diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS index 0f4298b7d..6e32ade9a 100644 --- a/.github/CODEOWNERS +++ b/.github/CODEOWNERS @@ -1 +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 4a1018188..e85fbe40d 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -27,12 +27,20 @@ jobs: - 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@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6 + - uses: actions/checkout@9c091bb21b7c1c1d1991bb908d89e4e9dddfe3e0 # v7.0.0 with: submodules: recursive - uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6 with: python-version: ${{ matrix.python }} + # 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 diff --git a/.gitignore b/.gitignore index 157a7418c..6f9d58c21 100644 --- a/.gitignore +++ b/.gitignore @@ -4,3 +4,8 @@ __pycache__ .vscode .DS_Store .claude +.mypy_cache/ +**/client.key +**/client.pem +*.egg-info/ +.env diff --git a/README.md b/README.md index d4d6a61bc..164263995 100644 --- a/README.md +++ b/README.md @@ -7,8 +7,10 @@ This is a collection of samples showing how to use the [Python SDK](https://gith Prerequisites: * [uv](https://docs.astral.sh/uv/) -* [Temporal CLI installed](https://docs.temporal.io/cli#install) -* [Local Temporal server running](https://docs.temporal.io/cli/server#start-dev) +* [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, @@ -65,28 +67,53 @@ Some examples require extra dependencies. See each sample's directory for specif * [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. -* [langchain](langchain) - Orchestrate workflows for LangChain. +* [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. -* [worker_multiprocessing](worker_multiprocessing) - Leverage Python multiprocessing to parallelize workflow tasks and other CPU bound operations by running multiple workers. +* [workflow_streams](workflow_streams) - Workflow-hosted durable event stream via `temporalio.contrib.workflow_streams`. **Experimental** ## Test diff --git a/bedrock/README.md b/bedrock/README.md index 42a1f4d50..5b294d64e 100644 --- a/bedrock/README.md +++ b/bedrock/README.md @@ -12,7 +12,11 @@ Demonstrates how Temporal and Amazon Bedrock can be used to quickly build bullet 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. See [Temporal's dev server docs](https://docs.temporal.io/cli#start-dev-server) for more information. +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). 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/nexus_sync_operations/__init__.py b/deepagents_plugin/continue_as_new/__init__.py similarity index 100% rename from nexus_sync_operations/__init__.py rename to deepagents_plugin/continue_as_new/__init__.py 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/nexus_sync_operations/caller/__init__.py b/deepagents_plugin/filesystem_backend/__init__.py similarity index 100% rename from nexus_sync_operations/caller/__init__.py rename to deepagents_plugin/filesystem_backend/__init__.py 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/nexus_sync_operations/handler/__init__.py b/deepagents_plugin/hello_world/__init__.py similarity index 100% rename from nexus_sync_operations/handler/__init__.py rename to deepagents_plugin/hello_world/__init__.py 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/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/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/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/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/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_sync_operations/caller/app.py b/nexus_messaging/callerpattern/caller/app.py similarity index 73% rename from nexus_sync_operations/caller/app.py rename to nexus_messaging/callerpattern/caller/app.py index 375628d21..933dcd5d5 100644 --- a/nexus_sync_operations/caller/app.py +++ b/nexus_messaging/callerpattern/caller/app.py @@ -6,15 +6,13 @@ from temporalio.envconfig import ClientConfig from temporalio.worker import Worker -from nexus_sync_operations.caller.workflows import CallerWorkflow +from nexus_messaging.callerpattern.caller.workflows import CallerWorkflow -NAMESPACE = "nexus-sync-operations-caller-namespace" -TASK_QUEUE = "nexus-sync-operations-caller-task-queue" +NAMESPACE = "nexus-messaging-caller-namespace" +TASK_QUEUE = "nexus-messaging-caller-task-queue" -async def execute_caller_workflow( - client: Optional[Client] = None, -) -> None: +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") @@ -28,7 +26,8 @@ async def