A new method for model interpretability, ADORE, is released as an open-source Python package. It uses first- and second-order derivatives to capture nonlinear feature interactions and provides a unified analytical framework for global feature importance and local sample contributions. This improves upon existing methods like LIME and SHAP, making it a useful tool for model explainability and decision-making in AI development.