Contributing

We'd love you to contribute to Pydantic!

Issues

Questions, feature requests and bug reports are all welcome as discussions or issues. However, to report a security vulnerability, please see our security policy.

To make it as simple as possible for us to help you, please include the output of the following call in your issue:

python -c "import pydantic.version; print(pydantic.version.version_info())"

If you're using Pydantic prior to v2.0 please use:

python -c "import pydantic.utils; print(pydantic.utils.version_info())"

Please try to always include the above unless you're unable to install Pydantic or know it's not relevant to your question or feature request.

Pull Requests

It should be extremely simple to get started and create a Pull Request. Pydantic is released regularly so you should see your improvements release in a matter of days or weeks 🚀.

Unless your change is trivial (typo, docs tweak etc.), please create an issue to discuss the change before creating a pull request.

Pydantic V1 is in maintenance mode

Pydantic v1 is in maintenance mode, meaning that only bug fixes and security fixes will be accepted. New features should be targeted at Pydantic v2.

To submit a fix to Pydantic v1, use the 1.10.X-fixes as a target branch.

If you're looking for something to get your teeth into, check out the "help wanted" label on github.

To make contributing as easy and fast as possible, you'll want to run tests and linting locally. Luckily, Pydantic has few dependencies, doesn't require compiling and tests don't need access to databases, etc. Because of this, setting up and running the tests should be very simple.

Tip

tl;dr: use make format to fix formatting, make to run tests and linting and make docs to build the docs.

Prerequisites

You'll need the following prerequisites:

• Any Python version between Python 3.9 and 3.12
• uv or other virtual environment tool
• git
• make

Installation and setup

Fork the repository on GitHub and clone your fork locally.

# Clone your fork and cd into the repo directory
git clone [email protected]:<your username>/pydantic.git
cd pydantic

# Install UV and pre-commit
# We use pipx here, for other options see:
# https://docs.astral.sh/uv/getting-started/installation/
# https://pre-commit.com/#install
# To get pipx itself:
# https://pypa.github.io/pipx/
pipx install uv
pipx install pre-commit

# Install pydantic, dependencies, test dependencies and doc dependencies
make install

Check out a new branch and make your changes

Create a new branch for your changes.

# Checkout a new branch and make your changes
git checkout -b my-new-feature-branch
# Make your changes...

Run tests and linting

Run tests and linting locally to make sure everything is working as expected.

# Run automated code formatting and linting
make format
# Pydantic uses ruff, an awesome Python linter written in rust
# https://github.com/astral-sh/ruff

# Run tests and linting
make
# There are a few sub-commands in Makefile like `test`, `testcov` and `lint`
# which you might want to use, but generally just `make` should be all you need.
# You can run `make help` to see more options.

Build documentation

If you've made any changes to the documentation (including changes to function signatures, class definitions, or docstrings that will appear in the API documentation), make sure it builds successfully.

We use mkdocs-material[imaging] to support social previews. You can find directions on how to install the required dependencies here.

# Build documentation
make docs
# If you have changed the documentation, make sure it builds successfully.
# You can also use `uv run mkdocs serve` to serve the documentation at localhost:8000

If this isn't working due to issues with the imaging plugin, try commenting out the social plugin line in mkdocs.yml and running make docs again.

Updating the documentation

We push a new version of the documentation with each minor release, and we push to a dev path with each commit to main.

If you're updating the documentation out of cycle with a minor release and want your changes to be reflected on latest, do the following:

1. Open a PR against main with your docs changes
2. Once the PR is merged, checkout the docs-update branch. This branch should be up to date with the latest patch release. For example, if the latest release is v2.9.2, you should make sure docs-update is up to date with the v2.9.2 tag.
3. Checkout a new branch from docs-update and cherry-pick your changes onto this branch.
4. Push your changes and open a PR against docs-update.
5. Once the PR is merged, the new docs will be built and deployed.

Note

Maintainer shortcut - as a maintainer, you can skip the second PR and just cherry pick directly onto the docs-update branch.

Commit and push your changes

Commit your changes, push your branch to GitHub, and create a pull request.

