Captum is implemented in Python and seamlessly integrates with PyTorch, supporting most PyTorch models with minimal modifications. Users can leverage its capabilities for crucial tasks like troubleshooting model issues, enhancing overall model performance, and providing clear, understandable explanations of model-driven outcomes to end-users. For instance, explaining why a movie was recommended becomes straightforward. The library provides an extensive suite of attribution algorithms, categorized for different interpretability needs. To use Captum, developers select an appropriate attribution algorithm from its three main groups. For understanding individual input feature contributions, Primary Attribution algorithms like Integrated Gradients, Gradient SHAP, DeepLIFT, Saliency, Input X Gradient, Guided Backpropagation, Deconvolution, Guided GradCAM, Feature Ablation, Feature Permutation, Occlusion, Shapley Value Sampling, LIME, and KernelSHAP can be applied directly to the model's inputs. If the goal is to assess the importance of specific neurons within a model's internal layers, Layer Attribution methods are employed. These methods help evaluate how each neuron in a given layer contributes to the final output, providing deeper insights into the model's internal workings. The extensibility of Captum allows researchers and developers to apply these diverse techniques to gain a comprehensive understanding of their complex AI systems, identifying key features or concepts driving predictions.