The Computational Psychiatry Modeling (cpm) toolbox is a Python library for theory-driven computational psychiatry. cpm integrates a wide range of state-of-the-art computational methods with the primary goal of enabling non-expert researchers to conduct cutting-edge computational modeling following the best practices of computational modeling and reporting. The toolbox’s architecture is built flexibly and modularly to easily adjust to different needs and is covering a wide range of tasks (such as risky decision-making, learning from rewards and punishments, and making perceptual decisions with confidence assessments), models (including associative and reinforcement learning, and signal detection theory) and methods (such as hierarchical hyperparameter estimation using empirical and variational Bayes techniques). The cpm toolbox thus provides the means for novices and experts in computational modeling alike to rapidly model their data and to adhere to the best practices. We believe that building such an overarching, customizable tool can facilitate access to computational modeling and thus jumpstart computational approaches in psychiatry and beyond.