Graph Neural Networks is a neural network architecture that has recently become more common in research publications and real-world applications. And since neural graph networks require modified convolution and pooling operators, many Python packages like PyTorch Geometric, StellarGraph, and DGL have emerged for working with graphs. In Keras Graph Convolutional Neural Network(kgcnn) a straightforward and flexible integration of graph operations into the TensorFlow-Keras framework is achieved using RaggedTensors. It contains a set of TensorFlow-Keras layer classes that can be used to build graph convolution models. The package also includes standard bench-mark graph datasets such as Cora,45 MUTAG46, and QM9.