AgentGym-RL provides a training framework and benchmark for developing autonomous LLM agents capable of sequential decision-making through multi-turn reinforcement learning interactions. It supports mainstream RL algorithms and evaluates agents across 27 tasks spanning diverse environments including web navigation, tool use, and code generation. The benchmark assesses open-source 7B-scale models against commercial baselines.