Over the years, the lack of an array data type in Python has resulted in the creation of numerous array libraries, each specializing in unique niches but still having some interoperability between each other. NumPy has become the de facto array library of Python, and the other array libraries try to keep their API close to that of NumPy. However, this often becomes infeasible, and the libraries deviate out of necessity. To make Python's array libraries shake hands with each other without any inconsistencies, the Consortium for Python Data API Standards has formalized an array API standard for libraries offering array creation and manipulation operations.
Though the consortium provides array-api-tests to check compatibility for libraries providing the array data type (provider libraries), there exists no testing frameworks for libraries consuming the array data type (consumer libraries). array-api-strict is a good step in the direction of testing consumer libraries, but for a more robust solution, the testing framework should be able to check against multiple provider libraries with flexible functionalities (such as testing with CuPy only when a GPU is available, or skipping a particular provider library on a few functions). SciPy has an internal mini testing framework to validate their array API compliance; this project starts there and aims to create a full-fledged testing suite that any consumer library can use.