Agent Lightning is an open-source framework developed by Microsoft to train and optimize AI agents using techniques like reinforcement learning (RL), supervised fine-tuning, and automatic prompt optimization, with minimal or zero changes to existing agent code. It’s designed to be compatible with a wide range of agent architectures and frameworks — from LangChain and OpenAI Agent SDKs to AutoGen and custom Python agents — making it broadly applicable across different agent tooling ecosystems. Agent-Lightning introduces a lightweight training pipeline that observes agents’ execution traces, converts them into structured data, and feeds them into training algorithms, enabling users to improve agent behaviors systematically. The project emphasizes minimalist integration, so you can drop this into existing systems without extensive rewrites, focusing instead on iterative performance improvement.

Features

  • Reinforcement learning-based agent optimization
  • Zero or minimal code integration required
  • Works with many agent frameworks (LangChain, AutoGen, etc.)
  • Structured trace collection and training pipeline
  • Trainer abstraction for iterative improvement
  • Support for multi-agent systems

Project Samples

Project Activity

See All Activity >

Categories

AI Agents

License

MIT License

Follow Agent Lightning

Agent Lightning Web Site

Other Useful Business Software
MongoDB Atlas runs apps anywhere Icon
MongoDB Atlas runs apps anywhere

Deploy in 115+ regions with the modern database for every enterprise.

MongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
Start Free
Rate This Project
Login To Rate This Project

User Reviews

Be the first to post a review of Agent Lightning!

Additional Project Details

Programming Language

Python

Related Categories

Python AI Agents

Registered

2026-02-06