Gaussian Processes (GPs) are a very flexible class of nonparametric models frequently used in supervised learning tasks because of their ability to fit data with very few assumptions, namely just the type of correlation (kernel) the data is expected to display. Automatic Bayesian Covariance Discovery (ABCD) is an iterative GP regression framework aimed at removing the requirement for even this initial correlation form assumption. An original ABCD implementation exists and is a complex stand-alone system designed to produce long-form text analyses of provided data. This paper presents a lighter, more functional and configurable implementation of the ABCD idea, outputting only fit models and short descriptions: the Python package GPy-ABCD, which was developed as part of an adaptive modelling component for the FRANK query-answering system. It uses a revised model-space search algorithm and removes a search bias which was required in order to retain model explainability in the original system.