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Agent Lightning v1.0 is a 3,500-line open-source framework from Microsoft Research for applying reinforcement learning to existing agent harnesses. Instead of forcing you to rewrite your agent's execution loop inside the training framework, it sits between your agent and the LLM API as an OpenAI-compatible proxy. It records model calls and handles collocated asynchronous training on a shared pool of GPUs.

It is worth a look if you are already maintaining a custom harness like OpenHands or mini-SWE-agent and want to tune it without rewriting your stack. The catch is that splitting rollouts into variable training samples introduces complex data alignment challenges, and it assumes you have the compute infrastructure to run heavy concurrent agent loops. Skip it if you are looking for an out-of-the-box training environment with zero systems overhead.

more: https://www.microsoft.com/en-us/research/blog/agent-lightning-v1-0-a-3500-line-lightweight-agentic-rl-framework-for-training-agents-with-real-harnesses/