ART

ART is an open-source training framework that allows developers to improve the performance and reliability of agentic LLMs using reinforcement learning.

ART is an open-source training framework designed to teach agentic LLMs through experience. It provides a wrapper around reinforcement learning techniques, such as Group Relative Policy Optimization (GRPO), to improve model performance while minimizing training costs. The software abstracts the training server into a modular service that functions independently of the user's codebase. It supports training on local GPUs or ephemeral GPU-enabled environments in the cloud. Integrations with observability tools like W&B, Langfuse, and OpenPipe facilitate debugging, while LangGraph and OpenEnv support specific agent workflows.

ART at a glance

Best for: Software engineer, Researcher
Runs on: open source, self-hosted
Pricing: Free
Released: March 2025
Category: AI coding tool
Website: art.openpipe.ai

Key features

Reinforcement learning via Group Relative Policy Optimization (GRPO)
Modular training server abstraction
Support for local or cloud-based GPU training
Integration with W&B, Langfuse, and OpenPipe
Checkpoint management including forking and deletion
Tracking metrics for model performance
MCP and SFT training capabilities

Use it for

Training deep research agents to parse information from knowledge stores
Correcting specific agent behaviors identified during production or QA
Building voice agents that follow strict scripts

Companions

Tools that articles about ART recommend using with it, each with its job.

Context7 — Provides up-to-date documentation for AI agents and code editors.
Ollama — Run local models at zero marginal cost, with code kept on your machine.
OpenRouter — Access over 500 models and 80 providers through one unified API.
Daytona — Runs sessions in isolated sandboxes synced to git branches.
Composio — Equips AI agents with tools, managed user auth, and sandboxes across 1,500+ apps
Oh My OpenCode Slim — Lightweight multi-agent orchestration and background tasks.

Alternatives to ART

rLLM — Enables reinforcement learning training for AI agents with minimal code changes.
Agent Lightning — Train existing artificial intelligence agents with reinforcement learning and zero code changes.
OpenRLHF — High-performance distributed architecture and unified agent-based execution
AReaL — Bridges foundation model training with modern agent-based applications.
RLinf — Scalable open-source framework supporting real-world robots and simulators.
NeMo Molt — It scales fully-async agentic RL to frontier MoE scale with a small PyTorch stack.
ARTEX — Runs autonomous multi-agent penetration testing with a dual-graph architecture.
OpenCode — Works with 75+ model providers, local models, and existing subscriptions
XTuner — High-efficiency training for 200B+ scale MoE models without expert parallelism.
RAGEN — Diagnoses agent failures and stabilizes reasoning training.
pstack — Improves AI coding agent outcomes with rigorous workflows.
Stagewise — Maintain full sovereignty over AI models while running agents in parallel.

All ART alternatives