{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/2"}],"enrichment":{"capability":"PyOD detects anomalies and outliers across tabular, time series, graph, text, image, and audio data using 61 detectors, with optional agentic workflows for AI-driven investigation.","skillfed_tags":["anomaly-detection","agentic-ai","multimodal"],"use_cases":["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."],"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.\n\nPyOD 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.","worth_installing":"Yes. PyOD is production-stable (BSD-2-Clause, no vulnerabilities, actively maintained), widely adopted (5M+ monthly downloads), and offers genuine breadth\u201461 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."},"id":"pyod","links":{"html":"https://skillfed.io/packages/pyod","md":"https://skillfed.io/packages/pyod.md","pypi":"https://pypi.org/project/pyod/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-02","license_spdx":"BSD-2-Clause","license_treatment":"permissive","name":"pyod","python_support":"supports_current","summary":"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."},"popularity":{"monthly_downloads":5096513,"position":2168,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"3.6.4"}