We are pleased to announce the first major version of Xopt, which has been almost completely rewritten from earlier versions to be more modular and general. Some highlights are: The core Xopt object now consists of Generator and Evaluator objects, strongly separating out running of a complex evaluation function from the optimization algorithm. Generator objects encapsulate the optimization algorithms. Evaluator objects encapsulate the user-defined evaluation function, and handle their parallel asyncronous execution using Executors that conform to PEP-3148 Xopt objects manage these and the data produced in the form of pandas DataFrames. Pydantic tooling is now used to handle parsing of all configurations and options, including the YAML formats. Users of previous versions will need to modify their YAML inputs Thank you to @roussel-ryan for initiating this rewrite and developing all of the Bayesian agorithms, and also to @jacquelinegarrahan for teaching us the ways of Pydandic and developing novel methods to handle complex objects using it. Finally, thank you to the users of Xopt who pose interesting and novel use cases for this software. New collaborators and helpful comments are welcome!