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Strict Mode

API Documentation

pydantic.types.Strict

By default, Pydantic will attempt to coerce values to the desired type when possible. For example, you can pass the string "123" as the input to an int field, and it will be converted to 123. This coercion behavior is useful in many scenarios — think: UUIDs, URL parameters, HTTP headers, environment variables, user input, etc.

However, there are also situations where this is not desirable, and you want Pydantic to error instead of coercing data.

To better support this use case, Pydantic provides a "strict mode" that can be enabled on a per-model, per-field, or even per-validation-call basis. When strict mode is enabled, Pydantic will be much less lenient when coercing data, and will instead error if the data is not of the correct type.

Here is a brief example showing the difference between validation behavior in strict and the default/"lax" mode:

from pydantic import BaseModel, ValidationError


class MyModel(BaseModel):
    x: int


print(MyModel.model_validate({'x': '123'}))  # lax mode
#> x=123

try:
    MyModel.model_validate({'x': '123'}, strict=True)  # strict mode
except ValidationError as exc:
    print(exc)
    """
    1 validation error for MyModel
    x
      Input should be a valid integer [type=int_type, input_value='123', input_type=str]
    """

There are various ways to get strict-mode validation while using Pydantic, which will be discussed in more detail below:

Type coercions in strict mode

For most types, when validating data from python in strict mode, only the instances of the exact types are accepted. For example, when validating an int field, only instances of int are accepted; passing instances of float or str will result in raising a ValidationError.

Note that we are looser when validating data from JSON in strict mode. For example, when validating a UUID field, instances of str will be accepted when validating from JSON, but not from python:

import json
from uuid import UUID

from pydantic import BaseModel, ValidationError


class MyModel(BaseModel):
    guid: UUID


data = {'guid': '12345678-1234-1234-1234-123456789012'}

print(MyModel.model_validate(data))  # OK: lax
#> guid=UUID('12345678-1234-1234-1234-123456789012')

print(
    MyModel.model_validate_json(json.dumps(data), strict=True)
)  # OK: strict, but from json
#> guid=UUID('12345678-1234-1234-1234-123456789012')

try:
    MyModel.model_validate(data, strict=True)  # Not OK: strict, from python
except ValidationError as exc:
    print(exc.errors(include_url=False))
    """
    [
        {
            'type': 'is_instance_of',
            'loc': ('guid',),
            'msg': 'Input should be an instance of UUID',
            'input': '12345678-1234-1234-1234-123456789012',
            'ctx': {'class': 'UUID'},
        }
    ]
    """

For more details about what types are allowed as inputs in strict mode, you can review the Conversion Table.

Strict mode in method calls

All the examples included so far get strict-mode validation through the use of strict=True as a keyword argument to the validation methods. While we have shown this for BaseModel.model_validate, this also works with arbitrary types through the use of TypeAdapter:

from pydantic import TypeAdapter, ValidationError

print(TypeAdapter(bool).validate_python('yes'))  # OK: lax
#> True

try:
    TypeAdapter(bool).validate_python('yes', strict=True)  # Not OK: strict
except ValidationError as exc:
    print(exc)
    """
    1 validation error for bool
      Input should be a valid boolean [type=bool_type, input_value='yes', input_type=str]
    """

Note this also works even when using more "complex" types in TypeAdapter: 

from dataclasses import dataclass

from pydantic import TypeAdapter, ValidationError


@dataclass
class MyDataclass:
    x: int


try:
    TypeAdapter(MyDataclass).validate_python({'x': '123'}, strict=True)
except ValidationError as exc:
    print(exc)
    """
    1 validation error for MyDataclass
      Input should be an instance of MyDataclass [type=dataclass_exact_type, input_value={'x': '123'}, input_type=dict]
    """

This also works with the TypeAdapter.validate_json and BaseModel.model_validate_json methods:

import json
from typing import List
from uuid import UUID

from pydantic import BaseModel, TypeAdapter, ValidationError

try:
    TypeAdapter(List[int]).validate_json('["1", 2, "3"]', strict=True)
except ValidationError as exc:
    print(exc)
    """
    2 validation errors for list[int]
    0
      Input should be a valid integer [type=int_type, input_value='1', input_type=str]
    2
      Input should be a valid integer [type=int_type, input_value='3', input_type=str]
    """


class Model(BaseModel):
    x: int
    y: UUID


data = {'x': '1', 'y': '12345678-1234-1234-1234-123456789012'}
try:
    Model.model_validate(data, strict=True)
except ValidationError as exc:
    # Neither x nor y are valid in strict mode from python:
    print(exc)
    """
    2 validation errors for Model
    x
      Input should be a valid integer [type=int_type, input_value='1', input_type=str]
    y
      Input should be an instance of UUID [type=is_instance_of, input_value='12345678-1234-1234-1234-123456789012', input_type=str]
    """

