modules + # and decrease startup time. Fork is not available on all + # operating systems, so we fallback to 'spawn' when not available + try: + mp_ctx = multiprocessing.get_context("fork") + except ValueError: + mp_ctx = multiprocessing.get_context("spawn") # type: ignore + + with concurrent.futures.ProcessPoolExecutor( + args.total_workers, mp_context=mp_ctx + ) as executor: + # Start workflow workers by submitting them to the + # ProcessPoolExecutor + worker_futures = [ + executor.submit(worker_entry, "workflow", i) + for i in range(args.num_workflow_workers) + ] + + # In this sample, we start activity workers as separate processes in the + # same way we do workflow workers. In production, activity workers + # are often deployed separately from workflow workers to account for + # differing scaling characteristics. + worker_futures.extend( + [ + executor.submit(worker_entry, "activity", i) + for i in range(args.num_activity_workers) + ] + ) + + try: + print("waiting for keyboard interrupt or for all workers to exit") + for worker in concurrent.futures.as_completed(worker_futures): + print("ERROR: worker exited unexpectedly") + if worker.exception(): + traceback.print_exception(worker.exception()) + except KeyboardInterrupt: + pass + + +def worker_entry(worker_type: Literal["workflow", "activity"], id: int): + Runtime.set_default(Runtime(telemetry=TelemetryConfig())) + + async def run_worker(): + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) + + if worker_type == "workflow": + worker = workflow_worker(client) + else: + worker = activity_worker(client) + + try: + print(f"{worker_type}-worker:{id} starting") + await asyncio.shield(worker.run()) + except asyncio.CancelledError: + print(f"{worker_type}-worker:{id} shutting down") + await worker.shutdown() + + asyncio.run(run_worker()) + + +def workflow_worker(client: Client) -> Worker: + """ + Create a workflow worker that is configured to leverage being run + as many child processes. + """ + return Worker( + client, + task_queue=WORKFLOW_TASK_QUEUE, + workflows=[ParallelizedWorkflow], + # Workflow tasks are CPU bound, but generally execute quickly. + # Because we're leveraging multiprocessing to achieve parallelism, + # we want each workflow worker to be confirgured for small workflow + # task processing. + max_concurrent_workflow_tasks=2, + workflow_task_poller_behavior=PollerBehaviorSimpleMaximum(2), + # Allow workflows to access the os module to access the pid + workflow_runner=SandboxedWorkflowRunner( + restrictions=dataclasses.replace( + SandboxRestrictions.default, + invalid_module_members=SandboxRestrictions.invalid_module_members_default.with_child_unrestricted( + "os" + ), + ) + ), + ) + + +def activity_worker(client: Client) -> Worker: + """ + Create a basic activity worker + """ + return Worker( + client, + task_queue=ACTIVITY_TASK_QUEUE, + activities=[echo_pid_activity], + ) + + +if __name__ == "__main__": + main() diff --git a/worker_multiprocessing/workflows.py b/worker_multiprocessing/workflows.py new file mode 100644 index 000000000..04f6b6351 --- /dev/null +++ b/worker_multiprocessing/workflows.py @@ -0,0 +1,22 @@ +import os +from datetime import timedelta + +from temporalio import workflow + +from worker_multiprocessing import ACTIVITY_TASK_QUEUE +from worker_multiprocessing.activities import echo_pid_activity + + +@workflow.defn +class ParallelizedWorkflow: + @workflow.run + async def run(self) -> str: + pid = os.getpid() + activity_result = await workflow.execute_activity( + echo_pid_activity, + f"wf-starting-pid:{pid}", + task_queue=ACTIVITY_TASK_QUEUE, + start_to_close_timeout=timedelta(seconds=10), + ) + + return f"{activity_result} | wf-ending-pid:{pid}" diff --git a/worker_specific_task_queues/README.md b/worker_specific_task_queues/README.md index 8e8e577a8..6fb17e6e3 100644 --- a/worker_specific_task_queues/README.md +++ b/worker_specific_task_queues/README.md @@ -21,19 +21,19 @@ Activities have been artificially slowed with `time.sleep(3)` to simulate doing ### Running This Sample -To run, first see [README.md](../README.md) for prerequisites. Then, run the following from this directory to start the +To run, first see [README.md](../README.md) for prerequisites. Then, run the following from the root directory to start the worker: - poetry run python worker.py + uv run worker_specific_task_queues/worker.py This will start the worker. Then, in another terminal, run the following to execute the workflow: - poetry run python starter.py + uv run worker_specific_task_queues/starter.py #### Example output: ```bash -(temporalio-samples-py3.10) user@machine:~/samples-python/activities_sticky_queues$ poetry run python starter.py +(temporalio-samples-py3.10) user@machine:~/samples-python/activities_sticky_queues$ uv run starter.py Output checksums: 49d7419e6cba3575b3158f62d053f922aa08b23c64f05411cda3213b56c84ba4 49d7419e6cba3575b3158f62d053f922aa08b23c64f05411cda3213b56c84ba4 diff --git a/worker_specific_task_queues/starter.py b/worker_specific_task_queues/starter.py index c55c63bec..61009436f 100644 --- a/worker_specific_task_queues/starter.py +++ b/worker_specific_task_queues/starter.py @@ -2,13 +2,16 @@ from uuid import uuid4 from temporalio.client import Client +from temporalio.envconfig import ClientConfig from worker_specific_task_queues.tasks import FileProcessing async def main(): # Connect client - client = await Client.connect("localhost:7233") + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) # Start 10 concurrent workflows futures = [] diff --git a/worker_specific_task_queues/worker.py b/worker_specific_task_queues/worker.py index 30ea18b6c..95824cfd0 100644 --- a/worker_specific_task_queues/worker.py +++ b/worker_specific_task_queues/worker.py @@ -6,6 +6,7 @@ from temporalio import activity from temporalio.client import Client +from temporalio.envconfig import ClientConfig from temporalio.worker import Worker from worker_specific_task_queues import tasks @@ -31,7 +32,9 @@ async def select_task_queue() -> str: return task_queue # Start client - client = await Client.connect("localhost:7233") + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) # Run a worker to distribute the workflows run_futures = [] diff --git a/worker_versioning/README.md b/worker_versioning/README.md index 04f958bf7..0ff423f1b 100644 --- a/worker_versioning/README.md +++ b/worker_versioning/README.md @@ -1,12 +1,26 @@ -# Worker Versioning Sample +## Worker Versioning -This sample shows you how you can use the [Worker Versioning](https://docs.temporal.io/workers#worker-versioning) -feature to deploy incompatible changes to workflow code more easily. +This sample demonstrates how to use Temporal's Worker Versioning feature to safely deploy updates to workflow and activity code. It shows the difference between auto-upgrading and pinned workflows, and how to manage worker deployments with different build IDs. -To run, first see [README.md](../README.md) for prerequisites. Then, run the following from this directory: +The sample creates multiple worker versions (1.0, 1.1, and 2.0) within one deployment and demonstrates: +- **Auto-upgrading workflows**: Automatically and controllably migrate to newer worker versions +- **Pinned workflows**: Stay on the original worker version throughout their lifecycle +- **Compatible vs incompatible changes**: How to make safe updates using `workflow.patched` - poetry run python example.py +### Steps to run this sample: -This will add some Build IDs to a Task Queue, and will also run Workers with those versions to show how you can -mark add versions, mark them as compatible (or not) with one another, and run Workers at specific versions. You'll -see that only the workers only process Workflow Tasks assigned versions they are compatible with. +1) Run a [Temporal service](https://github.com/temporalio/samples-python/tree/main/#how-to-use). + Ensure that you're using at least Server version 1.28.0 (CLI version 1.4.0). + +2) Start the main application (this will guide you through the sample): +```bash +uv run worker_versioning/app.py +``` + +3) Follow the prompts to start workers in separate terminals: + - When prompted, run: `uv run worker_versioning/workerv1.py` + - When prompted, run: `uv run worker_versioning/workerv1_1.py` + - When prompted, run: `uv run worker_versioning/workerv2.py` + +The sample will show how auto-upgrading workflows migrate to newer workers while pinned workflows +remain on their original version. diff --git a/worker_versioning/activities.py b/worker_versioning/activities.py index 4115e0fd2..50c0700f6 100644 --- a/worker_versioning/activities.py +++ b/worker_versioning/activities.py @@ -1,11 +1,23 @@ +from dataclasses import dataclass + from temporalio import activity +@dataclass +class IncompatibleActivityInput: + """Input for the incompatible activity.""" + + called_by: str + more_data: str + + @activity.defn -async def greet(inp: str) -> str: - return f"Hi from {inp}" +async def some_activity(called_by: str) -> str: + """Basic activity for the workflow.""" + return f"some_activity called by {called_by}" @activity.defn -async def super_greet(inp: str, some_number: int) -> str: - return f"Hi from {inp} with {some_number}" +async def some_incompatible_activity(input_data: IncompatibleActivityInput) -> str: + """Incompatible activity that takes different input.""" + return f"some_incompatible_activity called by {input_data.called_by} with {input_data.more_data}" diff --git a/worker_versioning/app.py b/worker_versioning/app.py new file mode 100644 index 000000000..78e1d6417 --- /dev/null +++ b/worker_versioning/app.py @@ -0,0 +1,142 @@ +"""Main application for the worker versioning sample.""" + +import asyncio +import logging +import uuid + +from temporalio.client import Client +from temporalio.envconfig import ClientConfig + +TASK_QUEUE = "worker-versioning" +DEPLOYMENT_NAME = "my-deployment" + +logging.basicConfig(level=logging.INFO) + + +async def main() -> None: + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) + + # Wait for v1 worker and set as current version + logging.info( + "Waiting for v1 worker to appear. Run `python worker_versioning/workerv1.py` in another terminal" + ) + await wait_for_worker_and_make_current(client, "1.0") + + # Start auto-upgrading and pinned workflows. Importantly, note that when we start the workflows, + # we are using a workflow type name which does *not* include the version number. We defined them + # with versioned names so we could show changes to the code, but here when the client invokes + # them, we're demonstrating that the client remains version-agnostic. + auto_upgrade_workflow_id = "worker-versioning-versioning-autoupgrade_" + str( + uuid.uuid4() + ) + auto_upgrade_execution = await client.start_workflow( + "AutoUpgrading", + id=auto_upgrade_workflow_id, + task_queue=TASK_QUEUE, + ) + + pinned_workflow_id = "worker-versioning-versioning-pinned_" + str(uuid.uuid4()) + pinned_execution = await client.start_workflow( + "Pinned", + id=pinned_workflow_id, + task_queue=TASK_QUEUE, + ) + + logging.info("Started auto-upgrading workflow: %s", auto_upgrade_execution.id) + logging.info("Started pinned workflow: %s", pinned_execution.id) + + # Signal both workflows a few times to drive them + await advance_workflows(auto_upgrade_execution, pinned_execution) + + # Now wait for the v1.1 worker to appear and become current + logging.info( + "Waiting for v1.1 worker to appear. Run `python worker_versioning/workerv1_1.py` in another terminal" + ) + await wait_for_worker_and_make_current(client, "1.1") + + # Once it has, we will continue to advance the workflows. + # The auto-upgrade workflow will now make progress on the new worker, while the pinned one will + # keep progressing on the old worker. + await advance_workflows(auto_upgrade_execution, pinned_execution) + + # Finally we'll start the v2 worker, and again it'll become the new current version + logging.info( + "Waiting for v2 worker to appear. Run `python worker_versioning/workerv2.py` in another terminal" + ) + await wait_for_worker_and_make_current(client, "2.0") + + # Once it has we'll start one more new workflow, another pinned one, to demonstrate that new + # pinned workflows start on the current version. + pinned_workflow_2_id = "worker-versioning-versioning-pinned-2_" + str(uuid.uuid4()) + pinned_execution_2 = await client.start_workflow( + "Pinned", + id=pinned_workflow_2_id, + task_queue=TASK_QUEUE, + ) + logging.info("Started pinned workflow v2: %s", pinned_execution_2.id) + + # Now we'll conclude all workflows. You should be able to see in your server UI that the pinned + # workflow always stayed on 1.0, while the auto-upgrading workflow migrated. + for handle in [auto_upgrade_execution, pinned_execution, pinned_execution_2]: + await handle.signal("do_next_signal", "conclude") + await handle.result() + + logging.info("All workflows completed") + + +async def advance_workflows(auto_upgrade_execution, pinned_execution): + """Signal both workflows a few times to drive them.""" + for i in range(3): + await auto_upgrade_execution.signal("do_next_signal", "do-activity") + await pinned_execution.signal("do_next_signal", "some-signal") + + +async def wait_for_worker_and_make_current(client: Client, build_id: str) -> None: + import temporalio.api.workflowservice.v1 as wsv1 + from temporalio.common import WorkerDeploymentVersion + + target_version = WorkerDeploymentVersion( + deployment_name=DEPLOYMENT_NAME, build_id=build_id + ) + + while True: + try: + describe_request = wsv1.DescribeWorkerDeploymentRequest( + namespace=client.namespace, + deployment_name=DEPLOYMENT_NAME, + ) + response = await client.workflow_service.describe_worker_deployment( + describe_request + ) + + for version_summary in response.worker_deployment_info.version_summaries: + if ( + version_summary.deployment_version.deployment_name + == target_version.deployment_name + and version_summary.deployment_version.build_id + == target_version.build_id + ): + break + else: + await asyncio.sleep(1) + continue + + break + + except Exception: + await asyncio.sleep(1) + continue + + # Once the version is available, set it as current + set_request = wsv1.SetWorkerDeploymentCurrentVersionRequest( + namespace=client.namespace, + deployment_name=DEPLOYMENT_NAME, + build_id=target_version.build_id, + ) + await client.workflow_service.set_worker_deployment_current_version(set_request) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/worker_versioning/example.py b/worker_versioning/example.py deleted file mode 100644 index 97354303b..000000000 --- a/worker_versioning/example.py +++ /dev/null @@ -1,116 +0,0 @@ -import asyncio -import uuid - -from temporalio.client import BuildIdOpAddNewCompatible, BuildIdOpAddNewDefault, Client -from temporalio.worker import Worker - -from worker_versioning.activities import greet, super_greet -from worker_versioning.workflow_v1 import MyWorkflow as MyWorkflowV1 -from worker_versioning.workflow_v1_1 import MyWorkflow as MyWorkflowV1_1 -from worker_versioning.workflow_v2 import MyWorkflow as MyWorkflowV2 - - -async def main(): - client = await Client.connect("localhost:7233") - task_queue = f"worker-versioning-{uuid.uuid4()}" - - # Start a 1.0 worker - async with Worker( - client, - task_queue=task_queue, - workflows=[MyWorkflowV1], - activities=[greet, super_greet], - build_id="1.0", - use_worker_versioning=True, - ): - # Add 1.0 as the default version for the queue - await client.update_worker_build_id_compatibility( - task_queue, BuildIdOpAddNewDefault("1.0") - ) - - # Start a workflow which will run on the 1.0 worker - handle = await client.start_workflow( - MyWorkflowV1.run, - task_queue=task_queue, - id=f"worker-versioning-v1-{uuid.uuid4()}", - ) - # Signal the workflow to proceed - await handle.signal(MyWorkflowV1.proceeder, "go") - - # Give a chance for the worker to process the signal - # TODO Better? - await asyncio.sleep(1) - - # Add 1.1 as the default version for the queue, compatible with 1.0 - await client.update_worker_build_id_compatibility( - task_queue, BuildIdOpAddNewCompatible("1.1", "1.0") - ) - - # Stop the old worker, and start a 1.1 worker. We do this to speed along the example, since the - # 1.0 worker may continue to process tasks briefly after we make 1.1 the new default. - async with Worker( - client, - task_queue=task_queue, - workflows=[MyWorkflowV1_1], - activities=[greet, super_greet], - build_id="1.1", - use_worker_versioning=True, - ): - # Continue driving the workflow. Take note that the new version of the workflow run by the 1.1 - # worker is the one that takes over! You might see a workflow task timeout, if the 1.0 worker is - # processing a task as the version update happens. That's normal. - await handle.signal(MyWorkflowV1.proceeder, "go") - - # Add a new *incompatible* version to the task queue, which will become the new overall default for the queue. - await client.update_worker_build_id_compatibility( - task_queue, BuildIdOpAddNewDefault("2.0") - ) - - # Start a 2.0 worker - async with Worker( - client, - task_queue=task_queue, - workflows=[MyWorkflowV2], - activities=[greet, super_greet], - build_id="2.0", - use_worker_versioning=True, - ): - # Start a new workflow. Note that it will run on the new 2.0 version, without the client invocation changing - # at all! Note here we can use `MyWorkflowV1.run` because the signature of the workflow has not changed. - handle2 = await client.start_workflow( - MyWorkflowV1.run, - task_queue=task_queue, - id=f"worker-versioning-v2-{uuid.uuid4()}", - ) - - # Drive both workflows once more before concluding them. The first workflow will continue running on the 1.1 - # worker. - await handle.signal(MyWorkflowV1.proceeder, "go") - await handle2.signal(MyWorkflowV1.proceeder, "go") - await handle.signal(MyWorkflowV1.proceeder, "finish") - await handle2.signal(MyWorkflowV1.proceeder, "finish") - - # Wait for both workflows to complete - await handle.result() - await handle2.result() - - # Lastly we'll demonstrate how you can use the gRPC api to determine if certain build IDs are ready to be - # retired. There's more information in the documentation, but here's a quick example that shows us how to - # tell when the 1.0 worker can be retired: - - # There is a 5 minute buffer before we will consider IDs no longer reachable by new workflows, to - # account for replication in multi-cluster setups. Uncomment the following line to wait long enough to see - # the 1.0 worker become unreachable. - # await asyncio.sleep(60 * 5) - reachability = await client.get_worker_task_reachability( - build_ids=["2.0", "1.0", "1.1"] - ) - - if not reachability.build_id_reachability["1.0"].task_queue_reachability[ - task_queue - ]: - print("1.0 is ready to be retired!") - - -if __name__ == "__main__": - asyncio.run(main()) diff --git a/worker_versioning/workerv1.py b/worker_versioning/workerv1.py new file mode 100644 index 000000000..221b5ca93 --- /dev/null +++ b/worker_versioning/workerv1.py @@ -0,0 +1,43 @@ +"""Worker v1 for the worker versioning sample.""" + +import asyncio +import logging + +from temporalio.client import Client +from temporalio.common import WorkerDeploymentVersion +from temporalio.envconfig import ClientConfig +from temporalio.worker import Worker, WorkerDeploymentConfig + +from worker_versioning.activities import some_activity, some_incompatible_activity +from worker_versioning.app