cattrs VS pydantic

I'd suggest you look at my cattrs (https://catt.rs) library as a good serde lookalike in Python (sum type support present and getting better), and to use attrs instead of dataclasses in general.

Pydantic is by far not the only library of its kind, with prominent members of the same class being attrs, cattrs or even plain dataclasses for some use cases.

That being said, data serialization is a very common thing to do, so naturally there are tons of libraries that automate it for you. Personally, using dataclasses and cattrs is my goto way for doing such things.

I'm fond of attrs and cattrs myself, attrs make creating data classes a snap, writing all of the stupid code python requires to have a dataclass. Note the new built in dataclass is actually a limited copy of attrs. https://www.attrs.org/en/stable/ and https://github.com/python-attrs/cattrs

This month, I've released version 0.17, and it's now blazing fast; there is in fact no more comparison with Pydantic, which more than 5x slower (up to 30x in serialization). It's also faster than alternatives like mashumaro or cattrs. (See the quick benchmark result in documentation, and the code)

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Cattrs has some problems with generics [1] [2]. Dacite and marshmallow-dataclasses don't support generics well either, with some issues around Union types.

They do work well for simple python types but what I'd like to see is guarantee that the serialisation operation is completely reversible and if not raise warning/exception.

[1] https://github.com/Tinche/cattrs/issues/149

The solution? Leverage Pydantic, a Python library that enables runtime data validation using type annotations.

Pydantic author here. We have plans for an improvement to pydantic where JSON is parsed iteratively, which will make way for reading a file as we parse it. Details in https://github.com/pydantic/pydantic/issues/10032.

Our JSON parser, jiter (https://github.com/pydantic/jiter) already supports iterative parsing, so it's "just" a matter of solving the lifetimes in pydantic-core to validate as we parse.

This should make pydantic around 3x faster at parsing JSON and significantly reduce the memory overhead.

Across this five-post series, we’ve journeyed from Pydantic’s basics—type validation and nested models—to advanced integrations with FastAPI, SQLAlchemy, and scalable techniques. You’ve learned how to build declarative, type-safe models, handle complex APIs, and optimize performance. To deepen your knowledge, explore the Pydantic documentation, contribute to the open-source project, or experiment with real-world use cases. Check out our GitHub repo for code samples and a Pydantic cheat sheet. Thank you for joining us—happy coding!

To dive deeper, check out the Pydantic documentation for advanced features like nested models, custom validators, and settings management. Happy coding!

Pydantic is bundled with FastAPI and is excellent for modelling, validating, and serialising API responses.

Pydantic Settings is a python library that extends 🚀 Pydantic for dealing with settings management.

And here is where FastAPI ✨ shines: It leverages Pydantic powerful data validation to offer out of the box JSON Schema and OpenAPI specs via Swagger and Redoc.

If you carefully think about it, the system that extracts raw text from the PDF needs to both detect and render PDF form elements like checkboxes and radiobuttons in a way that LLMs can understand. In this example, we’ll use LLMWhisperer to extract PDF raw text representing checkboxes and radiobuttons. You can use LLMWhisperer completely free for processing up to 100 pages per day. As for structuring the output from LLMWhisperer, we’ll use GPT3.5-Turbo and we’ll use Langchain and Pydantic to help make our job easy.