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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
151 lines
5.6 KiB
Python
151 lines
5.6 KiB
Python
# Copyright (c) Microsoft. All rights reserved.
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"""Example showing Agent with AGUIChatClient for hybrid tool execution.
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This demonstrates the HYBRID pattern matching .NET AGUIClient implementation:
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1. AgentSession Pattern (like .NET):
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- Create session with agent.create_session()
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- Pass session to agent.run(stream=True) on each turn
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- Session maintains conversation context via context providers
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2. Hybrid Tool Execution:
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- AGUIChatClient uses function invocation mixin
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- Client-side tools (get_weather) can execute locally when server requests them
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- Server may also have its own tools that execute server-side
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- Both work together: server LLM decides which tool to call, decorator handles client execution
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This matches .NET pattern: session maintains state, tools execute on appropriate side.
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"""
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from __future__ import annotations
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import asyncio
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import logging
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import os
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from agent_framework import Agent, tool
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from agent_framework.ag_ui import AGUIChatClient
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# Enable debug logging
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logging.basicConfig(
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level=logging.DEBUG,
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format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
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)
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logger = logging.getLogger(__name__)
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@tool(description="Get the current weather for a location.")
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def get_weather(location: str) -> str:
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"""Get the current weather for a location.
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Args:
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location: The city or location name
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"""
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print(f"[CLIENT] get_weather tool called with location: {location}")
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weather_data = {
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"seattle": "Rainy, 55°F",
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"san francisco": "Foggy, 62°F",
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"new york": "Sunny, 68°F",
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"london": "Cloudy, 52°F",
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}
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result = weather_data.get(location.lower(), f"Weather data not available for {location}")
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print(f"[CLIENT] get_weather returning: {result}")
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return result
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async def main():
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"""Demonstrate Agent + AGUIChatClient hybrid tool execution.
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This matches the .NET pattern from Program.cs where:
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- AIAgent agent = chatClient.CreateAIAgent(tools: [...])
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- AgentSession session = agent.CreateSession()
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- RunStreamingAsync(messages, session)
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Python equivalent:
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- agent = Agent(client=AGUIChatClient(...), tools=[...])
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- session = agent.create_session() # Creates session
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- agent.run(message, stream=True, session=session) # Session tracks context
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"""
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server_url = os.environ.get("AGUI_SERVER_URL", "http://127.0.0.1:5100/")
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print("=" * 70)
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print("Agent + AGUIChatClient: Hybrid Tool Execution")
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print("=" * 70)
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print(f"\nServer: {server_url}")
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print("\nThis example demonstrates:")
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print(" 1. AgentSession maintains conversation state (like .NET)")
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print(" 2. Client-side tools execute locally via function invocation mixin")
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print(" 3. Server may have additional tools that execute server-side")
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print(" 4. HYBRID: Client and server tools work together simultaneously\n")
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try:
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# Create remote client in async context manager
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async with AGUIChatClient(endpoint=server_url) as remote_client:
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# Wrap in Agent for conversation history management
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agent = Agent(
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name="remote_assistant",
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instructions="You are a helpful assistant. Remember user information across the conversation.",
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client=remote_client,
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tools=[get_weather],
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)
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# Create a session to maintain conversation state (like .NET AgentSession)
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session = agent.create_session()
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print("=" * 70)
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print("CONVERSATION WITH HISTORY")
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print("=" * 70)
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# Turn 1: Introduce
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print("\nUser: My name is Alice and I live in Seattle\n")
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async for chunk in agent.run("My name is Alice and I live in Seattle", stream=True, session=session):
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if chunk.text:
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print(chunk.text, end="", flush=True)
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print("\n")
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# Turn 2: Ask about name (tests history)
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print("User: What's my name?\n")
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async for chunk in agent.run("What's my name?", stream=True, session=session):
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if chunk.text:
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print(chunk.text, end="", flush=True)
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print("\n")
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# Turn 3: Ask about location (tests history)
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print("User: Where do I live?\n")
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async for chunk in agent.run("Where do I live?", stream=True, session=session):
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if chunk.text:
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print(chunk.text, end="", flush=True)
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print("\n")
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# Turn 4: Test client-side tool (get_weather is client-side)
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print("User: What's the weather forecast for today in Seattle?\n")
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async for chunk in agent.run(
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"What's the weather forecast for today in Seattle?",
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stream=True,
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session=session,
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):
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if chunk.text:
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print(chunk.text, end="", flush=True)
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print("\n")
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# Turn 5: Test server-side tool (get_time_zone is server-side only)
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print("User: What time zone is Seattle in?\n")
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async for chunk in agent.run("What time zone is Seattle in?", stream=True, session=session):
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if chunk.text:
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print(chunk.text, end="", flush=True)
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print("\n")
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except ConnectionError as e:
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print(f"\n\033[91mConnection Error: {e}\033[0m")
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print("\nMake sure an AG-UI server is running at the specified endpoint.")
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except Exception as e:
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print(f"\n\033[91mError: {e}\033[0m")
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import traceback
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traceback.print_exc()
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if __name__ == "__main__":
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asyncio.run(main())
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