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agent-framework/python/packages/lab
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Eduard van Valkenburg 838a7fd61d Python: [BREAKING] Types API Review improvements (#3647)
* Replace Role and FinishReason classes with NewType + Literal

- Remove EnumLike metaclass from _types.py
- Replace Role class with NewType('Role', str) + RoleLiteral
- Replace FinishReason class with NewType('FinishReason', str) + FinishReasonLiteral
- Update all usages across codebase to use string literals
- Remove .value access patterns (direct string comparison now works)
- Add backward compatibility for legacy dict serialization format
- Update tests to reflect new string-based types

Addresses #3591, #3615

* Simplify ChatResponse and AgentResponse type hints (#3592)

- Remove overloads from ChatResponse.__init__
- Remove text parameter from ChatResponse.__init__
- Remove | dict[str, Any] from finish_reason and usage_details params
- Remove **kwargs from AgentResponse.__init__
- Both now accept ChatMessage | Sequence[ChatMessage] | None for messages
- Update docstrings and examples to reflect changes
- Fix tests that were using removed kwargs
- Fix Role type hint usage in ag-ui utils

* Remove text parameter from ChatResponseUpdate and AgentResponseUpdate (#3597)

- Remove text parameter from ChatResponseUpdate.__init__
- Remove text parameter from AgentResponseUpdate.__init__
- Remove **kwargs from both update classes
- Simplify contents parameter type to Sequence[Content] | None
- Update all usages to use contents=[Content.from_text(...)] pattern
- Fix imports in test files
- Update docstrings and examples

* Rename from_chat_response_updates to from_updates (#3593)

- ChatResponse.from_chat_response_updates → ChatResponse.from_updates
- ChatResponse.from_chat_response_generator → ChatResponse.from_update_generator
- AgentResponse.from_agent_run_response_updates → AgentResponse.from_updates

* Remove try_parse_value method from ChatResponse and AgentResponse (#3595)

- Remove try_parse_value method from ChatResponse
- Remove try_parse_value method from AgentResponse
- Remove try_parse_value calls from from_updates and from_update_generator methods
- Update samples to use try/except with response.value instead
- Update tests to use response.value pattern
- Users should now use response.value with try/except for safe parsing

* Add agent_id to AgentResponse and clarify author_name documentation (#3596)

- Add agent_id parameter to AgentResponse class
- Document that author_name is on ChatMessage objects, not responses
- Update ChatResponse docstring with author_name note
- Update AgentResponse docstring with author_name note

* Simplify ChatMessage.__init__ signature (#3618)

- Make contents a positional argument accepting Sequence[Content | str]
- Auto-convert strings in contents to TextContent
- Remove overloads, keep text kwarg for backward compatibility with serialization
- Update _parse_content_list to handle string items
- Update all usages across codebase to use new format: ChatMessage("role", ["text"])

* Allow Content as input on run and get_response

- Update prepare_messages and normalize_messages to accept Content
- Update type signatures in _agents.py and _clients.py
- Add tests for Content input handling

* Fix ChatMessage usage across packages and samples

Update all remaining ChatMessage(role=..., text=...) to use new
ChatMessage('role', ['text']) signature.

* Fix Role string usage and response format parsing

- Fix redis provider: remove .value access on string literals
- Fix durabletask ensure_response_format: set _response_format before accessing .value

* Fix ollama .value and ai_model_id issues, handle None in content list

- Fix ollama _chat_client: remove .value on string literals
- Fix ollama _chat_client: rename ai_model_id to model_id
- Fix _parse_content_list: skip None values gracefully

* Fix A2AAgent type signature to include Content

* Fix Role/FinishReason NewType dict annotations and improve test coverage to 95%

* Fix mypy errors for Role/FinishReason NewType usage

* Fix Role.TOOL and Role.ASSISTANT usage in _orchestrator_helpers.py

* Fix Role NewType usage in durabletask _models.py
838a7fd61d · 2026-02-04 10:13:23 +00:00
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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