Atomistic simulations are an important tool in materials modeling. Interatomic potentials (IPs) are at the heart of such molecular models, and the accuracy of a model’s predictions depends strongly on the choice of IP. Uncertainty quantification (UQ) is an emerging tool for assessing the reliability of atomistic simulations. The Open Knowledgebase of Interatomic Models (OpenKIM) is a cyberinfrastructure project whose goal is to collect and standardize the study of IPs to enable transparent, reproducible research. Part of the OpenKIM framework is the Python package, KIM-based Learning-Integrated Fitting Framework (KLIFF), that provides tools for fitting parameters in an IP to data. We introduce UQ toolbox in KLIFF, which focuses on the uncertainty due to variations in parameters. There are two methods currently implemented in KLIFF, namely the Bayesian Markov Chain Monte Carlo (MCMC) and bootstrap methods. We apply the MCMC method to an empirical potential, namely the Stillinger–Weber potential for a silicon system. Then, we demonstrate the bootstrap method with a neural network potential and explain the interface in KLIFF. Finally, we discuss some best practices and the common pitfalls for each method.