---
name: agent-lightning-01e7011d84f9
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 tracer

# Wrap your agent with tracer
with tracer.trace("my-agent", input_data):
    result = your_agent.run(user_query)
```

### 2. Create Training Config

```yaml
# config.yaml
agent:
  name: "my-agent"
  type: "openai"  # openai, langchain, autogen, crewai

training:
  algorithm: "grpo"  # grpo, apo, sft, rloo
  episodes: 100
  batch_size: 16
  
environment:
  eval_tasks:
    - "math"
    - "coding"
    - "reasoning"
```

### 3. Run Training

```bash
agent-lightning train --config config.yaml
```

## Algorithms

| Algorithm | Use Case | Description |
|-----------|----------|-------------|
| **GRPO** | General RL | Group Relative Policy Optimization — stable, works well for most agents |
| **APO** | Prompt Tuning | Automatic Prompt Optimization — improves system prompts |
| **SFT** | Supervised Fine-tuning | Supervised Fine-tuning with preference data |
| **RLOO** | Long-horizon | RLOO for tasks with sparse rewards |

## Usage Commands

### `agent-lightning train`
Train your agent with configured algorithm.

### `agent-lightning eval`
Evaluate agent on benchmark tasks.

### `agent-lightning export`
Export trained model/prompts for deployment.

### `agent-lightning serve`
Launch serving endpoint for trained agent.

## Example: SQL Agent Training

See full example: [Train SQL Agent with RL](https://microsoft.github.io/agent-lightning/stable/how-to/train-sql-agent/)

```python
from agentlightning import Agent, RLConfig, GRPOTrainer

# 1. Define your agent
sql_agent = Agent(
    name="sql-agent",
    system_prompt="You are a SQL expert...",
    tools=[execute_sql, query_schema]
)

# 2. Configure RL training
config = RLConfig(
    algorithm="grpo",
    episodes=500,
    learning_rate=1e-4
)

# 3. Train
trainer = GRPOTrainer(config=config)
trainer.train(sql_agent, eval_tasks=["sql-generation"])
```

## Integration with Clawdbot

### Environment Variables

```bash
# Required for training
export OPENAI_API_KEY="sk-..."

# Optional: for remote storage
export AGL_STORAGE="s3://my-bucket/agent-lightning/"
```

### Python API

```python
from agentlightning import LightningStore, GRPOTrainer

# LightningStore keeps tasks, resources, and traces in sync
store = LightningStore()

# Read traces, learn, and update prompts
trainer = GRPOTrainer(store=store)
trainer.train(agent=my_agent)
```

## Monitoring Training

```bash
# Launch dashboard
agent-lightning dashboard --port 8080

# View logs
tail -f ~/.agent-lightning/logs/training.log
```

## Best Practices

1. **Start Small**: Begin with 10-50 episodes to verify setup
2. **Define Clear Rewards**: Design reward functions that match your goal
3. **Use Evaluation Tasks**: Always eval on held-out tasks
4. **Checkpoint Frequently**: Save model every N episodes
5. **Monitor Convergence**: Watch loss curves in dashboard

## Resources

- [Documentation](https://microsoft.github.io/agent-lightning/)
- [Examples](https://github.com/microsoft/agent-lightning/tree/main/examples)
- [API Reference](https://microsoft.github.io/agent-lightning/stable/reference/)
- [ArXiv Paper](https://arxiv.org/abs/2508.03680)
- [Discord Community](https://discord.gg/RYkC7dvDR7)

## Citation

If you use Agent Lightning in research:

```bibtex
@misc{luo2025agentlightningtrainai,
  title={Agent Lightning: Train ANY AI Agents with Reinforcement Learning},
  author={Xufang Luo and Yuge Zhang and Zhiyuan He and Zilong Wang and Siyun Zhao and Dongsheng Li and Luna K. Qiu and Yuqing Yang},
  year={2025},
  eprint={2508.03680},
  archivePrefix={arXiv},
  primaryClass={cs.AI}
}
```


---
Source attribution: modbender
Original project: https://github.com/modbender/skill-library-mcp/blob/d9c8fe903b5b4e82237dbe8d0df8567783022e18/data/agent-lightning/SKILL.md
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2025 skill-library-mcp contributors

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: modbender
Original project: https://github.com/modbender/skill-library-mcp/blob/d9c8fe903b5b4e82237dbe8d0df8567783022e18/data/agent-lightning/SKILL.md
Attribution basis: Source repository record

Fields of work: Data & Analytics, Development & Engineering, Product Management
Roles: Data Scientist, Growth Product Manager, ML / AI Engineer, Product Manager, Technical Product Manager
Specialties: API
Tools: LangChain, OpenAI API
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: ae09da67a7e4568c793cd0813053e3de3f26e8dd96768684a3bba01f28cf355c