CEBRA implements contrastive learning algorithms for joint behavioral and neural data analysis. It produces consistent latent spaces by jointly using behavioral and neural data in a hypothesis-driven or discovery-driven manner. The library improves decoding accuracy of behavioral variables over standard supervised learning approaches and supports a variety of datasets common in biology and neuroscience.