PyOD is a comprehensive Python library for anomaly/outlier detection offering 60+ detectors across tabular, time series, graph, text, image, and audio modalities. It provides a classic scikit-learn-compatible API (fit/predict/decision_function) via pyod.models, a high-level orchestration engine (ADEngine) for automatic profiling, detector selection, consensus scoring, and reporting, and an agentic workflow layer for LLM-driven anomaly detection through CLI, MCP server, and packaged AI skills. The library is primarily a software library (application) with strong data research influence: it comes with benchmark-backed routing rules, academic references, and Jupyter notebooks for research reprocibility. Its architecture follows a three-layer design (classic detectors, ADEngine orchestration, agentic skills) that allows progressive engagement from simple model fitting to full automated pipelines.