Agent Lightning

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.

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

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
• Examples
• API Reference
• ArXiv Paper
• Discord Community

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}
}