Agent Lightning is an optimization framework designed to refine the performance of individual AI agents within complex multi-agent systems. It provides a platform for improving decision-making and task execution by applying reinforcement learning, supervised fine-tuning, and automated prompt optimization.
The framework distinguishes itself through its ability to isolate specific agents for targeted tuning, allowing developers to enhance individual behaviors while maintaining the stability of the broader system architecture. By utilizing a modular interface, it integrates with diverse agent frameworks without requiring modifications to the underlying source code.
The system supports large-scale operations by distributing training workloads across compute clusters, enabling the processing of complex mathematical and coding tasks. It facilitates iterative performance improvements through feedback-driven learning loops and gradient-free instruction refinement, ensuring that agents can be systematically optimized for specific roles within a workflow.
