{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/6"}],"enrichment":{"capability":"Provides experimental lab modules (GAIA, TAU2, Lightning) built on Microsoft Agent Framework for benchmarking, evaluating, and training agents with research prototypes and incubating features.","skillfed_tags":["agent-framework","benchmarking","reinforcement-learning"],"use_cases":["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."],"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.\n\nThe 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.","worth_installing":"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\u2014use 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."},"id":"agent-framework-lab","links":{"html":"https://skillfed.io/packages/agent-framework-lab","md":"https://skillfed.io/packages/agent-framework-lab.md","pypi":"https://pypi.org/project/agent-framework-lab/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-10","license_spdx":null,"license_treatment":"permissive","name":"agent-framework-lab","python_support":"supports_current","summary":"Experimental modules for Microsoft Agent Framework"},"popularity":{"monthly_downloads":289271,"position":8003,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.0.0b260709"}