Initial Checks

• I have searched Google & GitHub for similar requests and couldn't find anything
• I have read and followed the docs and still think this feature is missing

Description

Please consider supporting Python 3.13 soon – or at least commenting on a timeline for doing so.

• Python 3.13 is now at 3.13.0b2, with release candidate 1 scheduled for the end of July and the final release scheduled for the beginning of October.
• Fedora now has Python 3.13 as the system Python in Rawhide, and Fedora 41 will release with Python 3.13 in October.
• PyO3 is planning to release 0.22 soon with “official” Python 3.13 support.

In Fedora, we’ve managed to rebuild the python-pydantic-core package with Python 3.13, and all of its tests pass.

However:

• Test collection errors due to the DeprecationWarning from CPython 3.13 warns on FutureRef._evaluate/typing._eval_type not passing type_params #9613.
• With pytest -W ignore::DeprecationWarning, test collection errors due to TypeError: typing.Annotated cannot be used with issubclass(), similar to the previous reports create_model fails to use type annotation of typing.Annotated #8534 and TypeError raised when using generics in python 3.13 #7942.
• With python -m pytest -W 'ignore::DeprecationWarning' -v --ignore=tests/test_type_adapter.py, test collection succeeds, but there are many errors. A large portion appear to be the same TypeError: typing.Annotated cannot be used with issubclass(), but it appears there are also some unrelated assertion failures.

It’s currently possible to test Pydantic in a Python 3.13 virtualenv with:

$ gh repo clone pydantic/pydantic
$ cd pydantic
$ python3.13 -m venv _e
$ . _e/bin/activate
(_e) $ UNSAFE_PYO3_SKIP_VERSION_CHECK=1 pip install -e .
(_e) $ pip install cloudpickle coverage[toml] dirty-equals eval-type-backport pytest pytest-mock pytest-pretty pytest-examples faker pytest-benchmark pytest-codspeed packaging
(_e) $ python -m pytest

Affected Components

• Compatibility between releases
• Data validation/parsing
• Data serialization - .model_dump() and .model_dump_json()
• JSON Schema
• Dataclasses
• Model Config
• Field Types - adding or changing a particular data type
• Function validation decorator
• Generic Models
• Other Model behaviour - model_construct(), pickling, private attributes, ORM mode
• Plugins and integration with other tools - mypy, FastAPI, python-devtools, Hypothesis, VS Code, PyCharm, etc.