pyod
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.
Decision gist · record as of 2026-08-14
Yes. PyOD is production-stable (BSD-2-Clause, no vulnerabilities, actively maintained), widely adopted (5M+ monthly downloads), and offers genuine breadth—61 detectors across six data modalities with both classical and agentic APIs. Install if you need anomaly detection; the low dependency friction and backward-compatible API make it a safe choice.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Low friction: pure Python wheel with six well-established scientific dependencies (numpy, scipy, scikit-learn, joblib, matplotlib, numba).
- Active maintenance—released 12 days ago with 9958 GitHub stars and no known vulnerabilities.
License · maintenance · safety
BSD-2-Clause (permissive) — BSD-2-Clause permissive license allows commercial and private use with minimal restrictions; attribution required but no copyleft obligations.
last release 2026-08-02 (12 days) · last repo commit 2026-08-02 · 9,958 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 5,096,513 downloads/mo, #2,168 on PyPI
Alternatives
Verify before relying
pip install pyod
from pyod.models.iforest import IForest
clf = IForest()
clf.fit(X_train)
y_test_scores = clf.decision_function(X_test)- Whether the 61 detectors cover all claimed modalities equally or if some modalities have fewer detector options.
- Performance characteristics and scalability limits for large datasets or real-time streaming scenarios.
- Whether the agentic workflow (od-expert skill, MCP server) is production-ready or still experimental.
What it is and what it does
PyOD is a comprehensive anomaly detection library that provides 61 detectors spanning tabular, time series, graph, text, image, and audio data under a single API. It supports three usage layers: a classic fit/predict interface for direct detector selection, an ADEngine orchestration layer that automatically selects and compares detectors, and an agentic investigation layer where AI agents drive detection workflows through natural language. The library depends on numpy, scipy, scikit-learn, joblib, matplotlib, and numba for numerical computation, parallel training, and JIT acceleration.
PyOD is built for both traditional machine learning workflows and modern AI-agent integration. It exposes detectors through a standard scikit-learn-compatible API, offers an MCP server for LLM-compatible agents, and provides a Claude Code skill for agentic anomaly investigations. The library has been actively maintained since 2017, supports Python 3.9 through 3.13, and carries no known security vulnerabilities.
Use it for
- Detect fraudulent transactions or pricing anomalies in financial data using tabular detectors.
- Identify equipment failures or system anomalies in time-series sensor or telemetry data.
- Automate anomaly detection workflows by letting an AI agent choose and tune detectors via natural language.
- Detect out-of-distribution or novel samples in image or text datasets for content moderation.
- Build ensemble anomaly detection systems by comparing multiple detectors on the same dataset.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
PyOD is production-stable (BSD-2-Clause, no vulnerabilities, actively maintained), widely adopted (5M+ monthly downloads), and offers genuine breadth—61 detectors across six data modalities with both classical and agentic APIs. Install if you need anomaly detection; the low dependency friction and backward-compatible API make it a safe choice.
Install
pyod on PyPI
Before you install
Low friction: pure Python wheel with six well-established scientific dependencies (numpy, scipy, scikit-learn, joblib, matplotlib, numba). Active maintenance—released 12 days ago with 9958 GitHub stars and no known vulnerabilities.
License in practice
BSD-2-Clause permissive license allows commercial and private use with minimal restrictions; attribution required but no copyleft obligations.
Quickstart
pip install pyod
from pyod.models.iforest import IForest
clf = IForest()
clf.fit(X_train)
y_test_scores = clf.decision_function(X_test)Verify before relying
- Whether the 61 detectors cover all claimed modalities equally or if some modalities have fewer detector options.
- Performance characteristics and scalability limits for large datasets or real-time streaming scenarios.
- Whether the agentic workflow (od-expert skill, MCP server) is production-ready or still experimental.
Package facts
| License | BSD-2-Clause permissive |
| Python support | Supports the current Python release >=3.9 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 6 packagesjoblibmatplotlibnumpynumbascipyscikit-learn |
| Maintenance | Actively maintained 12 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 5,096,513 / month, #2,168 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersIntended Audience :: EducationIntended Audience :: Financial and Insurance IndustryIntended Audience :: Information TechnologyIntended Audience :: Science/ResearchProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.9 |
Evidence: pyod-3.6.4-py3-none-any.whl
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