json_data = json.dumps(data)
try:
    Model.model_validate_json(json_data, strict=True)
except ValidationError as exc:
    # From JSON, x is still not valid in strict mode, but y is:
    print(exc)
    """
    1 validation error for Model
    x
      Input should be a valid integer [type=int_type, input_value='1', input_type=str]
    """

import json
from uuid import UUID

from pydantic import BaseModel, TypeAdapter, ValidationError

try:
    TypeAdapter(list[int]).validate_json('["1", 2, "3"]', strict=True)
except ValidationError as exc:
    print(exc)
    """
    2 validation errors for list[int]
    0
      Input should be a valid integer [type=int_type, input_value='1', input_type=str]
    2
      Input should be a valid integer [type=int_type, input_value='3', input_type=str]
    """


class Model(BaseModel):
    x: int
    y: UUID


data = {'x': '1', 'y': '12345678-1234-1234-1234-123456789012'}
try:
    Model.model_validate(data, strict=True)
except ValidationError as exc:
    # Neither x nor y are valid in strict mode from python:
    print(exc)
    """
    2 validation errors for Model
    x
      Input should be a valid integer [type=int_type, input_value='1', input_type=str]
    y
      Input should be an instance of UUID [type=is_instance_of, input_value='12345678-1234-1234-1234-123456789012', input_type=str]
    """

json_data = json.dumps(data)
try:
    Model.model_validate_json(json_data, strict=True)
except ValidationError as exc:
    # From JSON, x is still not valid in strict mode, but y is:
    print(exc)
    """
    1 validation error for Model
    x
      Input should be a valid integer [type=int_type, input_value='1', input_type=str]
    """

Strict mode with Field

For individual fields on a model, you can set strict=True on the field. This will cause strict-mode validation to be used for that field, even when the validation methods are called without strict=True.

Only the fields for which strict=True is set will be affected:

from pydantic import BaseModel, Field, ValidationError


class User(BaseModel):
    name: str
    age: int
    n_pets: int


user = User(name='John', age='42', n_pets='1')
print(user)
#> name='John' age=42 n_pets=1


class AnotherUser(BaseModel):
    name: str
    age: int = Field(strict=True)
    n_pets: int


try:
    anotheruser = AnotherUser(name='John', age='42', n_pets='1')
except ValidationError as e:
    print(e)
    """
    1 validation error for AnotherUser
    age
      Input should be a valid integer [type=int_type, input_value='42', input_type=str]
    """

Note that making fields strict will also affect the validation performed when instantiating the model class:

from pydantic import BaseModel, Field, ValidationError


class Model(BaseModel):
    x: int = Field(strict=True)
    y: int = Field(strict=False)


try:
    Model(x='1', y='2')
except ValidationError as exc:
    print(exc)
    """
    1 validation error for Model
    x
      Input should be a valid integer [type=int_type, input_value='1', input_type=str]
    """

Using Field as an annotation

Note that Field(strict=True) (or with any other keyword arguments) can be used as an annotation if necessary, e.g., when working with TypedDict:

from typing_extensions import Annotated, TypedDict

from pydantic import Field, TypeAdapter, ValidationError


class MyDict(TypedDict):
    x: Annotated[int, Field(strict=True)]


try:
    TypeAdapter(MyDict).validate_python({'x': '1'})
except ValidationError as exc:
    print(exc)
    """
    1 validation error for typed-dict
    x
      Input should be a valid integer [type=int_type, input_value='1', input_type=str]
    """

from typing_extensions import TypedDict
from typing import Annotated

from pydantic import Field, TypeAdapter, ValidationError


class MyDict(TypedDict):
    x: Annotated[int, Field(strict=True)]


try:
    TypeAdapter(MyDict).validate_python({'x': '1'})
except ValidationError as exc:
    print(exc)
    """
    1 validation error for typed-dict
    x
      Input should be a valid integer [type=int_type, input_value='1', input_type=str]
    """

Strict mode with Annotated[..., Strict()]

API Documentation

pydantic.types.Strict

Pydantic also provides the Strict class, which is intended for use as metadata with typing.Annotated class; this annotation indicates that the annotated field should be validated in strict mode:

from typing_extensions import Annotated

from pydantic import BaseModel, Strict, ValidationError


class User(BaseModel):
    name: str
    age: int
    is_active: Annotated[bool, Strict()]


User(name='David', age=33, is_active=True)
try:
    User(name='David', age=33, is_active='True')
except ValidationError as exc:
    print(exc)
    """
    1 validation error for User
    is_active
      Input should be a valid boolean [type=bool_type, input_value='True', input_type=str]
    """

from typing import Annotated

from pydantic import BaseModel, Strict, ValidationError


class User(BaseModel):
    name: str
    age: int
    is_active: Annotated[bool, Strict()]


User(name='David', age=33, is_active=True)
try:
    User(name='David', age=33, is_active='True')
except ValidationError as exc:
    print(exc)
    """
    1 validation error for User
    is_active
      Input should be a valid boolean [type=bool_type, input_value='True', input_type=str]
    """

This is, in fact, the method used to implement some of the strict-out-of-the-box types provided by Pydantic, such as StrictInt.

