OpenPipe/ART

Health v2 · maintenance only

Adoption not part of the score

10665 stars · 982 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

ART (Agent Reinforcement Trainer) is an open-source Python framework for training multi-step LLM agents using reinforcement learning techniques like GRPO. It provides an ergonomic client-server harness that lets agents learn from experience, with support for models like Qwen, GPT-OSS, and Llama via LoRA fine-tuning.

Use cases

• train llm agents with reinforcement learning
• improve agent reliability with grpo
• fine-tune qwen or llama for multi-step tasks
• rl training for agentic workflows
• on-the-job training for ai agents
• serverless rl training without gpu setup

When to choose

• you want to improve an existing LLM agent's reliability through RL
• you need GRPO-based training with minimal infrastructure management
• you want to train open models like Qwen or Llama with LoRA
• you prefer a client-server setup that can train from any Python machine

When to avoid

• you only need simple supervised fine-tuning or prompt engineering
• you have no reward signal or evaluation environment for your agent
• you need to train non-LLM models or non-agent workloads

Facets

framework · maturity active

llm-training reinforcement-learning agent-framework reinforcement-learning large-language-models machine-learning python cloud grpo lora rl-training agentic-ai qwen openpipe wandb multi-step-agents ai-agents gpu docker

Member repositories

For agents

markdown · JSON · MCP: product_card(name="OpenPipe/ART")