Probably, but I fail to see how that's relevant here. This is not a "dataclass heavy" library in any sense, they just used dataclass in the examples to make them shorter.

Based on everything I see in the documentation, you should be able to use Pydantic models as well, or standard python objects, or anything else, as long as it has a method `def htmy(self, context: Context) -> Component`.

Please don't! Pydantic demands 100% type correctness at runtime in a language that can't guarantee basically anything at "compile" (lint) time. Screw up one type annotation for one edge case and your entire system turns into one big ValidationError.

Dataclasses let you return "incorrect" data and that's a good thing. I'd rather get an unexpected None here and there (which can be handled) than have library code crash because the wrong type snuck into a field I don't even care about.

As for support, is any explicit support needed? You can Pydantic models into things expecting dataclasses and often the other way around too.