Type Annotations#

attrs comes with first class support for type annotations for both Python 3.6 (PEP 526) and legacy syntax.

However they will forever remain optional, therefore the example from the README could also be written as:

>>> from attrs import define, field

>>> @define
... class SomeClass:
...     a_number = field(default=42)
...     list_of_numbers = field(factory=list)

>>> sc = SomeClass(1, [1, 2, 3])
>>> sc
SomeClass(a_number=1, list_of_numbers=[1, 2, 3])

You can choose freely between the approaches, but please remember that if you choose to use type annotations, you must annotate all attributes!

Even when going all-in on type annotations, you will need attrs.field() for some advanced features though.

One of those features are the decorator-based features like defaults. It’s important to remember that attrs doesn’t do any magic behind your back. All the decorators are implemented using an object that is returned by the call to attrs.field().

Attributes that only carry a class annotation do not have that object so trying to call a method on it will inevitably fail.

Please note that types – regardless how added – are only metadata that can be queried from the class and they aren’t used for anything out of the box!

Because Python does not allow references to a class object before the class is defined, types may be defined as string literals, so-called forward references (PEP 526). You can enable this automatically for a whole module by using from __future__ import annotations (PEP 563) as of Python 3.7. In this case attrs simply puts these string literals into the type attributes. If you need to resolve these to real types, you can call attrs.resolve_types() which will update the attribute in place.

In practice though, types show their biggest usefulness in combination with tools like Mypy, pytype, or Pyright that have dedicated support for attrs classes.

The addition of static types is certainly one of the most exciting features in the Python ecosystem and helps you write correct and verified self-documenting code.

If you don’t know where to start, Carl Meyer gave a great talk on Type-checked Python in the Real World at PyCon US 2018 that will help you to get started in no time.


While having a nice syntax for type metadata is great, it’s even greater that Mypy as of 0.570 ships with a dedicated attrs plugin which allows you to statically check your code.

Imagine you add another line that tries to instantiate the defined class using SomeClass("23"). Mypy will catch that error for you:

$ mypy t.py
t.py:12: error: Argument 1 to "SomeClass" has incompatible type "str"; expected "int"

This happens without running your code!

And it also works with both Python 2-style annotation styles. To Mypy, this code is equivalent to the one above:

class SomeClass:
    a_number = attr.ib(default=42)  # type: int
    list_of_numbers = attr.ib(factory=list, type=list[int])


attrs provides support for Pyright through the dataclass_transform / PEP 681 specification. This provides static type inference for a subset of attrs equivalent to standard-library dataclasses, and requires explicit type annotations using the attrs.define() or @attr.s(auto_attribs=True) API.

Given the following definition, Pyright will generate static type signatures for SomeClass attribute access, __init__, __eq__, and comparison methods:

class SomeClass:
    a_number: int = 42
    list_of_numbers: list[int] = attr.field(factory=list)


The Pyright inferred types are a tiny subset of those supported by Mypy, including:

  • The generated __init__ signature only includes the attribute type annotations. It currently does not include attribute converter types.

  • The attrs.frozen decorator is not typed with frozen attributes, which are properly typed via attrs.define(frozen=True).

Your constructive feedback is welcome in both attrs#795 and pyright#1782. Generally speaking, the decision on improving attrs support in Pyright is entirely Microsoft’s prerogative, though.