A Python library for anomaly detection across tabular, time series, graph, text, image, and audio data. 61 detectors, benchmark-backed ADEngine orchestration, and an agentic workflow for AI agents.
Tentative categories were assigned with moderate confidence (0.5 to 0.75): they are shown, but they do not count toward a category’s momentum. How categories work
PyPI publishes no adoption figure, so there is no growth to derive. Nothing is estimated in its place.
7 days
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30 days
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90 days
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1 year
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Adoption history
Adoption history
PyPI publishes no adoption count, so there is no history to chart. That is a fact about the registry, not a gap in ours: the package’s releases and maintenance are measured instead.
A Python library for anomaly detection across tabular, time series, graph, text, image, and audio data. 60+ detectors, benchmark-backed ADEngine orchestration, and an agentic workflow for AI agents.