Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub ยท Developer Tools
Agent Lightning
Microsoft Research's agent training framework. Optimizes AI agents with Reinforcement Learning, Automatic Prompt Optimization, and Supervised Fine-tuning. Ze...
skill openclawclawhub Free
Microsoft Research's agent training framework. Optimizes AI agents with Reinforcement Learning, Automatic Prompt Optimization, and Supervised Fine-tuning. Ze...
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Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md, _meta.json, examples/config.yaml, examples/train_agent.py
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.0
Provenance
- Publisher
- olmmlo-cmd
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
Agent Lightning โก
Microsoft Research's agent training framework. Turn your AI agents into optimizable beasts with (almost) zero code changes.
Core Features
๐ Universal Compatibility: Works with LangChain, OpenAI Agent SDK, AutoGen, CrewAI, Microsoft Agent Framework, or plain Python OpenAI ๐ฏ Selective Optimization: Optimize one or more agents in a multi-agent system ๐ง Multiple Algorithms: Reinforcement Learning (RL), Automatic Prompt Optimization (APO), Supervised Fine-tuning (SFT) โก Zero Code Change: Add agl.emit_xxx() helpers or use tracer โ 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
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
AlgorithmUse CaseDescriptionGRPOGeneral RLGroup Relative Policy Optimization โ stable, works well for most agentsAPOPrompt TuningAutomatic Prompt Optimization โ improves system promptsSFTSupervised Fine-tuningSupervised Fine-tuning with preference dataRLOOLong-horizonRLOO for tasks with sparse rewards
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 ) # 3. Train trainer = GRPOTrainer(config=config) trainer.train(sql_agent, eval_tasks=["sql-generation"])
Environment Variables
# Required for training export OPENAI_API_KEY="sk-..." # Optional: for remote storage export AGL_STORAGE="s3://my-bucket/agent-lightning/"
Python API
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
# Launch dashboard agent-lightning dashboard --port 8080 # View logs tail -f ~/.agent-lightning/logs/training.log
Best Practices
Start Small: Begin with 10-50 episodes to verify setup Define Clear Rewards: Design reward functions that match your goal Use Evaluation Tasks: Always eval on held-out tasks Checkpoint Frequently: Save model every N episodes Monitor Convergence: Watch loss curves in dashboard
Resources
Documentation Examples API Reference ArXiv Paper Discord Community
Citation
If you use Agent Lightning in research: @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} }
Category context
Code helpers, APIs, CLIs, browser automation, testing, and developer operations.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
2 Config1 Docs1 Scripts
- SKILL.md
- examples/train_agent.py
- _meta.json
- examples/config.yaml