Captum is an essential open-source library for anyone working with PyTorch models who seeks to understand and explain their behavior. It offers a robust suite of interpretability tools that enable users to delve into the inner workings of machine learning models. With support for various algorithms like attribution methods, feature importance analysis, and layer-wise relevance propagation, Captum helps identify the key inputs and features driving model predictions. This capability is invaluable for debugging, validating, and optimizing models, especially in high-stakes domains such as healthcare, finance, and autonomous systems. The library is designed with usability in mind, featuring a straightforward API that integrates seamlessly with PyTorch. Additionally, Captum provides comprehensive documentation and tutorials, making it accessible to both experienced practitioners and beginners. By leveraging Captum, users can enhance the transparency and reliability of their models, ensuring they are both effective and trustworthy.