Poker

It provides the core computational components that game developers, poker study tools, and AI poker agents need: deck and card representations, hand history parsing, equity calculation across hand ranges, and simulation of multi-player scenarios under various board run-outs.

The library's hand evaluator is optimized for speed, enabling the rapid equity computations that real-time poker analysis tools require.

The hand evaluation engine supports all major poker variantsTexas Hold'em, Omaha, and othersand can enumerate or sample from the space of possible board run-outs to calculate equity percentages across ranges of starting hands. This range vs.

range equity calculation is the foundation of modern poker study: understanding not just how two specific hands fare against each other, but how an entire range of hands performs under given board conditions and betting patterns.

The library's API is designed for programmatic use, making it straightforward to build custom study tools, training simulators, or bot engines on top of.

Poker AI researchers building game-theoretically optimal (GTO) solvers, poker training application developers, and data scientists studying game theory applications use this library as a foundation.

The clean Python implementation makes it accessible for prototyping without the performance overhead of production C++ engines like those used in commercial GTO tools.