Agent Lightning solves : Days spent tweaking prompts, adding examples, hoping for improvement.
No system, just constant trial and error.
🟢 How it works?
The agent works as usual with any framework. You just add a simple call to agl.emit() or let the tracer collect data itself.
Agent Lightning collects every prompt, tool call, and reward, saves everything as structured events.
You choose an algorithm (RL, prompt optimization, fine-tuning). It reads events, finds patterns, generates improved prompts or policy weights.
Trainer loads updates back into the agent. The agent gets smarter without rewriting code.
You can optimize each agent in a system of multiple agents.
Suitable for LangChain, AutoGen, CrewAI, OpenAI SDK, or just Python.
It is open-source framework that trains ANY AI agent using reinforcement learning.
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