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Boolean satisfiability (SAT) solvers are a family of highly efficient reasoning engines, which are frequently used for solving a large and diverse variety of practical challenges. This applies to multidisciplinary problems belonging to the class NP but also those arising at higher levels of the polynomial hierarchy. Unfortunately, encoding a problem of user’s interest to a (series of) propositional formula(s) in conjunctive normal form (CNF), let alone dealing with a SAT solver, is rarely a simple task even for an experienced SAT practitioner. This situation gets aggravated further when the user has little to no knowledge on the operation of the modern SAT solving technology. In 2018, the PySAT framework was proposed to address the issue of fast and "painless" prototyping with SAT solvers in Python allowing researchers to get SAT-based solutions to their problems without investing substantial time in the development process and yet sacrificing only a little in terms of performance. Since then, PySAT has proved a useful instrument for solving a wide range of practical problems and is now a critical package for the PyPI infrastructure. In the meantime, there have been advances in SAT solving and enhancements to PySAT functionality to extend its modelling and solving capabilities in order to make modern SAT technology accessible and deployable on a massive scale. This paper provides a high-level overview of the current architecture of PySAT and some of its capabilities including arbitrary Boolean formula manipulation, CNF preprocessing, and support for external user-defined propagators.