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

pip install agentlightning

For latest nightly build:

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)

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)

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

# 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

agent-lightning train --config config.yaml

Algorithms

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

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
)