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agent-framework/python/samples/02-agents/tools/function_tool_with_approval.py
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Eduard van Valkenburg 1e350ea22f Python: [BREAKING] PR2 — Wire context provider pipeline, remove old types, update all consumers (#3850)
* PR2: Wire context provider pipeline and update all internal consumers

- Replace AgentThread with AgentSession across all packages
- Replace ContextProvider with BaseContextProvider across all packages
- Replace context_provider param with context_providers (Sequence)
- Replace thread= with session= in run() signatures
- Replace get_new_thread() with create_session()
- Add get_session(service_session_id) to agent interface
- DurableAgentThread -> DurableAgentSession
- Remove _notify_thread_of_new_messages from WorkflowAgent
- Wire before_run/after_run context provider pipeline in RawAgent
- Auto-inject InMemoryHistoryProvider when no providers configured

* fix: update all tests for context provider pipeline, fix lazy-loaders, remove old test files

* refactor: update all sample files for context provider pipeline (AgentThread→AgentSession, ContextProvider→BaseContextProvider)

* fix: update remaining ag-ui references (client docstring, getting_started sample)

* fix: make get_session service_session_id keyword-only to avoid confusion with session_id

* refactor: rename _RunContext.thread_messages to session_messages

* refactor: remove _threads.py, _memory.py, and old provider files; migrate devui to use plain message lists

* rename: remove _new_ prefix from test files

* refactor: rewrite SlidingWindowChatMessageStore as SlidingWindowHistoryProvider(InMemoryHistoryProvider)

* fix: read full history from session state directly instead of reaching into provider internals

* fix: update stale .pyi stubs, sample imports, and README references for new provider types

* fix: remove stale message_store, _notify_thread_of_new_messages, and session_id.key references in samples

* refactor: merge context_providers and sessions sample folders into sessions, remove aggregate_context_provider

* refactor: UserInfoMemory stores state in session.state instead of instance attributes

* feat: add Pydantic BaseModel support to session state serialization

Pydantic models stored in session.state are now automatically serialized
via model_dump() and restored via model_validate() during to_dict()/from_dict()
round-trips. Models are auto-registered on first serialization; use
register_state_type() for cold-start deserialization.

Also export register_state_type as a public API.

* fix mem0

* Update sample README links and descriptions for session terminology

- Replace 'thread' with 'session' in sample descriptions across all READMEs
- Update file links for renamed samples (mem0_sessions, redis_sessions, etc.)
- Fix Threads section → Sessions section in main samples/README.md
- Update tools, middleware, workflows, durabletask, azure_functions READMEs
- Update architecture diagrams in concepts/tools/README.md
- Update migration guides (autogen, semantic-kernel)

* Fix broken Redis README link to renamed sample

* Fix Mem0 OSS client search: pass scoping params as direct kwargs

AsyncMemory (OSS) expects user_id/agent_id/run_id as direct kwargs,
while AsyncMemoryClient (Platform) expects them in a filters dict.
Adds tests for both client types.

Port of fix from #3844 to new Mem0ContextProvider.

* Fix rebase issues: restore missing _conversation_state.py and checkpoint decode logic

- Add back _conversation_state.py (encode/decode_chat_messages) lost in rebase
- Fix on_checkpoint_restore to decode cache/conversation with decode_chat_messages
- Fix on_checkpoint_restore to use decode_checkpoint_value for pending requests
- Add tests/workflow/__init__.py for relative import support
- Fix test_agent_executor checkpoint selection (checkpoints[1] not superstep)

* Add STORES_BY_DEFAULT ClassVar to skip redundant InMemoryHistoryProvider injection

Chat clients that store history server-side by default (OpenAI Responses API,
Azure AI Agent) now declare STORES_BY_DEFAULT = True. The agent checks this
during auto-injection and skips InMemoryHistoryProvider unless the user
explicitly sets store=False.

* Fix broken markdown links in azure_ai and redis READMEs

* Fix getting-started samples to use session API instead of removed thread/ContextProvider API

* updates to workflow as agent

* fix group chat import

* Rename Thread→Session throughout, fix service_session_id propagation, remove stale AGUIThread

- Fix: Propagate conversation_id from ChatResponse back to session.service_session_id
  in both streaming and non-streaming paths in _agents.py
- Rename AgentThreadException → AgentSessionException
- Remove stale AGUIThread from ag_ui lazy-loader
- Rename use_service_thread → use_service_session in ag-ui package
- Rename test functions from *_thread_* to *_session_*
- Rename sample files from *_thread* to *_session*
- Update docstrings and comments: thread → session
- Update _mcp.py kwargs filter: add 'session' alongside 'thread'
- Fix ContinuationToken docstring example: thread=thread → session=session
- Fix _clients.py docstring: 'Agent threads' → 'Agent sessions'

