Safety-Gymnasium offers a comprehensive set of environments for benchmarking safe reinforcement learning algorithms. It provides constrained RL scenarios where agents must optimize rewards while satisfying safety requirements. The benchmark includes various task types (navigation, manipulation) and is designed for reproducible evaluation of constraint-satisfaction and safe policy optimization methods.