mirror of
https://github.com/microsoft/agent-framework.git
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1e350ea22f
* 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
122 lines
3.7 KiB
Python
122 lines
3.7 KiB
Python
# Copyright (c) Microsoft. All rights reserved.
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"""Client application for interacting with a Durable Task hosted agent.
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This client connects to the Durable Task Scheduler and sends requests to
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registered agents, demonstrating how to interact with agents from external processes.
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Prerequisites:
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- The worker must be running with the agent registered
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- Set AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_CHAT_DEPLOYMENT_NAME
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(plus AZURE_OPENAI_API_KEY or Azure CLI authentication)
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- Durable Task Scheduler must be running
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"""
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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.azure import DurableAIAgentClient
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from azure.identity import DefaultAzureCredential
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from durabletask.azuremanaged.client import DurableTaskSchedulerClient
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# Configure logging
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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def get_client(
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taskhub: str | None = None,
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endpoint: str | None = None,
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log_handler: logging.Handler | None = None
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) -> DurableAIAgentClient:
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"""Create a configured DurableAIAgentClient.
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Args:
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taskhub: Task hub name (defaults to TASKHUB env var or "default")
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endpoint: Scheduler endpoint (defaults to ENDPOINT env var or "http://localhost:8080")
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log_handler: Optional logging handler for client logging
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Returns:
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Configured DurableAIAgentClient instance
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"""
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taskhub_name = taskhub or os.getenv("TASKHUB", "default")
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endpoint_url = endpoint or os.getenv("ENDPOINT", "http://localhost:8080")
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logger.debug(f"Using taskhub: {taskhub_name}")
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logger.debug(f"Using endpoint: {endpoint_url}")
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credential = None if endpoint_url == "http://localhost:8080" else DefaultAzureCredential()
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dts_client = DurableTaskSchedulerClient(
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host_address=endpoint_url,
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secure_channel=endpoint_url != "http://localhost:8080",
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taskhub=taskhub_name,
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token_credential=credential,
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log_handler=log_handler
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)
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return DurableAIAgentClient(dts_client)
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def run_client(agent_client: DurableAIAgentClient) -> None:
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"""Run client interactions with the Joker agent.
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Args:
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agent_client: The DurableAIAgentClient instance
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"""
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# Get a reference to the Joker agent
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logger.debug("Getting reference to Joker agent...")
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joker = agent_client.get_agent("Joker")
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# Create a new session for the conversation
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session = joker.create_session()
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logger.debug(f"Session ID: {session.session_id}")
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logger.info("Start chatting with the Joker agent! (Type 'exit' to quit)")
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# Interactive conversation loop
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while True:
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# Get user input
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try:
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user_message = input("You: ").strip()
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except (EOFError, KeyboardInterrupt):
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logger.info("\nExiting...")
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break
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# Check for exit command
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if user_message.lower() == "exit":
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logger.info("Goodbye!")
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break
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# Skip empty messages
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if not user_message:
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continue
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# Send message to agent and get response
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try:
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response = joker.run(user_message, session=session)
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logger.info(f"Joker: {response.text} \n")
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except Exception as e:
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logger.error(f"Error getting response: {e}")
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logger.info("Conversation completed.")
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async def main() -> None:
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"""Main entry point for the client application."""
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logger.debug("Starting Durable Task Agent Client...")
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# Create client using helper function
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agent_client = get_client()
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try:
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run_client(agent_client)
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except Exception as e:
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logger.exception(f"Error during agent interaction: {e}")
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finally:
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logger.debug("Client shutting down")
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if __name__ == "__main__":
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asyncio.run(main())
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