BayBE implements Bayesian optimization methods for efficiently finding optimal configurations in high-dimensional parameter spaces. It provides surrogate model-based strategies, acquisition functions, and experimental design primitives that iteratively select promising evaluation candidates. The library is designed for scenarios like hyperparameter tuning, materials discovery, and process optimization.