verl-agent extends the veRL framework to enable training LLM and VLM agents via reinforcement learning. It introduces a step-independent multi-turn rollout mechanism that allows fully customizable per-step input structures and history management, replacing traditional full-interaction concatenation. The project implements Group-in-Group Policy Optimization (GIGPO) for improved agent training, as published at NeurIPS 2025.