The absolute trainer to light up AI agents.
- Turn your agent into an optimizable beast with ZERO CODE CHANGE (almost)! 💤
- Build with ANY agent framework (LangChain, OpenAI Agent SDK, AutoGen, CrewAI, Microsoft Agent Framework...); or even WITHOUT agent framework (Python OpenAI). You name it! 🤖
- Selectively optimize one or more agents in a multi-agent system. 🎯
- Embraces Algorithms like Reinforcement Learning, Automatic Prompt Optimization, Supervised Fine-tuning and more. 🤗
Agent Lightning keeps the moving parts to a minimum so you can focus on your idea, not the plumbing. No rewrites, no lock-in, just a clear path from first rollout to steady improvement.
| Workflow | Status |
|---|---|
| CPU Tests |  |
| GPU Tests | |
| Examples Integration |  |
| Latest Dependency Compatibility |  |
| Legacy Examples Compatibility |  |
If you find Agent Lightning useful in your research or projects, please cite our paper:
@misc{luo2025agentlightningtrainai,
title={Agent Lightning: Train ANY AI Agents with Reinforcement Learning},
author={Xufang Luo and Yuge Zhang and Zhiyuan He and Zilong Wang and Siyun Zhao and Dongsheng Li and Luna K. Qiu and Yuqing Yang},
year={2025},
eprint={2508.03680},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2508.03680},
}This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.
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This project is licensed under the MIT License. See the LICENSE file for details.