There exists a wide range of DFT packages, each with their own strengths and set of unique features. The atomate package was centered around the use of VASP for periodic sys-tems and Q-Chem 15 for molecular systems. In atomate2, we have expanded support to a wider array of computational methods including FHI-aims, ABINIT, and CP2K, in addition to many state-of-the-art machine learning interatomic potentials (MLIPs). Throughout this paper these methods and codes are termed Calculators. A key challenge is to enable heterogeneous workflows where different parts of a workflow are performed using different computational methods. Such workflows are necessary to take advantage of the range of features implemented in different DFT packages. For example, hybrid DFT calculations in CP2K can be significantly accelerated by the auxiliary density matrix method (ADMM), but this implementation is currently limited to the use of a single k -point in reciprocal space.