In this work, we introduce Atlas, an Object-Oriented Python library for BO that was designed with the broadest applicability to SDLs and experimental science in mind. Figure shows a summary of the main experiment-planning capabilities of Atlas along with its place within the closed-loop experimentation paradigm. Atlas intends to provide practitioners of autonomous science with state-of-the-art BO algorithms while abstracting away all complex implementation details. We strived to provide researchers with the numerous, application-agnostic features often required for the successful deployment of BO in practice. This flexibility is expected to allow researchers to focus on customizing their experimental or computational protocols and expand the set of design and discovery problems that BO and SDLs can tackle. This paper is organized as follows: Section II provides a brief review of BO and its components. Section III lists the notable features of the Atlas library and gives code snippet examples for each feature. Finally, Section IV describes a real-world demonstration of Atlas used in conjunction with ChemOS 2.0, an SDL orchestration software, to optimize the oxidation potential of metal complexes in a cyclic voltammetry experiment.