Agent Lightning

Agent Lightning tackles a specific problem in agent development: once you have a working agent built in LangChain, AutoGen, CrewAI, the OpenAI Agents SDK, or even plain Python, improving it usually means manual prompt tweaking with no systematic feedback loop. Agent Lightning instead wraps your existing agent and applies training algorithms — reinforcement learning, automatic prompt optimization, or supervised fine-tuning — to optimize its behavior, with the project’s stated goal of requiring almost zero code changes to the agent itself.

It supports selectively optimizing one or more agents within a multi-agent system, rather than requiring an all-or-nothing training setup, and works regardless of which (if any) agent framework the target agent was built with. The project ships as the PyPI package agentlightning.

MIT licensed and maintained by Microsoft, Agent Lightning positions itself less as an agent framework and more as a training harness that sits alongside whatever framework you already use.

What You Get

• A training wrapper that applies to agents built with LangChain, AutoGen, CrewAI, OpenAI Agents SDK, or no framework
• Support for multiple optimization algorithms: reinforcement learning, automatic prompt optimization, and supervised fine-tuning
• Selective optimization of specific agents within a multi-agent system, rather than an all-or-nothing setup
• A CLI and Python package (agentlightning on PyPI) for integrating training into an existing agent pipeline

Common Use Cases

• Systematically improving an existing agent’s task performance instead of manually iterating on prompts
• Applying reinforcement learning to optimize one agent’s behavior within a larger multi-agent system without retraining everything
• Automatically tuning prompts for an agent built in any framework, rather than hand-writing prompt variations
• Fine-tuning an agent’s underlying model based on real task performance data collected during runs

Under The Hood

Architecture The agentlightning package separates concerns into algorithm (the training methods — RL, prompt optimization, fine-tuning), adapter (framework-specific integration points for LangChain/AutoGen/CrewAI/etc.), runner and execution (running agent episodes for training), emitter/instrumentation (capturing signals from agent runs), and reward.py (defining what “better” means for the optimization loop) — a design that keeps the training algorithm decoupled from how any specific agent framework executes, which is what allows near-zero-code-change integration.

Tech Stack Python, distributed via PyPI, with a CLI for running training jobs and an llm_proxy.py component suggesting requests are intercepted/proxied to collect training signal from live agent calls. Documentation is published via GitHub Pages, with unit test coverage tracked through GitHub Actions.

Code Quality The project has dedicated unit test CI badges and structured documentation, though GitHub activity metrics show relatively low recent commit velocity for a project of its visibility — worth checking current activity before depending on it for cutting-edge features.

What Makes It Unique Most agent-optimization approaches require rewriting an agent to work within a specific training framework; Agent Lightning’s adapter-based design instead lets it sit alongside an agent built in any framework (or none), applying RL/prompt-optimization/fine-tuning as an external training loop rather than requiring the agent to be restructured around it.

Self-Hosting

Licensing Model MIT licensed — fully open source with no license key.

Self-Hosting Restrictions None found; the package runs entirely within your own training/execution environment.

License Key Required No.