KaHIP (Karlsruhe High Quality Partitioning) is a high-performance C++ library and command-line toolkit for graph partitioning, node ordering, and mapping. It implements multilevel partitioning with advanced refinement techniques including Fiduccia-Mattheyses local search, flow-based min-cut refinement, cycle improvements, and tabu search. The library provides a C API for integration into external applications, along with Python bindings and a Java JNI wrapper. The codebase includes both a sequential version and a distributed-memory parallel version (ParHIP) using MPI, enabling partitioning of large graphs on clusters. Core algorithms are well-structured with modular coarsening, initial partitioning, and uncoarsening/refinement phases, supporting multiple matching strategies (GPA, random, label propagation) and quality/speed trade-offs via preconfigured modes (Strong, Eco, Fast, Social). Despite research-grade complexity and some code duplication between serial and parallel variants, the library is actively maintained and widely used in scientific computing and graph analytics.