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agent-framework/python/samples/02-agents/tools/function_tool_with_kwargs.py
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Eduard van Valkenburg a4b9539b62 [BREAKING] Python: clean up kwargs across agents, chat clients, tools, and sessions (#4581)
* Python: clean up kwargs across agents, chat clients, tools, and sessions (#3642)

Audit and refactor public **kwargs usage across core agents, chat clients,
tools, sessions, and provider packages per the migration strategy codified
in CODING_STANDARD.md.

Key changes:
- Add explicit runtime buckets: function_invocation_kwargs and client_kwargs
  on RawAgent.run() and chat client get_response() layers.
- Refactor FunctionTool to prefer explicit ctx: FunctionInvocationContext
  injection; legacy **kwargs tools still work via _forward_runtime_kwargs.
- Refactor Agent.as_tool() to use direct JSON schema, always-streaming
  wrapper, approval_mode parameter, and UserInputRequiredException
  propagation (integrates PR #4568 behavior).
- Remove implicit session bleeding into FunctionInvocationContext; tools
  that need a session must receive it via function_invocation_kwargs.
- Lower chat-client layers after FunctionInvocationLayer accept only
  compatibility **kwargs (client_kwargs flattened, function_invocation_kwargs
  ignored).
- Add layered docstring composition from Raw... implementations via
  _docstrings.py helper.
- Clean up provider constructors to use explicit additional_properties.
- Deprecation warnings on legacy direct kwargs paths.
- Update samples, tests, and typing across all 23 packages.

Resolves #3642

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* clarified docstring

* feedback fixes

* Add unit tests for _docstrings.py build/apply helpers

Tests cover: no docstring source, no extra kwargs, appending to existing
Keyword Args section, inserting after Args, inserting in plain docstrings,
multiline descriptions, ordering, and apply_layered_docstring.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* Add test for propagate_session TypeError on non-AgentSession values

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* Add tests for multi-content and empty UserInputRequiredException propagation

Cover the branching logic in _try_execute_function_calls for:
- Multiple user_input_request items in a single exception (extra_user_input_contents path)
- Empty contents list (fallback function_result path)

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* Add tests for DurableAIAgent.get_session forwarding service_session_id

Verifies get_session correctly forwards service_session_id and session_id
to the executor's get_new_session, replacing the removed kwargs test.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* Simplify ag-ui test stub to read session from client_kwargs only

Remove dual-mode detection (client_kwargs vs raw kwargs fallback) from
the test mock. Session is now read exclusively from client_kwargs,
matching the settled public calling convention.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* updated create and get sessions in durable

* fixed docstrings

* fix test

* updated session handling

* updated from main

* updated tests

---------

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
2026-03-13 08:58:32 +00:00

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Python

# Copyright (c) Microsoft. All rights reserved.
import asyncio
from typing import Annotated
from agent_framework import FunctionInvocationContext, tool
from agent_framework.openai import OpenAIResponsesClient
from dotenv import load_dotenv
from pydantic import Field
# Load environment variables from .env file
load_dotenv()
"""
AI Function with kwargs Example
This example demonstrates how to inject runtime context into an AI function
from the agent's run method, without exposing it to the AI model.
This is useful for passing runtime information like access tokens, user IDs, or
request-specific context that the tool needs but the model shouldn't know about
or provide. The injected context parameter can be typed as
``FunctionInvocationContext`` as shown here, or left untyped as ``ctx`` when you
prefer a lighter-weight sample setup.
"""
# Define the function tool with explicit invocation context.
# The context parameter can also be declared as an untyped ``ctx`` parameter.
@tool(approval_mode="never_require")
def get_weather(
location: Annotated[str, Field(description="The location to get the weather for.")],
ctx: FunctionInvocationContext,
) -> str:
"""Get the weather for a given location."""
# Extract the injected argument from the explicit context
user_id = ctx.kwargs.get("user_id", "unknown")
# Simulate using the user_id for logging or personalization
print(f"Getting weather for user: {user_id}")
return f"The weather in {location} is cloudy with a high of 15°C."
async def main() -> None:
agent = OpenAIResponsesClient().as_agent(
name="WeatherAgent",
instructions="You are a helpful weather assistant.",
tools=[get_weather],
)
# Pass the runtime context explicitly when running the agent.
response = await agent.run(
"What is the weather like in Amsterdam?",
function_invocation_kwargs={"user_id": "user_123"},
)
print(f"Agent: {response.text}")
if __name__ == "__main__":
asyncio.run(main())