Please follow the pull request template and fill in as much information as possible. Link to any relevant issues and include a description of your changes.

When your pull request is ready for review, add a comment with the message "please review" and we'll take a look as soon as we can.

Documentation style

Documentation is written in Markdown and built using Material for MkDocs. API documentation is build from docstrings using mkdocstrings.

Code documentation

When contributing to Pydantic, please make sure that all code is well documented. The following should be documented using properly formatted docstrings:

• Modules
• Class definitions
• Function definitions
• Module-level variables

Pydantic uses Google-style docstrings formatted according to PEP 257 guidelines. (See Example Google Style Python Docstrings for further examples.)

pydocstyle is used for linting docstrings. You can run make format to check your docstrings.

Where this is a conflict between Google-style docstrings and pydocstyle linting, follow the pydocstyle linting hints.

Class attributes and function arguments should be documented in the format "name: description." When applicable, a return type should be documented with just a description. Types are inferred from the signature.

class Foo:
    """A class docstring.

    Attributes:
        bar: A description of bar. Defaults to "bar".
    """

    bar: str = 'bar'

def bar(self, baz: int) -> str:
    """A function docstring.

    Args:
        baz: A description of `baz`.

    Returns:
        A description of the return value.
    """

    return 'bar'

You may include example code in docstrings. This code should be complete, self-contained, and runnable. Docstring examples are tested, so make sure they are correct and complete. See FieldInfo.from_annotated_attribute for an example.

Class and instance attributes

Class attributes should be documented in the class docstring.

Instance attributes should be documented as "Args" in the __init__ docstring.

Documentation Style

In general, documentation should be written in a friendly, approachable style. It should be easy to read and understand, and should be as concise as possible while still being complete.

Code examples are encouraged, but should be kept short and simple. However, every code example should be complete, self-contained, and runnable. (If you're not sure how to do this, ask for help!) We prefer print output to naked asserts, but if you're testing something that doesn't have a useful print output, asserts are fine.

Pydantic's unit test will test all code examples in the documentation, so it's important that they are correct and complete. When adding a new code example, use the following to test examples and update their formatting and output:

# Run tests and update code examples
pytest tests/test_docs.py --update-examples

Debugging Python and Rust

If you're working with pydantic and pydantic-core, you might find it helpful to debug Python and Rust code together. Here's a quick guide on how to do that. This tutorial is done in VSCode, but you can use similar steps in other IDEs.

Badges

Pydantic has a badge that you can use to show that your project uses Pydantic. You can use this badge in your README.md:

With Markdown

[![Pydantic v1](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/pydantic/pydantic/main/docs/badge/v1.json)](https://pydantic.dev)

[![Pydantic v2](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/pydantic/pydantic/main/docs/badge/v2.json)](https://pydantic.dev)

With reStructuredText

.. image:: https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/pydantic/pydantic/main/docs/badge/v1.json
    :target: https://pydantic.dev
    :alt: Pydantic

.. image:: https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/pydantic/pydantic/main/docs/badge/v2.json
    :target: https://pydantic.dev
    :alt: Pydantic

With HTML

<a href="https://pydantic.dev"><img src="https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/pydantic/pydantic/main/docs/badge/v1.json" alt="Pydantic Version 1" style="max-width:100%;"></a>

<a href="https://pydantic.dev"><img src="https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/pydantic/pydantic/main/docs/badge/v2.json" alt="Pydantic Version 2" style="max-width:100%;"></a>

Adding your library as part of Pydantic's third party test suite

To be able to identify regressions early during development, Pydantic runs tests on various third-party projects using Pydantic. We consider adding support for testing new open source projects (that rely heavily on Pydantic) if your said project matches some of the following criteria:

• The project is actively maintained.
• The project makes use of Pydantic internals (e.g. relying on the BaseModel metaclass, typing utilities).
• The project is popular enough (although small projects can still be included depending on how Pydantic is being used).
• The project CI is simple enough to be ported into Pydantic's testing workflow.

If your project meets some of these criteria, you can open feature request to discuss the inclusion of your project.