This repository provides a PyTorch implementation of E(n)-Equivariant Graph Neural Networks, a graph neural network architecture that maintains equivariance under geometric transformations. The library includes both EGNN layers and full network constructs, supporting operations on graph-structured data where both features and coordinates are updated. It has been applied to molecular activity prediction, dynamical system modeling, and benchmarks against other geometric deep learning methods like SE3 Transformers.