execute_caller_workflow( ): log = await client.execute_workflow( CallerWorkflow.run, - id=str(uuid.uuid4()), + arg="user-1", + id=f"nexus-messaging-caller-{uuid.uuid4()}", task_queue=TASK_QUEUE, ) for line in log: 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_sync_operations/handler/worker.py b/nexus_messaging/ondemandpattern/handler/worker.py similarity index 58% rename from nexus_sync_operations/handler/worker.py rename to nexus_messaging/ondemandpattern/handler/worker.py index 97c8eb04a..5eec9cfc2 100644 --- a/nexus_sync_operations/handler/worker.py +++ b/nexus_messaging/ondemandpattern/handler/worker.py @@ -6,14 +6,16 @@ from temporalio.envconfig import ClientConfig from temporalio.worker import Worker -from message_passing.introduction.activities import call_greeting_service -from message_passing.introduction.workflows import GreetingWorkflow -from nexus_sync_operations.handler.service_handler import GreetingServiceHandler +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-sync-operations-handler-namespace" -TASK_QUEUE = "nexus-sync-operations-handler-task-queue" +NAMESPACE = "nexus-messaging-handler-namespace" +TASK_QUEUE = "nexus-messaging-handler-task-queue" async def main(client: Optional[Client] = None): @@ -25,20 +27,14 @@ async def main(client: Optional[Client] = None): config.setdefault("namespace", NAMESPACE) client = await Client.connect(**config) - # Create the nexus service handler instance, starting the long-running entity workflow that - # backs the Nexus service - greeting_service_handler = await GreetingServiceHandler.create( - "nexus-sync-operations-greeting-workflow", client, TASK_QUEUE - ) - async with Worker( client, task_queue=TASK_QUEUE, workflows=[GreetingWorkflow], activities=[call_greeting_service], - nexus_service_handlers=[greeting_service_handler], + nexus_service_handlers=[NexusRemoteGreetingServiceHandler()], ): - logging.info("Worker started, ctrl+c to exit") + logging.info("Handler worker started, ctrl+c to exit") await interrupt_event.wait() logging.info("Shutting down") 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_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/nexus_sync_operations/README.md b/nexus_sync_operations/README.md deleted file mode 100644 index 10e266eca..000000000 --- a/nexus_sync_operations/README.md +++ /dev/null @@ -1,39 +0,0 @@ -This sample shows how to create a Nexus service that is backed by a long-running workflow and -exposes operations that execute updates and queries against that workflow. The long-running -workflow, and the updates/queries are private implementation detail of the nexus service: the caller -does not know how the operations are implemented. - -### 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, activity, 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 nexus-sync-operations-handler-namespace -temporal operator namespace create --namespace nexus-sync-operations-caller-namespace - -temporal operator nexus endpoint create \ - --name nexus-sync-operations-nexus-endpoint \ - --target-namespace nexus-sync-operations-handler-namespace \ - --target-task-queue nexus-sync-operations-handler-task-queue \ - --description-file nexus_sync_operations/endpoint_description.md -``` - -In one terminal, run the Temporal worker in the handler namespace: -``` -uv run nexus_sync_operations/handler/worker.py -``` - -In another terminal, run the Temporal worker in the caller namespace and start the caller -workflow: -``` -uv run nexus_sync_operations/caller/app.py -``` diff --git a/nexus_sync_operations/caller/workflows.py b/nexus_sync_operations/caller/workflows.py deleted file mode 100644 index a358d7646..000000000 --- a/nexus_sync_operations/caller/workflows.py +++ /dev/null @@ -1,46 +0,0 @@ -""" -This is a workflow that calls nexus operations. The caller does not have information about how these -operations are implemented by the nexus service. -""" - -from temporalio import workflow - -from message_passing.introduction import Language -from message_passing.introduction.workflows import GetLanguagesInput, SetLanguageInput - -with workflow.unsafe.imports_passed_through(): - from nexus_sync_operations.service import GreetingService - -NEXUS_ENDPOINT = "nexus-sync-operations-nexus-endpoint" - - -@workflow.defn -class CallerWorkflow: - @workflow.run - async def run(self) -> list[str]: - log = [] - nexus_client = workflow.create_nexus_client( - service=GreetingService, - endpoint=NEXUS_ENDPOINT, - ) - - # Get