agent-lightning

Reinforcement learning for AI agents without code changes is achieved by initializing the library in your project and applying a tracing decorator to your agent function, enabling rapid experimentation and deployment.

Yes, zero-code reinforcement learning training works with LangChain, AutoGen, and CrewAI frameworks. It integrates directly with these popular agent frameworks or the OpenAI API without requiring any changes to your existing code.

Yes, you can optimize specific agents within a multi-agent system in parallel. Selective optimization allows you to target specific agents within your multi-agent setups, supporting rapid reinforcement learning experimentation across various environments.

Besides reinforcement learning, supported algorithms for AI agent training include automatic prompt optimization and supervised fine-tuning, with ongoing updates planned to expand available optimization techniques.

Yes, you need a Python environment with the agent-lightning library installed and a configured OpenAI API key. These prerequisites are required to run end-to-end reinforcement learning training for your AI agents.