From 85db80fbb11860e3f08f3dc60b449d96bc725867 Mon Sep 17 00:00:00 2001 From: Shashi-Stackone Date: Thu, 16 Apr 2026 16:08:35 +0100 Subject: [PATCH 1/7] Example Cleanup - Python --- .env.example | 6 + .gitignore | 1 + README.md | 74 ++-- examples/README.md | 52 +++ examples/agent_tool_search.py | 202 ----------- examples/auth_management.py | 101 ++++++ examples/crewai_integration.py | 42 ++- examples/file_uploads.py | 83 ----- examples/index.py | 105 ------ examples/langchain_integration.py | 63 ++-- examples/openai_integration.py | 66 ++-- examples/search_tool_example.py | 66 ---- examples/search_tools.py | 171 +++++++++ examples/semantic_search_example.py | 423 ---------------------- examples/stackone_account_ids.py | 40 -- examples/test_examples.py | 10 +- examples/workday_integration.py | 91 ----- justfile | 4 + {examples => scripts}/benchmark_search.py | 0 19 files changed, 496 insertions(+), 1104 deletions(-) create mode 100644 .env.example create mode 100644 examples/README.md delete mode 100644 examples/agent_tool_search.py create mode 100644 examples/auth_management.py delete mode 100644 examples/file_uploads.py delete mode 100644 examples/index.py delete mode 100644 examples/search_tool_example.py create mode 100644 examples/search_tools.py delete mode 100644 examples/semantic_search_example.py delete mode 100644 examples/stackone_account_ids.py delete mode 100644 examples/workday_integration.py rename {examples => scripts}/benchmark_search.py (100%) diff --git a/.env.example b/.env.example new file mode 100644 index 0000000..0f527f9 --- /dev/null +++ b/.env.example @@ -0,0 +1,6 @@ +# Required for all examples +STACKONE_API_KEY=your-stackone-api-key +STACKONE_ACCOUNT_ID=your-account-id + +# Required for OpenAI-based examples (openai_integration, langchain_integration, crewai_integration, search_tools) +OPENAI_API_KEY=your-openai-api-key diff --git a/.gitignore b/.gitignore index 14d2d5c..371a7f1 100644 --- a/.gitignore +++ b/.gitignore @@ -1,5 +1,6 @@ .env .env.* +!.env.example .venv .pytest_cache diff --git a/README.md b/README.md index 293e986..62cc692 100644 --- a/README.md +++ b/README.md @@ -52,17 +52,18 @@ uv add 'stackone-ai[mcp,examples]' ## Quick Start ```python +import os from stackone_ai import StackOneToolSet -# Initialize with API key -toolset = StackOneToolSet() # Uses STACKONE_API_KEY env var -# Or explicitly: toolset = StackOneToolSet(api_key="your-api-key") +# Initialize — reads STACKONE_API_KEY from environment +toolset = StackOneToolSet() -# Get HRIS-related tools with glob patterns -tools = toolset.fetch_tools(actions=["bamboohr_*"], account_ids=["your-account-id"]) +# Fetch tools — pass account ID from STACKONE_ACCOUNT_ID env var +account_id = os.getenv("STACKONE_ACCOUNT_ID") +tools = toolset.fetch_tools(actions=["workday_*"], account_ids=[account_id]) # Use a specific tool with the call method -employee_tool = tools.get_tool("bamboohr_get_employee") +employee_tool = tools.get_tool("workday_get_worker") # Call with keyword arguments employee = employee_tool.call(id="employee-id") # Or with traditional execute method @@ -86,7 +87,7 @@ toolset = StackOneToolSet() tools = toolset.fetch_tools(account_ids=["acc-123", "acc-456"]) # Filter by providers (case-insensitive) -tools = toolset.fetch_tools(providers=["hibob", "bamboohr"]) +tools = toolset.fetch_tools(providers=["hibob", "workday"]) # Filter by action patterns with glob support tools = toolset.fetch_tools(actions=["*_list_employees"]) @@ -106,11 +107,11 @@ tools = toolset.fetch_tools(providers=["hibob"]) **Filtering Options:** - **`account_ids`**: Filter tools by account IDs. Tools will be loaded for each specified account. -- **`providers`**: Filter by provider names (e.g., `["hibob", "bamboohr"]`). Case-insensitive matching. +- **`providers`**: Filter by provider names (e.g., `["hibob", "workday"]`). Case-insensitive matching. - **`actions`**: Filter by action patterns with glob support: - - Exact match: `["bamboohr_list_employees"]` + - Exact match: `["workday_list_workers"]` - Glob pattern: `["*_list_employees"]` matches all tools ending with `_list_employees` - - Provider prefix: `["bamboohr_*"]` matches all BambooHR tools + - Provider prefix: `["workday_*"]` matches all Workday tools ## Implicit Feedback (Beta) @@ -165,12 +166,14 @@ When two calls for the same session happen within a few seconds, the SDK emits a StackOne tools work seamlessly with LangChain, enabling powerful AI agent workflows: ```python +import os from langchain_openai import ChatOpenAI from stackone_ai import StackOneToolSet # Initialize StackOne tools toolset = StackOneToolSet() -tools = toolset.fetch_tools(actions=["bamboohr_*"], account_ids=["your-account-id"]) +account_id = os.getenv("STACKONE_ACCOUNT_ID") +tools = toolset.fetch_tools(actions=["workday_*"], account_ids=[account_id]) # Convert to LangChain format langchain_tools = tools.to_langchain() @@ -204,6 +207,7 @@ pip install langgraph langchain-openai ``` ```python +import os from langchain_openai import ChatOpenAI from typing import Annotated from typing_extensions import TypedDict @@ -217,7 +221,8 @@ from stackone_ai.integrations.langgraph import to_tool_node, bind_model_with_too # Prepare tools toolset = StackOneToolSet() -tools = toolset.fetch_tools(actions=["bamboohr_*"], account_ids=["your-account-id"]) +account_id = os.getenv("STACKONE_ACCOUNT_ID") +tools = toolset.fetch_tools(actions=["workday_*"], account_ids=[account_id]) langchain_tools = tools.to_langchain() class State(TypedDict): @@ -250,12 +255,14 @@ _ = app.invoke({"messages": [("user", "Get employee with id emp123") ]}) CrewAI uses LangChain tools natively, making integration seamless: ```python +import os from crewai import Agent, Crew, Task from stackone_ai import StackOneToolSet # Get tools and convert to LangChain format toolset = StackOneToolSet() -tools = toolset.fetch_tools(actions=["bamboohr_*"], account_ids=["your-account-id"]) +account_id = os.getenv("STACKONE_ACCOUNT_ID") +tools = toolset.fetch_tools(actions=["workday_*"], account_ids=[account_id]) langchain_tools = tools.to_langchain() # Create CrewAI agent with StackOne tools @@ -297,7 +304,7 @@ feedback_tool = tools.get_tool("tool_feedback") result = feedback_tool.call( feedback="The HRIS tools are working great! Very fast response times.", account_id="acc_123456", - tool_names=["bamboohr_list_employees", "bamboohr_get_employee"] + tool_names=["workday_list_workers", "workday_get_worker"] ) ``` @@ -313,9 +320,12 @@ Search for tools using natural language queries. Works with both semantic (cloud ### Basic Usage ```python +import os + # Get a callable search tool toolset = StackOneToolSet() -all_tools = toolset.fetch_tools(account_ids=["your-account-id"]) +account_id = os.getenv("STACKONE_ACCOUNT_ID") +all_tools = toolset.fetch_tools(account_ids=[account_id]) search_tool = toolset.get_search_tool() # Search for relevant tools — returns a Tools collection @@ -327,15 +337,17 @@ tools[0](limit=10) ## Semantic Search -Discover tools using natural language instead of exact names. Queries like "onboard new hire" resolve to the right actions even when the tool is called `bamboohr_create_employee`. +Discover tools using natural language instead of exact names. Queries like "onboard new hire" resolve to the right actions even when the tool is called `workday_create_employee`. ```python +import os from stackone_ai import StackOneToolSet toolset = StackOneToolSet() # Search by intent — returns Tools collection ready for any framework -tools = toolset.search_tools("manage employee records", account_ids=["your-account-id"], top_k=5) +account_id = os.getenv("STACKONE_ACCOUNT_ID") +tools = toolset.search_tools("manage employee records", account_ids=[account_id], top_k=5) openai_tools = tools.to_openai() # Lightweight: inspect results without fetching full tool definitions @@ -357,19 +369,31 @@ tools = toolset.search_tools("manage employees", search="semantic") tools = toolset.search_tools("manage employees", search="local") ``` -Results are automatically scoped to connectors in your linked accounts. See [Semantic Search Example](examples/semantic_search_example.py) for `SearchTool` (`get_search_tool`) integration, OpenAI, and LangChain patterns. +Results are automatically scoped to connectors in your linked accounts. See [Search Tools Example](examples/search_tools.py) for `SearchTool` (`get_search_tool`) integration, OpenAI, and LangChain patterns. ## Examples For more examples, check out the [examples/](examples/) directory: -- [StackOne Account IDs](examples/stackone_account_ids.py) -- [File Uploads](examples/file_uploads.py) -- [OpenAI Integration](examples/openai_integration.py) -- [LangChain Integration](examples/langchain_integration.py) -- [CrewAI Integration](examples/crewai_integration.py) -- [Search Tool](examples/search_tool_example.py) -- [Semantic Search](examples/semantic_search_example.py) +- [OpenAI Integration](examples/openai_integration.py) — OpenAI function calling +- [LangChain Integration](examples/langchain_integration.py) — LangChain tools +- [CrewAI Integration](examples/crewai_integration.py) — CrewAI agent +- [Search Tools](examples/search_tools.py) — Tool discovery (semantic, local, auto search) +- [Auth Management](examples/auth_management.py) — API key and account ID patterns + +### Running Examples + +```bash +# 1. Set up credentials +cp .env.example .env +# Edit .env with your API keys + +# 2. Install dependencies +uv sync --all-extras + +# 3. Run any example +uv run examples/search_tools.py +``` ## Development diff --git a/examples/README.md b/examples/README.md new file mode 100644 index 0000000..ed9092b --- /dev/null +++ b/examples/README.md @@ -0,0 +1,52 @@ +# StackOne AI SDK Examples + +## Setup + +Install dependencies: + +```bash +uv sync --all-extras +``` + +Set your credentials using either approach: + +**Option A: Environment variables (shell)** + +```bash +export STACKONE_API_KEY=your-stackone-api-key +export STACKONE_ACCOUNT_ID=your-account-id +export OPENAI_API_KEY=your-openai-api-key # for OpenAI/LangChain/CrewAI examples +``` + +**Option B: `.env` file** + +```bash +cp .env.example .env +# Edit .env with your keys +``` + +## Running Examples + +```bash +uv run examples/search_tools.py +``` + +Test all examples (no API keys needed): + +```bash +uv run pytest examples +``` + +## Examples + +| File | Description | +|------|-------------| +| `openai_integration.py` | OpenAI function calling with Workday tools | +| `langchain_integration.py` | LangChain tools integration | +| `crewai_integration.py` | CrewAI agent with Workday tools | +| `search_tools.py` | Tool discovery: direct fetch, semantic/local/auto search, search & execute | +| `auth_management.py` | API key and account ID configuration patterns | + +## Environment Variables + +See `.env.example` in the project root for all required variables. diff --git a/examples/agent_tool_search.py b/examples/agent_tool_search.py deleted file mode 100644 index e3c90c2..0000000 --- a/examples/agent_tool_search.py +++ /dev/null @@ -1,202 +0,0 @@ -"""Search and execute example: LLM-driven tool discovery and execution. - -There are two ways to give tools to an LLM: - -1. ``toolset.openai()`` — fetches ALL tools and converts them to OpenAI format. - Token cost scales with the number of tools in your catalog. - -2. ``toolset.openai(mode="search_and_execute")`` — returns just 2 tools - (tool_search + tool_execute). The LLM discovers and runs tools on-demand, - keeping token usage constant regardless of catalog size. - -This example demonstrates approach 2 with two patterns: -- Raw client (OpenAI): manual agent loop with ``toolset.execute()`` -- LangChain: framework handles tool execution automatically - -Prerequisites: - - STACKONE_API_KEY environment variable - - STACKONE_ACCOUNT_ID environment variable - - OPENAI_API_KEY environment variable - -Run with: - uv run python examples/agent_tool_search.py -""" - -from __future__ import annotations - -import json -import os - -try: - from dotenv import load_dotenv - - load_dotenv() -except ModuleNotFoundError: - pass - -from stackone_ai import StackOneToolSet - - -def example_openai() -> None: - """Raw client: OpenAI. - - Shows: init toolset -> get OpenAI tools -> manual agent loop with toolset.execute(). - """ - print("=" * 60) - print("Example 1: Raw client (OpenAI) — manual execution") - print("=" * 60) - print() - - try: - from openai import OpenAI - except ImportError: - print("Skipped: pip install openai") - print() - return - - if not os.getenv("OPENAI_API_KEY"): - print("Skipped: Set OPENAI_API_KEY to run this example.") - print() - return - - # 1. Init toolset - account_id = os.getenv("STACKONE_ACCOUNT_ID") - toolset = StackOneToolSet( - account_id=account_id, - search={"method": "semantic", "top_k": 3}, - execute={"account_ids": [account_id]} if account_id else None, - ) - - # 2. Get tools in OpenAI format - openai_tools = toolset.openai(mode="search_and_execute") - - # 3. Create OpenAI client and run agent loop - client = OpenAI() - messages: list[dict] = [ - { - "role": "system", - "content": ( - "You are a helpful scheduling assistant. Use tool_search to find relevant tools, " - "then tool_execute to run them. Always read the parameter schemas from tool_search " - "results carefully. If a tool needs a user URI, first search for and call a " - '"get current user" tool to obtain it. If a tool execution fails, try different ' - "parameters or a different tool." - ), - }, - {"role": "user", "content": "List my upcoming Calendly events for the next week."}, - ] - - for _step in range(10): - response = client.chat.completions.create( - model="gpt-5.4", - messages=messages, - tools=openai_tools, - tool_choice="auto", - ) - - choice = response.choices[0] - - # 4. If no tool calls, print final answer and stop - if not choice.message.tool_calls: - print(f"Answer: {choice.message.content}") - break - - # 5. Execute tool calls manually and feed results back - messages.append(choice.message.model_dump(exclude_none=True)) - for tool_call in choice.message.tool_calls: - print(f" -> {tool_call.function.name}({tool_call.function.arguments})") - result = toolset.execute(tool_call.function.name, tool_call.function.arguments) - messages.append( - { - "role": "tool", - "tool_call_id": tool_call.id, - "content": json.dumps(result), - } - ) - - print() - - -def example_langchain() -> None: - """Framework: LangChain with auto-execution. - - Shows: init toolset -> get LangChain tools -> bind to model -> framework executes tools. - No toolset.execute() needed — the framework calls _run() on tools automatically. - """ - print("=" * 60) - print("Example 2: LangChain — framework handles execution") - print("=" * 60) - print() - - try: - from langchain_core.messages import AIMessage, HumanMessage, SystemMessage, ToolMessage - from langchain_openai import ChatOpenAI - except ImportError: - print("Skipped: pip install langchain-openai") - print() - return - - if not os.getenv("OPENAI_API_KEY"): - print("Skipped: Set OPENAI_API_KEY to run this example.") - print() - return - - # 1. Init toolset - account_id = os.getenv("STACKONE_ACCOUNT_ID") - toolset = StackOneToolSet( - account_id=account_id, - search={"method": "semantic", "top_k": 3}, - execute={"account_ids": [account_id]} if account_id else None, - ) - - # 2. Get tools in LangChain format and bind to model - langchain_tools = toolset.langchain(mode="search_and_execute") - tools_by_name = {tool.name: tool for tool in langchain_tools} - model = ChatOpenAI(model="gpt-5.4").bind_tools(langchain_tools) - - # 3. Run agent loop - messages = [ - SystemMessage( - content=( - "You are a helpful scheduling assistant. Use tool_search to find relevant tools, " - "then tool_execute to run them. Always read the parameter schemas from tool_search " - "results carefully. If a tool needs a user URI, first search for and call a " - '"get current user" tool to obtain it. If a tool execution fails, try different ' - "parameters or a different tool." - ), - ), - HumanMessage(content="List my upcoming Calendly events for the next week."), - ] - - for _step in range(10): - response: AIMessage = model.invoke(messages) - - # 4. If no tool calls, print final answer and stop - if not response.tool_calls: - print(f"Answer: {response.content}") - break - - # 5. Framework-compatible execution — invoke LangChain tools directly - messages.append(response) - for tool_call in response.tool_calls: - print(f" -> {tool_call['name']}({json.dumps(tool_call['args'])})") - tool = tools_by_name[tool_call["name"]] - result = tool.invoke(tool_call["args"]) - messages.append(ToolMessage(content=json.dumps(result), tool_call_id=tool_call["id"])) - - print() - - -def main() -> None: - """Run all examples.""" - api_key = os.getenv("STACKONE_API_KEY") - if not api_key: - print("Set STACKONE_API_KEY to run these examples.") - return - - example_openai() - example_langchain() - - -if __name__ == "__main__": - main() diff --git a/examples/auth_management.py b/examples/auth_management.py new file mode 100644 index 0000000..3e2edd2 --- /dev/null +++ b/examples/auth_management.py @@ -0,0 +1,101 @@ +""" +Comprehensive auth management example showing all ways to configure API keys and account IDs. + +```bash +uv run examples/auth_management.py +``` +""" + +from __future__ import annotations + +import os + +try: + from dotenv import load_dotenv + + load_dotenv() +except ImportError: + pass + +from stackone_ai import StackOneToolSet + + +def api_key_setup() -> None: + """Demonstrate the two ways to provide an API key.""" + print("\n--- API Key Setup ---") + + # 1. Default: reads STACKONE_API_KEY from the environment + toolset_from_env = StackOneToolSet() + print("Created toolset from STACKONE_API_KEY env var (default)") + + # 2. Explicit: pass the key directly + api_key = os.environ["STACKONE_API_KEY"] + toolset_explicit = StackOneToolSet(api_key=api_key) + print("Created toolset with explicit api_key") + + # Both toolsets are functionally equivalent + print(f"Toolset from env: {toolset_from_env}") + print(f"Toolset from explicit: {toolset_explicit}") + + +def single_account(account_id: str) -> None: + """Fetch tools scoped to a single account.""" + print("\n--- Single Account via fetch_tools ---") + + toolset = StackOneToolSet() + tools = toolset.fetch_tools(actions=["workday_*"], account_ids=[account_id]) + print(f"Fetched {len(tools)} tools for configured account") + + + +def set_accounts_globally(account_id: str) -> None: + """Use set_accounts() to bind accounts at the toolset level.""" + print("\n--- set_accounts() (global on toolset) ---") + + toolset = StackOneToolSet() + toolset.set_accounts([account_id]) + print("Set global accounts via set_accounts()") + + tools = toolset.fetch_tools(actions=["workday_*"]) + print(f"Fetched {len(tools)} tools (account inherited from toolset)") + + +def per_tool_override(account_id: str) -> None: + """Override the account ID on a tools collection or an individual tool.""" + print("\n--- Per-Tool Account Override ---") + + toolset = StackOneToolSet() + tools = toolset.fetch_tools(actions=["workday_*"], account_ids=[account_id]) + + # Override on the entire tools collection + tools.set_account_id("overridden-account") + print("Called tools.set_account_id('overridden-account')") + + # Override on a single tool + tool = tools.get_tool("workday_list_workers") + if tool: + tool.set_account_id("per-tool-account") + current = tool.get_account_id() + print(f"Single tool account_id is now: '{current}'") + else: + print("Tool 'workday_list_workers' not found -- skipping single-tool override") + + +def main() -> None: + api_key = os.getenv("STACKONE_API_KEY") + if not api_key: + print("STACKONE_API_KEY is not set. Please export it or add it to .env") + return + + account_id = os.getenv("STACKONE_ACCOUNT_ID", "test-account-id") + + api_key_setup() + single_account(account_id) + set_accounts_globally(account_id) + per_tool_override(account_id) + + print("\nAll auth management examples completed.") + + +if __name__ == "__main__": + main() diff --git a/examples/crewai_integration.py b/examples/crewai_integration.py index 0792809..ac2b62b 100644 --- a/examples/crewai_integration.py +++ b/examples/crewai_integration.py @@ -8,39 +8,53 @@ ``` """ +from __future__ import annotations + +import os + +try: + from dotenv import load_dotenv + + load_dotenv() +except ModuleNotFoundError: + pass + from crewai import Agent, Crew, Task from stackone_ai import StackOneToolSet -account_id = "45072196112816593343" -employee_id = "c28xIQaWQ6MzM5MzczMDA2NzMzMzkwNzIwNA" - def crewai_integration(): + account_id = os.getenv("STACKONE_ACCOUNT_ID") + if not os.getenv("STACKONE_API_KEY"): + print("Set STACKONE_API_KEY to run this example.") + return + if not account_id: + print("Set STACKONE_ACCOUNT_ID to run this example.") + return + toolset = StackOneToolSet() - tools = toolset.fetch_tools(actions=["bamboohr_*"], account_ids=[account_id]) + tools = toolset.fetch_tools( + actions=["workday_list_workers", "workday_get_worker", "workday_get_current_user"], + account_ids=[account_id], + ) # CrewAI uses LangChain tools natively langchain_tools = tools.to_langchain() - assert len(langchain_tools) > 0, "Expected at least one LangChain tool" - - for tool in langchain_tools: - assert hasattr(tool, "name"), "Expected tool to have name" - assert hasattr(tool, "description"), "Expected tool to have description" - assert hasattr(tool, "_run"), "Expected tool to have _run method" + print(f"Loaded {len(langchain_tools)} tools for CrewAI.") agent = Agent( role="HR Manager", - goal=f"What is the employee with the id {employee_id}?", + goal="List the first 5 employees in the company", backstory="With over 10 years of experience in HR and employee management, " "you excel at finding patterns in complex datasets.", - llm="gpt-5.4", + llm="gpt-5.1", tools=langchain_tools, max_iter=2, ) task = Task( - description="What is the employee with the id c28xIQaWQ6MzM5MzczMDA2NzMzMzkwNzIwNA?", + description="List the first 5 employees in the company", agent=agent, expected_output="A JSON object containing the employee's information", ) @@ -48,7 +62,7 @@ def crewai_integration(): crew = Crew(agents=[agent], tasks=[task]) result = crew.kickoff() - assert result is not None, "Expected result to be returned" + print(f"Crew result:\n{result}") if __name__ == "__main__": diff --git a/examples/file_uploads.py b/examples/file_uploads.py deleted file mode 100644 index 78c1fcd..0000000 --- a/examples/file_uploads.py +++ /dev/null @@ -1,83 +0,0 @@ -""" -Example demonstrating file upload functionality using StackOne. -Shows how to upload an employee document using a BambooHR integration. - -This example is runnable with the following command: -```bash -uv run examples/file_upload_example.py -``` -""" - -import base64 -import tempfile -from pathlib import Path - -from dotenv import load_dotenv - -from stackone_ai import StackOneToolSet - -load_dotenv() - -account_id = "45072196112816593343" -employee_id = "c28xIQaWQ6MzM5MzczMDA2NzMzMzkwNzIwNA" - -""" -# Resume content - -This is a sample resume content that will be uploaded using the `bamboohr_upload_employee_document` tool. -""" - -resume_content = """ - JOHN DOE - Software Engineer - - EXPERIENCE - Senior Developer - Tech Corp - 2020-Present - - Led development of core features - - Managed team of 5 engineers - - EDUCATION - BS Computer Science - University of Technology - 2016-2020 """ - - -""" -# Upload employee document - -This function uploads a resume using the `bamboohr_upload_employee_document` tool. - -""" - - -def upload_employee_document() -> None: - with tempfile.TemporaryDirectory() as temp_dir: - resume_file = Path(temp_dir) / "resume.pdf" - resume_file.write_text(resume_content) - - toolset = StackOneToolSet() - tools = toolset.fetch_tools(actions=["bamboohr_*"], account_ids=[account_id]) - - upload_tool = tools.get_tool("bamboohr_upload_employee_document") - assert upload_tool is not None - - with open(resume_file, "rb") as f: - file_content = base64.b64encode(f.read()).decode() - - upload_params = { - "x-account-id": account_id, - "id": employee_id, - "name": "resume", - "content": file_content, - "category": {"value": "shared"}, - "file_format": {"value": "txt"}, - } - - result = upload_tool.execute(upload_params) - assert result is not None - assert result.get("message") == "File uploaded successfully" - - -if __name__ == "__main__": - upload_employee_document() diff --git a/examples/index.py b/examples/index.py deleted file mode 100644 index cedfdbf..0000000 --- a/examples/index.py +++ /dev/null @@ -1,105 +0,0 @@ -""" -StackOne AI SDK provides an AI-friendly interface for accessing various SaaS tools through the StackOne API. - -This SDK is available on [PyPI](https://pypi.org/project/stackone-ai/) for python projects. There is a node version in the works. - -# Installation - -```bash -# Using uv -uv add stackone-ai - -# Using pip -pip install stackone-ai -``` - -# How to use these docs - -All examples are complete and runnable. -We use [uv](https://docs.astral.sh/uv/getting-started/installation/) for easy python dependency management. - -Install uv: - -```bash -curl -LsSf https://astral.sh/uv/install.sh | sh -``` -To run this example, clone the repo, install the dependencies (one-time setup) and run the script: - -```bash -git clone https://github.com/stackoneHQ/stackone-ai-python.git -cd stackone-ai-python - -# Install dependencies -uv sync --all-extras - -# Run the example -uv run examples/index.py -``` - -# Authentication - -Set the `STACKONE_API_KEY` environment variable: - -```bash -export STACKONE_API_KEY= -``` - -or load from a .env file: -""" - -from dotenv import load_dotenv - -load_dotenv() - -""" -# Account IDs - -StackOne uses account IDs to identify different integrations. -See the example [stackone-account-ids.md](stackone-account-ids.md) for more details. - -This example will hardcode the account ID: -""" - -account_id = "45072196112816593343" - -""" -# Quickstart -""" - -from stackone_ai import StackOneToolSet - - -def quickstart(): - toolset = StackOneToolSet() - - # Get all BambooHR-related tools using MCP-backed fetch_tools() - tools = toolset.fetch_tools(actions=["bamboohr_*"], account_ids=[account_id]) - - # Use a specific tool - employee_tool = tools.get_tool("bamboohr_list_employees") - assert employee_tool is not None - - employees = employee_tool.execute() - assert employees is not None - - -if __name__ == "__main__": - quickstart() - -""" -# Next Steps - -Check out some more documentation: - -- [StackOne Account IDs](stackone-account-ids.md) -- [Error Handling](error-handling.md) -- [Available Tools](available-tools.md) -- [File Uploads](file-uploads.md) - -Or get started with an integration: - -- [OpenAI](openai-integration.md) -- [LangChain](langchain-integration.md) -- [CrewAI](crewai-integration.md) -- [LangGraph](langgraph-tool-node.md) -""" diff --git a/examples/langchain_integration.py b/examples/langchain_integration.py index c08a2ba..1dfe5cc 100644 --- a/examples/langchain_integration.py +++ b/examples/langchain_integration.py @@ -6,45 +6,60 @@ ``` """ -from dotenv import load_dotenv -from langchain_openai import ChatOpenAI +from __future__ import annotations -from stackone_ai import StackOneToolSet +import os + +try: + from dotenv import load_dotenv + + load_dotenv() +except ModuleNotFoundError: + pass -load_dotenv() +from langchain_openai import ChatOpenAI -account_id = "45072196112816593343" -employee_id = "c28xIQaWQ6MzM5MzczMDA2NzMzMzkwNzIwNA" +from stackone_ai import StackOneToolSet def langchain_integration() -> None: + account_id = os.getenv("STACKONE_ACCOUNT_ID") + if not os.getenv("STACKONE_API_KEY"): + print("Set STACKONE_API_KEY to run this example.") + return + if not account_id: + print("Set STACKONE_ACCOUNT_ID to run this example.") + return + toolset = StackOneToolSet() - tools = toolset.fetch_tools(actions=["bamboohr_*"], account_ids=[account_id]) + tools = toolset.fetch_tools( + actions=["workday_list_workers", "workday_get_worker", "workday_get_current_user"], + account_ids=[account_id], + ) - # Convert to LangChain format and verify + # Convert to LangChain format langchain_tools = tools.to_langchain() - assert len(langchain_tools) > 0, "Expected at least one LangChain tool" - - # Verify tool structure + print(f"Loaded {len(langchain_tools)} LangChain tools.") for tool in langchain_tools: - assert hasattr(tool, "name"), "Expected tool to have name" - assert hasattr(tool, "description"), "Expected tool to have description" - assert