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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
This commit is contained in:
Eduard van Valkenburg
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@@ -9,7 +9,7 @@ This folder contains examples demonstrating how to use the Mem0 context provider
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| File | Description |
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|------|-------------|
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| [`mem0_basic.py`](mem0_basic.py) | Basic example of using Mem0 context provider to store and retrieve user preferences across different conversation threads. |
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| [`mem0_threads.py`](mem0_threads.py) | Advanced example demonstrating different thread scoping strategies with Mem0. Covers global thread scope (memories shared across all operations), per-operation thread scope (memories isolated per thread), and multiple agents with different memory configurations for personal vs. work contexts. |
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| [`mem0_sessions.py`](mem0_sessions.py) | Advanced example demonstrating different thread scoping strategies with Mem0. Covers global thread scope (memories shared across all operations), per-operation thread scope (memories isolated per thread), and multiple agents with different memory configurations for personal vs. work contexts. |
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| [`mem0_oss.py`](mem0_oss.py) | Example of using the Mem0 Open Source self-hosted version as the context provider. Demonstrates setup and configuration for local deployment. |
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## Prerequisites
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@@ -5,11 +5,11 @@ import uuid
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from agent_framework import tool
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from agent_framework.azure import AzureAIAgentClient
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from agent_framework.mem0 import Mem0Provider
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from agent_framework.mem0 import Mem0ContextProvider
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from azure.identity.aio import AzureCliCredential
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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_threads.py.
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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 retrieve_company_report(company_code: str, detailed: bool) -> str:
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if company_code != "CNTS":
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@@ -39,7 +39,7 @@ async def main() -> None:
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name="FriendlyAssistant",
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instructions="You are a friendly assistant.",
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tools=retrieve_company_report,
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context_provider=Mem0Provider(user_id=user_id),
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context_providers=[Mem0ContextProvider(user_id=user_id)],
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) as agent,
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):
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# First ask the agent to retrieve a company report with no previous context.
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@@ -64,17 +64,17 @@ async def main() -> None:
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print("Waiting for memories to be processed...")
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await asyncio.sleep(12) # Empirically determined delay for Mem0 indexing
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print("\nRequest within a new thread:")
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# Create a new thread for the agent.
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# The new thread has no context of the previous conversation.
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thread = agent.get_new_thread()
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print("\nRequest within a new session:")
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# Create a new session for the agent.
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# The new session has no context of the previous conversation.
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session = agent.create_session()
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# Since we have the mem0 component in the thread, the agent should be able to
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# Since we have the mem0 component in the session, the agent should be able to
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# retrieve the company report without asking for clarification, as it will
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# be able to remember the user preferences from Mem0 component.
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query = "Please retrieve my company report"
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print(f"User: {query}")
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result = await agent.run(query, thread=thread)
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result = await agent.run(query, session=session)
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print(f"Agent: {result}\n")
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@@ -5,12 +5,12 @@ import uuid
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from agent_framework import tool
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from agent_framework.azure import AzureAIAgentClient
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from agent_framework.mem0 import Mem0Provider
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from agent_framework.mem0 import Mem0ContextProvider
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from azure.identity.aio import AzureCliCredential
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from mem0 import AsyncMemory
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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_threads.py.
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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 retrieve_company_report(company_code: str, detailed: bool) -> str:
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if company_code != "CNTS":
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@@ -42,7 +42,7 @@ async def main() -> None:
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name="FriendlyAssistant",
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instructions="You are a friendly assistant.",
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tools=retrieve_company_report,
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context_provider=Mem0Provider(user_id=user_id, mem0_client=local_mem0_client),
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context_providers=[Mem0ContextProvider(user_id=user_id, mem0_client=local_mem0_client)],
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) as agent,
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):
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# First ask the agent to retrieve a company report with no previous context.
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@@ -60,18 +60,18 @@ async def main() -> None:
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result = await agent.run(query)
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print(f"Agent: {result}\n")
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print("\nRequest within a new thread:")
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print("\nRequest within a new session:")
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# Create a new thread for the agent.
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# The new thread has no context of the previous conversation.
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thread = agent.get_new_thread()
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# Create a new session for the agent.
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# The new session has no context of the previous conversation.
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session = agent.create_session()
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# Since we have the mem0 component in the thread, the agent should be able to
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# Since we have the mem0 component in the session, the agent should be able to
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# retrieve the company report without asking for clarification, as it will
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# be able to remember the user preferences from Mem0 component.
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query = "Please retrieve my company report"
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print(f"User: {query}")
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result = await agent.run(query, thread=thread)
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result = await agent.run(query, session=session)
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print(f"Agent: {result}\n")
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+32
-32
@@ -5,11 +5,11 @@ import uuid
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from agent_framework import tool
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from agent_framework.azure import AzureAIAgentClient
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from agent_framework.mem0 import Mem0Provider
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from agent_framework.mem0 import Mem0ContextProvider
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from azure.identity.aio import AzureCliCredential
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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_threads.py.
