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OpenPipe/ART

An open-source framework for training multi-step LLM agents using reinforcement learning (GRPO) to improve their reliability and performance on real-world tasks, with an optional serverless training service.

Core Features

Enables LLM agents (e.g., Qwen, Llama) to learn from experience via reinforcement learning.

Integrates the GRPO algorithm for robust policy optimization.

Provides an ergonomic Python harness for easy application integration.

Offers an optional serverless training service (W&B Training) with managed infrastructure, lower costs, and faster feedback cycles.

Includes practical examples for various agent tasks like email search and game playing.

Quick Start

pip install openpipe-art

Detailed Introduction

ART (Agent Reinforcement Trainer) is an open-source framework designed to enhance the reliability and performance of multi-step AI agents, particularly those powered by Large Language Models (LLMs). It achieves this by enabling agents to learn from real-world experience through the application of the GRPO (Generalized Reinforcement Policy Optimization) algorithm. ART provides an ergonomic Python interface for seamless integration into existing applications. Additionally, it offers an optional serverless training service, W&B Training, which automates infrastructure management, reduces costs, and accelerates the reinforcement learning feedback loop, allowing developers to focus solely on defining data, environments, and reward functions.