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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

orchestrations

Python: [BREAKING] Simplify API: ChatAgent -> Agent, ChatMessage -> Message (#3747 )

2026-02-10 23:04:32 +00:00

single_agent

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

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

.gitignore

Python: AutoGen migration samples (#1738 )

2025-10-28 00:47:31 +00:00

pyrightconfig.json

Python: AutoGen migration samples (#1738 )

2025-10-28 00:47:31 +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

AutoGen → Microsoft Agent Framework Migration Samples

This gallery helps AutoGen developers move to the Microsoft Agent Framework (AF) with minimal guesswork. Each script pairs AutoGen code with its AF equivalent so you can compare primitives, tooling, and orchestration patterns side by side while you migrate production workloads.

What's Included

Single-Agent Parity

01_basic_assistant_agent.py — Minimal AutoGen AssistantAgent and AF Agent comparison.
02_assistant_agent_with_tool.py — Function tool integration in both SDKs.
03_assistant_agent_thread_and_stream.py — Session management and streaming responses.
04_agent_as_tool.py — Using agents as tools (hierarchical agent pattern) and streaming with tools.

Multi-Agent Orchestration

01_round_robin_group_chat.py — AutoGen RoundRobinGroupChat → AF GroupChatBuilder/SequentialBuilder.
02_selector_group_chat.py — AutoGen SelectorGroupChat → AF GroupChatBuilder.
03_swarm.py — AutoGen Swarm pattern → AF HandoffBuilder.
04_magentic_one.py — AutoGen MagenticOneGroupChat → AF MagenticBuilder.

Each script is fully async and the main() routine runs both implementations back to back so you can observe their outputs in a single execution.

Prerequisites

Python 3.10 or later.
Access to the necessary model endpoints (Azure OpenAI, OpenAI, etc.).
Installed SDKs: Install AutoGen and the Microsoft Agent Framework with:
```
pip install "autogen-agentchat autogen-ext[openai] agent-framework"
```
Service credentials exposed through environment variables (e.g., OPENAI_API_KEY).

Running Single-Agent Samples

From the repository root:

python samples/autogen-migration/single_agent/01_basic_assistant_agent.py

Every script accepts no CLI arguments and will first call the AutoGen implementation, followed by the AF version. Adjust the prompt or credentials inside the file as necessary before running.

Running Orchestration Samples

Advanced comparisons are in autogen-migration/orchestrations (RoundRobin, Selector, Swarm, Magentic). You can run them directly:

python samples/autogen-migration/orchestrations/01_round_robin_group_chat.py
python samples/autogen-migration/orchestrations/04_magentic_one.py

Tips for Migration

Default behavior differences: AutoGen's AssistantAgent is single-turn by default (max_tool_iterations=1), while AF's Agent is multi-turn and continues tool execution automatically.
Thread management: AF agents are stateless by default. Use agent.create_session() and pass it to run() to maintain conversation state, similar to AutoGen's conversation context.
Tools: AutoGen uses FunctionTool wrappers; AF uses @tool decorators with automatic schema inference.
Orchestration patterns:
- RoundRobinGroupChat → SequentialBuilder or WorkflowBuilder
- SelectorGroupChat → GroupChatBuilder with LLM-based speaker selection
- Swarm → HandoffBuilder for agent handoff coordination
- MagenticOneGroupChat → MagenticBuilder for orchestrated multi-agent workflows