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