This study addresses pressing issues in the efficient control of nuclear reactors using modern methods. The primary focus is on developing a point kinetics model of a reactor that accounts for nonlinear factors while ensuring computational efficiency. The Python programming language is used for model development, offering access to a wide range of libraries and simulation tools. The research outlines the principal methods of nuclear reactor modeling, including the Monte Carlo method, diffusion approximation, and kinetics modeling, as well as the principles underlying the dynamic reactor model. The developed Python library integrates both static and dynamic components to determine reactor parameters and their temporal variations. Simulation results are presented, showcasing reactor behavior during sudden reactivity changes while considering feedback effects. The developed model is open-source, enabling scientists and engineers to use it for analyzing, optimizing, and managing nuclear reactors, thereby contributing to technological advancements and knowledge dissemination in this field.