3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses!
Documentation · Technical Report · MIT License
Agent Lightning was completely refactored in v1.0. For legacy releases earlier than v1.0, see this branch.
⚡ Key Features
- 🪶 ~3,500 lines of code: We treat simplicity as the first principle.
- 🧩 Train with real agent harnesses: Agents interact with the model through the Agent Lightning v1.0 proxy with ZERO changes, while keeping tools, context, control flow, and environments in the loop.
- ☸️ Native Kubernetes support: Run agents directly as Kubernetes Jobs without relying on external sandbox services.
- 💻 Full coding agent training example: Using only 6K training samples, an end-to-end Qwen3.5-9B workflow improves SWE-bench Verified from 41.8% to 56.4%, a gain of 14.6 percentage points. We release the full pipeline, including data cleaning, reward-hacking prevention, and training scripts.
⚡ Installation
The following is an example installation on a CUDA 13.0 machine:
cd <this-repo>
uv sync
bash scripts/setup_verl.sh 0.8.0 cu130
See the Installation Guide for details.
⚡ Architecture
Agent Lightning v1.0 keeps the training architecture simple with three lightweight components:
- Trainer: Runs
verl and vLLM, builds training samples, and updates the policy.
- API Gateway: Proxies model requests and captures training data.
- Rollout Controller: Runs agents locally or as Kubernetes Jobs.
The Trainer creates rollouts, the Controller launches agents, and the Gateway turns interactions into training data, while agents continue to run with their real harnesses.
⚡ Results
We evaluate Agent Lightning v1.0 across several practical training domains, including Search R1, LLM-in-Sandbox, and Coding Agent. Pure RL delivers substantial improvements across all three domains, as shown below.
⚡ Documentation
⚡ Examples
| Example |
Description |
| Calc-X |
POC math reasoning example with AutoGen and MCP calculator tools, requiring only one GPU. |
| GSM8K |
POC grade-school math reasoning example. |
| ScienceWorld |
Interactive science tasks in a text-based environment. |
| Search-R1 |
Multi-turn retrieval and reasoning agent. |
| LLM-in-Sandbox |
General agent with computer and code execution tools. |
| Coding Agent |
Coding agent trained with repository tests. |
⚡ Articles
⚡ Citation
If you use Agent Lightning v1.0 in your research or projects, please cite the technical report:
@misc{he2026agentlightningv10harnessed,
title={Agent Lightning v1.0: Towards Harnessed Agentic RL},
author={Zhiyuan He and Siwei Zhang and Zhiwen Zhou and Yuqing Yang and Yu Kang and Yuge Zhang and Luna K. Qiu and Tin Yan Tsui and Jiahang Xu and Chong Luo},
year={2026},
eprint={2608.17528},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2608.17528},
}
For the original Agent Lightning paper, please use:
@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},
}
⚡ Contributing
This project welcomes contributions and suggestions. Start by reading the Contributing Guide for recommended contribution points, environment setup, branching conventions, and pull request expectations. 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.
When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.
⚡ Trademarks
This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.
⚡ Responsible AI
This project has been evaluated and certified to comply with the Microsoft Responsible AI Standard. The team will continue to monitor and maintain the repository, addressing any severe issues, including potential harms, if they arise.
⚡ License
Agent Lightning v1.0 is released under the MIT License.
Agent Lightning 是微软开发的开源强化学习训练框架，几乎不需要改动智能体自身的代码，就能用强化学习训练你现有的任何 AI 智能体。