Agent Lightning is a training framework for optimizing AI agents built with reinforcement learning and other algorithms.
The tool addresses the challenge of improving agent performance without requiring developers to rewrite their code. It works by wrapping existing agents from any framework—LangChain, OpenAI Agent SDK, AutoGen, CrewAI, or custom implementations—and applying optimization algorithms including reinforcement learning, automatic prompt optimization, and supervised fine-tuning. The framework claims to require minimal or zero code changes to integrate, allowing selective optimization of individual agents within multi-agent systems.
Developers should adopt this tool if they have deployed agents and want to improve their performance through training rather than manual iteration. It suits projects using established agent frameworks where rewriting is impractical, and particularly those where multiple agents operate together but only some need optimization. The README does not compare the tool to alternatives.
The project maintains a substantial user base, with nearly all open issues originating from outside users reporting real-world problems rather than from the core team. Maintainers respond to new issues and pull requests within a day. Work in the issue tracker centers on user questions, the VERL component, and example implementations.
