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AgentJet

Distributed reinforcement learning training for agentic workflows

Trains and fine-tunes LLM-powered agents with reinforcement learning across distributed GPU clusters. Eliminates manual cluster setup, reward wiring, and training monitoring through ready-made swarm server and client workflows. Includes skills for installation, config mapping, experiment execution, log monitoring, and evaluation pipelines to speed up agent tuning.
npx skills add modelscope/AgentJet --all -g -y

All Skills in This Repository (1)

Pure Emerald Level Indicators

Frequently Asked Questions

FAQPage Schema
How to install AgentJet?โ–ผ

Run `npx skills add modelscope/AgentJet --all -g -y` in your terminal to install all skills in this suite globally.

What does AgentJet do?โ–ผ

AgentJet is a reinforcement learning framework that fine-tunes the models behind your agent workflows. It supports distributed swarm training, so you can launch training from a laptop while GPU servers do the heavy lifting.

Can I train agents without a local GPU?โ–ผ

Yes. Start the swarm server on a remote GPU machine with `ajet-swarm start`, then run a lightweight client script from any laptop to drive the training.

Which agent frameworks work with AgentJet?โ–ผ

It works with agents built on the OpenAI SDK, AgentScope, LangChain, or plain HTTP requests, as long as the agent can call an OpenAI-compatible endpoint.

How do I monitor an AgentJet training run?โ–ผ

Use the included tmux monitoring skill to watch training logs at increasing intervals, detect errors early, and get fix suggestions without sitting in front of the console.

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