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agent-lightning

Train AI agents with reinforcement learning, prompt optimization, and fine-tuning.

1|Updated Apr 29, 2026
Search Tags:#openai#reinforcement learning#agent optimization#ai-training#training algorithms
One-click install
npx skills add https://github.com/bailynlove/STARK-TOWER --skill agent-lightning-bailynlove
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: agent-lightning
Source: https://github.com/bailynlove/STARK-TOWER/tree/main/jarvis/skills/agent-lightning
Command: npx skills add https://github.com/bailynlove/STARK-TOWER --skill agent-lightning-bailynlove

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires agentlightning, and includes scripts (resource) components.

What problem does it solve?

This Skill provides a seamless solution to train AI agents using reinforcement learning without the need for coding changes, allowing users to optimize their agents with ease.

Core Features & Use Cases

  • Zero Code Change: Use agl.emit_xxx() helpers or the tracer to train agents without altering your existing codebase.
  • Universal Compatibility: Supports LangChain, OpenAI Agent SDK, AutoGen, CrewAI, Microsoft Agent Framework, or plain Python OpenAI.
  • Multiple Algorithms: Offers Reinforcement Learning (RL), Automatic Prompt Optimization (APO), and Supervised Fine-tuning (SFT) for optimal agent training.
  • Quick Start: With minimal steps, users can train and evaluate agents, and export trained models.

Quick Start

To get started, instrument your agent by adding emit helpers to your code.

Use the command:

agent-lightning train --config config.yaml

Frequently Asked Questions about agent-lightning

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I train AI agents with reinforcement learning without modifying my codebase?▼

You can train AI agents with reinforcement learning without code changes by using emit helpers or a tracer to instrument your existing codebase. This allows you to optimize agents seamlessly without altering your current Python scripts.

Can I apply reinforcement learning to agents built with LangChain, AutoGen, or CrewAI?▼

Yes, you can apply reinforcement learning to agents built with LangChain, OpenAI Agent SDK, AutoGen, CrewAI, Microsoft Agent Framework, or plain Python OpenAI. The training tool offers universal compatibility across these frameworks.

What algorithms are available for AI agent optimization besides reinforcement learning?▼

Besides reinforcement learning, AI agent optimization algorithms include Automatic Prompt Optimization (APO) and Supervised Fine-tuning (SFT). These methods allow developers to comprehensively fine-tune and improve agent performance.

Do I need a specific Python environment to run agent training and prompt optimization?▼

Yes, you need a Python environment with specified libraries and dependencies, including the agentlightning package, to run agent training and prompt optimization. Ensure these prerequisites are installed before starting.

How do I start training an AI agent using a configuration file?▼

To start training an AI agent using a configuration file, instrument your agent by adding emit helpers to your code, then run the command `agent-lightning train --config config.yaml` to train, evaluate, and export your model.

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