import DEPLOYMENT_NAME, TASK_QUEUE +from worker_versioning.workflows import AutoUpgradingWorkflowV1, PinnedWorkflowV1 + +logging.basicConfig(level=logging.INFO) + + +async def main() -> None: + """Run worker v1.""" + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect("localhost:7233") + + # Create worker v1 + worker = Worker( + client, + task_queue=TASK_QUEUE, + workflows=[AutoUpgradingWorkflowV1, PinnedWorkflowV1], + activities=[some_activity, some_incompatible_activity], + deployment_config=WorkerDeploymentConfig( + version=WorkerDeploymentVersion( + deployment_name=DEPLOYMENT_NAME, build_id="1.0" + ), + use_worker_versioning=True, + ), + ) + + logging.info("Starting worker v1 (build 1.0)") + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/worker_versioning/workerv1_1.py b/worker_versioning/workerv1_1.py new file mode 100644 index 000000000..4f21d6166 --- /dev/null +++ b/worker_versioning/workerv1_1.py @@ -0,0 +1,43 @@ +"""Worker v1.1 for the worker versioning sample.""" + +import asyncio +import logging + +from temporalio.client import Client +from temporalio.common import WorkerDeploymentVersion +from temporalio.envconfig import ClientConfig +from temporalio.worker import Worker, WorkerDeploymentConfig + +from worker_versioning.activities import some_activity, some_incompatible_activity +from worker_versioning.app import DEPLOYMENT_NAME, TASK_QUEUE +from worker_versioning.workflows import AutoUpgradingWorkflowV1b, PinnedWorkflowV1 + +logging.basicConfig(level=logging.INFO) + + +async def main() -> None: + """Run worker v1.1.""" + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) + + # Create worker v1.1 + worker = Worker( + client, + task_queue=TASK_QUEUE, + workflows=[AutoUpgradingWorkflowV1b, PinnedWorkflowV1], + activities=[some_activity, some_incompatible_activity], + deployment_config=WorkerDeploymentConfig( + version=WorkerDeploymentVersion( + deployment_name=DEPLOYMENT_NAME, build_id="1.1" + ), + use_worker_versioning=True, + ), + ) + + logging.info("Starting worker v1.1 (build 1.1)") + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/worker_versioning/workerv2.py b/worker_versioning/workerv2.py new file mode 100644 index 000000000..557f70abf --- /dev/null +++ b/worker_versioning/workerv2.py @@ -0,0 +1,43 @@ +"""Worker v2 for the worker versioning sample.""" + +import asyncio +import logging + +from temporalio.client import Client +from temporalio.common import WorkerDeploymentVersion +from temporalio.envconfig import ClientConfig +from temporalio.worker import Worker, WorkerDeploymentConfig + +from worker_versioning.activities import some_activity, some_incompatible_activity +from worker_versioning.app import DEPLOYMENT_NAME, TASK_QUEUE +from worker_versioning.workflows import AutoUpgradingWorkflowV1b, PinnedWorkflowV2 + +logging.basicConfig(level=logging.INFO) + + +async def main() -> None: + """Run worker v2.""" + config = ClientConfig.load_client_connect_config() + config.setdefault("target_host", "localhost:7233") + client = await Client.connect(**config) + + # Create worker v2 + worker = Worker( + client, + task_queue=TASK_QUEUE, + workflows=[AutoUpgradingWorkflowV1b, PinnedWorkflowV2], + activities=[some_activity, some_incompatible_activity], + deployment_config=WorkerDeploymentConfig( + version=WorkerDeploymentVersion( + deployment_name=DEPLOYMENT_NAME, build_id="2.0" + ), + use_worker_versioning=True, + ), + ) + + logging.info("Starting worker v2 (build 2.0)") + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/worker_versioning/workflow_v1.py b/worker_versioning/workflow_v1.py deleted file mode 100644 index 1cef90958..000000000 --- a/worker_versioning/workflow_v1.py +++ /dev/null @@ -1,27 +0,0 @@ -from datetime import timedelta - -from temporalio import workflow - -with workflow.unsafe.imports_passed_through(): - from worker_versioning.activities import greet - - -@workflow.defn -class MyWorkflow: - """The 1.0 version of the workflow we'll be making changes to""" - - should_finish: bool = False - - @workflow.run - async def run(self) -> str: - workflow.logger.info("Running workflow V1") - await workflow.wait_condition(lambda: self.should_finish) - return "Concluded workflow on V1" - - @workflow.signal - async def proceeder(self, inp: str): - await workflow.execute_activity( - greet, "V1", start_to_close_timeout=timedelta(seconds=5) - ) - if inp == "finish": - self.should_finish = True diff --git a/worker_versioning/workflow_v1_1.py b/worker_versioning/workflow_v1_1.py deleted file mode 100644 index e2f229433..000000000 --- a/worker_versioning/workflow_v1_1.py +++ /dev/null @@ -1,45 +0,0 @@ -from datetime import timedelta - -from temporalio import workflow - -with workflow.unsafe.imports_passed_through(): - from worker_versioning.activities import greet, super_greet - - -@workflow.defn -class MyWorkflow: - """ - The 1.1 version of the workflow, which is compatible with the first version. - - The compatible changes we've made are: - - Altering the log lines - - Using the `patched` API to properly introduce branching behavior while maintaining - compatibility - """ - - should_finish: bool = False - - @workflow.run - async def run(self) -> str: - workflow.logger.info("Running workflow V1.1") - await workflow.wait_condition(lambda: self.should_finish) - return "Concluded workflow on V1.1" - - @workflow.signal - async def proceeder(self, inp: str): - if workflow.patched("different-activity"): - await workflow.execute_activity( - super_greet, - args=["V1.1", 100], - start_to_close_timeout=timedelta(seconds=5), - ) - else: - # Note it is a valid compatible change to alter the input to an activity. However, because - # we're using the patched API, this branch would only be taken if the workflow was started on - # a v1 worker. - await workflow.execute_activity( - greet, "V1.1", start_to_close_timeout=timedelta(seconds=5) - ) - - if inp == "finish": - self.should_finish = True diff --git a/worker_versioning/workflow_v2.py b/worker_versioning/workflow_v2.py deleted file mode 100644 index dcb0a20e4..000000000 --- a/worker_versioning/workflow_v2.py +++ /dev/null @@ -1,36 +0,0 @@ -import asyncio -from datetime import timedelta - -from temporalio import workflow - -with workflow.unsafe.imports_passed_through(): - from worker_versioning.activities import greet - - -@workflow.defn -class MyWorkflow: - """ - The 2.0 version of the workflow, which is fully incompatible with the other workflows, since it - alters the sequence of commands without using `patched`. - """ - - should_finish: bool = False - - @workflow.run - async def run(self) -> str: - workflow.logger.info("Running workflow V2") - await workflow.wait_condition(lambda: self.should_finish) - return "Concluded workflow on V2" - - @workflow.signal - async def proceeder(self, inp: str): - await asyncio.sleep(1) - await workflow.execute_activity( - greet, "V2", start_to_close_timeout=timedelta(seconds=5) - ) - await workflow.execute_activity( - greet, "V2", start_to_close_timeout=timedelta(seconds=5) - ) - - if inp == "finish": - self.should_finish = True diff --git a/worker_versioning/workflows.py b/worker_versioning/workflows.py new file mode 100644 index 000000000..a09c371a8 --- /dev/null +++ b/worker_versioning/workflows.py @@ -0,0 +1,189 @@ +"""Workflow definitions for the worker versioning sample.""" + +from datetime import timedelta + +from temporalio import common, workflow + +with workflow.unsafe.imports_passed_through(): + from worker_versioning.activities import ( + IncompatibleActivityInput, + some_activity, + some_incompatible_activity, + ) + + +@workflow.defn( + name="AutoUpgrading", versioning_behavior=common.VersioningBehavior.AUTO_UPGRADE +) +class AutoUpgradingWorkflowV1: + """AutoUpgradingWorkflowV1 will automatically move to the latest worker version. We'll be making + changes to it, which must be replay safe. + + Note that generally you won't want or need to include a version number in your workflow name if + you're using the worker versioning feature. This sample does it to illustrate changes to the + same code over time - but really what we're demonstrating here is the evolution of what would + have been one workflow definition. + """ + + def __init__(self) -> None: + self.signals: list[str] = [] + + @workflow.run + async def run(self) -> None: + workflow.logger.info( + "Changing workflow v1 started.", extra={"StartTime": workflow.now()} + ) + + # This workflow will listen for signals from our starter, and upon each signal either run + # an activity, or conclude execution. + while True: + await workflow.wait_condition(lambda: len(self.signals) > 0) + signal = self.signals.pop(0) + + if signal == "do-activity": + workflow.logger.info("Changing workflow v1 running activity") + await workflow.execute_activity( + some_activity, "v1", start_to_close_timeout=timedelta(seconds=10) + ) + else: + workflow.logger.info("Concluding workflow v1") + return + + @workflow.signal + async def do_next_signal(self, signal: str) -> None: + """Signal to perform next action.""" + self.signals.append(signal) + + +@workflow.defn( + name="AutoUpgrading", versioning_behavior=common.VersioningBehavior.AUTO_UPGRADE +) +class AutoUpgradingWorkflowV1b: + """AutoUpgradingWorkflowV1b represents us having made *compatible* changes to + AutoUpgradingWorkflowV1. + + The compatible changes we've made are: + - Altering the log lines + - Using the workflow.patched API to properly introduce branching behavior while maintaining + compatibility + """ + + def __init__(self) -> None: + self.signals: list[str] = [] + + @workflow.run + async def run(self) -> None: + workflow.logger.info( + "Changing workflow v1b started.", extra={"StartTime": workflow.now()} + ) + + # This