supported languages - supported_languages = await nexus_client.execute_operation( - GreetingService.get_languages, GetLanguagesInput(include_unsupported=False) - ) - log.append(f"supported languages: {supported_languages}") - - # Set language - previous_language = await nexus_client.execute_operation( - GreetingService.set_language, - SetLanguageInput(language=Language.ARABIC), - ) - assert ( - await nexus_client.execute_operation(GreetingService.get_language, None) - == Language.ARABIC - ) - log.append( - f"language changed: {previous_language.name} -> {Language.ARABIC.name}" - ) - - return log diff --git a/nexus_sync_operations/endpoint_description.md b/nexus_sync_operations/endpoint_description.md deleted file mode 100644 index a33b60cfb..000000000 --- a/nexus_sync_operations/endpoint_description.md +++ /dev/null @@ -1,4 +0,0 @@ -## Service: [GreetingService](https://github.com/temporalio/samples-python/blob/main/nexus_sync_operations/service.py) -- operation: `get_languages` -- operation: `get_language` -- operation: `set_language` diff --git a/nexus_sync_operations/handler/service_handler.py b/nexus_sync_operations/handler/service_handler.py deleted file mode 100644 index 626948f0b..000000000 --- a/nexus_sync_operations/handler/service_handler.py +++ /dev/null @@ -1,83 +0,0 @@ -""" -This file demonstrates how to implement a Nexus service that is backed by a long-running workflow -and exposes operations that perform updates and queries against that workflow. -""" - -from __future__ import annotations - -import nexusrpc -from temporalio import nexus -from temporalio.client import Client, WorkflowHandle -from temporalio.common import WorkflowIDConflictPolicy - -from message_passing.introduction import Language -from message_passing.introduction.workflows import ( - GetLanguagesInput, - GreetingWorkflow, - SetLanguageInput, -) -from nexus_sync_operations.service import GreetingService - - -@nexusrpc.handler.service_handler(service=GreetingService) -class GreetingServiceHandler: - def __init__(self, workflow_id: str): - self.workflow_id = workflow_id - - @classmethod - async def create( - cls, workflow_id: str, client: Client, task_queue: str - ) -> GreetingServiceHandler: - # Start the long-running "entity" workflow, if it is not already running. - await client.start_workflow( - GreetingWorkflow.run, - id=workflow_id, - task_queue=task_queue, - id_conflict_policy=WorkflowIDConflictPolicy.USE_EXISTING, - ) - return cls(workflow_id) - - @property - def greeting_workflow_handle(self) -> WorkflowHandle[GreetingWorkflow, str]: - # In nexus operation handler code, nexus.client() is always available, returning a client - # connected to the handler namespace (it's the same client instance that your nexus worker - # is using to poll the server for nexus tasks). This client can be used to interact with the - # handler namespace, for example to send signals, queries, or updates. Remember however, - # that a sync_operation handler must return quickly (no more than a few seconds). To do - # long-running work in a nexus operation handler, use - # temporalio.nexus.workflow_run_operation (see the hello_nexus sample). - return nexus.client().get_workflow_handle_for( - GreetingWorkflow.run, self.workflow_id - ) - - # 👉 This is a handler for a nexus operation whose internal implementation involves executing a - # query against a long-running workflow that is private to the nexus service. - @nexusrpc.handler.sync_operation - async def get_languages( - self, ctx: nexusrpc.handler.StartOperationContext, input: GetLanguagesInput - ) -> list[Language]: - return await self.greeting_workflow_handle.query( - GreetingWorkflow.get_languages, input - ) - - # 👉 This is a handler for a nexus operation whose internal implementation involves executing a - # query against a long-running workflow that is private to the nexus service. - @nexusrpc.handler.sync_operation - async def get_language( - self, ctx: nexusrpc.handler.StartOperationContext, input: None - ) -> Language: - return await self.greeting_workflow_handle.query(GreetingWorkflow.get_language) - - # 👉 This is a handler for a nexus operation whose internal implementation involves executing an - # update against a long-running workflow that is private to the nexus service. Although updates - # can run for an arbitrarily long time, when exposing an update via a nexus sync operation the - # update should execute quickly (sync operations must complete in under 