# Awesome-OL: An Extensible Toolkit for Online Learning

arXiv: https://arxiv.org/abs/2507.20144 · HTML: https://mlsanity.com/paper/2507.20144/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 24 references that are in the map.
- First posted on arXiv 27 Jul 2025.

## Builds on (most cited first, 5 of 5)

- [Learning under Concept Drift: A Review](https://mlsanity.com/paper/2004.05785/) (2020)
- [Online Learning: A Comprehensive Survey](https://mlsanity.com/paper/1802.02871/) (2018)
- [River: machine learning for streaming data in Python](https://mlsanity.com/paper/2012.04740/) (2020)
- [Open-environment Machine Learning](https://mlsanity.com/paper/2206.00423/) (2022)
- [Multi-Condition Fault Diagnosis of Dynamic Systems: A Survey, Insights, and Prospects](https://mlsanity.com/paper/2412.19497/) (2024)

## Abstract (arXiv)

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.