workflow will listen for signals from our starter, and upon each signal either run + # an activity, or conclude execution. + while True: + await workflow.wait_condition(lambda: len(self.signals) > 0) + signal = self.signals.pop(0) + + if signal == "do-activity": + workflow.logger.info("Changing workflow v1b running activity") + if workflow.patched("DifferentActivity"): + await workflow.execute_activity( + some_incompatible_activity, + IncompatibleActivityInput(called_by="v1b", more_data="hello!"), + start_to_close_timeout=timedelta(seconds=10), + ) + else: + # Note it is a valid compatible change to alter the input to an activity. + # However, because we're using the patched API, this branch will never be + # taken. + await workflow.execute_activity( + some_activity, + "v1b", + start_to_close_timeout=timedelta(seconds=10), + ) + else: + workflow.logger.info("Concluding workflow v1b") + break + + @workflow.signal + async def do_next_signal(self, signal: str) -> None: + """Signal to perform next action.""" + self.signals.append(signal) + + +@workflow.defn(name="Pinned", versioning_behavior=common.VersioningBehavior.PINNED) +class PinnedWorkflowV1: + """PinnedWorkflowV1 demonstrates a workflow that likely has a short lifetime, and we want to always + stay pinned to the same version it began on. + + Note that generally you won't want or need to include a version number in your workflow name if + you're using the worker versioning feature. This sample does it to illustrate changes to the + same code over time - but really what we're demonstrating here is the evolution of what would + have been one workflow definition. + """ + + def __init__(self) -> None: + self.signals: list[str] = [] + + @workflow.run + async def run(self) -> None: + workflow.logger.info( + "Pinned Workflow v1 started.", extra={"StartTime": workflow.now()} + ) + + while True: + await workflow.wait_condition(lambda: len(self.signals) > 0) + signal = self.signals.pop(0) + if signal == "conclude": + break + + await workflow.execute_activity( + some_activity, + "Pinned-v1", + start_to_close_timeout=timedelta(seconds=10), + ) + + @workflow.signal + async def do_next_signal(self, signal: str) -> None: + """Signal to perform next action.""" + self.signals.append(signal) + + +@workflow.defn(name="Pinned", versioning_behavior=common.VersioningBehavior.PINNED) +class PinnedWorkflowV2: + """PinnedWorkflowV2 has changes that would make it incompatible with v1, and aren't protected by + a patch. + """ + + def __init__(self) -> None: + self.signals: list[str] = [] + + @workflow.run + async def run(self) -> None: + workflow.logger.info( + "Pinned Workflow v2 started.", extra={"StartTime": workflow.now()} + ) + + # Here we call an activity where we didn't before, which is an incompatible change. + await workflow.execute_activity( + some_activity, + "Pinned-v2", + start_to_close_timeout=timedelta(seconds=10), + ) + + while True: + await workflow.wait_condition(lambda: len(self.signals) > 0) + signal = self.signals.pop(0) + if signal == "conclude": + break + + # We've also changed the activity type here, another incompatible change + await workflow.execute_activity( + some_incompatible_activity, + IncompatibleActivityInput(called_by="Pinned-v2", more_data="hi"), + start_to_close_timeout=timedelta(seconds=10), + ) + + @workflow.signal + async def do_next_signal(self, signal: str) -> None: + """Signal to perform next action.""" + self.signals.append(signal) diff --git a/workflow_streams/README.md b/workflow_streams/README.md new file mode 100644 index 000000000..f06bed399 --- /dev/null +++ b/workflow_streams/README.md @@ -0,0 +1,120 @@ +# Workflow Streams + +> **Experimental.** These samples use +> `temporalio.contrib.workflow_streams`, which ships in +> `temporalio>=1.27.0`. The module is considered experimental and its +> API may change in future versions. + +`temporalio.contrib.workflow_streams` lets a workflow host a durable, +offset-addressed event channel. The workflow holds an append-only log; +external clients (activities, starters, web backends) publish to topics via +signals and subscribe via long-poll updates. This packages the +boilerplate — batching, offset tracking, topic filtering, +continue-as-new hand-off — into a reusable stream. + +This directory has five scenarios. The first four share one worker; +the fifth has its own worker because it needs the `openai` package +and an `OPENAI_API_KEY`. + +**Scenario 1 — basic publish/subscribe with heterogeneous topics:** + +* `workflows/order_workflow.py` — a workflow that hosts a + `WorkflowStream` and publishes status events as it processes an order. +* `activities/payment_activity.py` — an activity that publishes + intermediate progress to the stream via + `WorkflowStreamClient.from_within_activity()`. +* `run_publisher.py` — starts the workflow, subscribes to both topics, + decodes each by `item.topic`, and prints events as they arrive. + +**Scenario 2 — reconnecting subscriber:** + +* `workflows/pipeline_workflow.py` — a multi-stage pipeline that + publishes stage transitions over ~10 seconds, leaving room for a + consumer to disconnect and reconnect mid-run. +* `run_reconnecting_subscriber.py` — connects, reads a couple of + events, "disconnects," then reopens a fresh client and resumes via + `subscribe(from_offset=...)`. This is the central Workflow Streams + use case: a consumer can disappear (page refresh, server restart, + laptop closed) and resume later without missing events or seeing + duplicates. + +**Scenario 3 — external (non-Activity) publisher:** + +* `workflows/hub_workflow.py` — a passive workflow that does no work + of its own; it exists only to host a `WorkflowStream` and shut down + when signaled. +* `run_external_publisher.py` — starts the hub, then publishes events + into it from a plain Python coroutine using + `WorkflowStreamClient.create(client, workflow_id)`. A subscriber + task runs alongside; when the publisher is done it emits a sentinel + event and signals `HubWorkflow.close`. The shape that fits a + backend service or scheduled job pushing events into a workflow it + didn't itself start. + +**Scenario 4 — bounded log via `truncate()`:** + +* `workflows/ticker_workflow.py` — a long-running workflow that + publishes events at a fixed cadence and calls + `self.stream.truncate(...)` periodically to bound log growth, + keeping only the most recent N entries. +* `run_truncating_ticker.py` — runs a fast subscriber and a slow + subscriber side by side. The fast one keeps up and sees every + offset in order; the slow one falls behind a truncation and + silently jumps forward to the new base offset. The output makes + the trade visible: bounded log size in exchange for intermediate + events being invisible to slow consumers. + +**Scenario 5 — LLM streaming:** + +* `workflows/llm_workflow.py` — hosts a `WorkflowStream` and runs + `stream_completion` as a single activity. The workflow itself + does no streaming; the activity owns the non-deterministic OpenAI + call. +* `activities/llm_activity.py` — calls + `openai.AsyncOpenAI().chat.completions.create(stream=True)`, + publishes each token chunk on the `delta` topic, the final + accumulated text on `complete`, and a `RetryEvent` on `retry` + when running on attempt > 1. +* `run_llm.py` — subscribes to all three topics, renders deltas to + the terminal as they arrive, and on a `retry` event uses ANSI + escapes to rewind the printed output before the retried attempt + re-publishes. + +Scenario 5 runs on its own worker (`run_llm_worker.py`, on +`workflow-stream-llm-task-queue`) because it needs the `openai` +dependency and an `OPENAI_API_KEY`, and because killing this worker +mid-stream is the easiest way to demonstrate retry handling without +disrupting the other four scenarios. + +## Run it + +For scenarios 1–4, start the shared worker: + +```bash +uv run workflow_streams/run_worker.py +``` + +For scenario 5, install the extra, export the key, and start the +LLM worker: + +```bash +uv sync --group llm-stream +export OPENAI_API_KEY=... +uv run workflow_streams/run_llm_worker.py +``` + +Then in another terminal, pick a scenario: + +```bash +uv run workflow_streams/run_publisher.py # scenario 1 +uv run workflow_streams/run_reconnecting_subscriber.py # scenario 2 +uv run workflow_streams/run_external_publisher.py # scenario 3 +uv run workflow_streams/run_truncating_ticker.py # scenario 4 +uv run workflow_streams/run_llm.py # scenario 5 +``` + +To exercise scenario 5's retry path, kill `run_llm_worker.py` +(`Ctrl-C`) while output is streaming and start it again. The +activity's next attempt sends a `RetryEvent` first; the consumer +clears its on-screen output via ANSI escapes and re-renders from +scratch. diff --git a/workflow_streams/__init__.py b/workflow_streams/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/workflow_streams/activities/__init__.py b/workflow_streams/activities/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/workflow_streams/activities/llm_activity.py b/workflow_streams/activities/llm_activity.py new file mode 100644 index 000000000..aebe80ec7 --- /dev/null +++ b/workflow_streams/activities/llm_activity.py @@ -0,0 +1,71 @@ +from __future__ import annotations + +from datetime import timedelta + +from openai import AsyncOpenAI +from temporalio import activity +from temporalio.contrib.workflow_streams import WorkflowStreamClient + +from workflow_streams.llm_shared import ( + TOPIC_COMPLETE, + TOPIC_DELTA, + TOPIC_RETRY, + LLMInput, + RetryEvent, + TextComplete, + TextDelta, +) + + +@activity.defn +async def stream_completion(input: LLMInput) -> str: + """Stream an LLM completion to the parent workflow's stream. + + Activity-as-publisher: each delta from the OpenAI streaming API is + pushed to the workflow's stream as a ``TextDelta`` event on the + ``delta`` topic. The accumulated full text returns as the + activity's result and is also published on the ``complete`` topic + as a terminator. On retry attempts (``activity.info().attempt > 1``) + a ``RetryEvent`` lands on the ``retry`` topic before the new + attempt's deltas, so consumers can reset their accumulated state + instead of concatenating the failed attempt's partial output with + the retried attempt's full output. + + No ``force_flush=True``: the 200ms ``batch_interval`` is fast + enough for an interactive feel, and the WorkflowStreamClient's + ``__aexit__`` cancels a sleeping flusher cleanly. + """ + stream_client = WorkflowStreamClient.from_within_activity( + batch_interval=timedelta(milliseconds=200), + ) + # Disable provider-side retries; let Temporal own retry policy at + # the activity layer. + openai_client = AsyncOpenAI(max_retries=0) + + async with stream_client: + deltas = stream_client.topic(TOPIC_DELTA, type=TextDelta) + complete = stream_client.topic(TOPIC_COMPLETE, type=TextComplete) + retry = stream_client.topic(TOPIC_RETRY, type=RetryEvent) + + attempt = activity.info().attempt + if attempt > 1: + retry.publish(RetryEvent(attempt=attempt)) + + full: list[str] = [] + oai_stream = await openai_client.chat.completions.create( + model=input.model, + messages=[{"role": "user", "content": input.prompt}], + stream=True, + ) + async for chunk in oai_stream: + if not chunk.choices: + continue + text = chunk.choices[0].delta.content + if not text: + continue + deltas.publish(TextDelta(text=text)) + full.append(text) + + full_text = "".join(full) + complete.publish(TextComplete(full_text=full_text)) + return full_text diff --git a/workflow_streams/activities/payment_activity.py b/workflow_streams/activities/payment_activity.py new file mode 100644 index 000000000..b3b2aa297 --- /dev/null +++ b/workflow_streams/activities/payment_activity.py @@ -0,0 +1,30 @@ +from __future__ import annotations + +import asyncio +from datetime import timedelta + +from temporalio import activity +from temporalio.contrib.workflow_streams import WorkflowStreamClient + +from workflow_streams.shared import TOPIC_PROGRESS, ProgressEvent + + +@activity.defn +async def charge_card(order_id: str) -> str: + """Pretend to charge a card, publishing progress to the parent workflow. + + `WorkflowStreamClient.from_within_activity()` reads the parent + workflow id and the Temporal client from the activity context, so + this activity can push events back without any wiring. + """ + client = WorkflowStreamClient.from_within_activity( + batch_interval=timedelta(milliseconds=200) + ) + async with client: + progress = client.topic(TOPIC_PROGRESS, type=ProgressEvent) + progress.publish(ProgressEvent(message="charging card...")) + await asyncio.sleep(1.0) + progress.publish( + ProgressEvent(message="card charged"), + ) + return f"charge-{order_id}" diff --git a/workflow_streams/llm_shared.py b/workflow_streams/llm_shared.py new file mode 100644 index 000000000..2780fd0b0 --- /dev/null +++ b/workflow_streams/llm_shared.py @@ -0,0 +1,44 @@ +"""Types and constants for the LLM-streaming scenario. + +Kept separate from ``shared.py`` because the other scenarios don't +use these — and this scenario runs on its own worker and task queue +so the ``openai`` dependency stays out of everyone else's path. +""" + +from __future__ import annotations + +from dataclasses import dataclass + +from temporalio.contrib.workflow_streams import WorkflowStreamState + +# Scenario 5 runs on its own worker so the openai dependency only +# matters for that scenario. +LLM_TASK_QUEUE = "workflow-stream-llm-task-queue" + +# Topics published by the activity. +TOPIC_DELTA = "delta" +TOPIC_COMPLETE = "complete" +TOPIC_RETRY = "retry" + + +@dataclass +class LLMInput: + prompt: str + model: str = "gpt-5-mini" + # Carries stream state across continue-as-new. None on a fresh start. + stream_state: WorkflowStreamState | None = None + + +@dataclass +class TextDelta: + text: str + + +@dataclass +class TextComplete: + full_text: str + + +@dataclass +class RetryEvent: + attempt: int diff --git a/workflow_streams/run_external_publisher.py b/workflow_streams/run_external_publisher.py new file mode 100644 index 000000000..8e7d38f81 --- /dev/null +++ b/workflow_streams/run_external_publisher.py @@ -0,0 +1,99 @@ +"""External publisher: a non-Activity process pushes events into a workflow. + +The two earlier scenarios publish from inside the workflow itself +(``OrderWorkflow``, ``PipelineWorkflow``) or from an Activity it runs +(``charge_card``). This scenario shows the third shape: a backend +service, scheduled job, or anything else with a Temporal ``Client`` +publishing into a *running* workflow it didn't start. Same factory as +the subscribe path — :py:meth:`WorkflowStreamClient.create` — used for +publishing instead. + +The script starts a ``HubWorkflow`` (which does no work of its own — +it exists only to host the stream), then runs a publisher and a +subscriber concurrently. When the publisher is done it signals +``HubWorkflow.close``, the workflow's run finishes, and the +subscriber's iterator exits normally. + +Run the worker first (``uv run workflow_streams/run_worker.py``), then:: + + uv run workflow_streams/run_external_publisher.py +""" + +from __future__ import annotations + +import asyncio +import uuid + +from temporalio.client import Client +from temporalio.contrib.workflow_streams import WorkflowStreamClient + +from workflow_streams.shared import ( + TASK_QUEUE, + TOPIC_NEWS, + HubInput, + NewsEvent, +) +from workflow_streams.workflows.hub_workflow import HubWorkflow + +HEADLINES = [ + "rates held", + "merger announced", + "outage resolved", + "earnings beat", + "regulator opens probe", +] + +# In-band terminator the publisher emits before signaling close. The +# subscriber recognizes this value and stops polling — without an +# explicit terminator the consumer would have to rely on the workflow +# returning to break the iterator, which means racing the last item +# delivery against workflow completion. +DONE_HEADLINE = "__done__" + + +async def main() -> None: + client = await Client.connect("localhost:7233") + + workflow_id = f"workflow-stream-hub-{uuid.uuid4().hex[:8]}" + handle = await client.start_workflow( + HubWorkflow.run, + HubInput(hub_id=workflow_id), + id=workflow_id, + task_queue=TASK_QUEUE, + ) + + async def publish_news() -> None: + # WorkflowStreamClient.create takes a Temporal client and a + # workflow id — the same factory used elsewhere for subscribing. + # The async context manager batches publishes and flushes on + # exit; we additionally call flush() before signaling close so + # we know the events landed before the workflow shuts down. + producer = WorkflowStreamClient.create(client, workflow_id) + async with producer: + news = producer.topic(TOPIC_NEWS, type=NewsEvent) + for headline in HEADLINES: + news.publish(NewsEvent(headline=headline)) + print(f"[publisher] sent: {headline}") + await asyncio.sleep(0.5) + news.publish(NewsEvent(headline=DONE_HEADLINE), force_flush=True) + await producer.flush() + # Tell the hub it can stop. The subscriber has already broken + # out of its async-for loop on the sentinel above. + await handle.signal(HubWorkflow.close) + print("[publisher] signaled close") + + async def consume_news() -> None: + consumer = WorkflowStreamClient.create(client, workflow_id) + async for item in consumer.subscribe([TOPIC_NEWS], result_type=NewsEvent): + if item.data.headline == DONE_HEADLINE: + return + print(f"[subscriber] offset={item.offset}: {item.data.headline}") + + await asyncio.gather(publish_news(), consume_news()) + + result = await handle.result() + print(f"\nworkflow result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/workflow_streams/run_llm.py b/workflow_streams/run_llm.py new file mode 100644 index 000000000..1b2a9d7ae --- /dev/null +++ b/workflow_streams/run_llm.py @@ -0,0 +1,132 @@ +"""Stream LLM output to the terminal, handling retries. + +Starts an ``LLMWorkflow``, subscribes to its delta / complete / retry +topics, and renders the model's output to stdout as it arrives. On a +``RETRY`` event (the activity is on attempt > 1), the consumer rewinds +its rendered output with ANSI escapes and starts fresh — so a killed +worker doesn't leave a half-finished response stuck on screen +followed by the retried attempt's full output. + +Requires ``OPENAI_API_KEY`` in the environment and the ``llm-stream`` +extra:: + + uv sync --group llm-stream + export OPENAI_API_KEY=... + +Run the LLM worker first (``uv run workflow_streams/run_llm_worker.py``), +then:: + + uv run workflow_streams/run_llm.py + +To see retry handling in action, kill the LLM worker mid-stream +(Ctrl-C in its terminal) and start it again. The consumer will clear +its accumulated output on the ``RETRY`` event and re-render the +retried attempt's output from scratch. +""" + +from __future__ import annotations + +import asyncio +import sys +import uuid + +from temporalio.client import Client +from temporalio.common import RawValue +from temporalio.contrib.workflow_streams import WorkflowStreamClient + +from workflow_streams.llm_shared import ( + LLM_TASK_QUEUE, + TOPIC_COMPLETE, + TOPIC_DELTA, + TOPIC_RETRY, + LLMInput, + RetryEvent, + TextComplete, + TextDelta, +) +from workflow_streams.workflows.llm_workflow import LLMWorkflow + +# Long enough that you can comfortably kill the worker mid-stream and +# watch the retry render. Adjust to taste. +DEFAULT_PROMPT = ( + "Write a 500-word comparison of Paxos, Raft, and Viewstamped " + "Replication