BOA Constrictor is an open-source implementation of a Bytewise Online Autoregressive (BOA) compressor built on Mamba state-space models. It targets lossless compression of High Energy Physics (HEP) datasets. The compressor couples a compact Mamba model (order of a few megabytes) with a parallel range coder to predict and encode bytes in a fully online fashion. On representative ATLAS and CMS datasets, BOA achieves substantially higher compression ratios than a strong classical baseline (LZMA-9), while remaining strictly lossless and respecting the original serialisation. The repository includes: Training and evaluation pipelines for Mamba-based bytewise models A streaming encoder/decoder interface suitable for large HEP files Scripts to reproduce key metrics from the paper (compression ratio, throughput, reliability diagrams, Top-k accuracy, confusion matrices) Example configs for adapting BOA to new datasets This Zenodo record provides an archived snapshot of the BOA Constrictor code corresponding to the results in the paper “Boa Constrictor: ML-Enhanced Lossless Compression Algorithms for HEP”. If you use this software in academic work, please cite both this software record and the associated paper.