Understanding ion transport in functional materials is crucial to unravel complex chemical reactions, improve rate per- formance of materials for energy storage and conversion, and optimize catalysts. To model ionic transport, atomistic simulations, including molecular dynamics (MD) and kinetic Monte Carlo (kMC) have been developed and applied to shed light on intricate materials science and chemistry problems. Typically, kMC simulations are utilized to a lower extent compared to MD due to a lack of systematic workflows to construct a model for predicting transition rates. Here, we propose kMCpy, a light-weight, customizable, and modular python package to compute the ionic transport prop- erties in crystalline materials using kMC that can be combined with a (local) cluster expansion Hamiltonian derived from first-principles calculations. kMCpy is versatile with respect to any type of crystalline material, bearing any dimen- sionality, such as 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, which can be used in high-throughput simulations for the discovery and optimization of novel functional materials.