Strict Mode

??? api "API Documentation" [pydantic.types.Strict][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 for the int number type, and it will be converted to the value 123. This coercion behavior is useful in many scenarios — think: UUIDs, URL parameters, HTTP headers, environment variables, dates, 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". 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.

Most of the time, strict mode will only allow instances of the type to be provided, although looser rules may apply to JSON input (for instance, the date and time types allow strings even in strict mode).

The strict behavior for each type can be found in the standard library types documentation, and is summarized in the conversion table.

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

python

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]
    """

Strict mode can be enabled in various ways:

As a validation parameter, such as when using [model_validate()][pydantic.BaseModel.model_validate], on Pydantic models.
At the field level.
At the configuration level (with the possibility to override at the field level).

{#strict-mode-in-method-calls}

As a validation parameter

Strict mode can be enaled on a per-validation-call basis, when using the validation methods on Pydantic models and type adapters.

python

from datetime import date

from pydantic import TypeAdapter, ValidationError

print(TypeAdapter(date).validate_python('2000-01-01'))  # OK: lax
#> 2000-01-01

try:
    # Not OK: strict:
    TypeAdapter(date).validate_python('2000-01-01', strict=True)
except ValidationError as exc:
    print(exc)
    """
    1 validation error for date
      Input should be a valid date [type=date_type, input_value='2000-01-01', input_type=str]
    """

TypeAdapter(date).validate_json('"2000-01-01"', strict=True)  # (1)!
#> 2000-01-01

As mentioned, strict mode is looser when validating from JSON.

{#strict-mode-with-field}

At the field level

Strict mode can be enabled on specific fields, by setting the strict parameter of the [Field()][pydantic.Field] function to True. Strict mode will be applied for such fields, even when the validation methods are called in lax mode.

python

from pydantic import BaseModel, Field, ValidationError


class User(BaseModel):
    name: str
    age: int = Field(strict=True)  # (1)!


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


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

The strict constraint can also be applied using the annotated pattern: Annotated[int, Field(strict=True)]

{#strict-mode-with-annotated-strict}

Using the `Strict()` metadata class

??? api "API Documentation" [pydantic.types.Strict][pydantic.types.Strict]

As an alternative to the [Field()][pydantic.Field] function, Pydantic provides the [Strict][pydantic.types.Strict] metadata class, meant to be used with the annotated pattern. It also provides convenience aliases for the most common types (namely [StrictBool][pydantic.types.StrictBool], [StrictInt][pydantic.types.StrictInt], [StrictFloat][pydantic.types.StrictFloat], [StrictStr][pydantic.types.StrictStr] and [StrictBytes][pydantic.types.StrictBytes]).

python

from typing import Annotated
from uuid import UUID

from pydantic import BaseModel, Strict, StrictInt


class User(BaseModel):
    id: Annotated[UUID, Strict()]
    age: StrictInt  # (1)!

Equivalent to Annotated[int, Strict()].

{#strict-mode-with-configdict}

As a configuration value

Strict mode behavior can be controlled at the configuration level. When used on a Pydantic model (or model like class such as dataclasses), strictness can still be overridden at the field level:

python

from pydantic import BaseModel, ConfigDict, Field


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

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


print(User(name='John', age='18'))
#> name='John' age=18

As a validation parameter

At the field level

Using the Strict() metadata class

As a configuration value

Using the `Strict()` metadata class