Understanding ion transport in functional materials is crucial to unravel complex chemical reactions, improving the rate performance of materials for energy storage and conversion, and optimizing catalysts. To model ion transport, atomistic simulations, including molecular dynamics (MD) and kinetic Monte Carlo (kMC) have been developed and applied. However, kMC simulations are utilized to a lower extent than MDs due to a lack of systematic workflows to construct models for predicting transition rates. Here, we present kMCpy, a lightweight, customizable, and modular python package to compute the ionic transport properties in crystalline materials using kMC. kMCpy is remarkably versatile and user-friendly, making it a powerful code for studying materials kinetics in crystalline systems. kMCpy can be combined with (local) cluster expansion Hamiltonians derived from first-principles calculations. kMCpy is versatile with respect to any type of crystalline material, bearing any dimensionality, i.e., 1D, 2D, and 3D. kMCpy provides (i) a comprehensive workflow to enumerate all possible migration events in crystalline systems, (ii) to derive transition rates efficiently and at the accuracy of first-principles calculations, and (iii) a robust kMC solver to study kinetic phenomena in materials. The workflow implemented in kMCpy provides a systematic way to compute highly accurate kinetic properties. Hence, kMCpy can be used in high-throughput simulations for the discovery and optimization of novel functional materials.