Strict mode with ConfigDict

BaseModel

If you want to enable strict mode for all fields on a complex input type, you can use ConfigDict(strict=True) in the model_config:

from pydantic import BaseModel, ConfigDict, ValidationError


class User(BaseModel):
    model_config = ConfigDict(strict=True)

    name: str
    age: int
    is_active: bool


try:
    User(name='David', age='33', is_active='yes')
except ValidationError as exc:
    print(exc)
    """
    2 validation errors for User
    age
      Input should be a valid integer [type=int_type, input_value='33', input_type=str]
    is_active
      Input should be a valid boolean [type=bool_type, input_value='yes', input_type=str]
    """

Note

When using strict=True through a model's model_config, you can still override the strictness of individual fields by setting strict=False on individual fields:

from pydantic import BaseModel, ConfigDict, Field


class User(BaseModel):
    model_config = ConfigDict(strict=True)

    name: str
    age: int = Field(strict=False)

Note that strict mode is not recursively applied to nested model fields:

from pydantic import BaseModel, ConfigDict, ValidationError


class Inner(BaseModel):
    y: int


class Outer(BaseModel):
    model_config = ConfigDict(strict=True)

    x: int
    inner: Inner


print(Outer(x=1, inner=Inner(y='2')))
#> x=1 inner=Inner(y=2)

try:
    Outer(x='1', inner=Inner(y='2'))
except ValidationError as exc:
    print(exc)
    """
    1 validation error for Outer
    x
      Input should be a valid integer [type=int_type, input_value='1', input_type=str]
    """

(This is also the case for dataclasses and TypedDict.)

If this is undesirable, you should make sure that strict mode is enabled for all the types involved. For example, this can be done for model classes by using a shared base class with model_config = ConfigDict(strict=True):

from pydantic import BaseModel, ConfigDict, ValidationError


class MyBaseModel(BaseModel):
    model_config = ConfigDict(strict=True)


class Inner(MyBaseModel):
    y: int


class Outer(MyBaseModel):
    x: int
    inner: Inner


try:
    Outer.model_validate({'x': 1, 'inner': {'y': '2'}})
except ValidationError as exc:
    print(exc)
    """
    1 validation error for Outer
    inner.y
      Input should be a valid integer [type=int_type, input_value='2', input_type=str]
    """

Dataclasses and TypedDict

Pydantic dataclasses behave similarly to the examples shown above with BaseModel, just that instead of model_config you should use the config keyword argument to the @pydantic.dataclasses.dataclass decorator.

When possible, you can achieve nested strict mode for vanilla dataclasses or TypedDict subclasses by annotating fields with the pydantic.types.Strict annotation.

However, if this is not possible (e.g., when working with third-party types), you can set the config that Pydantic should use for the type by setting the __pydantic_config__ attribute on the type:

from typing_extensions import TypedDict

from pydantic import ConfigDict, TypeAdapter, ValidationError


class Inner(TypedDict):
    y: int


Inner.__pydantic_config__ = ConfigDict(strict=True)


class Outer(TypedDict):
    x: int
    inner: Inner


adapter = TypeAdapter(Outer)
print(adapter.validate_python({'x': '1', 'inner': {'y': 2}}))
#> {'x': 1, 'inner': {'y': 2}}


try:
    adapter.validate_python({'x': '1', 'inner': {'y': '2'}})
except ValidationError as exc:
    print(exc)
    """
    1 validation error for typed-dict
    inner.y
      Input should be a valid integer [type=int_type, input_value='2', input_type=str]
    """

TypeAdapter

You can also get strict mode through the use of the config keyword argument to the TypeAdapter class:

from pydantic import ConfigDict, TypeAdapter, ValidationError

adapter = TypeAdapter(bool, config=ConfigDict(strict=True))

try:
    adapter.validate_python('yes')
except ValidationError as exc:
    print(exc)
    """
    1 validation error for bool
      Input should be a valid boolean [type=bool_type, input_value='yes', input_type=str]
    """

@validate_call

Strict mode is also usable with the @validate_call decorator by passing the config keyword argument:

from pydantic import ConfigDict, ValidationError, validate_call


@validate_call(config=ConfigDict(strict=True))
def foo(x: int) -> int:
    return x


try:
    foo('1')
except ValidationError as exc:
    print(exc)
    """
    1 validation error for foo
    0
      Input should be a valid integer [type=int_type, input_value='1', input_type=str]
    """