Qlib is an open-source, AI-oriented quantitative investment platform developed by Microsoft. It covers the full pipeline for quantitative research and trading: data ingestion and feature engineering (Alpha158, Alpha360, high-frequency), model training (traditional ML like LightGBM/XGBoost, deep learning in PyTorch including LSTM, GRU, Transformer, GATs, and reinforcement learning), backtesting with realistic costs and slippage, portfolio optimization, and online serving for production-like deployment. The platform is highly modular and config-driven, with a core library (qlib) and a separate contrib layer for community extensions. It includes a CLI (qrun) for running full workflows from YAML configs, extensive experiment tracking via MLflow, and a rich set of evaluation metrics. While primarily a library/application, it also functions as a research benchmark suite with many example models and data collectors for global markets. The codebase is active and well-maintained, though some components (e.g., TFT model, older RL code) show signs of technical drift due to dependency version locks and deprecated patterns.