hasattr(tool, "_run"), "Expected tool to have _run method" - assert hasattr(tool, "args_schema"), "Expected tool to have args_schema" + print(f" - {tool.name}") # Create model with tools - model = ChatOpenAI(model="gpt-5.4") + model = ChatOpenAI(model="gpt-5.1") model_with_tools = model.bind_tools(langchain_tools) - result = model_with_tools.invoke(f"Can you get me information about employee with ID: {employee_id}?") + result = model_with_tools.invoke("List the first 5 employees") + print(f"LLM response: {result.content}") - assert result.tool_calls is not None - for tool_call in result.tool_calls: - tool = tools.get_tool(tool_call["name"]) - if tool: - result = tool.execute(tool_call["args"]) - assert result is not None - assert result.get("data") is not None + if result.tool_calls: + print(f"LLM made {len(result.tool_calls)} tool call(s).") + for tool_call in result.tool_calls: + print(f" - {tool_call['name']}({tool_call['args']})") + tool = tools.get_tool(tool_call["name"]) + if tool: + call_result = tool.execute(tool_call["args"]) + print(f" Result: {str(call_result)[:200]}...") + else: + print("No tool calls were made by the model.") if __name__ == "__main__": diff --git a/examples/openai_integration.py b/examples/openai_integration.py index 236a310..eff426a 100644 --- a/examples/openai_integration.py +++ b/examples/openai_integration.py @@ -9,15 +9,20 @@ You can find out more about the OpenAI Function Calling API format [here](https://platform.openai.com/docs/guides/function-calling). """ -from dotenv import load_dotenv -from openai import OpenAI +from __future__ import annotations -from stackone_ai import StackOneToolSet +import os + +try: + from dotenv import load_dotenv + + load_dotenv() +except ModuleNotFoundError: + pass -load_dotenv() +from openai import OpenAI -account_id = "45072196112816593343" -employee_id = "c28xIQaWQ6MzM5MzczMDA2NzMzMzkwNzIwNA" +from stackone_ai import StackOneToolSet def handle_tool_calls(tools, tool_calls) -> list[dict]: @@ -30,63 +35,78 @@ def handle_tool_calls(tools, tool_calls) -> list[dict]: def openai_integration() -> None: + account_id = os.getenv("STACKONE_ACCOUNT_ID") + if not os.getenv("STACKONE_API_KEY"): + print("Set STACKONE_API_KEY to run this example.") + return + if not account_id: + print("Set STACKONE_ACCOUNT_ID to run this example.") + return + client = OpenAI() toolset = StackOneToolSet() # Filter tools to only the ones we need to avoid context window limits tools = toolset.fetch_tools( actions=[ - "bamboohr_get_employee", - "bamboohr_list_employee_employments", - "bamboohr_get_employee_employment", + "workday_list_workers", + "workday_get_worker", + "workday_get_current_user", ], account_ids=[account_id], ) openai_tools = tools.to_openai() + print(f"Loaded {len(openai_tools)} tools for OpenAI function calling.") messages = [ {"role": "system", "content": "You are a helpful HR assistant."}, { "role": "user", - "content": f"Can you get me information about employee with ID: {employee_id}?", + "content": "List the first 5 employees", }, ] response = client.chat.completions.create( - model="gpt-5.4", + model="gpt-5.1", messages=messages, tools=openai_tools, tool_choice="auto", ) - # Verify we got a response with tool calls - assert response.choices[0].message.tool_calls is not None, "Expected tool calls in response" + tool_calls = response.choices[0].message.tool_calls + if not tool_calls: + print("No tool calls were made by the model.") + return + + print(f"LLM made {len(tool_calls)} tool call(s):") + for tc in tool_calls: + print(f" - {tc.function.name}({tc.function.arguments})") - # Handle the tool calls and verify results - results = handle_tool_calls(tools, response.choices[0].message.tool_calls) - assert results is not None and len(results) > 0, "Expected tool call results" - assert "data" in results[0], "Expected data in tool call result" + # Handle the tool calls + results = handle_tool_calls(tools, tool_calls) + print(f"Received {len(results)} tool call result(s).") + for i, result in enumerate(results): + print(f" Result {i + 1}: {str(result)[:200]}...") - # Verify we can continue the conversation with the results + # Continue the conversation with the results messages.extend( [ - {"role": "assistant", "content": None, "tool_calls": response.choices[0].message.tool_calls}, + {"role": "assistant", "content": None, "tool_calls": tool_calls}, { "role": "tool", - "tool_call_id": response.choices[0].message.tool_calls[0].id, + "tool_call_id": tool_calls[0].id, "content": str(results[0]), }, ] ) - # Verify the final response final_response = client.chat.completions.create( - model="gpt-5.4", + model="gpt-5.1", messages=messages, tools=openai_tools, tool_choice="auto", ) - assert final_response.choices[0].message.content is not None, "Expected final response content" + print(f"Final response:\n{final_response.choices[0].message.content}") if __name__ == "__main__": diff --git a/examples/search_tool_example.py b/examples/search_tool_example.py deleted file mode 100644 index 21f8882..0000000 --- a/examples/search_tool_example.py +++ /dev/null @@ -1,66 +0,0 @@ -"""Search tool patterns: callable wrapper and config overrides. - -For semantic search basics, see semantic_search_example.py. -For full agent execution, see agent_tool_search.py. - -Prerequisites: - - STACKONE_API_KEY environment variable - - STACKONE_ACCOUNT_ID environment variable - -Run with: - uv run python examples/search_tool_example.py -""" - -from __future__ import annotations - -import os - -try: - from dotenv import load_dotenv - - load_dotenv() -except ModuleNotFoundError: - pass - -from stackone_ai import StackOneToolSet - - -def main() -> None: - api_key = os.getenv("STACKONE_API_KEY") - account_id = os.getenv("STACKONE_ACCOUNT_ID") - - if not api_key: - print("Set STACKONE_API_KEY to run this example.") - return - if not account_id: - print("Set STACKONE_ACCOUNT_ID to run this example.") - return - - # --- Example 1: get_search_tool() callable --- - print("=== get_search_tool() callable ===\n") - - toolset = StackOneToolSet(api_key=api_key, account_id=account_id, search={}) - search_tool = toolset.get_search_tool() - - queries = ["cancel an event", "list employees", "send a message"] - for query in queries: - tools = search_tool(query, top_k=3) - names = [t.name for t in tools] - print(f' "{query}" -> {", ".join(names) or "(none)"}') - - # --- Example 2: Constructor top_k vs per-call override --- - print("\n=== Constructor top_k vs per-call override ===\n") - - toolset_3 = StackOneToolSet(api_key=api_key, account_id=account_id, search={"top_k": 3}) - - query = "manage employee records" - - tools_3 = toolset_3.search_tools(query) - print(f"Constructor top_k=3: got {len(tools_3)} tools") - - tools_override = toolset_3.search_tools(query, top_k=10) - print(f"Per-call top_k=10 (overrides constructor 3): got {len(tools_override)} tools") - - -if __name__ == "__main__": - main() diff --git a/examples/search_tools.py b/examples/search_tools.py new file mode 100644 index 0000000..629e5e7 --- /dev/null +++ b/examples/search_tools.py @@ -0,0 +1,171 @@ +""" +Tool discovery with the StackOne AI SDK. + +Covers the full spectrum of tool discovery: direct fetch, semantic search, +local search, auto search, and the search-and-execute pattern with OpenAI. + +```bash +uv run examples/search_tools.py +``` +""" + +from __future__ import annotations + +import json +import os + +try: + from dotenv import load_dotenv + + load_dotenv() +except ModuleNotFoundError: + pass + +from stackone_ai import StackOneToolSet + + +def direct_fetch(account_id: str) -> None: + """Fetch tools by action glob pattern -- no search involved.""" + print("=== Direct Fetch ===") + + toolset = StackOneToolSet() + tools = toolset.fetch_tools(actions=["workday_*"], account_ids=[account_id]) + + print(f"Fetched {len(tools)} tools matching 'workday_*'") + for tool in tools: + print(f" - {tool.name}") + + tool = tools.get_tool("workday_list_workers") + if tool: + print(f"Found tool: {tool.name}") + print(f" Description: {tool.description[:80]}...") + else: + print("Tool 'workday_list_workers' not found in fetched tools") + + +def semantic_search(account_id: str) -> None: + """Search tools using the semantic search API (default).""" + print("\n=== Semantic Search ===") + + toolset = StackOneToolSet(search={"method": "semantic", "top_k": 5}) + tools = toolset.search_tools("manage employees", account_ids=[account_id], top_k=5) + + print(f"Semantic search returned {len(tools)} tools for 'manage employees'") + for tool in tools: + print(f" - {tool.name}: {tool.description[:60]}...") + + +def local_search(account_id: str) -> None: + """Search tools using local BM25+TF-IDF (no API call to search endpoint).""" + print("\n=== Local Search ===") + + toolset = StackOneToolSet(search={"method": "local", "top_k": 5}) + tools = toolset.search_tools("create time off request", account_ids=[account_id], search="local") + + print(f"Local search returned {len(tools)} tools for 'create time off request'") + for tool in tools: + print(f" - {tool.name}: {tool.description[:60]}...") + + +def auto_search(account_id: str) -> None: + """Auto search: tries semantic first, falls back to local on failure.""" + print("\n=== Auto Search ===") + + toolset = StackOneToolSet(search={"method": "auto", "top_k": 5}) + tools = toolset.search_tools("list employee compensation", account_ids=[account_id], search="auto") + + print(f"Auto search returned {len(tools)} tools for 'list employee compensation'") + for tool in tools: + print(f" - {tool.name}: {tool.description[:60]}...") + + # The get_search_tool() helper returns a callable for agent loops + search_tool = toolset.get_search_tool() + tools = search_tool("send a message", top_k=3, account_ids=[account_id]) + print(f"SearchTool callable returned {len(tools)} tools for 'send a message'") + + +def search_and_execute(account_id: str) -> None: + """Two-tool pattern: tool_search + tool_execute driven by an OpenAI model.""" + print("\n=== Search & Execute (OpenAI) ===") + + try: + from openai import OpenAI + except ImportError: + print("Skipping: 'openai' package is required (pip install openai)") + return + + if not os.getenv("OPENAI_API_KEY"): + print("Skipping: OPENAI_API_KEY is required") + return + + # Configure toolset with search + execute defaults + toolset = StackOneToolSet( + search={"method": "semantic", "top_k": 3}, + execute={"account_ids": [account_id]}, + timeout=120, # increase for slow providers (default: 60s) + ) + openai_tools = toolset.openai(mode="search_and_execute") + + print(f"Registered {len(openai_tools)} meta-tools with OpenAI:") + for t in openai_tools: + print(f" - {t['function']['name']}") + + # Simple agent loop + client = OpenAI() + messages: list[dict] = [ + {"role": "system", "content": "You are a helpful HR assistant."