yzhao062/pyod
Some data may be outdated — see provenance labels.
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. observed · 2026-10-04
Health v2 · maintenance only
96/100
- Activity 95
- Release rhythm 95
- Longevity 100
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-10-21. Adoption (stars, forks) is never an input.
- gap_med: 12
- age_days: 3304
- days_rel: 34
- days_push: 34
- n_releases_24m: 22
Adoption not part of the score
10026 stars · 1510 forks observed · 2026-10-04
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded
PyOD is the most comprehensive Python library for anomaly detection, offering 60+ detectors across tabular, time series, graph, text, image, and audio data under a unified fit/predict API. Version 3 adds ADEngine orchestration, benchmark-backed planning, and agentic/MCP workflows for AI agents.
Use cases
- detect anomalies in tabular data
- find outliers in time series
- detect fraud in transactions
- identify anomalous images
- detect outliers in graph data
- flag novelty in text documents
- run anomaly detection from an AI agent
When to choose
- you need a wide selection of anomaly detection algorithms behind one consistent API
- you work with multiple data types (tabular, time series, graph, text, image)
- you want a mature, benchmark-backed, widely adopted library with 50M+ installs
When to avoid
- you need supervised classification rather than unsupervised outlier detection
- you need real-time streaming anomaly detection at very low latency
- you want a turnkey GUI product rather than a Python library
Facets
library · maturity stable
machine-learning nlp data-science agent-framework mcp machine-learning data-science artificial-intelligence analytics time-series python cross-platform anomaly-detection outlier-detection unsupervised-learning fraud-detection novelty-detection time-series graph multimodal agentic-ai benchmarking
3 sources
- readme: https://github.com/yzhao062/pyod · fetched 2026-10-04 · 299918a183e2
- homepage: https://pyod.dev/ · fetched 2026-08-29 · bdc72e350364
- registry_pypi: https://pypi.org/pypi/pyod/json · fetched 2026-08-29 · 20c6e2f97340
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| yzhao062/pyod | main | 96 |
For agents
Data as of 2026-10-19T08:05:51.212177+00:00 · Report a problem