RAGEN is a modular system for training and evaluating LLM agents using multi-turn reinforcement learning. Built on the StarPO framework, it implements the full training loop including rollout generation, reward assignment, and trajectory optimization. RAGEN serves as research infrastructure to analyze LLM agent training dynamics, focusing on challenges like stability, generalization, and the emergence of reasoning in interactive environments.