ART (Agent Reinforcement Trainer) is an early alpha release of a reinforcement learning framework designed to easily train large language model-based agents using GRPO and other techniques. The framework addresses key limitations of existing frameworks, including multi-turn roll-outs, GPU efficiency, and integration with existing codebases. ART's new architecture separates the frontend and backend, allowing for easier embedding in existing production applications, and provides an industry-standard OpenAI-compatible chat completion endpoint for maximizing ecosystem compatibility. Early results include successful training of models on tasks such as title generation, 2048, tic-tac-toe, Clue, and more. The project is open-source and invites community participation to shape its direction.