Spearmint is an open-source software package designed to perform Bayesian optimization, a framework particularly useful for optimizing hyperparameters in machine learning models. Hosted on GitHub, Spearmint is maintained by the Harvard Intelligent Probabilistic Systems (HIPS) group. It employs Gaussian processes to model the objective function robustly, allowing for efficient exploration and exploitation of the search space. Spearmint's algorithm is useful for tuning algorithms where objective function evaluations are costly or time-consuming. The project provides a practical and sophisticated implementation for researchers and developers seeking to automate the hyperparameter tuning process.