---
id: agent-framework-lab
version: "1.0.0b260709"
license: unclear
license_treatment: permissive
maintenance: active
---

# agent-framework-lab — Experimental modules for Microsoft Agent Framework

License: permissive · Maintenance: active · Downloads: 289.3K/mo

## What it is and what it does

Agent Framework Lab is an experimental package from Microsoft that extends the core Agent Framework with research prototypes, benchmarking tools, and incubating features. It provides three main lab modules: GAIA for evaluating agents on general assistant tasks, TAU2 for customer support task evaluation, and Lightning for reinforcement learning agent training. The package is explicitly designed for experimentation and research rather than production use; its documentation warns that lab modules may experience breaking changes or deprecation without notice.

The package is installed via extras syntax (e.g., `pip install agent-framework-lab[gaia]`) and imported from the `agent_framework.lab` namespace. It depends only on agent-framework-core at runtime, keeping the dependency surface minimal. The beta status and active maintenance indicate ongoing development, with recent releases and community interest in the broader Agent Framework ecosystem.

Use it for:

- Benchmark your agent implementations against GAIA or TAU2 evaluation suites to measure performance on general or customer support tasks.
- Prototype and experiment with new agent features in a lab environment before considering them for the core framework.
- Train agents using reinforcement learning with the Lightning module for custom task optimization.
- Evaluate different agent architectures and approaches in a research setting without production stability constraints.
- Contribute community-maintained lab modules by building on top of the core framework and Agent Framework Lab infrastructure.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Provides experimental lab modules (GAIA, TAU2, Lightning) built on Microsoft Agent Framework for benchmarking, evaluating, and training agents with research prototypes and incubating features.

Yes, if you are actively experimenting with agent frameworks, benchmarking agent performance, or researching RL-based agent training. No, if you need stable, production-ready agent features—use the core framework instead. The package is well-maintained, has no known vulnerabilities, and carries low install friction, but its beta status and explicit warning about breaking changes mean you should only adopt it for non-critical experimentation.

## Install

pip install agent-framework-lab
uv add agent-framework-lab
poetry add agent-framework-lab

## Installing agent-framework-lab

Before you install: Low install friction with a single runtime dependency. Active maintenance with recent releases and no known vulnerabilities. Beta status signals ongoing development; breaking changes are explicitly expected.

License in practice: MIT license (permissive) means you can use, modify, and distribute this package freely with minimal restrictions, though you must include the license notice.

Quickstart:

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

from agent_framework.lab.gaia import GAIA

Requires Python 3.10 or later; agent-framework-core must be installed as a runtime dependency.

Verify before relying:

- Whether GAIA, TAU2, and Lightning modules are fully functional in the current beta release or still under active development.
- Whether breaking changes between releases are documented in a changelog.
- Performance characteristics and resource requirements for Lightning RL training and benchmark evaluation.

## Package facts

- License: not declared (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 289.3K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

agent framework benchmarking, agent evaluation GAIA TAU2, reinforcement learning agent training, agent framework extensions, experimental agent modules, agent performance evaluation, agent framework lab modules, agent-framework, benchmarking, reinforcement-learning

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