Agent Lightning¶
Agent Lightning is the absolute trainer to light up AI agents.
Join our Discord community to connect with other users and contributors.
Features¶
- 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. 🤗
How to Read this Documentation¶
This documentation is organized into the following parts:
- Installation - Get started with Agent Lightning
- How-to Recipes (e.g., Train SQL Agent with RL) - Practical examples of training agents and customizing algorithms.
- Learning More (e.g., Debugging) - Guides on specific topics like debugging or parallelization.
- Algorithm Zoo (e.g., APO) - References for built-in algorithms.
- Deep Dive (e.g., Bird's Eye View) - For a deeper understanding of what Agent-lightning is doing under the hood.
- API References (e.g., Agent) - References for the Agent-lightning Python API.
Resources¶
- 10/22/2025 No More Retokenization Drift: Returning Token IDs via the OpenAI Compatible API Matters in Agent RL vLLM blog. See also Zhihu writeup.
- 8/11/2025 Training AI Agents to Write and Self-correct SQL with Reinforcement Learning Medium.
- 8/5/2025 Agent Lightning: Train ANY AI Agents with Reinforcement Learning arXiv paper.
- 7/26/2025 We discovered an approach to train any AI agent with RL, with (almost) zero code changes. Reddit.
- 6/6/2025 Agent Lightning - Microsoft Research Project page.
Community Projects¶
- DeepWerewolf — A case study of agent RL training for the Chinese Werewolf game built with AgentScope and Agent Lightning.
- AgentFlow — A modular multi-agent framework that combines planner, executor, verifier, and generator agents with the Flow-GRPO algorithm to tackle long-horizon, sparse-reward tasks.
Citation¶
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},
}
License¶
See the LICENSE file for details.