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agent-framework/python/packages/lab
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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
..
2026-02-11 00:20:29 +00:00

Agent Framework Lab

This is the experimental package for Microsoft Agent Framework, agent-framework-lab, which contains various lab modules built on top of the core framework. Lab modules are not part of the core framework and may experience breaking changes or be deprecated in the future.

What are Lab Modules?

Lab modules are extensions to the core Agent Framework that fall into one of the following categories:

  1. Incubation of new features that may get incorporated by the core framework.
  2. Research prototypes built on the core framework.
  3. Benchmarks and experimentation tools.

Lab Modules

  • gaia: Evaluate your agents using the GAIA benchmark for general assistant tasks
  • tau2: Evaluate your agents using the TAU2 benchmark for customer support tasks
  • lightning: RL training for agents using Agent Lightning

Repository Structure

agent-framework-lab/
├── pyproject.toml          # Single package configuration for agent-framework-lab
├── README.md               # This file
├── LICENSE                 # License file
├── namespace/              # Centralized namespace package files
│   └── agent_framework/
│       └── lab/
│           ├── gaia/       # Re-exports from agent_framework_lab_gaia
│           ├── lightning/  # Re-exports from agent_framework_lab_lightning
│           └── tau2/       # Re-exports from agent_framework_lab_tau2
├── gaia/                   # GAIA module implementation
│   └── agent_framework_lab_gaia/
├── lightning/              # Lightning module implementation
│   └── agent_framework_lab_lightning/
└── tau2/                   # TAU2 module implementation
    └── agent_framework_lab_tau2/

This structure maintains a single PyPI package agent-framework-lab while supporting modular imports through the namespace package mechanism.

Installation

To install each lab module, use the extras syntax with pip:

pip install "agent-framework-lab[gaia]"
pip install "agent-framework-lab[tau2]"
pip install "agent-framework-lab[lightning]"

Usage

Import and use lab modules from the agent_framework.lab namespace. For example, to use the GAIA module:

# Using GAIA module
from agent_framework.lab.gaia import GAIA

Should I consume Lab Modules?

If you are looking for stable and production-ready features, you should not use lab modules. Stick to the core framework.

If you are looking for experimentation, research, or want to benchmark different approaches -- most importantly, if you don't mind breaking changes and potential deprecations -- then lab modules are for you.

Contributing to Lab Modules

Microsoft-maintained modules

For Microsoft-maintained modules in this repository, please follow standard contribution guidelines and submit pull requests directly to this repository.

Community modules

If you want to contribute a community-maintained lab module:

  1. Create a new repository on GitHub for your module
  2. Tag your repository with agent-framework-lab for discoverability
  3. Submit a PR to add a link to your repository in the Lab Modules section above
  4. Use the PR title format: [New Lab Module] Your Module Name

We will review your submission based on the guidelines below.

Guidelines

  1. Purpose: Community modules should fit into one of the three categories of lab modules (incubation, research, benchmarks)
  2. Namespace: Community modules should avoid the agent_framework.lab namespace (reserved for modules maintained in this repository)
  3. Dependencies: Minimize external dependencies, always include agent-framework as a base dependency
  4. Documentation: Include comprehensive README with installation instructions and usage examples
  5. Tests: Write comprehensive tests with good coverage
  6. Type hints: Always include type hints and a py.typed file
  7. Versioning: Use semantic versioning, start with 0.1.0 for initial releases