agent-lightning

---
name: agent-lightning
description: Microsoft Research's agent training framework. Optimizes AI agents with Reinforcement Learning, Automatic Prompt Optimization, and Supervised Fine-tuning. Zero code change required. Works with LangChain, AutoGen, CrewAI, OpenAI Agent SDK.
version: "1.0.0"
author: "Microsoft Research"
license: "MIT"
repository: "https://github.com/microsoft/agent-lightning"
homepage: "https://microsoft.github.io/agent-lightning/"
tags:
  - "agent-training"
  - "reinforcement-learning"
  - "prompt-optimization"
  - "fine-tuning"
  - "microsoft"
  - "rlhf"
  - "agent-improvement"
keywords:
  - "AI agent training"
  - "reinforcement learning agents"
  - "automatic prompt optimization"
  - "agent fine-tuning"
  - "RL for agents"
category: "ai-training"
---

# Agent Lightning ⚡

Microsoft Research's agent training framework. Turn your AI agents into optimizable beasts with (almost) zero code changes.

## Core Features

- **🔌 Universal Compatibility**: Works with LangChain, OpenAI Agent SDK, AutoGen, CrewAI, Microsoft Agent Framework, or plain Python OpenAI
- **🎯 Selective Optimization**: Optimize one or more agents in a multi-agent system
- **🧠 Multiple Algorithms**: Reinforcement Learning (RL), Automatic Prompt Optimization (APO), Supervised Fine-tuning (SFT)
- **⚡ Zero Code Change**: Add `agl.emit_xxx()` helpers or use tracer — your agent keeps running as usual

## Installation

```bash
pip install agentlightning
```

For latest nightly build:
```bash
pip install --upgrade --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple/ --pre agentlightning
```

## Quick Start

### 1. Instrument Your Agent

**Option A: Add emit helpers (recommended)**
```python
import agentlightning as agl

# In your agent's tool calls
response = agl.emit_tool_call(
    model=model,
    messages=messages,
    tools=tools,
    context={"task": "search"}
)
```

**Option B: Use tracer (zero code change)**
```python
from agentlightning import tra