Eduard van Valkenburg
8ed50009c6
* Centralize tool result parsing in FunctionTool.invoke() - Add parse_result static method to FunctionTool that converts raw function return values to strings at invocation time - Add result_parser parameter to FunctionTool and @tool decorator for custom parsing - Remove prepare_function_call_results from all 9 consumer files and from the public API - Update MCPTool to parse MCP types directly to strings via _parse_tool_result_from_mcp and _parse_prompt_result_from_mcp - Change MCPTool parse_tool_results/parse_prompt_results type from Literal[True] | Callable | None to Callable | None - Remove ReturnT type parameter from FunctionTool (now single generic ArgsT since invoke() always returns str) - Update all subclass signatures and docstrings Fixes #1147 * Fix test_mcp_tool_call_tool_with_meta_integration for string results The test was still accessing result[0].additional_properties but invoke() now returns a string, not a list of Content objects. * Fix SIM108 lint: use binary operator for output assignment * Fix bedrock: use FunctionTool.parse_result instead of str() fallback str(result) turns None into literal 'None' and dicts into Python reprs with single quotes, breaking JSON parsing. Use the shared parse_result which handles None as '' and serializes via json.dumps. * updated lock * updates from feedback
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:
- Incubation of new features that may get incorporated by the core framework.
- Research prototypes built on the core framework.
- 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:
- Create a new repository on GitHub for your module
- Tag your repository with
agent-framework-labfor discoverability - Submit a PR to add a link to your repository in the Lab Modules section above
- Use the PR title format:
[New Lab Module] Your Module Name
We will review your submission based on the guidelines below.
Guidelines
- Purpose: Community modules should fit into one of the three categories of lab modules (incubation, research, benchmarks)
- Namespace: Community modules should avoid the
agent_framework.labnamespace (reserved for modules maintained in this repository) - Dependencies: Minimize external dependencies, always include
agent-frameworkas a base dependency - Documentation: Include comprehensive README with installation instructions and usage examples
- Tests: Write comprehensive tests with good coverage
- Type hints: Always include type hints and a
py.typedfile - Versioning: Use semantic versioning, start with
0.1.0for initial releases