In summary, we introduce Atlas, a Bayesian optimization package with a comprehensive set of features designed to suit most experimental settings and enable model-guided optimization in SDLs. Among the capabilities currently available are optimizations over mixed-parameter, constrained, and molecular domains, in addition to supporting multi-objective and multi-fidelity optimizations, as well as robust optimization and the incorporation of past knowledge