MAGSAC is a C++ library with Python bindings (pymagsac) implementing robust model fitting algorithms for computer vision, including homography, fundamental matrix, essential matrix, rigid transformation, and 2D line estimation. The core algorithm is a template class `MAGSAC<DatumType, ModelEstimator>` that supports sigma-consensus and MAGSAC++ scoring, with interchangeable sampling strategies (uniform, PROSAC, P-NAPSAC, NG-RANSAC, AR-Sampler) and adaptive inlier selection. The Python API mirrors the C++ functionality via pybind11, accepting numpy arrays for point correspondences and returning estimated models with inlier masks. The repository also includes benchmark datasets and Jupyter notebook examples demonstrating usage. While the library is primarily a software package, it contains research-grade data and notebooks, giving it a hybrid character. The code is well-architected with clean separation of concerns, though it has minor maintenance risks such as memory management caveats and limited platform support (Linux/Windows only).