Deep Java Library (DJL) is a comprehensive, engine-agnostic deep learning framework for Java, providing a unified API for model loading, training, inference, and deployment across multiple backends (PyTorch, TensorFlow, MXNet, ONNX Runtime, XGBoost, LightGBM, and a Rust-based engine). Its architecture is modular: a core API defines abstractions like `NDArray`, `NDManager`, `Model`, `Predictor`, `Trainer`, and `Translator`; engine-specific implementations adapt these to native libraries; model zoos and repositories supply pre-trained models; and extensions add capabilities for generative AI, audio, timeseries, Spark integration, and more. The codebase is well-organized with extensive use of the builder pattern, ServiceLoader-based plugin discovery, and a rich set of training utilities (losses, optimizers, trackers, evaluators, listeners). It supports both training and inference workflows and includes runnable examples for CV, NLP, audio, and tabular tasks. While the project is actively maintained with frequent commits and CI, it exhibits medium technical drift due to some outdated engine dependencies (e.g., MXNet) and the need to synchronize multiple native library versions.