10s). - @nexusrpc.handler.sync_operation - async def set_language( - self, - ctx: nexusrpc.handler.StartOperationContext, - input: SetLanguageInput, - ) -> Language: - return await self.greeting_workflow_handle.execute_update( - GreetingWorkflow.set_language_using_activity, input - ) diff --git a/nexus_sync_operations/service.py b/nexus_sync_operations/service.py deleted file mode 100644 index 3436d5f3f..000000000 --- a/nexus_sync_operations/service.py +++ /dev/null @@ -1,20 +0,0 @@ -""" -This module defines a Nexus service that exposes three operations. - -It is used by the nexus service handler to validate that the operation handlers implement the -correct input and output types, and by the caller workflow to create a type-safe client. It does not -contain the implementation of the operations; see nexus_sync_operations.handler.service_handler for -that. -""" - -import nexusrpc - -from message_passing.introduction import Language -from message_passing.introduction.workflows import GetLanguagesInput, SetLanguageInput - - -@nexusrpc.service -class GreetingService: - get_languages: nexusrpc.Operation[GetLanguagesInput, list[Language]] - get_language: nexusrpc.Operation[None, Language] - set_language: nexusrpc.Operation[SetLanguageInput, Language] diff --git a/openai_agents/README.md b/openai_agents/README.md index 9404278fd..f6857d795 100644 --- a/openai_agents/README.md +++ b/openai_agents/README.md @@ -17,7 +17,10 @@ This approach ensures that AI agent workflows are durable, observable, and can h ## Prerequisites -- Temporal server [running locally](https://docs.temporal.io/cli/server#start-dev) +- [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` @@ -36,3 +39,4 @@ Each directory contains a complete example with its own README for detailed inst - **[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/agent_patterns/README.md b/openai_agents/agent_patterns/README.md index 337847478..c86fdfd21 100644 --- a/openai_agents/agent_patterns/README.md +++ b/openai_agents/agent_patterns/README.md @@ -40,7 +40,7 @@ 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 for non-streaming): +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 ``` @@ -94,4 +94,4 @@ This is really useful for latency: for example, you might have a very fast model 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**: Requires streaming capabilities which are not yet available in the Temporal integration \ No newline at end of file +- **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/basic/README.md b/openai_agents/basic/README.md index e593ee48c..128ec7cac 100644 --- a/openai_agents/basic/README.md +++ b/openai_agents/basic/README.md @@ -76,4 +76,5 @@ uv run openai_agents/basic/run_previous_response_id_workflow.py 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** - Streaming is not supported in Temporal OpenAI Agents SDK integration \ No newline at end of file + +**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/workflows/agent_lifecycle_workflow.py b/openai_agents/basic/workflows/agent_lifecycle_workflow.py index c016d1500..32bce03b4 100644 --- a/openai_agents/basic/workflows/agent_lifecycle_workflow.py +++ b/openai_agents/basic/workflows/agent_lifecycle_workflow.py @@ -41,7 +41,7 @@ async def on_tool_start( ) async def on_tool_end( - self, context: RunContextWrapper, agent: Agent, tool, result: str + self, context: RunContextWrapper, agent: Agent, tool, result: Any ) -> None: self.event_counter += 1 print( diff --git a/openai_agents/basic/workflows/lifecycle_workflow.py b/openai_agents/basic/workflows/lifecycle_workflow.py index cf72de4a3..ded120a61 100644 --- a/openai_agents/basic/workflows/lifecycle_workflow.py +++ b/openai_agents/basic/workflows/lifecycle_workflow.py @@ -43,7 +43,7 @@ async def on_tool_start( ) async def on_tool_end( - self, context: RunContextWrapper, agent: Agent, tool: Tool, result: str + self, context: RunContextWrapper, agent: Agent, tool: Tool, result: Any ) -> None: self.event_counter += 1 print( diff --git a/openai_agents/basic/workflows/local_image_workflow.py b/openai_agents/basic/workflows/local_image_workflow.py index b536f517e..9c82a5991 100644 --- a/openai_agents/basic/workflows/local_image_workflow.py +++ b/openai_agents/basic/workflows/local_image_workflow.py @@ -1,3 +1,5 @@ +from datetime import timedelta + from agents import Agent, Runner from temporalio import workflow @@ -24,7 +26,7 @@ async def run( b64_image = await