}, + {"role": "user", "content": "Find tools that can list employees and then list them."}, + ] + + for step in range(5): + response = client.chat.completions.create( + model="gpt-5.1", + messages=messages, + tools=openai_tools, + tool_choice="auto", + ) + message = response.choices[0].message + + if not message.tool_calls: + print(f"Assistant: {message.content}") + break + + # Append the assistant message (with tool calls) to history + messages.append({"role": "assistant", "content": message.content, "tool_calls": message.tool_calls}) + + for tool_call in message.tool_calls: + name = tool_call.function.name + args = json.loads(tool_call.function.arguments) + print(f" Step {step + 1}: calling {name}({json.dumps(args, indent=2)})") + + result = toolset.execute(name, args) + messages.append( + { + "role": "tool", + "tool_call_id": tool_call.id, + "content": json.dumps(result), + } + ) + + print("Agent loop complete.") + + +def main() -> None: + if not os.getenv("STACKONE_API_KEY"): + print("Set STACKONE_API_KEY to run this example.") + return + + account_id = os.getenv("STACKONE_ACCOUNT_ID") + if not account_id: + print("Set STACKONE_ACCOUNT_ID to run this example.") + return + + direct_fetch(account_id) + semantic_search(account_id) + local_search(account_id) + auto_search(account_id) + search_and_execute(account_id) + + +if __name__ == "__main__": + main() diff --git a/examples/semantic_search_example.py b/examples/semantic_search_example.py deleted file mode 100644 index 1631ad2..0000000 --- a/examples/semantic_search_example.py +++ /dev/null @@ -1,423 +0,0 @@ -#!/usr/bin/env python -""" -Example demonstrating semantic search for AI-powered tool discovery. - -Semantic search understands natural language intent and synonyms, so queries like -"book a meeting" or "cancel an event" resolve to the right StackOne actions — -unlike keyword matching which requires exact tool names. - -This example uses a Calendly-linked account to demonstrate how semantic search -discovers scheduling, event, and organization management tools from natural -language queries. - - -How Semantic Search Works (Overview) -===================================== - -The SDK provides three paths for semantic tool discovery, each with a different -trade-off between speed, filtering, and completeness: - -1. search_tools(query) — Full discovery (recommended for agent frameworks) - - This is the method you should use when integrating with OpenAI, LangChain, - CrewAI, or any other agent framework. - - Recommended usage — pass ``connector`` to scope to a single provider: - - tools = toolset.search_tools("book a meeting", connector="calendly") - - This is faster and returns more relevant results than searching all - connectors. When the target provider is known, always pass ``connector``. - - When ``connector`` is not specified, the SDK searches all connectors - available in the user's linked accounts in parallel: - - a) Fetch tools from the user's linked accounts via MCP - b) Extract available connectors (e.g. {bamboohr, calendly}) - c) Search each connector in parallel via the semantic search API - d) Collect results, sort by relevance score - e) If top_k was specified, keep only the top K results - f) Match results back to the fetched tool definitions - g) Return a Tools collection sorted by relevance score - - Key point: only the user's own connectors are searched — no wasted results - from connectors the user doesn't have. When top_k is not specified, the - backend decides how many results to return per connector. If the semantic - API is unavailable, the SDK falls back to local BM25+TF-IDF search - automatically. - -2. search_action_names(query) — Lightweight preview - - Queries the semantic API directly and returns metadata (name, connector, - score, description) without fetching full tool definitions. Useful for - inspecting results before committing to a full fetch. When account_ids are - provided, each connector is searched in parallel (same as search_tools). - -3. get_search_tool() — Agent-loop pattern - - Returns a callable SearchTool that wraps search_tools(). Call it - with a natural language query to get a Tools collection back. - Designed for agent loops where the LLM decides what to search for. - - -This example is runnable with the following command: -```bash -uv run examples/semantic_search_example.py -``` - -Prerequisites: -- STACKONE_API_KEY environment variable set -- STACKONE_ACCOUNT_ID environment variable set (required for examples that fetch tools) -- At least one linked account in StackOne (this example uses Calendly) - -Note: search_action_names() works with just STACKONE_API_KEY — no account ID needed. -""" - -import logging -import os - -from stackone_ai import StackOneToolSet - -try: - from dotenv import load_dotenv - - load_dotenv() -except ModuleNotFoundError: - pass - -# Show SDK warnings (e.g., semantic search fallback to local search) -logging.basicConfig(level=logging.WARNING) - -# Read account IDs from environment — supports comma-separated values -_account_ids = [aid.strip() for aid in os.getenv("STACKONE_ACCOUNT_ID", "").split(",") if aid.strip()] - - -def example_search_action_names(): - """Lightweight search returning action names and scores without fetching tools. - - search_action_names() queries the semantic search API directly — it does NOT - need account IDs or MCP. This makes it the simplest way to try semantic search. - - When called without account_ids, results come from the full StackOne catalog - (all connectors). When called with account_ids, results are filtered to only - connectors available in your linked accounts. - """ - print("=" * 60) - print("Example 1: search_action_names() — lightweight discovery") - print("=" * 60) - print() - print("This searches the StackOne action catalog using semantic vectors.") - print("No account ID needed — results come from all available connectors.") - print() - - # Constructor-level config sets defaults; per-call params override them. - # Here we set method="semantic" at the constructor level. - toolset = StackOneToolSet(search={"method": "semantic"}) - - query = "get user schedule" - - # --- top_k behavior --- - # When top_k is NOT specified, the backend decides how many results to return. - # When top_k IS specified, results are explicitly limited to that number. - print(f'Searching for: "{query}" (no top_k — backend decides count)') - results_default = toolset.search_action_names(query) - print(f" Backend returned {len(results_default)} results (its default)") - print() - - print(f'Searching for: "{query}" (top_k=3 — explicitly limited)') - results_limited = toolset.search_action_names(query, top_k=3) - print(f" Got exactly {len(results_limited)} results") - print() - - # Show the limited results - print(f"Top {len(results_limited)} matches from the full catalog:") - for r in results_limited: - print(f" [{r.similarity_score:.2f}] {r.id}") - print() - - # Show filtering effect when account_ids are available - if _account_ids: - print(f"Now filtering to your linked accounts ({', '.join(_account_ids)})...") - print(" (Each connector is searched in parallel — only your connectors are queried)") - filtered = toolset.search_action_names(query, account_ids=_account_ids, top_k=5) - print(f" Filtered to {len(filtered)} matches (only your connectors):") - for r in filtered: - print(f" [{r.similarity_score:.2f}] {r.id}") - else: - print("Tip: Set STACKONE_ACCOUNT_ID to see results filtered to your linked connectors.") - - print() - - -def example_search_tools(): - """High-level semantic search returning a Tools collection. - - search_tools() is the recommended way to use semantic search. It: - 1. Fetches tool definitions from your linked accounts via MCP - 2. Searches each of your connectors in parallel via the semantic search API - 3. Sorts results by relevance and matches back to tool definitions - 4. Returns a Tools collection ready for any framework (.to_openai(), .to_langchain(), etc.) - - Search config can be set at the constructor level: - toolset = StackOneToolSet(search={"method": "semantic", "top_k": 5}) - Per-call parameters (e.g. top_k, search) override the constructor defaults. - """ - print("=" * 60) - print("Example 2: