Garage provides a comprehensive framework for developing and evaluating reinforcement learning algorithms. It includes composable neural network models, replay buffers, high-performance samplers, and an expressive experiment definition interface. The toolkit supports reproducibility through global random seeds, logging to TensorBoard, and reliable experiment checkpointing and resuming. It ships with implementations of state-of-the-art RL algorithms and interfaces for popular benchmark suites.