workflow.execute_activity( read_image_as_base64, image_path, - start_to_close_timeout=workflow.timedelta(seconds=30), + start_to_close_timeout=timedelta(seconds=30), ) agent = Agent( diff --git a/openai_agents/handoffs/README.md b/openai_agents/handoffs/README.md index c38b09198..21717e9ff 100644 --- a/openai_agents/handoffs/README.md +++ b/openai_agents/handoffs/README.md @@ -41,4 +41,4 @@ The workflow returns both the final response and complete message history for in 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**: Streaming capabilities are not yet available in the Temporal integration \ No newline at end of file +- **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/reasoning_content/README.md b/openai_agents/reasoning_content/README.md index c654d2666..52ad48372 100644 --- a/openai_agents/reasoning_content/README.md +++ b/openai_agents/reasoning_content/README.md @@ -34,4 +34,4 @@ uv run openai_agents/reasoning_content/run_reasoning_content_workflow.py ## Note on Streaming -The original OpenAI Agents SDK example includes streaming capabilities, but since Temporal workflows do not support streaming yet, this example contains only the non-streaming approach. \ No newline at end of file +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/workflows/reasoning_content_workflow.py b/openai_agents/reasoning_content/workflows/reasoning_content_workflow.py index 0a9f0a158..2a584a4bc 100644 --- a/openai_agents/reasoning_content/workflows/reasoning_content_workflow.py +++ b/openai_agents/reasoning_content/workflows/reasoning_content_workflow.py @@ -1,4 +1,5 @@ from dataclasses import dataclass +from datetime import timedelta from temporalio import workflow @@ -22,7 +23,7 @@ async def run(self, prompt: str, model_name: str | None = None) -> ReasoningResu reasoning_content, regular_content = await workflow.execute_activity( get_reasoning_response, args=[prompt, model_name], - start_to_close_timeout=workflow.timedelta(minutes=5), + start_to_close_timeout=timedelta(minutes=5), ) return ReasoningResult( 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/pyproject.toml b/pyproject.toml index 0927eea3e..4d8422610 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -6,7 +6,7 @@ authors = [{ name = "Temporal Technologies Inc", email = "sdk@temporal.io" }] requires-python = ">=3.10" readme = "README.md" license = "MIT" -dependencies = ["temporalio>=1.26.0,<2"] +dependencies = ["temporalio>=1.31.0,<2", "protobuf>=5.29.6,<6"] [project.urls] Homepage = "https://github.com/temporalio/samples-python" @@ -20,6 +20,7 @@ dev = [ "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", @@ -27,14 +28,48 @@ dev = [ "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>=3.8.1,<4"] +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]>=1.26.0", + "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]", @@ -42,66 +77,39 @@ open-telemetry = [ ] openai-agents = [ "openai-agents[litellm] >= 0.14.1", - "temporalio[openai-agents,opentelemetry] >= 1.26.0", + "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>=2.2.2,<3 ; python_version >= '3.10' and python_version < '4.0'", + "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.hatch.metadata] -allow-direct-references = true - -[tool.hatch.build.targets.sdist] -include = ["./**/*.py"] - -[tool.hatch.build.targets.wheel] -include = ["./**/*.py"] -packages = [ - "activity_worker", - "bedrock", - "cloud_export_to_parquet", - "context_propagation", - "custom_converter", - "custom_decorator", - "custom_metric", - "dsl", - "encryption", - "gevent_async", - "hello", - "langsmith_tracing", - "message_passing", - "nexus", - "open_telemetry", - "patching", - "polling", - "prometheus", - "pydantic_converter", - "pydantic_converter_v1", - "pyproject.toml", - "replay", - "schedules", - "sentry", - "sleep_for_days", - "tests", - "trio_async", - "updatable_timer", - "worker_specific_task_queues", - "worker_versioning", +[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.hatch.build.targets.wheel.sources] -"./**/*.py" = "**/*.py" +[tool.setuptools.packages.find] +exclude = ["lambda_worker*"] [build-system] -requires = ["hatchling"] -build-backend = "hatchling.build" +requires = ["setuptools>=77"] +build-backend = "setuptools.build_meta" [tool.poe.tasks] format = [ @@ -113,8 +121,8 @@ lint = [ { cmd = "uv run ruff format --check" }, { ref = "lint-types" }, ] -lint-types = "uv run --all-groups mypy --check-untyped-defs --namespace-packages ." -test = "uv run --all-groups pytest" +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" @@ -124,10 +132,12 @@ log_cli_format = "%(asctime)s [%(levelname)8s] %(message)s (%(filename)s:%(linen [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/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 65de246ea..b857e0d96 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -42,12 +42,7 @@ async def env(request) -> AsyncGenerator[WorkflowEnvironment, None]: env_type = request.config.getoption("--workflow-environment") if env_type == "local": env = await WorkflowEnvironment.start_local( - dev_server_extra_args=[ - "--dynamic-config-value", - "frontend.enableExecuteMultiOperation=true", - "--dynamic-config-value", - "system.enableEagerWorkflowStart=true", - ] + dev_server_download_version="v1.7.4-standalone-nexus-operations", ) elif env_type == "time-skipping": env = await WorkflowEnvironment.start_time_skipping() 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/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/helpers/__init__.py b/tests/helpers/__init__.py index e69de29bb..a387e5291 100644 --- a/tests/helpers/__init__.py +++ 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 index dee8dc18a..b9ce68995 100644 --- a/tests/helpers/nexus.py +++ b/tests/helpers/nexus.py @@ -1,7 +1,4 @@ import temporalio.api -import temporalio.api.common -import temporalio.api.common.v1 -import temporalio.api.enums.v1 import temporalio.api.nexus import temporalio.api.nexus.v1 import temporalio.api.operatorservice 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/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/test_basic.py b/tests/langsmith_tracing/test_basic.py index b5be2f353..cef49054a 100644 --- a/tests/langsmith_tracing/test_basic.py +++ b/tests/langsmith_tracing/test_basic.py @@ -1,4 +1,5 @@ import uuid +from unittest.mock import MagicMock from temporalio import activity from temporalio.client import Client @@ -10,7 +11,9 @@ from langsmith_tracing.basic.workflows import BasicLLMWorkflow -async def test_basic_workflow(client: Client, env: WorkflowEnvironment): +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") @@ -22,7 +25,7 @@ async def mock_call_openai(request: OpenAIRequest) -> str: task_queue="test-langsmith-basic", workflows=[BasicLLMWorkflow], activities=[mock_call_openai], - plugins=[LangSmithPlugin()], + plugins=[LangSmithPlugin(client=mock_ls_client)], ): result = await client.execute_workflow( BasicLLMWorkflow.run, diff --git a/tests/langsmith_tracing/test_chatbot.py b/tests/langsmith_tracing/test_chatbot.py index 2febb654d..88d98202a 100644 --- a/tests/langsmith_tracing/test_chatbot.py +++ b/tests/langsmith_tracing/test_chatbot.py @@ -1,5 +1,6 @@ import json import uuid +from unittest.mock import MagicMock from temporalio import activity from temporalio.client import Client @@ -27,7 +28,9 @@ def _make_function_call_response( ) -async def test_chatbot_save_note(client: Client, env: WorkflowEnvironment): +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 @@ -47,7 +50,7 @@ async def mock_call_openai(request: OpenAIRequest) -> ChatResponse: task_queue="test-langsmith-chatbot", workflows=[ChatbotWorkflow], activities=[mock_call_openai], - plugins=[LangSmithPlugin()], + plugins=[LangSmithPlugin(client=mock_ls_client)], ): wf_handle = await client.start_workflow( ChatbotWorkflow.run, @@ -68,8 +71,10 @@ async def mock_call_openai(request: OpenAIRequest) -> ChatResponse: assert result == "Session ended." -async def test_chatbot_read_note(client: Client, env: WorkflowEnvironment): - """Test read_note tool call loop — read_note runs as a workflow method.""" +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") @@ -97,7 +102,7 @@ async def mock_call_openai(request: OpenAIRequest) -> ChatResponse: task_queue="test-langsmith-chatbot-read", workflows=[ChatbotWorkflow], activities=[mock_call_openai], - plugins=[LangSmithPlugin()], + plugins=[LangSmithPlugin(client=mock_ls_client)], ): wf_handle = await client.start_workflow( ChatbotWorkflow.run, 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/nexus_sync_operations/nexus_sync_operations_test.py b/tests/nexus_messaging/callerpattern_test.py similarity index 55% rename from tests/nexus_sync_operations/nexus_sync_operations_test.py rename to tests/nexus_messaging/callerpattern_test.py index d74168cb9..d14b4c29e 100644 --- a/tests/nexus_sync_operations/nexus_sync_operations_test.py +++ b/tests/nexus_messaging/callerpattern_test.py @@ -8,18 +8,18 @@ from temporalio.testing import WorkflowEnvironment from