search_tools() — full tool discovery") - print("=" * 60) - print() - - # Constructor-level search config: always use semantic search with top_k=5 - toolset = StackOneToolSet(search={"method": "semantic", "top_k": 5}) - - query = "cancel an event" - print(f'Step 1: Searching for "{query}" via semantic search (constructor config)...') - print() - - # top_k and method are already set via the constructor — no need to pass them here - tools = toolset.search_tools(query, account_ids=_account_ids) - - connectors = tools.get_connectors() - print(f"Found {len(tools)} tools from your linked account(s) ({', '.join(sorted(connectors))}):") - for tool in tools: - print(f" - {tool.name}") - print(f" {tool.description}") - print() - - -def example_search_tools_with_connector(): - """Semantic search filtered by connector. - - Use the connector parameter to scope results to a specific provider, - for example when you know the user works with Calendly. - """ - print("=" * 60) - print("Example 3: search_tools() with connector filter") - print("=" * 60) - print() - - toolset = StackOneToolSet(search={}) - - query = "book a meeting" - connector = "calendly" - print(f'Searching for "{query}" filtered to connector="{connector}"...') - print() - - tools = toolset.search_tools( - query, - connector=connector, - account_ids=_account_ids, - top_k=3, - ) - - print(f"Found {len(tools)} {connector} tools:") - for tool in tools: - print(f" - {tool.name}") - print(f" {tool.description}") - print() - - -def example_search_tool_agent_loop(): - """Using get_search_tool() for agent loops. - - get_search_tool() returns a callable that wraps search_tools(). - Call it with a query to get a Tools collection back — designed - for agent loops where the LLM decides what to search for. - """ - print("=" * 60) - print("Example 4: Search tool for agent loops") - print("=" * 60) - print() - - toolset = StackOneToolSet(search={}) - - print("Step 1: Fetching tools from your linked accounts via MCP...") - all_tools = toolset.fetch_tools(account_ids=_account_ids) - print(f"Loaded {len(all_tools)} tools.") - print() - - print("Step 2: Getting a callable search tool...") - search_tool = toolset.get_search_tool() - - query = "cancel an event or meeting" - print() - print(f'Step 3: Calling search_tool("{query}")...') - print(" (Searches are scoped to your linked connectors)") - print() - tools = search_tool(query, top_k=5, account_ids=_account_ids) - print(f"search_tool returned {len(tools)} tools:") - for tool in tools: - print(f" - {tool.name}") - print(f" {tool.description}") - - print() - - -def example_openai_agent_loop(): - """Complete agent loop: semantic search -> LLM -> execute. - - This demonstrates the full pattern for building an AI agent that - discovers tools via semantic search and executes them via an LLM. - - Supports both OpenAI and Google Gemini (via its OpenAI-compatible API). - Set OPENAI_API_KEY for OpenAI, or GOOGLE_API_KEY for Gemini. - """ - print("=" * 60) - print("Example 5: LLM agent loop with semantic search") - print("=" * 60) - print() - - try: - from openai import OpenAI - except ImportError: - print("Skipped: OpenAI library not installed. Install with: pip install openai") - print() - return - - # Support both OpenAI and Gemini (via OpenAI-compatible endpoint) - openai_key = os.getenv("OPENAI_API_KEY") - google_key = os.getenv("GOOGLE_API_KEY") - - if openai_key: - client = OpenAI() - model = "gpt-5.4" - provider = "OpenAI" - elif google_key: - client = OpenAI( - api_key=google_key, - base_url="https://generativelanguage.googleapis.com/v1beta/openai/", - ) - model = "gemini-2.5-flash" - provider = "Gemini" - else: - print("Skipped: Set OPENAI_API_KEY or GOOGLE_API_KEY to run this example.") - print() - return - - print(f"Using {provider} ({model})") - print() - - toolset = StackOneToolSet(search={}) - - query = "list upcoming events" - print(f'Step 1: Discovering tools for "{query}" via semantic search...') - tools = toolset.search_tools(query, account_ids=_account_ids, top_k=3) - print(f"Found {len(tools)} tools:") - for tool in tools: - print(f" - {tool.name}") - print() - - print(f"Step 2: Sending tools to {provider} as function definitions...") - openai_tools = tools.to_openai() - - messages = [ - {"role": "system", "content": "You are a helpful scheduling assistant."}, - {"role": "user", "content": "Can you show me my upcoming events?"}, - ] - - response = client.chat.completions.create( - model=model, - messages=messages, - tools=openai_tools, - tool_choice="auto", - ) - - if response.choices[0].message.tool_calls: - print(f"Step 3: {provider} chose to call these tools:") - for tool_call in response.choices[0].message.tool_calls: - print(f" - {tool_call.function.name}({tool_call.function.arguments})") - - tool = tools.get_tool(tool_call.function.name) - if tool: - result = tool.execute(tool_call.function.arguments) - print( - f" Response keys: {list(result.keys()) if isinstance(result, dict) else type(result)}" - ) - else: - print(f"{provider} responded with text: {response.choices[0].message.content}") - - print() - - -def example_langchain_semantic(): - """Semantic search with LangChain tools. - - search_tools() returns a Tools collection that converts directly - to LangChain format — no extra steps needed. - """ - print("=" * 60) - print("Example 6: Semantic search with LangChain") - print("=" * 60) - print() - - try: - from langchain_core.tools import BaseTool # noqa: F401 - except ImportError: - print("Skipped: LangChain not installed. Install with: pip install langchain-core") - print() - return - - toolset = StackOneToolSet(search={}) - - query = "remove a user from the team" - print(f'Step 1: Searching for "{query}" via semantic search...') - tools = toolset.search_tools(query, account_ids=_account_ids, top_k=5) - print(f"Found {len(tools)} tools.") - print() - - print("Step 2: Converting to LangChain tools...") - langchain_tools = tools.to_langchain() - - print(f"Created {len(langchain_tools)} LangChain tools (ready for use with agents):") - for tool in langchain_tools: - print(f" - {tool.name} (type: {type(tool).__name__})") - print(f" {tool.description}") - - print() - - -def main(): - """Run all semantic search examples.""" - print() - print("############################################################") - print("# StackOne AI SDK — Semantic Search Examples #") - print("############################################################") - print() - - if not os.getenv("STACKONE_API_KEY"): - print("Set STACKONE_API_KEY to run these examples.") - return - - # --- Examples that work without account IDs --- - example_search_action_names() - - # --- Examples that require account IDs (MCP needs x-account-id) --- - if not _account_ids: - print("=" * 60) - print("Remaining examples require STACKONE_ACCOUNT_ID") - print("=" * 60) - print() - print("Set STACKONE_ACCOUNT_ID (comma-separated for multiple) to run") - print("examples that fetch full tool definitions from your linked accounts:") - print(" - search_tools() with natural language queries") - print(" - search_tools() with connector filter") - print(" - Search tool for agent loops") - print(" - OpenAI agent loop") - print(" - LangChain integration") - return - - example_search_tools() - example_search_tools_with_connector() - example_search_tool_agent_loop() - - # Framework integration patterns - example_openai_agent_loop() - example_langchain_semantic() - - print("############################################################") - print("# All examples completed! #") - print("############################################################") - - -if __name__ == "__main__": - main() diff --git a/examples/stackone_account_ids.py b/examples/stackone_account_ids.py deleted file mode 100644 index 7926586..0000000 --- a/examples/stackone_account_ids.py +++ /dev/null @@ -1,40 +0,0 @@ -""" -Handling StackOne account IDs with the StackOne Tools. - -```bash -uv run examples/stackone_account_ids.py -``` -""" - -from dotenv import load_dotenv - -from stackone_ai import StackOneToolSet - -load_dotenv() - - -def stackone_account_ids(): - toolset = StackOneToolSet() - - """ - Set the account ID whilst getting tools using fetch_tools(). - """ - tools = toolset.fetch_tools(actions=["bamboohr_*"], account_ids=["test_id"]) - - """ - You can override the account ID on fetched tools. - """ - tools.set_account_id("a_different_id") - - employee_tool = tools.get_tool("bamboohr_get_employee") - assert employee_tool is not None - - """ - You can even set the account ID on a per-tool basis. - """ - employee_tool.set_account_id("again_another_id") - assert employee_tool.get_account_id() == "again_another_id" - - -if __name__ == "__main__": - stackone_account_ids() diff --git a/examples/test_examples.py b/examples/test_examples.py index 7727c08..e54a149 100644 --- a/examples/test_examples.py +++ b/examples/test_examples.py @@ -27,14 +27,8 @@ def get_example_files() -> list[str]: "openai_integration.py": ["openai", "mcp"], "langchain_integration.py": ["langchain_openai", "mcp"], "crewai_integration.py": ["crewai", "mcp"], - "index.py": ["mcp"], - "file_uploads.py": ["mcp"], - "stackone_account_ids.py": ["mcp"], - "search_tool_example.py": ["mcp"], - "semantic_search_example.py": ["mcp"], - "mcp_server.py": ["mcp"], - "workday_integration.py": ["openai", "mcp"], - "benchmark_search.py": ["mcp"], + "search_tools.py": ["mcp"], + "auth_management.py": ["mcp"], } diff --git a/examples/workday_integration.py b/examples/workday_integration.py deleted file mode 