for a new distributed-systems engineer. Cover the " + "core ideas, leader election, normal-case operation, " + "reconfiguration, and the practical tradeoffs that show up when " + "implementing each. Use short paragraphs." +) + + +# ANSI cursor save / restore. ``\033[s`` saves the current cursor +# position, ``\033[u`` restores it, ``\033[J`` clears from the cursor +# to the end of the screen. Save once before the first delta, and on +# RETRY restore + clear-to-end so the failed attempt's rendered output +# disappears regardless of how it was wrapped by the terminal. Save +# again afterwards so a second retry can rewind to the same point. +ANSI_SAVE = "\033[s" +ANSI_RESTORE_AND_CLEAR = "\033[u\033[J" + + +async def main() -> None: + client = await Client.connect("localhost:7233") + converter = client.data_converter.payload_converter + + workflow_id = f"workflow-stream-llm-{uuid.uuid4().hex[:8]}" + llm_input = LLMInput(prompt=DEFAULT_PROMPT) + handle = await client.start_workflow( + LLMWorkflow.run, + llm_input, + id=workflow_id, + task_queue=LLM_TASK_QUEUE, + ) + + # Print a header so the user sees something immediately. The + # response will start streaming below it once the first delta + # arrives — until then this is the only line on screen. + print( + f"[llm {workflow_id}] streaming response from {llm_input.model}, " + f"awaiting first token..." + ) + print() + sys.stdout.write(ANSI_SAVE) + sys.stdout.flush() + + stream = WorkflowStreamClient.create(client, workflow_id) + + # result_type=RawValue lets us dispatch on item.topic and decode + # against the right dataclass per topic. The loop ends either on + # the `complete` terminator (break) or because the iterator + # naturally exhausts when the workflow reaches a terminal state + # without one (activity exhausted retries, etc.). Either way the + # handle.result() below either returns the full text or raises + # the workflow's failure. + async for item in stream.subscribe( + [TOPIC_DELTA, TOPIC_RETRY, TOPIC_COMPLETE], + result_type=RawValue, + ): + if item.topic == TOPIC_RETRY: + evt = converter.from_payload(item.data.payload, RetryEvent) + sys.stdout.write(ANSI_RESTORE_AND_CLEAR) + sys.stdout.flush() + print(f"[retry attempt {evt.attempt}] resetting output") + print() + sys.stdout.write(ANSI_SAVE) + sys.stdout.flush() + elif item.topic == TOPIC_DELTA: + delta = converter.from_payload(item.data.payload, TextDelta) + sys.stdout.write(delta.text) + sys.stdout.flush() + elif item.topic == TOPIC_COMPLETE: + # The full text is also in the payload (and returned by + # the workflow), but the consumer has already rendered it + # incrementally. Just terminate the line. + converter.from_payload(item.data.payload, TextComplete) + print() + break + + result = await handle.result() + print(f"\n[workflow result: {len(result)} chars]") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/workflow_streams/run_llm_worker.py b/workflow_streams/run_llm_worker.py new file mode 100644 index 000000000..2bad59915 --- /dev/null +++ b/workflow_streams/run_llm_worker.py @@ -0,0 +1,40 @@ +"""Worker for the LLM-streaming scenario. + +Runs separately from ``run_worker.py`` so the ``openai`` dependency +and the ``OPENAI_API_KEY`` requirement stay isolated to this one +scenario. Different task queue too — the other four samples won't +route work to this worker. + +Kill this worker mid-stream while ``run_llm.py`` is running to +trigger a retry: Temporal restarts the activity on the next worker +to come up, the activity publishes a ``RetryEvent`` on its second +attempt, and the consumer resets its rendered output. +""" + +from __future__ import annotations + +import asyncio +import logging + +from temporalio.client import Client +from temporalio.worker import Worker + +from workflow_streams.activities.llm_activity import stream_completion +from workflow_streams.llm_shared import LLM_TASK_QUEUE +from workflow_streams.workflows.llm_workflow import LLMWorkflow + + +async def main() -> None: + logging.basicConfig(level=logging.INFO) + client = await Client.connect("localhost:7233") + worker = Worker( + client, + task_queue=LLM_TASK_QUEUE, + workflows=[LLMWorkflow], + activities=[stream_completion], + ) + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/workflow_streams/run_publisher.py b/workflow_streams/run_publisher.py new file mode 100644 index 000000000..9f76ee41c --- /dev/null +++ b/workflow_streams/run_publisher.py @@ -0,0 +1,60 @@ +from __future__ import annotations + +import asyncio +import uuid + +from temporalio.client import Client +from temporalio.common import RawValue +from temporalio.contrib.workflow_streams import WorkflowStreamClient + +from workflow_streams.shared import ( + TASK_QUEUE, + TOPIC_PROGRESS, + TOPIC_STATUS, + OrderInput, + ProgressEvent, + StatusEvent, +) +from workflow_streams.workflows.order_workflow import OrderWorkflow + + +async def main() -> None: + client = await Client.connect("localhost:7233") + + workflow_id = f"workflow-stream-order-{uuid.uuid4().hex[:8]}" + handle = await client.start_workflow( + OrderWorkflow.run, + OrderInput(order_id="order-1"), + id=workflow_id, + task_queue=TASK_QUEUE, + ) + + stream = WorkflowStreamClient.create(client, workflow_id) + converter = client.data_converter.payload_converter + + # Single iterator over both topics — avoids a cancellation race + # between two concurrent subscribers. result_type=RawValue + # delivers the underlying Payload so we can dispatch heterogeneous + # events on item.topic. The loop ends either on the in-band + # `complete` terminator (break) or because the iterator exhausts + # when the workflow reaches a terminal state without one (e.g. on + # failure). Either way we then await handle.result(), which raises + # if the workflow failed. + async for item in stream.subscribe( + [TOPIC_STATUS, TOPIC_PROGRESS], result_type=RawValue + ): + if item.topic == TOPIC_STATUS: + evt = converter.from_payload(item.data.payload, StatusEvent) + print(f"[status] {evt.kind}: order={evt.order_id}") + if evt.kind == "complete": + break + elif item.topic == TOPIC_PROGRESS: + progress = converter.from_payload(item.data.payload, ProgressEvent) + print(f"[progress] {progress.message}") + + result = await handle.result() + print(f"workflow result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/workflow_streams/run_reconnecting_subscriber.py b/workflow_streams/run_reconnecting_subscriber.py new file mode 100644 index 000000000..d0da5e32c --- /dev/null +++ b/workflow_streams/run_reconnecting_subscriber.py @@ -0,0 +1,164 @@ +"""Reconnecting subscriber: read a few events, disconnect, resume. + +Demonstrates the central Workflow Streams use case: a consumer can +disappear mid-stream — page refresh, server restart, laptop closed — +and resume later without missing events or seeing duplicates. The +event log lives in the Workflow, so the consumer just remembers where +it stopped. + +The script runs the pattern in two phases inside one process to keep +the demo short. The same code shape works across actual process +restarts because the resume offset is durable in the workflow, not in +the consumer. + +Output is one line per emit, with current stream stats in a left column +and a phase / event message in a right column. A background poller +calls ``WorkflowStreamClient.get_offset()`` for the whole demo and +emits a heartbeat line once a second so you can watch ``pending`` +(``available - processed``) grow while the consumer is disconnected +and shrink as phase 2 catches up. + +Run the worker first (``uv run workflow_streams/run_worker.py``), then:: + + uv run workflow_streams/run_reconnecting_subscriber.py +""" + +from __future__ import annotations + +import asyncio +import uuid +from dataclasses import dataclass + +from temporalio.client import Client +from temporalio.contrib.workflow_streams import WorkflowStreamClient + +from workflow_streams.shared import ( + TASK_QUEUE, + TOPIC_STATUS, + PipelineInput, + StageEvent, +) +from workflow_streams.workflows.pipeline_workflow import PipelineWorkflow + +# Number of events read in phase 1 before simulating a disconnect. +# Picked small enough that the workflow is still running after. +PHASE_1_EVENTS = 2 + +# How long to stay disconnected. +DISCONNECT_SECONDS = 3.0 + +# Background poller cadence. The poller refreshes state.available this +# often and emits a heartbeat line once per HEARTBEAT_SECONDS. +POLL_INTERVAL_SECONDS = 0.25 +HEARTBEAT_SECONDS = 1.0 + +# Width of the stats column. Picked to fit the longest stats string. +LEFT_WIDTH = 30 + + +@dataclass +class State: + processed: int = 0 + available: int = 0 + + @property + def pending(self) -> int: + return max(0, self.available - self.processed) + + +def emit(state: State, message: str) -> None: + left = ( + f"proc={state.processed:>2} " + f"avail={state.available:>2} " + f"pend={state.pending:>2}" + ) + print(f"{left:<{LEFT_WIDTH}}│ {message}", flush=True) + + +async def main() -> None: + client = await Client.connect("localhost:7233") + + workflow_id = f"workflow-stream-pipeline-{uuid.uuid4().hex[:8]}" + handle = await client.start_workflow( + PipelineWorkflow.run, + PipelineInput(pipeline_id=workflow_id), + id=workflow_id, + task_queue=TASK_QUEUE, + ) + + # In a production web backend the resume offset would live in + # durable storage keyed by (user_id, run_id) — a database row, a + # Redis key, etc. For an in-process demo a State.processed + # attribute works the same way. + state = State() + stream = WorkflowStreamClient.create(client, workflow_id) + emit(state, f"started {workflow_id}") + + stop = asyncio.Event() + + async def poller() -> None: + """Refresh state.available; emit a heartbeat line once a second.""" + loop = asyncio.get_running_loop() + last_emit = loop.time() + while not stop.is_set(): + try: + state.available = await stream.get_offset() + except Exception: + pass + now = loop.time() + if now - last_emit >= HEARTBEAT_SECONDS: + emit(state, "·") + last_emit = now + try: + await asyncio.wait_for(stop.wait(), timeout=POLL_INTERVAL_SECONDS) + except asyncio.TimeoutError: + pass + + poller_task = asyncio.create_task(poller()) + try: + # ---- Phase 1: connect, read a couple of events, "disconnect". + emit(state, "[phase 1] connecting") + seen = 0 + async for item in stream.subscribe([TOPIC_STATUS], result_type=StageEvent): + # Remember *one past* the offset just consumed: on resume we + # want the next unseen event, not the one we already showed. + state.processed = item.offset + 1 + emit(state, f" offset={item.offset:2d} stage={item.data.stage}") + seen += 1 + if seen >= PHASE_1_EVENTS: + break + emit(state, "[phase 1] disconnecting") + + # ---- Disconnect window: nobody reads. The workflow keeps + # publishing — `pend` grows on the heartbeat lines as the offset + # advances past `processed`. + await asyncio.sleep(DISCONNECT_SECONDS) + + # ---- Phase 2: brand-new client + stream, resume from saved + # offset. Same shape as a different process picking up where the + # first one left off. + emit(state, "[phase 2] reconnecting") + client2 = await Client.connect("localhost:7233") + stream2 = WorkflowStreamClient.create(client2, workflow_id) + async for item in stream2.subscribe( + [TOPIC_STATUS], + from_offset=state.processed, + result_type=StageEvent, + ): + state.processed = item.offset + 1 + emit(state, f" offset={item.offset:2d} stage={item.data.stage}") + if item.data.stage == "complete": + break + + result = await handle.result() + emit(state, f"workflow result: {result}") + finally: + stop.set() + try: + await poller_task + except asyncio.CancelledError: + pass + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/workflow_streams/run_truncating_ticker.py b/workflow_streams/run_truncating_ticker.py new file mode 100644 index 000000000..26399447c --- /dev/null +++ b/workflow_streams/run_truncating_ticker.py @@ -0,0 +1,129 @@ +"""Truncating ticker: bounded log + slow vs. fast subscribers. + +The ``TickerWorkflow`` publishes ``count`` events at a fixed interval, +calling ``self.stream.truncate(...)`` periodically to bound log +growth. This script subscribes twice — once fast, once slow — and +prints them in two lanes so the trade is visible at a glance: + +* **Fast lane** (left). Keeps up. Sees every published offset. +* **Slow lane** (right). Sleeps between iterations. When a truncation + has dropped its position by the time it polls again, the iterator + silently jumps forward to the new base offset; the slow lane prints + a ``↪ jumped N → M (K dropped)`` marker for each gap and resumes + at the new offset. + +``truncate()`` is unilateral: the workflow does not know who is +subscribed and does not wait for them. The implicit alternative — +never truncating — keeps every event around forever, lets slow +consumers eventually catch up without losses, and pays for it in +unbounded workflow history. The truncation model is the opposite +trade: bounded log, at-best-effort delivery to slow consumers, no +backpressure on the publisher. Pair it with set-semantic events where +each event carries enough state to make missing the prior ones +recoverable. (If you actually need lossless delivery to slow +consumers, the workflow has to coordinate acknowledgements +explicitly — that is a different sample.) + +Run the worker first (``uv run workflow_streams/run_worker.py``), then:: + + uv run workflow_streams/run_truncating_ticker.py +""" + +from __future__ import annotations + +import asyncio +import uuid + +from temporalio.client import Client +from temporalio.contrib.workflow_streams import WorkflowStreamClient + +from workflow_streams.shared import ( + TASK_QUEUE, + TOPIC_TICK, + TickerInput, + TickEvent, +) +from workflow_streams.workflows.ticker_workflow import TickerWorkflow + +# Aggressive truncation so the log stays at most KEEP_LAST entries +# right after each truncation, which keeps the slow subscriber's +# per-poll batch tiny. Small batches + a slow per-event sleep mean the +# slow subscriber re-polls often, and most of those polls land after a +# truncation that has passed its position — so it sees several jumps +# during the run rather than one batched at the end. +TICKER_COUNT = 30 +INTERVAL_MS = 200 +TRUNCATE_EVERY = 2 +KEEP_LAST = 1 +SLOW_SUBSCRIBER_DELAY_S = 1.5 + +LANE_WIDTH = 32 +SEP = "│" + + +def emit_fast(message: str) -> None: + print(f"{message:<{LANE_WIDTH}} {SEP}", flush=True) + + +def emit_slow(message: str) -> None: + print(f"{' ' * LANE_WIDTH} {SEP} {message}", flush=True) + + +def emit_header() -> None: + rule = "─" * LANE_WIDTH + print( + f"{'fast (every event)':<{LANE_WIDTH}} {SEP} " + f"slow (sleeps {SLOW_SUBSCRIBER_DELAY_S}s between events)" + ) + print(f"{rule} {SEP} {rule}") + + +async def main() -> None: + client = await Client.connect("localhost:7233") + + workflow_id = f"workflow-stream-ticker-{uuid.uuid4().hex[:8]}" + handle = await client.start_workflow( + TickerWorkflow.run, + TickerInput( + count=TICKER_COUNT, + keep_last=KEEP_LAST, + truncate_every=TRUNCATE_EVERY, + interval_ms=INTERVAL_MS, + ), + id=workflow_id, + task_queue=TASK_QUEUE, + ) + stream = WorkflowStreamClient.create(client, workflow_id) + last_n = TICKER_COUNT - 1 + + emit_header() + + async def fast_subscriber() -> None: + async for item in stream.subscribe([TOPIC_TICK], result_type=TickEvent): + emit_fast(f"offset={item.offset:>3} n={item.data.n}") + if item.data.n == last_n: + return + + async def slow_subscriber() -> None: + last_offset = -1 + async for item in stream.subscribe([TOPIC_TICK], result_type=TickEvent): + if last_offset >= 0 and item.offset > last_offset + 1: + gap = item.offset - last_offset - 1 + emit_slow( + f"↪ jumped offset={last_offset} → {item.offset} ({gap} dropped)" + ) + emit_slow(f"offset={item.offset:>3} n={item.data.n}") + last_offset = item.offset + if item.data.n == last_n: + return + await asyncio.sleep(SLOW_SUBSCRIBER_DELAY_S) + + await asyncio.gather(fast_subscriber(), slow_subscriber()) + + result = await handle.result() + print() + print(f"workflow result: {result}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/workflow_streams/run_worker.py b/workflow_streams/run_worker.py new file mode 100644 index 000000000..8aa12edca --- /dev/null +++ b/workflow_streams/run_worker.py @@ -0,0 +1,30 @@ +from __future__ import annotations + +import asyncio +import logging + +from temporalio.client import Client +from temporalio.worker import Worker + +from workflow_streams.activities.payment_activity import charge_card +from workflow_streams.shared import TASK_QUEUE +from workflow_streams.workflows.hub_workflow import HubWorkflow +from workflow_streams.workflows.order_workflow import OrderWorkflow +from workflow_streams.workflows.pipeline_workflow import PipelineWorkflow +from workflow_streams.workflows.ticker_workflow import TickerWorkflow + + +async def main() -> None: + logging.basicConfig(level=logging.INFO) + client = await Client.connect("localhost:7233") + worker = Worker( + client, + task_queue=TASK_QUEUE, + workflows=[HubWorkflow, OrderWorkflow, PipelineWorkflow, TickerWorkflow], + activities=[charge_card], + ) + await worker.run() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/workflow_streams/shared.py b/workflow_streams/shared.py new file mode 100644 index 000000000..9bf5a4b79 --- /dev/null +++ b/workflow_streams/shared.py @@ -0,0 +1,70 @@ +from __future__ import annotations + +from dataclasses import dataclass + +from temporalio.contrib.workflow_streams import WorkflowStreamState + +TASK_QUEUE = "workflow-stream-sample-task-queue" + +# Topics published by the workflow / activity. +TOPIC_STATUS = "status" +TOPIC_PROGRESS = "progress" +TOPIC_NEWS = "news" +TOPIC_TICK = "tick" + + +@dataclass +class OrderInput: + order_id: str + # Carries stream state across continue-as-new. None on a fresh start. + stream_state: WorkflowStreamState | None = None + + +@dataclass +class StatusEvent: + kind: str + order_id: str + + +@dataclass +class ProgressEvent: + message: str + + +@dataclass +class PipelineInput: + pipeline_id: str + # Carries stream state across continue-as-new. None on a fresh start. + stream_state: WorkflowStreamState | None = None + + +@dataclass +class StageEvent: + stage: str + + +@dataclass +class HubInput: + hub_id: str + # Carries stream state across continue-as-new. None on a fresh start. + stream_state: WorkflowStreamState | None = None + + +@dataclass +class NewsEvent: + headline: str + + +@dataclass +class TickerInput: + count: int = 20 + keep_last: int = 3 + truncate_every: int = 5 + interval_ms: int = 400 + # Carries stream state across continue-as-new. None on a fresh start. + stream_state: WorkflowStreamState | None = None + + +@dataclass +class TickEvent: + n: int diff --git a/workflow_streams/workflows/__init__.py b/workflow_streams/workflows/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/workflow_streams/workflows/hub_workflow.py b/workflow_streams/workflows/hub_workflow.py new file mode 100644 index 000000000..5dcc3c5f3 --- /dev/null +++ b/workflow_streams/workflows/hub_workflow.py @@ -0,0 +1,40 @@ +from __future__ import annotations + +from datetime import timedelta + +from temporalio import workflow +from temporalio.contrib.workflow_streams import WorkflowStream + +from workflow_streams.shared import HubInput + + +@workflow.defn +class HubWorkflow: + """Passive stream host: starts up, waits, closes when told. + + Unlike OrderWorkflow or PipelineWorkflow, this workflow does