Validation Errors

Pydantic attempts to provide useful validation errors. Below are details on common validation errors users may encounter when working with pydantic, together with some suggestions on how to fix them.

The entries below explain what each error type means. To see the rejected values and surrounding trace when one occurs in a live service, Logfire can record failed validations with their structured errors. See Troubleshooting Validation Errors for setup and sensitive-data considerations.

`arguments_type`

This error is raised when an object that would be passed as arguments to a function during validation is not a tuple, list, or dict:

Python

from pydantic import TypeAdapter, ValidationError


def func(x: int) -> None: ...


ta = TypeAdapter(func)

try:
    ta.validate_python('invalid')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'arguments_type'

`assertion_error`

This error is raised when a failing assert statement is encountered during validation:

Python

from pydantic import BaseModel, ValidationError, field_validator


class Model(BaseModel):
    x: int

    @field_validator('x')
    @classmethod
    def force_x_positive(cls, v):
        assert v > 0
        return v


try:
    Model(x=-1)
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'assertion_error'

`bool_parsing`

This error is raised when the input value is a string that is not valid for coercion to a boolean:

Python

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    x: bool


Model(x='true')  # OK

try:
    Model(x='test')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'bool_parsing'

`bool_type`

This error is raised when the input value's type is not valid for a bool field:

Python

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    x: bool


try:
    Model(x=None)
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'bool_type'

This error is also raised for strict fields when the input value is not an instance of bool.

`bytes_invalid_encoding`

This error is raised when a bytes value is invalid under the configured encoding. In the following example, 'a' is invalid hex (odd number of digits).

Python

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    x: bytes
    model_config = {'val_json_bytes': 'hex'}


try:
    Model(x='a')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'bytes_invalid_encoding'

`bytes_too_long`

This error is raised when the length of a bytes value is greater than the field's max_length constraint:

Python

from pydantic import BaseModel, Field, ValidationError


class Model(BaseModel):
    x: bytes = Field(max_length=3)


try:
    Model(x=b'test')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'bytes_too_long'

`bytes_too_short`

This error is raised when the length of a bytes value is less than the field's min_length constraint:

Python

from pydantic import BaseModel, Field, ValidationError


class Model(BaseModel):
    x: bytes = Field(min_length=3)


try:
    Model(x=b't')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'bytes_too_short'

`bytes_type`

This error is raised when the input value's type is not valid for a bytes field:

Python

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    x: bytes


try:
    Model(x=123)
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'bytes_type'

This error is also raised for strict fields when the input value is not an instance of bytes.

`callable_type`

This error is raised when the input value is not valid as a callable:

Python

from collections.abc import Callable
from typing import Any

from pydantic import BaseModel, ImportString, ValidationError


class Model(BaseModel):
    x: ImportString[Callable[[Any], Any]]


Model(x='math:cos')  # OK

try:
    Model(x='os.path')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'callable_type'

`complex_str_parsing`

This error is raised when the input value is a string but cannot be parsed as a complex number because it does not follow the rule in Python:

Python

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    num: complex


try:
    # Complex numbers in json are expected to be valid complex strings.
    # This value `abc` is not a valid complex string.
    Model.model_validate_json('{"num": "abc"}')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'complex_str_parsing'

`complex_type`

This error is raised when the input value cannot be interpreted as a complex number:

Python

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    num: complex


try:
    Model(num='not a complex number')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'complex_type'

`counter_type`

This error is raised when the input value's type is not valid for a Counter field:

Python

from collections import Counter

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    x: Counter[str]


try:
    Model(x='test')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'counter_type'

`dataclass_exact_type`

This error is raised when validating a dataclass with strict=True and the input is not an instance of the dataclass:

Python

import pydantic.dataclasses
from pydantic import TypeAdapter, ValidationError


@pydantic.dataclasses.dataclass
class MyDataclass:
    x: str


adapter = TypeAdapter(MyDataclass)

print(adapter.validate_python(MyDataclass(x='test'), strict=True))
#> MyDataclass(x='test')
print(adapter.validate_python({'x': 'test'}))
#> MyDataclass(x='test')

try:
    adapter.validate_python({'x': 'test'}, strict=True)
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'dataclass_exact_type'

`dataclass_type`

This error is raised when the input value is not valid for a dataclass field:

Python

from pydantic import ValidationError, dataclasses


@dataclasses.dataclass
class Inner:
    x: int


@dataclasses.dataclass
class Outer:
    y: Inner


Outer(y=Inner(x=1))  # OK

try:
    Outer(y=1)
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'dataclass_type'

`date_from_datetime_inexact`

This error is raised when the input datetime value provided for a date field has a nonzero time component. For a timestamp to parse into a field of type date, the time components must all be zero:

Python

from datetime import date, datetime

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    x: date


Model(x='2023-01-01')  # OK
Model(x=datetime(2023, 1, 1))  # OK

try:
    Model(x=datetime(2023, 1, 1, 12))
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'date_from_datetime_inexact'

`date_from_datetime_parsing`

This error is raised when the input value is a string that cannot be parsed for a date field:

Python

from datetime import date

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    x: date


try:
    Model(x='XX1494012000')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'date_from_datetime_parsing'

`date_future`

This error is raised when the input value provided for a FutureDate field is not in the future:

Python

from datetime import date

from pydantic import BaseModel, FutureDate, ValidationError


class Model(BaseModel):
    x: FutureDate


try:
    Model(x=date(2000, 1, 1))
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'date_future'

`date_parsing`

This error is raised when validating JSON where the input value is string that cannot be parsed for a date field:

Python

import json
from datetime import date

from pydantic import BaseModel, Field, ValidationError


class Model(BaseModel):
    x: date = Field(strict=True)


try:
    Model.model_validate_json(json.dumps({'x': '1'}))
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'date_parsing'

`date_past`

This error is raised when the value provided for a PastDate field is not in the past:

Python

from datetime import date, timedelta

from pydantic import BaseModel, PastDate, ValidationError


class Model(BaseModel):
    x: PastDate


try:
    Model(x=date.today() + timedelta(1))
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'date_past'

`date_type`

This error is raised when the input value's type is not valid for a date field:

Python

from datetime import date

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    x: date


try:
    Model(x=None)
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'date_type'

This error is also raised for strict fields when the input value is not an instance of date.

`datetime_from_date_parsing`

This error is raised when the input value is a string that cannot be parsed for a datetime field:

Python

from datetime import datetime

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    x: datetime


try:
    # there is no 13th month
    Model(x='2023-13-01')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'datetime_from_date_parsing'

`datetime_future`

This error is raised when the value provided for a FutureDatetime field is not in the future:

Python

from datetime import datetime

from pydantic import BaseModel, FutureDatetime, ValidationError


class Model(BaseModel):
    x: FutureDatetime


try:
    Model(x=datetime(2000, 1, 1))
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'datetime_future'

`datetime_object_invalid`

This error is raised when something about the datetime object is not valid:

Python

from datetime import datetime, tzinfo

from pydantic import AwareDatetime, BaseModel, ValidationError


class CustomTz(tzinfo):
    # utcoffset is not implemented!

    def tzname(self, _dt):
        return 'CustomTZ'


class Model(BaseModel):
    x: AwareDatetime


try:
    Model(x=datetime(2023, 1, 1, tzinfo=CustomTz()))
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'datetime_object_invalid'

`datetime_parsing`

This error is raised when the value is a string that cannot be parsed for a datetime field:

Python

import json
from datetime import datetime

from pydantic import BaseModel, Field, ValidationError


class Model(BaseModel):
    x: datetime = Field(strict=True)


try:
    Model.model_validate_json(json.dumps({'x': 'not a datetime'}))
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'datetime_parsing'

`datetime_past`

This error is raised when the value provided for a PastDatetime field is not in the past:

Python

from datetime import datetime, timedelta

from pydantic import BaseModel, PastDatetime, ValidationError


class Model(BaseModel):
    x: PastDatetime


try:
    Model(x=datetime.now() + timedelta(100))
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'datetime_past'

`datetime_type`

This error is raised when the input value's type is not valid for a datetime field:

Python

from datetime import datetime

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    x: datetime


try:
    Model(x=None)
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'datetime_type'

This error is also raised for strict fields when the input value is not an instance of datetime.

