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yzhao062/pyod

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 · 2026-08-28

github.com/yzhao062/pyod · homepage · Python · BSD-2-Clause (permissive) observed · 2026-08-28

Health v2 · maintenance only

98/100

  • Activity 98
  • Release rhythm 98
  • Longevity 100
How is this computed?

round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.

  • gap_med: 12
  • age_days: 3256
  • days_rel: 16
  • days_push: 14
  • n_releases_24m: 22

Full methodology

Adoption not part of the score

9977 stars · 1499 forks observed · 2026-08-28

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

Member repositories

RepositoryRoleHealth v2
yzhao062/pyodmain98

For agents

markdown · JSON · MCP: product_card(name="yzhao062/pyod")

Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem