# Strict Mode

:::accordion{title="API Documentation"}
[`pydantic.types.Strict`](/guides/pydantic-types)
:::

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](/guides/pydantic-standard-library-types#integers),
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](/guides/pydantic-standard-library-types#date-and-time-types) allow strings
even in strict mode).

The strict behavior for each type can be found in the [standard library types](/guides/pydantic-standard-library-types) documentation,
and is summarized in the [conversion table](/guides/concepts-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]
    """
```

One caveat when enabling strict mode on models an existing application relies on: inputs that were
being quietly coerced will start failing. If you can't audit every caller, it helps to watch validation
failures while you roll the change out. [Logfire](/guides/integrations-logfire) counts successful and
failed validations as metrics, and can record the rejected values included in structured errors.

Strict mode can be enabled in various ways:

- [As a validation parameter](#as-a-validation-parameter), such as when using [`model_validate()`](/guides/pydantic-base-model),
  on Pydantic models.
- [At the field level](#at-the-field-level).
- [At the configuration level](#as-a-configuration-value) (with the possibility to override at the field level).

[]()

## As a validation parameter

Strict mode can be enabled on a per-validation-call basis, when using the [validation methods](/guides/concepts-models#validating-data)
on [Pydantic models](/guides/concepts-models) and [type adapters](/guides/concepts-type-adapter).

```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
```

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

[]()

## At the field level

Strict mode can be enabled on specific fields, by setting the `strict` parameter of the
`Field()` function to `True`. Strict mode will be applied for such fields,
even when the [validation methods](/guides/concepts-models#validating-data) 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]
    """
```

1. The strict constraint can also be applied using the [annotated pattern](/guides/concepts-fields#the-annotated-pattern):
   `Annotated[int, Field(strict=True)]`

[]()

### Using the `Strict()` metadata class

:::accordion{title="API Documentation"}
[`pydantic.types.Strict`](/guides/pydantic-types)
:::

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

```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)!
```

1. Equivalent to `Annotated[int, Strict()]`.

[]()

## As a configuration value

Strict mode behavior can be controlled at the [configuration](/guides/concepts-config) level. When used on
a Pydantic model (or model like class such as [dataclasses](/guides/concepts-dataclasses)), strictness can still
be overridden at the [field level](#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
```

## Related pages

- [API Documentation](./api-documentation-index.md)
- [Concepts](./concepts-index.md)
- [Dev Tools](./dev-tools-index.md)
- [Error Messages](./error-messages-index.md)
- [Examples](./examples-index.md)
- [Integrations](./integrations-index.md)
- [Internals](./internals-index.md)
- [Production Tools](./production-tools-index.md)
- [Pydantic](./pydantic-index.md)
- [Pydantic Core](./pydantic-core-index.md)

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