Recent years have seen the vast potential of Graph Neural Networks (GNN) in many fields where data is structured as graphs (e.g., chemistry, logistics). However, implementing a GNN prototype is still a cumbersome task that requires strong skills in neural network programming. This poses an important barrier to researchers and practitioners that want to apply GNN to their specific problems but do not have the needed Machine Learning expertise. In this paper, we present IGNNITION, a novel open-source framework for fast prototyping of GNNs. This framework is built on top of TensorFlow, and offers an intuitive high-level abstraction that allows the user to define its GNN model via a YAML file, being completely oblivious to the tensor-wise operations made internally by the model. At the same time, IGNNITION offers great flexibility to build any GNN-based architecture. To showcase its versatility, we implement two state-of-the-art GNN models applied to the field of computer networks, which differ considerably from well-known standard GNN architectures. Our evaluation results show that the GNNs produced by IGNNITION are equivalent in performance to implementations directly coded in TensorFlow.