KWIVER is a large C++/Python computer vision and video analytics toolkit. Its architecture is a plugin-driven algorithm library (vital/algo), a dataflow pipeline framework (sprokit), and many backend implementations (arrows) that wrap OpenCV, VXL, Ceres, FFmpeg, GDAL, VTK, and other dependencies. In production it performs video/image I/O, feature detection and matching, structure-from-motion, object detection/tracking, geospatial metadata parsing, and serialization. It exposes command-line applets, an embedded pipeline API, Qt-based viewers, and pybind11 Python bindings. The system is configuration-driven: algorithms and processes are registered through macros, configured via vital::config_block, and instantiated dynamically through a plugin registry. The sprokit subsystem provides a pipeline DSL, schedulers, process clusters, and adapter ports for external data injection, making the project both a reusable library and a runtime pipeline environment. Although dominated by application/library code, the repository also serves research and defense-oriented use cases. It contains extensive MISB/KLV metadata parsers for multiple standards, multi-view geometry algorithms, track-file format readers/writers (kw18, KPF, XGTF, etc.), and PyTorch-based feature extractors. The hybrid nature is visible in the tension between production-grade pipeline infrastructure and research-oriented algorithms that remain experimental or under-documented. The codebase is actively maintained with recent commits and substantial CI infrastructure, but technical drift is medium due to the enormous optional dependency footprint, vendored copies of cereal/rapidjson/DBoW2, legacy modules such as track_oracle, and some deprecated APIs.