  The realization of novel technological opportunities given by computational
and autonomous materials design requires efficient and effective frameworks.
For more than two decades, aflow++ (Automatic-Flow Framework for Materials
Discovery) has provided an interconnected collection of algorithms and
workflows to address this challenge. This article contains an overview of the
software and some of its most heavily-used functionalities, including
algorithmic details, standards, and examples. Key thrusts are highlighted: the
calculation of structural, electronic, thermodynamic, and thermomechanical
properties in addition to the modeling of complex materials, such as
high-entropy ceramics and bulk metallic glasses. The aflow++ software
prioritizes interoperability, minimizing the number of independent parameters
and tolerances. It ensures consistency of results across property sets -
facilitating machine learning studies. The software also features various
validation schemes, offering real-time quality assurance for data generated in
a high-throughput fashion. Altogether, these considerations contribute to the
development of large and reliable materials databases that can ultimately
deliver future materials systems
