Abstract Weighted Model Integration (WMI) is a popular technique for probabilistic inference that extends Weighted Model Counting (WMC) – the standard inference technique for inference in discrete domains – to domains with both discrete and continuous variables. However, existing WMI solvers each have different interfaces and use different formats for representing WMI problems. Therefore, we introduce pywmi (http://pywmi.org), an open source framework and toolbox for probabilistic inference using WMI, to address these shortcomings. Crucially, pywmi fixes a common internal format for WMI problems and introduces a common interface for WMI solvers. To assist users in modeling WMI problems, pywmi introduces modeling languages based on SMT-LIB.v2 or MiniZinc and parsers for both. To assist users in comparing WMI solvers, pywmi includes implementations of several stateof-the-art solvers, a fast approximate WMI solver, and a command-line interface to solve WMI problems. Finally, to assist developers in implementing new solvers, pywmi provides Python implementations of commonly used subroutines