temporalio.worker import Worker -import nexus_sync_operations.handler.service_handler -import nexus_sync_operations.handler.worker -from message_passing.introduction import Language -from message_passing.introduction.workflows import GetLanguagesInput, SetLanguageInput -from nexus_sync_operations.caller.workflows import CallerWorkflow +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 -with workflow.unsafe.imports_passed_through(): - from nexus_sync_operations.service import GreetingService - - -NEXUS_ENDPOINT = "nexus-sync-operations-nexus-endpoint" +NEXUS_ENDPOINT = "nexus-messaging-nexus-endpoint" @workflow.defn @@ -27,89 +27,90 @@ class TestCallerWorkflow: """Test workflow that calls Nexus operations and makes assertions.""" @workflow.run - async def run(self) -> None: + async def run(self, user_id: str) -> None: nexus_client = workflow.create_nexus_client( - service=GreetingService, + service=NexusGreetingService, endpoint=NEXUS_ENDPOINT, ) supported_languages = await nexus_client.execute_operation( - GreetingService.get_languages, GetLanguagesInput(include_unsupported=False) + NexusGreetingService.get_languages, + GetLanguagesInput(include_unsupported=False, user_id=user_id), ) - assert supported_languages == [Language.CHINESE, Language.ENGLISH] + assert supported_languages.languages == [Language.CHINESE, Language.ENGLISH] initial_language = await nexus_client.execute_operation( - GreetingService.get_language, None + NexusGreetingService.get_language, + GetLanguageInput(user_id=user_id), ) assert initial_language == Language.ENGLISH previous_language = await nexus_client.execute_operation( - GreetingService.set_language, - SetLanguageInput(language=Language.CHINESE), + NexusGreetingService.set_language, + SetLanguageInput(language=Language.CHINESE, user_id=user_id), ) assert previous_language == Language.ENGLISH current_language = await nexus_client.execute_operation( - GreetingService.get_language, None + NexusGreetingService.get_language, + GetLanguageInput(user_id=user_id), ) assert current_language == Language.CHINESE previous_language = await nexus_client.execute_operation( - GreetingService.set_language, - SetLanguageInput(language=Language.ARABIC), + NexusGreetingService.set_language, + SetLanguageInput(language=Language.ARABIC, user_id=user_id), ) assert previous_language == Language.CHINESE current_language = await nexus_client.execute_operation( - GreetingService.get_language, None + NexusGreetingService.get_language, + GetLanguageInput(user_id=user_id), ) assert current_language == Language.ARABIC -async def test_nexus_sync_operations(client: Client, env: WorkflowEnvironment): +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_nexus_sync_operations_caller_workflow( - client: Client, env: WorkflowEnvironment -): - """ - Runs the CallerWorkflow from the sample to ensure it executes without errors. - """ +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, workflow: Type): +async def _run_caller_workflow(client: Client, wf: Type): create_response = await create_nexus_endpoint( name=NEXUS_ENDPOINT, - task_queue=nexus_sync_operations.handler.worker.TASK_QUEUE, + task_queue=nexus_messaging.callerpattern.handler.worker.TASK_QUEUE, client=client, ) try: handler_worker_task = asyncio.create_task( - nexus_sync_operations.handler.worker.main(client) + nexus_messaging.callerpattern.handler.worker.main(client) ) try: async with Worker( client, task_queue="test-caller-task-queue", - workflows=[workflow], + workflows=[wf], ): await client.execute_workflow( - workflow.run, + wf.run, + arg="user-1", id=str(uuid.uuid4()), task_queue="test-caller-task-queue", ) finally: - nexus_sync_operations.handler.worker.interrupt_event.set() + nexus_messaging.callerpattern.handler.worker.interrupt_event.set() await handler_worker_task - nexus_sync_operations.handler.worker.interrupt_event.clear() + nexus_messaging.callerpattern.handler.worker.interrupt_event.clear() finally: await delete_nexus_endpoint( id=create_response.endpoint.id, 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_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/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/uv.lock b/uv.lock index 3c9990eb9..202bb9448 100644 --- a/uv.lock +++ b/uv.lock @@ -2,12 +2,62 @@ version = 1 revision = 3 requires-python = ">=3.10" resolution-markers = [ - "python_full_version >= '3.13'", + 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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"