Agent Lightning

Microsoft's open-source trainer that fine-tunes AI agents with RL and prompt optimization, framework-agnostic.

Free· Free, MIT-licensed open sourceAgentsMulti-model

Best for

Pick Agent Lightning if you're an ML engineer or researcher who wants to systematically train and optimize agents already running in LangChain, AutoGen, or CrewAI.

Skip if

Skip it if you want a hosted no-code agent builder or you haven't yet shipped a baseline agent worth optimizing.

Agent Lightning is an open-source training framework from Microsoft Research that applies reinforcement learning, automatic prompt optimization, and supervised fine-tuning to existing AI agent systems. Its headline claim is "zero code change (almost)" — you point it at agents built in LangChain, OpenAI Agents SDK, AutoGen, CrewAI, the Microsoft Agent Framework, or even raw Python, and it learns to improve them without forcing you to rewrite the orchestration layer.

The framework is aimed squarely at developers and researchers building serious multi-agent systems who have hit the ceiling of prompt engineering and want a principled way to optimize specific agents inside a pipeline. Because it's framework-agnostic and supports selective optimization, you can target one weak agent in a chain without retraining the others. It's MIT-licensed, free to use, and lives on GitHub at microsoft/agent-lightning, with a Python API surface covering agents, algorithms, runners, and trainers.

Documentation includes recipes, algorithm deep-dives, and an active Discord community. There's no managed service or hosted offering — you bring your own compute and your own base models. That makes it a researcher-and-engineer tool rather than a no-code product, but it's one of the more credible attempts to make agent training a first-class workflow rather than a one-off project.

Editor's take

This is one of the few credible attempts to treat agent training as a real ML discipline rather than yet-another prompt-tuning UI. The framework-agnostic angle is the killer feature — most teams don't want to rewrite their stack just to try RL. Useful if you're past the demo phase and have real failure modes to fix.

— The AI Tool Bible editorial team

Pros

✅ Framework-agnostic — works with LangChain, AutoGen, CrewAI, OpenAI Agents SDK and more
✅ Combines RL, prompt optimization, and SFT in one trainer
✅ Minimal code changes to integrate with existing agent stacks
✅ Backed by Microsoft Research with active development and Discord support
✅ MIT-licensed and fully open source