In recent years, online learning has attracted increasing attention due to its adaptive capability to process streaming and non-stationary data. To facilitate algorithm development and practical deployment in this area, we introduce Awesome-OL, an extensible Python toolkit tailored for online learning research. Awesome-OL integrates state-of-the-art algorithm, which provides a unified framework for reproducible comparisons, curated benchmark datasets, and multi-modal visualization. Built upon the scikit-multiflow open-source infrastructure, Awesome-OL emphasizes user-friendly interactions without compromising research flexibility or extensibility. The source code is publicly available at: https://github.com/liuzy0708/Awesome-OL.
@article{arxiv.2507.20144,
title = {Awesome-OL: An Extensible Toolkit for Online Learning},
author = {Zeyi Liu and Songqiao Hu and Pengyu Han and Jiaming Liu and Xiao He},
journal= {arXiv preprint arXiv:2507.20144},
year = {2025}
}