Because validation belongs to the edges.

cattrs is a Swiss Army knife for (un)structuring and validating data in Python. In practice, that means it converts unstructured dictionaries into proper classes and back, while validating their contents.

cattrs works best with attrs classes, and dataclasses where simple (un-)structuring works out of the box, even for nested data, without polluting your data model with serialization details:

>>> from attrs import define
>>> from cattrs import structure, unstructure
>>> @define
... class C:
...     a: int
...     b: list[str]
>>> instance = structure({'a': 1, 'b': ['x', 'y']}, C)
>>> instance
C(a=1, b=['x', 'y'])
>>> unstructure(instance)
{'a': 1, 'b': ['x', 'y']}

Have a look at Why cattrs? for more examples!

• attrs classes and dataclasses are converted into dictionaries in a way similar to attrs.asdict(), or into tuples in a way similar to attrs.astuple().
• Enumeration instances are converted to their values.
• Other types are let through without conversion. This includes types such as integers, dictionaries, lists and instances of non-attrs classes.
• Custom converters for any type can be registered using register_unstructure_hook.

Converts unstructured data into structured data, recursively, according to your specification given as a type. The following types are supported:

• typing.Optional[T] and its 3.10 form, T | None.
• list[T], typing.List[T], typing.MutableSequence[T], typing.Sequence[T] convert to lists.
• tuple and typing.Tuple (both variants, tuple[T, ...] and tuple[X, Y, Z]).
• set[T], typing.MutableSet[T], and typing.Set[T] convert to sets.
• frozenset[T], and typing.FrozenSet[T] convert to frozensets.
• dict[K, V], typing.Dict[K, V], typing.MutableMapping[K, V], and typing.Mapping[K, V] convert to dictionaries.
• typing.TypedDict, both ordinary and generic.
• typing.NewType
• PEP 695 type aliases on 3.12
• attrs classes with simple attributes and the usual __init__¹.
• All attrs classes and dataclasses with the usual __init__, if their complex attributes have type metadata.
• Unions of supported attrs classes, given that all of the classes have a unique field.
• Unions of anything, if you provide a disambiguation function for it.
• Custom converters for any type can be registered using register_structure_hook.

cattrs comes with pre-configured converters for a number of serialization libraries, including JSON (standard library, orjson, UltraJSON), msgpack, cbor2, bson, PyYAML, tomlkit and msgspec (supports only JSON at this time).

For details, see the cattrs.preconf package.

cattrs is based on a few fundamental design decisions:

• Un/structuring rules are separate from the models. This allows models to have a one-to-many relationship with un/structuring rules, and to create un/structuring rules for models which you do not own and you cannot change. (cattrs can be configured to use un/structuring rules from models using the use_class_methods strategy.)
• Invent as little as possible; reuse existing ordinary Python instead. For example, cattrs did not have a custom exception type to group exceptions until the sanctioned Python exceptiongroups. A side-effect of this design decision is that, in a lot of cases, when you're solving cattrs problems you're actually learning Python instead of learning cattrs.
• Resist the temptation to guess. If there are two ways of solving a problem, cattrs should refuse to guess and let the user configure it themselves.

A foolish consistency is the hobgoblin of little minds, so these decisions can and are sometimes broken, but they have proven to be a good foundation.

Major credits to Hynek Schlawack for creating attrs and its predecessor, characteristic.

cattrs is tested with Hypothesis, by David R. MacIver.

cattrs is benchmarked using perf and pytest-benchmark.

This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.