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It addresses the lack of sophisticated packages for CAT in the Python programming language. Moreover, it bridges the gap between the construction and simulation of adaptive tests and their practical application by providing a dedicated API for integration with experiment software. Thereby, it eliminates the need for major code rewrites when transitioning from simulated to real-world adaptive testing. By leveraging Python\u2019s object-oriented programming approach, such as abstract classes, protocols, and inheritance, the package allows for easy extension and customization of its functionality. For example, Bayesian estimators can be modified to incorporate custom priors. This paper outlines the relevance and practical use of the  package through a walkthrough example. The package is fully documented, and its source code is published on GitHub. 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The dataset used in the application examples is obtained from Bohn et al. (\n                      \n                      ) and is available in the following GitHub repository under the Creative Commons Attribution 4.0 International License (CC BY 4.0):\n                      \n                      . No changes were made to the original dataset.","order":6,"name":"Ethics","label":"Open practices statement","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"250"}}