All 47 Dependencies
Every package ART depends on, ranked by repo health score.
ART (Agent Reinforcement Trainer) is an open-source Python framework by OpenPipe that brings reinforcement learning directly into multi-step LLM agent workflows. Rather than requiring practitioners to become RL researchers, ART wraps the complexity of GRPO training behind an ergonomic client/server architecture that slots into any existing Python application. You define your agent workflow, assign rewards at the end of each rollout, and ART handles the rest—gradient updates, LoRA checkpointing, and inference routing through vLLM.
The framework ships two execution modes: a local backend for teams with their own GPU infrastructure, and a serverless backend powered by W&B Training, which manages GPU provisioning, inference clusters, and checkpoint publishing automatically. Both expose an identical API so switching between them requires only a one-line backend swap. Out of the box, ART supports Qwen 3, Llama, Mistral, and any model compatible with vLLM and Unsloth.
Beyond vanilla GRPO, ART introduces RULER—a zero-labeled-data reward function that uses an LLM judge to relatively rank trajectories within each training group, removing the need for handcrafted reward engineering for most tasks. AutoRL extends this further by generating training inputs automatically, enabling training on tasks where no dataset exists at all. Integrations with LangGraph, MCP servers, W&B, Langfuse, and OpenPipe's own observability platform provide production-grade debugging and monitoring throughout the training loop.
Since its release in March 2025 ART has accumulated over 10,000 GitHub stars and 58 releases, driven by demonstrated results—a Qwen 2.5 14B email-retrieval agent trained with ART outperformed OpenAI's o3 on the benchmark the team designed.