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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_user_preferences(user_id: str) -> str:
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"""Mock function to get user preferences."""
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@@ -34,11 +34,11 @@ async def example_global_thread_scope() -> None:
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name="GlobalMemoryAssistant",
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instructions="You are an assistant that remembers user preferences across conversations.",
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tools=get_user_preferences,
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context_provider=Mem0Provider(
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context_providers=[Mem0ContextProvider(
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user_id=user_id,
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thread_id=global_thread_id,
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scope_to_per_operation_thread_id=False, # Share memories across all threads
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),
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scope_to_per_operation_thread_id=False, # Share memories across all sessions
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)],
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) as global_agent,
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):
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# Store some preferences in the global scope
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@@ -47,19 +47,19 @@ async def example_global_thread_scope() -> None:
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result = await global_agent.run(query)
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print(f"Agent: {result}\n")
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# Create a new thread - but memories should still be accessible due to global scope
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new_thread = global_agent.get_new_thread()
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# Create a new session - but memories should still be accessible due to global scope
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new_session = global_agent.create_session()
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query = "What do you know about my preferences?"
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print(f"User (new thread): {query}")
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result = await global_agent.run(query, thread=new_thread)
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print(f"User (new session): {query}")
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result = await global_agent.run(query, session=new_session)
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print(f"Agent: {result}\n")
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async def example_per_operation_thread_scope() -> None:
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"""Example 2: Per-operation thread scope (memories isolated per thread).
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"""Example 2: Per-operation thread scope (memories isolated per session).
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Note: When scope_to_per_operation_thread_id=True, the provider is bound to a single thread
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throughout its lifetime. Use the same thread object for all operations with that provider.
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Note: When scope_to_per_operation_thread_id=True, the provider is bound to a single session
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throughout its lifetime. Use the same session object for all operations with that provider.
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"""
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print("2. Per-Operation Thread Scope Example:")
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print("-" * 40)
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@@ -72,37 +72,37 @@ async def example_per_operation_thread_scope() -> None:
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name="ScopedMemoryAssistant",
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instructions="You are an assistant with thread-scoped memory.",
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tools=get_user_preferences,
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context_provider=Mem0Provider(
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context_providers=[Mem0ContextProvider(
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user_id=user_id,
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scope_to_per_operation_thread_id=True, # Isolate memories per thread
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),
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scope_to_per_operation_thread_id=True, # Isolate memories per session
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)],
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) as scoped_agent,
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):
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# Create a specific thread for this scoped provider
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dedicated_thread = scoped_agent.get_new_thread()
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# Create a specific session for this scoped provider
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dedicated_session = scoped_agent.create_session()
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# Store some information in the dedicated thread
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# Store some information in the dedicated session
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query = "Remember that for this conversation, I'm working on a Python project about data analysis."
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print(f"User (dedicated thread): {query}")
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result = await scoped_agent.run(query, thread=dedicated_thread)
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print(f"User (dedicated session): {query}")
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result = await scoped_agent.run(query, session=dedicated_session)
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print(f"Agent: {result}\n")
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# Test memory retrieval in the same dedicated thread
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# Test memory retrieval in the same dedicated session
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query = "What project am I working on?"
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print(f"User (same dedicated thread): {query}")
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result = await scoped_agent.run(query, thread=dedicated_thread)
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print(f"User (same dedicated session): {query}")
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result = await scoped_agent.run(query, session=dedicated_session)
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print(f"Agent: {result}\n")
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# Store more information in the same thread
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# Store more information in the same session
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query = "Also remember that I prefer using pandas and matplotlib for this project."
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print(f"User (same dedicated thread): {query}")
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result = await scoped_agent.run(query, thread=dedicated_thread)
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print(f"User (same dedicated session): {query}")
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result = await scoped_agent.run(query, session=dedicated_session)
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print(f"Agent: {result}\n")
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# Test comprehensive memory retrieval
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query = "What do you know about my current project and preferences?"
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print(f"User (same dedicated thread): {query}")
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result = await scoped_agent.run(query, thread=dedicated_thread)
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print(f"User (same dedicated session): {query}")
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result = await scoped_agent.run(query, session=dedicated_session)
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print(f"Agent: {result}\n")
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@@ -119,16 +119,16 @@ async def example_multiple_agents() -> None:
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AzureAIAgentClient(credential=credential).as_agent(
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name="PersonalAssistant",
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instructions="You are a personal assistant that helps with personal tasks.",
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context_provider=Mem0Provider(
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context_providers=[Mem0ContextProvider(
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agent_id=agent_id_1,
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),
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)],
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) as personal_agent,
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AzureAIAgentClient(credential=credential).as_agent(
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name="WorkAssistant",
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instructions="You are a work assistant that helps with professional tasks.",
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context_provider=Mem0Provider(
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context_providers=[Mem0ContextProvider(
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agent_id=agent_id_2,
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),
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)],
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) as work_agent,
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):
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# Store personal information
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