Neuroscientists record brain activity using probes that capture rapid voltage changes ('spikes') from neurons. Spike sorting, the process of isolating these signals and attributing them to specific neurons, faces significant challenges: incompatible file formats, diverse algorithms, and inconsistent quality control. SpikeInterface provides a unified Python framework that standardizes data handling across technologies and enables reproducibility. In this talk, we will discuss: 1) SpikeInterface's modular components for I/O, processing, and sorting; 2) containerized dependency management that eliminates complex installation conflicts between diverse spike sorters; and 3) parallelization tools optimized for the memory-intensive nature of large-scale electrophysiology recordings.