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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

1e350ea22f · 2026-02-12 21:00:32 +00:00

History

mem0_basic.py

Python: [BREAKING] PR2 — Wire context provider pipeline, remove old types, update all consumers (#3850 )

2026-02-12 21:00:32 +00:00

mem0_oss.py

Python: [BREAKING] PR2 — Wire context provider pipeline, remove old types, update all consumers (#3850 )

2026-02-12 21:00:32 +00:00

mem0_sessions.py

Python: [BREAKING] PR2 — Wire context provider pipeline, remove old types, update all consumers (#3850 )

2026-02-12 21:00:32 +00:00

README.md

Python: [BREAKING] PR2 — Wire context provider pipeline, remove old types, update all consumers (#3850 )

2026-02-12 21:00:32 +00:00

README.md

Mem0 Context Provider Examples

Mem0 is a self-improving memory layer for Large Language Models that enables applications to have long-term memory capabilities. The Agent Framework's Mem0 context provider integrates with Mem0's API to provide persistent memory across conversation sessions.

This folder contains examples demonstrating how to use the Mem0 context provider with the Agent Framework for persistent memory and context management across conversations.

Examples

File	Description
`mem0_basic.py`	Basic example of using Mem0 context provider to store and retrieve user preferences across different conversation sessions.
`mem0_sessions.py`	Advanced example demonstrating different session scoping strategies with Mem0. Covers global session scope (memories shared across all operations), per-operation session scope (memories isolated per session), and multiple agents with different memory configurations for personal vs. work contexts.
`mem0_oss.py`	Example of using the Mem0 Open Source self-hosted version as the context provider. Demonstrates setup and configuration for local deployment.

Prerequisites

Required Resources

Mem0 API Key - Sign up for a Mem0 account and get your API key - or self-host Mem0 Open Source
Azure AI project endpoint (used in these examples)
Azure CLI authentication (run az login)

Configuration

Environment Variables

Set the following environment variables:

For Mem0 Platform:

MEM0_API_KEY: Your Mem0 API key (alternatively, pass it as api_key parameter to Mem0Provider). Not required if you are self-hosting Mem0 Open Source

For Mem0 Open Source:

OPENAI_API_KEY: Your OpenAI API key (used by Mem0 OSS for embedding generation and automatic memory extraction)

For Azure AI:

AZURE_AI_PROJECT_ENDPOINT: Your Azure AI project endpoint
AZURE_AI_MODEL_DEPLOYMENT_NAME: The name of your model deployment

Key Concepts

Memory Scoping

The Mem0 context provider supports different scoping strategies:

Global Scope (scope_to_per_operation_thread_id=False): Memories are shared across all conversation sessions
Session Scope (scope_to_per_operation_thread_id=True): Memories are isolated per conversation session

Memory Association

Mem0 records can be associated with different identifiers:

user_id: Associate memories with a specific user
agent_id: Associate memories with a specific agent
thread_id: Associate memories with a specific conversation session
application_id: Associate memories with an application context