cattrs VS pydantic

Compare cattrs vs pydantic and see what are their differences.

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cattrs pydantic
7 181
919 24,550
2.9% 2.2%
8.2 9.7
5 days ago 1 day ago
Python Python
MIT License MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

cattrs

Posts with mentions or reviews of cattrs. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-05-20.

pydantic

Posts with mentions or reviews of pydantic. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2025-07-08.
  • Dict Unpacking in Python
    4 projects | news.ycombinator.com | 8 Jul 2025
  • A Practical Guide on Structuring LLM Outputs with Pydantic
    2 projects | dev.to | 12 Jun 2025
    The solution? Leverage Pydantic, a Python library that enables runtime data validation using type annotations.
  • Loading Pydantic models from JSON without running out of memory
    4 projects | news.ycombinator.com | 22 May 2025
    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.

  • Advanced Pydantic: Generic Models, Custom Types, and Performance Tricks
    1 project | dev.to | 5 May 2025
    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!
  • Getting Started with Pydantic: Type-Safe Data Models in Python
    2 projects | dev.to | 3 May 2025
    To dive deeper, check out the Pydantic documentation for advanced features like nested models, custom validators, and settings management. Happy coding!
  • Resumindo características da linguagem Python
    2 projects | dev.to | 3 Mar 2025
  • FastAPI, Pydantic, Psycopg3: the holy trinity for Python web APIs
    13 projects | dev.to | 24 Oct 2024
    Pydantic is bundled with FastAPI and is excellent for modelling, validating, and serialising API responses.
  • Pydantic Settings + AWS the easy way
    4 projects | dev.to | 28 Jul 2024
    Pydantic Settings is a python library that extends 🚀 Pydantic for dealing with settings management.
  • Understanding FastAPI: How OpenAPI works
    3 projects | dev.to | 24 Jul 2024
    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.
  • Checkbox Extraction from PDFs - A Tutorial
    3 projects | dev.to | 16 Jul 2024
    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.

What are some alternatives?

When comparing cattrs and pydantic you can also consider the following projects:

marshmallow - A lightweight library for converting complex objects to and from simple Python datatypes.

msgspec - A fast serialization and validation library, with builtin support for JSON, MessagePack, YAML, and TOML

Fast JSON schema for Python - Fast JSON schema validator for Python.

typeguard - Run-time type checker for Python

serpy - ridiculously fast object serialization

Lark - Lark is a parsing toolkit for Python, built with a focus on ergonomics, performance and modularity.

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InfluxDB 3 OSS is now GA. Transform, enrich, and act on time series data directly in the database. Automate critical tasks and eliminate the need to move data externally. Download now.
www.influxdata.com
featured
Stream - Scalable APIs for Chat, Feeds, Moderation, & Video.
Stream helps developers build engaging apps that scale to millions with performant and flexible Chat, Feeds, Moderation, and Video APIs and SDKs powered by a global edge network and enterprise-grade infrastructure.
getstream.io
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