Python programs often use dictionaries with string keys to represent objects. TypedDict lets you give precise types for dictionaries that represent objects with a fixed schema, such as {'id': 1, 'items': ['x']}.

Here is a typical example:

movie = {'name': 'Blade Runner', 'year': 1982}

Only a fixed set of string keys is expected ('name' and 'year' above), and each key has an independent value type (str for 'name' and int for 'year' above). We’ve previously seen the dict[K, V] type, which lets you declare uniform dictionary types, where every value has the same type, and arbitrary keys are supported. This is clearly not a good fit for movie above. Instead, you can use a TypedDict to give a precise type for objects like movie, where the type of each dictionary value depends on the key:

from typing import TypedDict

Movie = TypedDict('Movie', {'name': str, 'year': int})

movie: Movie = {'name': 'Blade Runner', 'year': 1982}

Movie is a TypedDict type with two items: 'name' (with type str) and 'year' (with type int). Note that we used an explicit type annotation for the movie variable. This type annotation is important – without it, mypy will try to infer a regular, uniform dict type for movie, which is not what we want here.


If you pass a TypedDict object as an argument to a function, no type annotation is usually necessary since mypy can infer the desired type based on the declared argument type. Also, if an assignment target has been previously defined, and it has a TypedDict type, mypy will treat the assigned value as a TypedDict, not dict.

Now mypy will recognize these as valid:

name = movie['name']  # Okay; type of name is str
year = movie['year']  # Okay; type of year is int

Mypy will detect an invalid key as an error:

director = movie['director']  # Error: 'director' is not a valid key

Mypy will also reject a runtime-computed expression as a key, as it can’t verify that it’s a valid key. You can only use string literals as TypedDict keys.

The TypedDict type object can also act as a constructor. It returns a normal dict object at runtime – a TypedDict does not define a new runtime type:

toy_story = Movie(name='Toy Story', year=1995)

This is equivalent to just constructing a dictionary directly using { ... } or dict(key=value, ...). The constructor form is sometimes convenient, since it can be used without a type annotation, and it also makes the type of the object explicit.

Like all types, TypedDicts can be used as components to build arbitrarily complex types. For example, you can define nested TypedDicts and containers with TypedDict items. Unlike most other types, mypy uses structural compatibility checking (or structural subtyping) with TypedDicts. A TypedDict object with extra items is compatible with (a subtype of) a narrower TypedDict, assuming item types are compatible (totality also affects subtyping, as discussed below).

A TypedDict object is not a subtype of the regular dict[...] type (and vice versa), since dict allows arbitrary keys to be added and removed, unlike TypedDict. However, any TypedDict object is a subtype of (that is, compatible with) Mapping[str, object], since Mapping only provides read-only access to the dictionary items:

def print_typed_dict(obj: Mapping[str, object]) -> None:
    for key, value in obj.items():
        print(f'{key}: {value}')

print_typed_dict(Movie(name='Toy Story', year=1995))  # OK


Unless you are on Python 3.8 or newer (where TypedDict is available in standard library typing module) you need to install typing_extensions using pip to use TypedDict:

python3 -m pip install --upgrade typing-extensions


By default mypy ensures that a TypedDict object has all the specified keys. This will be flagged as an error:

# Error: 'year' missing
toy_story: Movie = {'name': 'Toy Story'}

Sometimes you want to allow keys to be left out when creating a TypedDict object. You can provide the total=False argument to TypedDict(...) to achieve this:

GuiOptions = TypedDict(
    'GuiOptions', {'language': str, 'color': str}, total=False)
options: GuiOptions = {}  # Okay
options['language'] = 'en'

You may need to use get() to access items of a partial (non-total) TypedDict, since indexing using [] could fail at runtime. However, mypy still lets use [] with a partial TypedDict – you just need to be careful with it, as it could result in a KeyError. Requiring get() everywhere would be too cumbersome. (Note that you are free to use get() with total TypedDicts as well.)

Keys that aren’t required are shown with a ? in error messages:

# Revealed type is "TypedDict('GuiOptions', {'language'?: builtins.str,
#                                            'color'?: builtins.str})"

Totality also affects structural compatibility. You can’t use a partial TypedDict when a total one is expected. Also, a total TypedDict is not valid when a partial one is expected.

Supported operations

TypedDict objects support a subset of dictionary operations and methods. You must use string literals as keys when calling most of the methods, as otherwise mypy won’t be able to check that the key is valid. List of supported operations:


clear() and popitem() are not supported since they are unsafe – they could delete required TypedDict items that are not visible to mypy because of structural subtyping.

Class-based syntax

An alternative, class-based syntax to define a TypedDict is supported in Python 3.6 and later:

from typing import TypedDict  # "from typing_extensions" in Python 3.7 and earlier

class Movie(TypedDict):
    name: str
    year: int

The above definition is equivalent to the original Movie definition. It doesn’t actually define a real class. This syntax also supports a form of inheritance – subclasses can define additional items. However, this is primarily a notational shortcut. Since mypy uses structural compatibility with TypedDicts, inheritance is not required for compatibility. Here is an example of inheritance:

class Movie(TypedDict):
    name: str
    year: int

class BookBasedMovie(Movie):
    based_on: str

Now BookBasedMovie has keys name, year and based_on.

Mixing required and non-required items

In addition to allowing reuse across TypedDict types, inheritance also allows you to mix required and non-required (using total=False) items in a single TypedDict. Example:

class MovieBase(TypedDict):
    name: str
    year: int

class Movie(MovieBase, total=False):
    based_on: str

Now Movie has required keys name and year, while based_on can be left out when constructing an object. A TypedDict with a mix of required and non-required keys, such as Movie above, will only be compatible with another TypedDict if all required keys in the other TypedDict are required keys in the first TypedDict, and all non-required keys of the other TypedDict are also non-required keys in the first TypedDict.

Unions of TypedDicts

Since TypedDicts are really just regular dicts at runtime, it is not possible to use isinstance checks to distinguish between different variants of a Union of TypedDict in the same way you can with regular objects.

Instead, you can use the tagged union pattern. The referenced section of the docs has a full description with an example, but in short, you will need to give each TypedDict the same key where each value has a unique Literal type. Then, check that key to distinguish between your TypedDicts.