| 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
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
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
with tracer.trace("my-agent", input_data):
result = your_agent.run(user_query)
2. Create Training Config
agent:
name: "my-agent"
type: "openai"
training:
algorithm: "grpo"