TextGrad implements automatic differentiation via text by treating LLM outputs as differentiable signals. It provides a PyTorch-like API that enables defining custom loss functions and optimizing them through text feedback from LLMs. The framework treats prompts, model outputs, and system components as optimizable variables, computing textual gradients that guide the optimization process.