PaCMAP is a production-ready Python library for dimensionality reduction and manifold learning, implementing the Pairwise Controlled Manifold Approximation Projection algorithm. The core package (source/pacmap/) provides a scikit-learn compatible API with BaseEstimator inheritance, enabling seamless integration into ML pipelines. The library supports multiple KNN backends (FAISS, Annoy, Voyager) through a unified abstraction layer, with heavy use of Numba JIT compilation for performance-critical operations like distance calculation and gradient updates. Research components (experiments/, evaluation/, data/) support algorithm validation against benchmarks like t-SNE and UMAP, but the primary deliverable is the reusable library itself. The codebase demonstrates strong engineering practices with comprehensive pytest coverage, model persistence mechanisms, and deterministic random state management across Numba functions.