GraphGym provides a highly modularized pipeline for GNN research covering data loading, model architecture, node/edge/graph-level tasks, and evaluation metrics. It enables reproducible experiments via configuration files and scalable management of thousands of parallel experiments with automated analysis across random seeds. The platform supports PyTorch Geometric and DeepSNAP backends, allowing researchers to register custom modules including data loaders, GNN layers, and loss functions.