* Fix broken markdown links after thread→session file renames

* fix azure ai test
2026-02-12 21:00:32 +00:00

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6.1 KiB
Python

# Copyright (c) Microsoft. All rights reserved.
import asyncio
from random import randrange
from typing import TYPE_CHECKING, Annotated, Any
from agent_framework import Agent, AgentResponse, Message, tool
from agent_framework.openai import OpenAIResponsesClient
if TYPE_CHECKING:
from agent_framework import SupportsAgentRun
"""
Demonstration of a tool with approvals.
This sample demonstrates using AI functions with user approval workflows.
It shows how to handle function call approvals without using threads.
"""
conditions = ["sunny", "cloudy", "raining", "snowing", "clear"]
# NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/02-agents/tools/function_tool_with_approval.py and samples/02-agents/tools/function_tool_with_approval_and_sessions.py.
@tool(approval_mode="never_require")
def get_weather(location: Annotated[str, "The city and state, e.g. San Francisco, CA"]) -> str:
"""Get the current weather for a given location."""
# Simulate weather data
return f"The weather in {location} is {conditions[randrange(0, len(conditions))]} and {randrange(-10, 30)}°C."
# Define a simple weather tool that requires approval
@tool(approval_mode="always_require")
def get_weather_detail(location: Annotated[str, "The city and state, e.g. San Francisco, CA"]) -> str:
"""Get the current weather for a given location."""
# Simulate weather data
return (
f"The weather in {location} is {conditions[randrange(0, len(conditions))]} and {randrange(-10, 30)}°C, "
"with a humidity of 88%. "
f"Tomorrow will be {conditions[randrange(0, len(conditions))]} with a high of {randrange(-10, 30)}°C."
)
async def handle_approvals(query: str, agent: "SupportsAgentRun") -> AgentResponse:
"""Handle function call approvals.
When we don't have a thread, we need to ensure we include the original query,
the approval request, and the approval response in each iteration.
"""
result = await agent.run(query)
while len(result.user_input_requests) > 0:
# Start with the original query
new_inputs: list[Any] = [query]
for user_input_needed in result.user_input_requests:
print(
f"\nUser Input Request for function from {agent.name}:"
f"\n Function: {user_input_needed.function_call.name}"
f"\n Arguments: {user_input_needed.function_call.arguments}"
)
# Add the assistant message with the approval request
new_inputs.append(Message("assistant", [user_input_needed]))
# Get user approval
user_approval = await asyncio.to_thread(input, "\nApprove function call? (y/n): ")
# Add the user's approval response
new_inputs.append(
Message("user", [user_input_needed.to_function_approval_response(user_approval.lower() == "y")])
)
# Run again with all the context
result = await agent.run(new_inputs)
return result
async def handle_approvals_streaming(query: str, agent: "SupportsAgentRun") -> None:
"""Handle function call approvals with streaming responses.
When we don't have a thread, we need to ensure we include the original query,
the approval request, and the approval response in each iteration.
"""
current_input: str | list[Any] = query
has_user_input_requests = True
while has_user_input_requests:
has_user_input_requests = False
user_input_requests: list[Any] = []
# Stream the response
async for chunk in agent.run(current_input, stream=True):
if chunk.text:
print(chunk.text, end="", flush=True)
# Collect user input requests from the stream
if chunk.user_input_requests:
user_input_requests.extend(chunk.user_input_requests)
if user_input_requests:
has_user_input_requests = True
# Start with the original query
new_inputs: list[Any] = [query]
for user_input_needed in user_input_requests:
print(
f"\n\nUser Input Request for function from {agent.name}:"
f"\n Function: {user_input_needed.function_call.name}"
f"\n Arguments: {user_input_needed.function_call.arguments}"
)
# Add the assistant message with the approval request
new_inputs.append(Message("assistant", [user_input_needed]))
# Get user approval
user_approval = await asyncio.to_thread(input, "\nApprove function call? (y/n): ")
# Add the user's approval response
new_inputs.append(
Message("user", [user_input_needed.to_function_approval_response(user_approval.lower() == "y")])
)
# Update input with all the context for next iteration
current_input = new_inputs
async def run_weather_agent_with_approval(stream: bool) -> None:
"""Example showing AI function with approval requirement."""
print(f"\n=== Weather Agent with Approval Required ({'Streaming' if stream else 'Non-Streaming'}) ===\n")
async with Agent(
client=OpenAIResponsesClient(),
name="WeatherAgent",
instructions=("You are a helpful weather assistant. Use the get_weather tool to provide weather information."),
tools=[get_weather, get_weather_detail],
) as agent:
query = "Can you give me an update of the weather in LA and Portland and detailed weather for Seattle?"
print(f"User: {query}")
if stream:
print(f"\n{agent.name}: ", end="", flush=True)
await handle_approvals_streaming(query, agent)
print()
else:
result = await handle_approvals(query, agent)
print(f"\n{agent.name}: {result}\n")
async def main() -> None:
print("=== Demonstration of a tool with approvals ===\n")
await run_weather_agent_with_approval(stream=False)
await run_weather_agent_with_approval(stream=True)
if __name__ == "__main__":
asyncio.run(main())