100644 index 8f89361..0000000 --- a/examples/workday_integration.py +++ /dev/null @@ -1,91 +0,0 @@ -"""Workday integration: timeout and account scoping for slow providers. - -Workday can take 10-15s to respond. This example shows how to configure -timeout and account_ids through the execute config. - -Prerequisites: - - STACKONE_API_KEY environment variable - - STACKONE_ACCOUNT_ID environment variable (a Workday-connected account) - - OPENAI_API_KEY environment variable - -Run with: - uv run python examples/workday_integration.py -""" - -from __future__ import annotations - -import json -import os - -try: - from dotenv import load_dotenv - - load_dotenv() -except ModuleNotFoundError: - pass - -from openai import OpenAI - -from stackone_ai import StackOneToolSet - - -def main() -> None: - api_key = os.getenv("STACKONE_API_KEY") - account_id = os.getenv("STACKONE_ACCOUNT_ID") - - if not api_key: - print("Set STACKONE_API_KEY to run this example.") - return - if not account_id: - print("Set STACKONE_ACCOUNT_ID to run this example.") - return - - # Timeout for slow providers, account_id for scoping - toolset = StackOneToolSet( - api_key=api_key, - account_id=account_id, - search={"method": "auto", "top_k": 5}, - timeout=120, - ) - client = OpenAI() - - def run_agent(messages: list[dict], tools: list[dict], max_steps: int = 10) -> None: - """Simple agent loop: call LLM, execute tools, repeat.""" - for _ in range(max_steps): - response = client.chat.completions.create(model="gpt-5.4", messages=messages, tools=tools) - choice = response.choices[0] - - if not choice.message.tool_calls: - print(f"Answer: {choice.message.content}") - return - - messages.append(choice.message.model_dump(exclude_none=True)) - for tc in choice.message.tool_calls: - print(f" -> {tc.function.name}({tc.function.arguments[:80]})") - result = toolset.execute(tc.function.name, tc.function.arguments) - messages.append({"role": "tool", "tool_call_id": tc.id, "content": json.dumps(result)}) - - # --- Example 1: Search and execute mode --- - print("=== Search and execute mode ===\n") - run_agent( - messages=[ - {"role": "system", "content": "Use tool_search to find tools, then tool_execute to run them."}, - {"role": "user", "content": "List the first 5 employees."}, - ], - tools=toolset.openai(mode="search_and_execute"), - ) - - # --- Example 2: Normal mode --- - print("\n=== Normal mode ===\n") - tools = toolset.fetch_tools(actions=["workday_*_employee*"]) - if len(tools) == 0: - print("No Workday tools found for this account.") - else: - run_agent( - messages=[{"role": "user", "content": "List the first 5 employees."}], - tools=tools.to_openai(), - ) - - -if __name__ == "__main__": - main() diff --git a/justfile b/justfile index 2ebc360..c040f97 100644 --- a/justfile +++ b/justfile @@ -26,6 +26,10 @@ test-tools: test-examples: uv run pytest examples +# Run a specific example +run-example file: + uv run examples/{{file}} + # Run type checking ty: uv run ty check stackone_ai diff --git a/examples/benchmark_search.py b/scripts/benchmark_search.py similarity index 100% rename from examples/benchmark_search.py rename to scripts/benchmark_search.py From 66ca944e86ac85ae48b95d7ebfe0b4fef27932b8 Mon Sep 17 00:00:00 2001 From: Shashi-Stackone Date: Thu, 16 Apr 2026 16:36:27 +0100 Subject: [PATCH 2/7] Fix Linter issue --- examples/auth_management.py | 1 - 1 file changed, 1 deletion(-) diff --git a/examples/auth_management.py b/examples/auth_management.py index 3e2edd2..28dd312 100644 --- a/examples/auth_management.py +++ b/examples/auth_management.py @@ -47,7 +47,6 @@ def single_account(account_id: str) -> None: print(f"Fetched {len(tools)} tools for configured account") - def set_accounts_globally(account_id: str) -> None: """Use set_accounts() to bind accounts at the toolset level.""" print("\n--- set_accounts() (global on toolset) ---") From 23d0fe8b9f20aed2e2aca69aa42534f3d5818fd8 Mon Sep 17 00:00:00 2001 From: Shashi-Stackone Date: Thu, 16 Apr 2026 17:02:23 +0100 Subject: [PATCH 3/7] Remove example readme --- examples/README.md | 52 ---------------------------------------------- 1 file changed, 52 deletions(-) delete mode 100644 examples/README.md diff --git a/examples/README.md b/examples/README.md deleted file mode 100644 index ed9092b..0000000 --- a/examples/README.md +++ /dev/null @@ -1,52 +0,0 @@ -# StackOne AI SDK Examples - -## Setup - -Install dependencies: - -```bash -uv sync --all-extras -``` - -Set your credentials using either approach: - -**Option A: Environment variables (shell)** - -```bash -export STACKONE_API_KEY=your-stackone-api-key -export STACKONE_ACCOUNT_ID=your-account-id -export OPENAI_API_KEY=your-openai-api-key # for OpenAI/LangChain/CrewAI examples -``` - -**Option B: `.env` file** - -```bash -cp .env.example .env -# Edit .env with your keys -``` - -## Running Examples - -```bash -uv run examples/search_tools.py -``` - -Test all examples (no API keys needed): - -```bash -uv run pytest examples -``` - -## Examples - -| File | Description | -|------|-------------| -| `openai_integration.py` | OpenAI function calling with Workday tools | -| `langchain_integration.py` | LangChain tools integration | -| `crewai_integration.py` | CrewAI agent with Workday tools | -| `search_tools.py` | Tool discovery: direct fetch, semantic/local/auto search, search & execute | -| `auth_management.py` | API key and account ID configuration patterns | - -## Environment Variables - -See `.env.example` in the project root for all required variables. From 04b723c161a601d9fc7ea52c3fc2838264c0b45b Mon Sep 17 00:00:00 2001 From: Shashi-Stackone Date: Thu, 16 Apr 2026 20:20:24 +0100 Subject: [PATCH 4/7] Address the PR comment --- README.md | 6 +++--- examples/auth_management.py | 5 ++++- examples/crewai_integration.py | 4 ++-- examples/langchain_integration.py | 5 ++++- examples/openai_integration.py | 22 +++++++++++----------- examples/search_tools.py | 4 ++-- 6 files changed, 26 insertions(+), 20 deletions(-) diff --git a/README.md b/README.md index 62cc692..3309f0c 100644 --- a/README.md +++ b/README.md @@ -179,7 +179,7 @@ tools = toolset.fetch_tools(actions=["workday_*"], account_ids=[account_id]) langchain_tools = tools.to_langchain() # Use with LangChain models -model = ChatOpenAI(model="gpt-4o-mini") +model = ChatOpenAI(model="gpt-5.4") model_with_tools = model.bind_tools(langchain_tools) # Execute AI-driven tool calls @@ -233,7 +233,7 @@ graph = StateGraph(State) graph.add_node("tools", to_tool_node(langchain_tools)) def call_llm(state: dict): - llm = ChatOpenAI(model="gpt-4o-mini") + llm = ChatOpenAI(model="gpt-5.4") llm = bind_model_with_tools(llm, langchain_tools) resp = llm.invoke(state["messages"]) # returns AIMessage with optional tool_calls return {"messages": state["messages"] + [resp]} @@ -271,7 +271,7 @@ agent = Agent( goal="Analyze employee data and generate insights", backstory="Expert in HR analytics and employee management", tools=langchain_tools, - llm="gpt-4o-mini" + llm="gpt-5.4" ) # Define task and execute diff --git a/examples/auth_management.py b/examples/auth_management.py index 28dd312..e05bc16 100644 --- a/examples/auth_management.py +++ b/examples/auth_management.py @@ -86,7 +86,10 @@ def main() -> None: print("STACKONE_API_KEY is not set. Please export it or add it to .env") return - account_id = os.getenv("STACKONE_ACCOUNT_ID", "test-account-id") + account_id = os.getenv("STACKONE_ACCOUNT_ID") + if not account_id: + print("Set STACKONE_ACCOUNT_ID to run this example.") + return api_key_setup() single_account(account_id) diff --git a/examples/crewai_integration.py b/examples/crewai_integration.py index ac2b62b..7f36831 100644 --- a/examples/crewai_integration.py +++ b/examples/crewai_integration.py @@ -48,7 +48,7 @@ def crewai_integration(): goal="List the first 5 employees in the company", backstory="With over 10 years of experience in HR and employee management, " "you excel at finding patterns in complex datasets.", - llm="gpt-5.1", + llm="gpt-5.4", tools=langchain_tools, max_iter=2, ) @@ -56,7 +56,7 @@ def crewai_integration(): task = Task( description="List the first 5 employees in the company", agent=agent, - expected_output="A JSON object containing the employee's information", + expected_output="A list of the first 5 employees with their details", ) crew = Crew(agents=[agent], tasks=[task]) diff --git a/examples/langchain_integration.py b/examples/langchain_integration.py index 1dfe5cc..2129cd3 100644 --- a/examples/langchain_integration.py +++ b/examples/langchain_integration.py @@ -30,6 +30,9 @@ def langchain_integration() -> None: if not account_id: print("Set STACKONE_ACCOUNT_ID to run this example.") return + if not os.getenv("OPENAI_API_KEY"): + print("Set OPENAI_API_KEY to run this example.") + return toolset = StackOneToolSet() tools = toolset.fetch_tools( @@ -44,7 +47,7 @@ def langchain_integration() -> None: print(f" - {tool.name}") # Create model with tools - model = ChatOpenAI(model="gpt-5.1") + model = ChatOpenAI(model="gpt-5.4") model_with_tools = model.bind_tools(langchain_tools) result = model_with_tools.invoke("List the first 5 employees") diff --git a/examples/openai_integration.py b/examples/openai_integration.py index eff426a..aa6bb80 100644 --- a/examples/openai_integration.py +++ b/examples/openai_integration.py @@ -11,6 +11,7 @@ from __future__ import annotations +import json import os try: @@ -67,7 +68,7 @@ def openai_integration() -> None: ] response = client.chat.completions.create( - model="gpt-5.1", + model="gpt-5.4", messages=messages, tools=openai_tools, tool_choice="auto", @@ -88,20 +89,19 @@ def openai_integration() -> None: for i, result in enumerate(results): print(f" Result {i + 1}: {str(result)[:200]}...") - # Continue the conversation with the results - messages.extend( - [ - {"role": "assistant", "content": None, "tool_calls": tool_calls}, + # Continue the conversation with all tool call results + messages.append(response.choices[0].message.model_dump(exclude_none=True)) + for tc, result in zip(tool_calls, results): + messages.append( { "role": "tool", - "tool_call_id": tool_calls[0].id, - "content": str(results[0]), - }, - ] - ) + "tool_call_id": tc.id, + "content": json.dumps(result), + } + ) final_response = client.chat.completions.create( - model="gpt-5.1", + model="gpt-5.4", messages=messages, tools=openai_tools, tool_choice="auto", diff --git a/examples/search_tools.py b/examples/search_tools.py index 629e5e7..16805dc 100644 --- a/examples/search_tools.py +++ b/examples/search_tools.py @@ -119,7 +119,7 @@ def search_and_execute(account_id: str) -> None: for step in range(5): response = client.chat.completions.create( - model="gpt-5.1", + model="gpt-5.4", messages=messages, tools=openai_tools, tool_choice="auto", @@ -131,7 +131,7 @@ def search_and_execute(account_id: str) -> None: break # Append the assistant message (with tool calls) to history - messages.append({"role": "assistant", "content": message.content, "tool_calls": message.tool_calls}) + messages.append(message.model_dump(exclude_none=True)) for tool_call in message.tool_calls: name = tool_call.function.name From 2a1df88be130be17e592dc502d62b355a52c4146 Mon Sep 17 00:00:00 2001 From: Shashi-Stackone Date: Thu, 16 Apr 2026 22:57:33 +0100 Subject: [PATCH 5/7] Add Pydantic and Langgraph examples in the from docs --- examples/langgraph_integration.py | 62 + examples/openai_integration.py | 2 +- examples/pydantic_ai_integration.py | 74 + examples/test_examples.py | 2 + pyproject.toml | 8 +- uv.lock | 2113 ++++++++++++--------------- 6 files changed, 1066 insertions(+), 1195 deletions(-) create mode 100644 examples/langgraph_integration.py create mode 100644 examples/pydantic_ai_integration.py diff --git a/examples/langgraph_integration.py b/examples/langgraph_integration.py new file mode 100644 index 0000000..56162d0 --- /dev/null +++ b/examples/langgraph_integration.py @@ -0,0 +1,62 @@ +""" +This example demonstrates how to use StackOne tools with LangGraph. + +LangGraph uses LangChain tools natively with its prebuilt ReAct agent. + +```bash +uv run examples/langgraph_integration.py +``` +""" + +from __future__ import annotations + +import os + +try: + from dotenv import load_dotenv + + load_dotenv() +except ModuleNotFoundError: + pass + +from langchain_openai import ChatOpenAI +from langgraph.prebuilt import create_react_agent + +from stackone_ai import StackOneToolSet + + +def langgraph_integration() -> None: + account_id = os.getenv("STACKONE_ACCOUNT_ID") + if not os.getenv("STACKONE_API_KEY"): + print("Set STACKONE_API_KEY to run this example.") + return + if not account_id: + print("Set STACKONE_ACCOUNT_ID to run this example.") + return + if not os.getenv("OPENAI_API_KEY"): + print("Set OPENAI_API_KEY to run this example.") + return + + toolset = StackOneToolSet() + tools = toolset.fetch_tools( + actions=["workday_list_workers", "workday_get_worker", "workday_get_current_user"], + account_ids=[account_id], + ) + + # LangGraph uses LangChain tools natively + langchain_tools = tools.to_langchain() + print(f"Loaded {len(langchain_tools)} LangGraph tools.") + for tool in langchain_tools: + print(f" - {tool.name}") + + # Create a ReAct agent with LangGraph + model = ChatOpenAI(model="gpt-5.4") + agent = create_react_agent(model, langchain_tools) + + result = agent.invoke({"messages": [("user", "List the first 5 employees")]}) + final_message = result["messages"][-1] + print(f"Agent response:\n{final_message.content}") + + +if __name__ == "__main__": + langgraph_integration() diff --git a/examples/openai_integration.py b/examples/openai_integration.py index aa6bb80..748930c 100644 --- a/examples/openai_integration.py +++ b/examples/openai_integration.py @@ -91,7 +91,7 @@ def openai_integration() -> None: # Continue the conversation with all tool call results messages.append(response.choices[0].message.model_dump(exclude_none=True)) - for tc, result in zip(tool_calls, results): + for tc, result in zip(tool_calls, results, strict=False): messages.append( { "role": "tool", diff --git a/examples/pydantic_ai_integration.py b/examples/pydantic_ai_integration.py new file mode 100644 index 0000000..e53575a --- /dev/null +++ b/examples/pydantic_ai_integration.py @@ -0,0 +1,74 @@ +"""StackOne tools with Pydantic AI. + +```bash +uv run examples/pydantic_ai_integration.py +``` +""" + +from __future__ import annotations + +import json +import os + +try: + from dotenv import load_dotenv + + load_dotenv() +except ModuleNotFoundError: + pass + +try: + from pydantic_ai import Agent, Tool + from pydantic_ai._function_schema import FunctionSchema +except ImportError: + print("Install pydantic-ai to run this example: pip install pydantic-ai") + raise SystemExit(1) from None + +from pydantic_core import SchemaValidator, core_schema + +from stackone_ai import StackOneToolSet + +_VALIDATOR = SchemaValidator(core_schema.dict_schema(core_schema.str_schema(), core_schema.any_schema())) + + +def _to_pydantic_ai_tool(stackone_tool) -> Tool: + """Convert a StackOneTool to a Pydantic AI Tool with proper JSON schema.""" + params_schema = stackone_tool.to_openai_function()["function"]["parameters"] + + def execute(**kwargs) -> str: + return json.dumps(stackone_tool.execute(kwargs)) + + fs = FunctionSchema( + function=execute, + name=stackone_tool.name, + description=stackone_tool.description, + validator=_VALIDATOR, + json_schema=params_schema, + takes_ctx=False, + is_async=False, + ) + return Tool(execute, name=stackone_tool.name, description=stackone_tool.description, function_schema=fs) + + +def pydantic_ai_integration() -> None: + account_id = os.getenv("STACKONE_ACCOUNT_ID") + for var in ["STACKONE_API_KEY", "STACKONE_ACCOUNT_ID", "OPENAI_API_KEY"]: + if not os.getenv(var): + print(f"Set {var} to run this example.") + return + + toolset = StackOneToolSet() + tools = toolset.fetch_tools( + actions=["workday_list_workers", "workday_get_worker", "workday_get_current_user"], + account_ids=[account_id], + ) + pydantic_tools = [_to_pydantic_ai_tool(t) for t in tools] + print(f"Loaded {len(pydantic_tools)} tools for Pydantic AI.") + + agent = Agent("openai:gpt-5.4", system_prompt="You are a helpful HR assistant.", tools=pydantic_tools) + result = agent.run_sync("List the first 5 employees") + print(f"Result:\n{result.output}") + + +if __name__ == "__main__": + pydantic_ai_integration() diff --git a/examples/test_examples.py b/examples/test_examples.py index e54a149..003a01d 100644 --- a/examples/test_examples.py +++ b/examples/test_examples.py @@ -27,6 +27,8 @@ def get_example_files() -> list[str]: "openai_integration.py": ["openai", "mcp"], "langchain_integration.py": ["langchain_openai", "mcp"], "crewai_integration.py": ["crewai", "mcp"], + "langgraph_integration.py": ["langgraph", "langchain_openai", "mcp"], + "pydantic_ai_integration.py": ["pydantic_ai", "mcp"], "search_tools.py": ["mcp"], "auth_management.py": ["mcp"], } diff --git a/pyproject.toml b/pyproject.toml index efe99d5..6de5d53 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -38,10 +38,10 @@ packages = ["stackone_ai"] [project.optional-dependencies] mcp = ["mcp>=1.3.0"] examples = [ - "crewai>=0.102.0", - 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Pydantic AI Tool with proper JSON schema.""" - params_schema = stackone_tool.to_openai_function()["function"]["parameters"] + """Convert a StackOneTool to a Pydantic AI Tool using the public API.""" - def execute(**kwargs) -> str: + def execute(**kwargs: object) -> str: return json.dumps(stackone_tool.execute(kwargs)) - fs = FunctionSchema( - function=execute, + return Tool( + execute, name=stackone_tool.name, description=stackone_tool.description, - validator=_VALIDATOR, - json_schema=params_schema, - takes_ctx=False, - is_async=False, ) - return Tool(execute, name=stackone_tool.name, description=stackone_tool.description, function_schema=fs) def pydantic_ai_integration() -> None: diff --git a/pyproject.toml b/pyproject.toml index 6de5d53..5715d85 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -38,6 +38,7 @@ packages = ["stackone_ai"] [project.optional-dependencies] mcp = ["mcp>=1.3.0"] examples = [ + "crewai>=0.102.0", "langchain-openai>=1.1.14", "langgraph>=1.1.6", 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examples/pydantic_ai_integration.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/examples/pydantic_ai_integration.py b/examples/pydantic_ai_integration.py index 5093270..4052d15 100644 --- a/examples/pydantic_ai_integration.py +++ b/examples/pydantic_ai_integration.py @@ -27,15 +27,17 @@ def _to_pydantic_ai_tool(stackone_tool) -> Tool: - """Convert a StackOneTool to a Pydantic AI Tool using the public API.""" + """Convert a StackOneTool to a Pydantic AI Tool with the proper JSON schema.""" + params_schema = stackone_tool.to_openai_function()["function"]["parameters"] def execute(**kwargs: object) -> str: return json.dumps(stackone_tool.execute(kwargs)) - return Tool( + return Tool.from_schema( execute, name=stackone_tool.name, description=stackone_tool.description, + json_schema=params_schema, )