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PySAD

github.com

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Spec score 7.4 #14 of 22 in Anomaly Detection Software

What it is

PySAD is a free, open-source Python framework for online anomaly detection in streaming data. Its models update when each new data instance arrives, supporting sequential detection for univariate and multivariate data. The project describes 16 online detectors, including xStream, LODA, RS-Hash, Half-Space Trees, and Robust Random Cut Forest. It also provides stream simulators, evaluators, preprocessors, statistic trackers, postprocessors, and probability calibrators. Integrations allow batch anomaly detectors from PyOD to run in a streaming setting. Experiments can use supervised, semi-supervised, or unsupervised settings. The documentation says streaming methods may retain one instance or a small recent window to meet memory and processing constraints. Installation is available through pip or the GitHub source; the current README lists Python 3.10 or newer on Linux, macOS, and Windows. PySAD is self-hosted and distributed under a BSD 3-Clause license. Its plan costs nothing, and 17 labelled benchmark datasets are downloaded on first use.

Spec sheet

Purpose
PySAD is an open-source Python framework for anomaly detection on streaming multivariate data.Read 2 Oct 2026 · pysad.readthedocs.io
Security
The repository links to a security policy, but the policy page could not be opened during this research.Read 2 Oct 2026 · github.com
Support
The maintainer says issues and pull requests are welcome and aims to reply within a few days; questions and show-and-tell go in GitHub Discussions.Read 2 Oct 2026 · github.com
License
The project is distributed under a BSD 3-Clause license.Read 2 Oct 2026 · github.com
Benchmarks
The README says 17 labelled benchmark datasets are downloaded on first use.Read 2 Oct 2026 · github.com
Data and learning settings
The framework supports models for univariate and multivariate data and experiments in supervised, semi-supervised, and unsupervised settings.Read 2 Oct 2026 · pysad.readthedocs.io
Detectors
The repository describes 16 online detectors, including xStream, LODA, RS-Hash, Half-Space Trees, and Robust Random Cut Forest.Read 2 Oct 2026 · github.com
Evaluation tools
PySAD includes stream simulators, evaluators, preprocessors, statistic trackers, postprocessors, and probability calibrators.Read 2 Oct 2026 · pysad.readthedocs.io
Installation
The project can be installed with pip or from its GitHub source, and its current README lists Python 3.10 or newer on Linux, macOS, and Windows.Read 2 Oct 2026 · github.com
Maintainer
The GitHub profile identifies Selim Firat Yilmaz as an AI researcher based in London, UK.Read 2 Oct 2026 · github.com
Online detection
Its models update as each new data instance arrives for online or sequential anomaly detection.Read 2 Oct 2026 · pysad.readthedocs.io
PyOD integration
PySAD provides integrations that let batch anomaly detectors from PyOD run in a streaming setting.Read 2 Oct 2026 · pysad.readthedocs.io
Resource use
The documentation says streaming methods may store only an instance or a small window of recent instances to meet memory and processing constraints.Read 2 Oct 2026 · pysad.readthedocs.io

Plans and prices

PySAD Free Open-source Python framework Read 2 Oct 2026 · github.com

Every PySAD plan and price →

Strong points and limits

Strong points

  • Models update as each data instance arrives.
  • Includes 16 online detectors.
  • Integrates batch detectors from PyOD for streaming use.
  • Supports Linux, macOS, and Windows.

Limits

  • Requires Python 3.10 or newer.
  • 17 labelled benchmark datasets download on first use.
  • Deployment is self-hosted.

Who it suits

PySAD suits Python users working on streaming anomaly detection, including those evaluating univariate or multivariate data in supervised, semi-supervised, or unsupervised settings.

Verdict

PySAD provides a broad set of streaming detection and evaluation components at no cost. Its Python version requirement and self-hosted deployment are important setup considerations.

Where Specifiction places it

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