OmniXAI is a comprehensive explainable AI (XAI) library providing a unified interface for generating explanations across multiple data types (tabular, vision, text, time series, ranking, vision-language) and ML models. It implements both model-agnostic (LIME, SHAP, L2X, MACE) and model-specific (Grad-CAM, Integrated Gradients, decision tree) explanation methods. The library features a modular architecture with separate modules for data handling, preprocessing, explanation generation, visualization (Dash-based dashboard), and deployment (BentoML integration). Key design patterns include base classes for explainers and explanations, factory patterns for explainer instantiation, and composable preprocessing pipelines. The system supports end-to-end workflows from data preprocessing to explanation visualization and model serving.