Agent Lightning is a Microsoft-developed training framework designed to optimize AI agents with minimal to zero code changes, supporting any existing agent framework (e.g., LangChain, OpenAI Agent SDK, AutoGen, CrewAI) or raw Python-based OpenAI integrations. It targets the selective optimization of individual agents within multi-agent systems and integrates various algorithmic approaches, including Reinforcement Learning (RL), Automatic Prompt Optimization (APOP), and Supervised Fine-tuning (SFT).

The core methodology revolves around a lightweight, modular architecture. Agent operation involves emitting events (prompts, tool calls, rewards) through direct helper calls (agl.emit_xxx()) or an automated tracer. These events are structured into "spans" and flow into the LightningStore, which serves as a centralized hub for managing tasks, resources, and execution traces. This store synchronizes all necessary data for the training process.

On the training side, an algorithm (either pre-built or custom-defined by the user) reads these structured spans from the LightningStore. This algorithm processes the collected data to learn and derive improvements, which it then translates into updated resources. These resources can manifest as refined prompt templates, updated policy weights, or other artifacts that enhance agent performance.

The Trainer component orchestrates the entire process. It is responsible for streaming datasets to agent runners for execution, facilitating the transfer of resources between the LightningStore and the chosen optimization algorithm, and applying the learned improvements to the inference engine. This iterative loop allows for continuous refinement of agents. The framework emphasizes avoiding code rewrites and vendor lock-in, providing a clear pathway from initial agent rollout to sustained performance improvement. While versatile, a foundational aspect of its development, as highlighted by an associated arXiv paper, focuses on training AI agents using Reinforcement Learning techniques.