AutoGL automates graph learning tasks by handling neural architecture search, hyperparameter optimization, and model selection. Built on PyTorch and PyTorch-Geometric, it provides a full pipeline for graph neural network development including graph robustness algorithms, self-supervised learning modules, and integration with NAS-Bench-Graph for benchmark-based architecture search.