Trace generalizes back-propagation to capture and propagate AI system execution traces for optimization. It is implemented as a PyTorch-like Python library where users write standard Python code and use Trace primitives to optimize specific parts of AI systems, similar to training neural networks. The platform supports diverse feedback signals enabling end-to-end training of compound AI systems and LLMs.