To build effective agents for real-world file and tool interactions, you need systematic data synthesis, training on realistic rollout trajectories, and careful evaluation—ClawGym provides all three components together.
ClawGym is a framework for building AI agents that work with files, tools, and persistent workspaces through multi-step tasks. It includes a dataset of 13.5K synthesized tasks with realistic mock environments, trained agent models using supervised learning and reinforcement learning, and a benchmark for evaluation.