AutoKeras is an automated machine learning (AutoML) library built on Keras 3, designed to make deep learning accessible to non-experts. It provides high-level task-specific APIs (e.g., ImageClassifier, TextClassifier, StructuredDataClassifier) that automate neural architecture search and hyperparameter tuning. The core architecture uses a modular block-based system—blocks (e.g., DenseBlock, ConvBlock, ResNetBlock) are composed into a graph via a functional API, and a tuner (Greedy, Bayesian, Hyperband, Random) searches for the best model. The library includes a data preprocessing pipeline (Pipeline, HyperPipeline), adapters for input/output conversion, and serialization for model persistence. While primarily an application library, it also contains research-oriented components like benchmark experiments and tuner implementations, reflecting its academic origins. Key entry points are the AutoModel class and task classes, which expose fit/predict/evaluate workflows similar to Keras.