OCTIS is a Python package for training, optimizing, and evaluating topic models. It supports classical approaches like Latent Dirichlet Allocation, Non-negative Matrix Factorization, and neural topic models with Bayesian optimization for hyperparameter tuning. The library provides evaluation metrics for comparing model performance and includes pre-built benchmark datasets. It was presented at the EACL 2021 demo track.