There is an endeavor in the Human-Computer Interaction (HCI) community to create novel authentication schemes so that passwords finally become obsolete and practical security is enhanced. For this purpose, researchers combine behavioral biometrics with deep learning. However, because the process of creating neural networks is inherently complex and each model architecture has certain limitations, implementing research prototypes is a time-consuming and challenging task. Therefore, we present useckit, an open-source toolkit that provides deep learning algorithms to support the creation of scientific evaluations in authentication research. Useckit provides multiple paradigms for implementing user verification and identification, functions to calculate common metrics, and neural network architectures founded in literature. It is written in Python and supports researchers and practitioners in creating, implementing, and rigorously evaluating novel deep learning-based authentication schemes.