Attrs¶

All of the documentation uses dataclasses specifically, because it is built into the standard library since python 3.7.

With that said, any dataclass-like class description pattern should be able to be supported with relatively little effort. Today dataclass-settings ships with adapters for dataclasses, Pydantic, and attrs. msgspec models.

Pydantic¶

Pydantic models will generally be the most well supported and most flexible option. This stems from the fact that they infer the type parsing of input values from the annotated types on the fields.

This also applies to pydantic’s dataclasses module.

Note

At this time, it seems as though pydantic 1.x does not retain the original Annotated type information, which is required to support this API.

Attrs¶

Attrs will likely work next-best, because it has an optional converters, which does construction-time casting of the input arguments. While not as automatic/integrated as pydantic, it can work just as well.

Dataclasses¶

Dataclasses primarily suffer from lack of a native API for casting input values. As such, this library performs very basic mapping of annotated types, where the output type can be constructed from a raw input string.

For example, int, float, Decimal, bool, str, etc. For anything more complex, dataclasses will probably not be the most productive choice (at least until/if PEP-712 is accepted/merged)

Msgspec¶

Msgspec models are supported as of v0.4.0.