Agent Lightning: A Comprehensive Framework for AI Agent Training
Agent Lightning is a flexible and extensible open-source framework from Microsoft Research designed to revolutionize the training of AI agents. It enables reinforcement learning (RL)-based optimization for large language models (LLMs) in any AI agent setup, decoupling the training process from agent execution to allow seamless integration without significant code modifications. Whether you're building agents with popular frameworks like LangChain, OpenAI Agents SDK, AutoGen, CrewAI, or even from scratch using Python and OpenAI APIs, Agent Lightning supports them all.
Key Features and Capabilities
- Zero-Code Integration: Instrument your existing agents with lightweight helpers like
agl.emit_xxx()or use automatic tracing to capture prompts, tool calls, and rewards as structured spans. This turns your agent's rollouts into optimizable data without rewriting core logic. - Multi-Agent Support: Selectively optimize individual agents in complex multi-agent systems, handling scenarios like dynamic workflows and parallel executions.
- Advanced Algorithms: Embrace RL (via the novel LightningRL algorithm with credit assignment for decomposing trajectories), automatic prompt optimization, supervised fine-tuning, and more. It formalizes agent execution as a Markov Decision Process (MDP) for unified data interfaces that capture all state changes, including non-LLM components.
- Architecture Overview: The framework uses a Training-Agent Disaggregation design, separating client-side agent running from server-side training. The LightningStore acts as a central hub for tasks, resources (e.g., prompt templates), and traces. Algorithms process these spans to update policies, while the Trainer orchestrates loops for continuous improvement.
- Practical Applications: Demonstrated in tasks like text-to-SQL, retrieval-augmented generation (RAG), and math tool-use, showing stable performance gains. It also handles sparse rewards through Automatic Intermediate Rewarding, converting runtime signals into dense feedback.
Installation and Usage
Getting started is straightforward: Install via pip install agentlightning. Follow the documentation for tutorials, such as training a SQL agent with RL. Examples include debugging, parallelization, and community projects like DeepWerewolf (for game AI) and AgentFlow (for long-horizon tasks).
Community and Resources
Join the Discord community for support. The project includes CI workflows for CPU/GPU tests and compatibility checks. It's licensed under MIT and follows Microsoft's Responsible AI Standard. For research, cite the associated arXiv paper (2508.03680). Explore the GitHub repo for examples, contributing guidelines, and more—it's garnered over 5,000 stars since its release.
This framework bridges the gap between agent development and RL training, making it easier to deploy learning agents in real-world scenarios without lock-in to specific tools.
