TorchOpt provides efficient, functional differentiable optimization primitives built on PyTorch. It focuses on implicit differentiation and bilevel optimization, supporting use cases like meta-learning, meta-reinforcement learning, and gradient-based neural architecture search. The library enables computing higher-order gradients through optimization problems, making it particularly suited for training scenarios requiring gradient-through-gradient computations.