no + work of its own — it exists only to host a ``WorkflowStream`` that + external publishers push events into and external subscribers read + from. The shape that fits a backend service or "event bus" pattern, + where the workflow owns durable state but the events come from + outside. + """ + + @workflow.init + def __init__(self, input: HubInput) -> None: + self.stream = WorkflowStream(prior_state=input.stream_state) + self._closed = False + + @workflow.run + async def run(self, input: HubInput) -> str: + await workflow.wait_condition(lambda: self._closed) + # The publisher publishes its own terminator into the stream + # before signaling close (see run_external_publisher.py). + # Hold the run open briefly so subscribers' final poll + # delivers any items still in the log. + await workflow.sleep(timedelta(milliseconds=500)) + return f"hub {input.hub_id} closed" + + @workflow.signal + def close(self) -> None: + self._closed = True diff --git a/workflow_streams/workflows/llm_workflow.py b/workflow_streams/workflows/llm_workflow.py new file mode 100644 index 000000000..b26cfbe4d --- /dev/null +++ b/workflow_streams/workflows/llm_workflow.py @@ -0,0 +1,51 @@ +from __future__ import annotations + +from datetime import timedelta + +from temporalio import workflow +from temporalio.common import RetryPolicy +from temporalio.contrib.workflow_streams import WorkflowStream + +from workflow_streams.llm_shared import LLMInput + +with workflow.unsafe.imports_passed_through(): + from workflow_streams.activities.llm_activity import stream_completion + + +@workflow.defn +class LLMWorkflow: + """Wrapper for an LLM-streaming activity. + + The workflow does no streaming of its own; it hosts the + `WorkflowStream` so external subscribers can attach by workflow + id, kicks off the streaming activity, and returns the full text + the activity produced. + + Streaming is delegated to the activity because the OpenAI call is + non-deterministic. If the activity fails partway through, Temporal + retries it (up to ``max_attempts``); the retried attempt + re-publishes from the start, so the consumer must reset on the + activity's ``RETRY`` event. See + `activities/llm_activity.py` and `run_llm.py`. + """ + + @workflow.init + def __init__(self, input: LLMInput) -> None: + # Construct the stream from `@workflow.init` so the + # publish-Signal handler is registered before any external + # publisher (the activity, here) tries to publish. + self.stream = WorkflowStream(prior_state=input.stream_state) + + @workflow.run + async def run(self, input: LLMInput) -> str: + result = await workflow.execute_activity( + stream_completion, + input, + start_to_close_timeout=timedelta(minutes=5), + retry_policy=RetryPolicy(maximum_attempts=3), + ) + # Hold the run open briefly so the consumer's next poll + # delivers the activity's terminal `complete` event before the + # workflow exits and the in-memory log is gone. + await workflow.sleep(timedelta(milliseconds=500)) + return result diff --git a/workflow_streams/workflows/order_workflow.py b/workflow_streams/workflows/order_workflow.py new file mode 100644 index 000000000..099634cd7 --- /dev/null +++ b/workflow_streams/workflows/order_workflow.py @@ -0,0 +1,58 @@ +from __future__ import annotations + +from datetime import timedelta + +from temporalio import workflow +from temporalio.contrib.workflow_streams import WorkflowStream + +from workflow_streams.shared import ( + TOPIC_PROGRESS, + TOPIC_STATUS, + OrderInput, + ProgressEvent, + StatusEvent, +) + +with workflow.unsafe.imports_passed_through(): + from workflow_streams.activities.payment_activity import charge_card + + +@workflow.defn +class OrderWorkflow: + """Process a fake order, publishing status and progress events. + + The workflow itself publishes status changes; an activity it runs + publishes finer-grained progress events using a + `WorkflowStreamClient`. A single stream carries both topics — + subscribers can filter on the topic(s) they care about. + """ + + @workflow.init + def __init__(self, input: OrderInput) -> None: + # Construct the stream from @workflow.init so it can register + # signal/update/query handlers before the workflow accepts any + # messages. Threading prior_state lets the workflow survive + # continue-as-new without losing buffered items. + self.stream = WorkflowStream(prior_state=input.stream_state) + self.status = self.stream.topic(TOPIC_STATUS, type=StatusEvent) + self.progress = self.stream.topic(TOPIC_PROGRESS, type=ProgressEvent) + + @workflow.run + async def run(self, input: OrderInput) -> str: + self.status.publish(StatusEvent(kind="received", order_id=input.order_id)) + + charge_id = await workflow.execute_activity( + charge_card, + input.order_id, + start_to_close_timeout=timedelta(seconds=30), + ) + + self.status.publish(StatusEvent(kind="shipped", order_id=input.order_id)) + self.progress.publish(ProgressEvent(message=f"charge id: {charge_id}")) + self.status.publish(StatusEvent(kind="complete", order_id=input.order_id)) + # The "complete" status event above is the in-band terminator + # subscribers break on (see run_publisher.py). Hold the run + # open briefly so subscribers' next poll delivers it before + # this task returns and the in-memory log is gone. + await workflow.sleep(timedelta(milliseconds=500)) + return charge_id diff --git a/workflow_streams/workflows/pipeline_workflow.py b/workflow_streams/workflows/pipeline_workflow.py new file mode 100644 index 000000000..833369056 --- /dev/null +++ b/workflow_streams/workflows/pipeline_workflow.py @@ -0,0 +1,48 @@ +from __future__ import annotations + +from datetime import timedelta + +from temporalio import workflow +from temporalio.contrib.workflow_streams import WorkflowStream + +from workflow_streams.shared import ( + TOPIC_STATUS, + PipelineInput, + StageEvent, +) + + +@workflow.defn +class PipelineWorkflow: + """Multi-stage pipeline that publishes stage transitions over time. + + Stages are spaced out with ``workflow.sleep`` so a subscriber can + realistically disconnect partway through and reconnect without the + pipeline finishing in the meantime — the shape needed to demo the + "show up late and still see what happened" pattern. + """ + + @workflow.init + def __init__(self, input: PipelineInput) -> None: + self.stream = WorkflowStream(prior_state=input.stream_state) + self.status = self.stream.topic(TOPIC_STATUS, type=StageEvent) + + @workflow.run + async def run(self, input: PipelineInput) -> str: + stages = [ + "validating", + "loading data", + "transforming", + "writing output", + "verifying", + "complete", + ] + for stage in stages: + self.status.publish(StageEvent(stage=stage)) + if stage != "complete": + await workflow.sleep(timedelta(seconds=2)) + # The "complete" stage above is the in-band terminator + # subscribers break on. Hold the run open briefly so the final + # poll delivers it. + await workflow.sleep(timedelta(milliseconds=500)) + return f"pipeline {input.pipeline_id} done" diff --git a/workflow_streams/workflows/ticker_workflow.py b/workflow_streams/workflows/ticker_workflow.py new file mode 100644 index 000000000..c3f37b9f3 --- /dev/null +++ b/workflow_streams/workflows/ticker_workflow.py @@ -0,0 +1,66 @@ +from __future__ import annotations + +from datetime import timedelta + +from temporalio import workflow +from temporalio.contrib.workflow_streams import WorkflowStream + +from workflow_streams.shared import ( + TOPIC_TICK, + TickerInput, + TickEvent, +) + + +@workflow.defn +class TickerWorkflow: + """Long-running ticker that bounds its event log via ``truncate``. + + Long-running workflows that publish high volumes of events would + otherwise grow their event log unboundedly. This workflow shows + the truncation pattern: every ``truncate_every`` events, drop + everything except the last ``keep_last`` entries by calling + ``self.stream.truncate(safe_offset)``. + + Subscribers that fall behind a truncation jump forward to the new + base offset transparently (the iterator handles the + ``TruncatedOffset`` error internally), so consumers stay live but + may not see every intermediate event. That is the trade: bounded + log size in exchange for at-best-effort delivery to slow + consumers. + + To compute the truncation offset the workflow tracks its own + published count. ``WorkflowStream`` does not expose a workflow-side + head-offset accessor, but the running count plus the carried + ``base_offset`` (in continue-as-new chains) is sufficient. + """ + + @workflow.init + def __init__(self, input: TickerInput) -> None: + self.stream = WorkflowStream(prior_state=input.stream_state) + self.tick = self.stream.topic(TOPIC_TICK, type=TickEvent) + # Running count of events published by THIS run. To compute a + # global offset, add the prior_state's base_offset (omitted + # here — this sample doesn't continue-as-new). + self._published = 0 + + @workflow.run + async def run(self, input: TickerInput) -> str: + for n in range(input.count): + self.tick.publish(TickEvent(n=n)) + self._published += 1 + await workflow.sleep(timedelta(milliseconds=input.interval_ms)) + if ( + self._published % input.truncate_every == 0 + and self._published > input.keep_last + ): + # Drop everything except the last `keep_last` entries. + truncate_to = self._published - input.keep_last + self.stream.truncate(truncate_to) + # The final tick (n == count - 1) is the in-band terminator + # subscribers break on. ``keep_last`` guarantees that final + # offset survives the last truncation so even slow consumers + # eventually see it. Hold the run open briefly so the final + # poll delivers it. + await workflow.sleep(timedelta(milliseconds=500)) + return f"ticker emitted {self._published} events"