Although continual learning, wherein AI systems learn continually from dynamic data, is a critical problem in machine learning, research on this topic is often difficult to reproduce, thereby inhibiting fast iteration and advancement. As such, Lomonaco et al. have proposed and released Avalanche, an OSS PyTorch-based library for continual learning, designed to support fast prototyping, training, and evaluation of continual learning models. In this paper, the authors describe a general framework for continual learning algorithms that includes a set of building blocks to compose continual learning solutions without imposing strong nomenclature, constraining abstractions, or assumptions.