`decimal_max_digits`

This error is raised when the value provided for a Decimal has too many digits:

Python

from decimal import Decimal

from pydantic import BaseModel, Field, ValidationError


class Model(BaseModel):
    x: Decimal = Field(max_digits=3)


try:
    Model(x='42.1234')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'decimal_max_digits'

`decimal_max_places`

This error is raised when the value provided for a Decimal has too many digits after the decimal point:

Python

from decimal import Decimal

from pydantic import BaseModel, Field, ValidationError


class Model(BaseModel):
    x: Decimal = Field(decimal_places=3)


try:
    Model(x='42.1234')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'decimal_max_places'

`decimal_parsing`

This error is raised when the value provided for a Decimal could not be parsed as a decimal number:

Python

from decimal import Decimal

from pydantic import BaseModel, Field, ValidationError


class Model(BaseModel):
    x: Decimal = Field(decimal_places=3)


try:
    Model(x='test')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'decimal_parsing'

`decimal_type`

This error is raised when the value provided for a Decimal is of the wrong type:

Python

from decimal import Decimal

from pydantic import BaseModel, Field, ValidationError


class Model(BaseModel):
    x: Decimal = Field(decimal_places=3)


try:
    Model(x=[1, 2, 3])
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'decimal_type'

This error is also raised for strict fields when the input value is not an instance of Decimal.

`decimal_whole_digits`

This error is raised when the value provided for a Decimal has more digits before the decimal point than max_digits - decimal_places (as long as both are specified):

Python

from decimal import Decimal

from pydantic import BaseModel, Field, ValidationError


class Model(BaseModel):
    x: Decimal = Field(max_digits=6, decimal_places=3)


try:
    Model(x='12345.6')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'decimal_whole_digits'

`default_factory_not_called`

This error is raised when a default factory taking validated data can't be called, because validation failed on previous fields:

Python

from pydantic import BaseModel, Field, ValidationError


class Model(BaseModel):
    a: int = Field(gt=10)
    b: int = Field(default_factory=lambda data: data['a'])


try:
    Model(a=1)
except ValidationError as exc:
    print(exc)
    """
    2 validation errors for Model
    a
      Input should be greater than 10 [type=greater_than, input_value=1, input_type=int]
    b
      The default factory uses validated data, but at least one validation error occurred [type=default_factory_not_called]
    """
    print(repr(exc.errors()[1]['type']))
    #> 'default_factory_not_called'

`deque_type`

This error is raised when the input value's type is not valid for a deque field:

Python

from collections import deque

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    x: deque[int]


try:
    Model(x=1)
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'deque_type'

`dict_type`

This error is raised when the input value's type is not dict for a dict field:

Python

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    x: dict


try:
    Model(x=['1', '2'])
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'dict_type'

`ellipsis_error`

This error is raised when the input isn't the Ellipsis literal:

Python

from types import EllipsisType

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    e: EllipsisType


try:
    Model(e=1)
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'ellipsis_error'

`enum`

This error is raised when the input value does not exist in an enum field members:

Python

from enum import Enum

from pydantic import BaseModel, ValidationError


class MyEnum(str, Enum):
    option = 'option'


class Model(BaseModel):
    x: MyEnum


try:
    Model(x='other_option')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'enum'

`extra_forbidden`

This error is raised when the input value contains extra fields, but model_config['extra'] == 'forbid':

Python

from pydantic import BaseModel, ConfigDict, ValidationError


class Model(BaseModel):
    x: str

    model_config = ConfigDict(extra='forbid')


try:
    Model(x='test', y='test')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'extra_forbidden'

You can read more about the extra configuration in the Extra Attributes section.

`finite_number`

This error is raised when the value is infinite, or too large to be represented as a 64-bit floating point number during validation:

Python

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    x: int


try:
    Model(x=2.2250738585072011e308)
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'finite_number'

`float_parsing`

This error is raised when the value is a string that can't be parsed as a float:

Python

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    x: float


try:
    Model(x='test')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'float_parsing'

`float_type`

This error is raised when the input value's type is not valid for a float field:

Python

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    x: float


try:
    Model(x=None)
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'float_type'

`fraction_parsing`

This error is raised when the value provided for an input that could not be parsed as a fraction:

Python

from fractions import Fraction

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    x: Fraction


try:
    Model(x='invalid')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'fraction_parsing'

`fraction_type`

This error is raised when the value provided for a Fraction is of the wrong type:

Python

from fractions import Fraction

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    x: Fraction


try:
    Model.model_validate_json('{"x": [1, 2]}')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'fraction_type'

`frozen_dict_type`

This error is raised when the input value's type is not valid for a frozendict field:

Python

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    x: frozendict


try:
    model = Model(x='test')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'frozen_dict_type'

`frozen_field`

This error is raised when you attempt to assign a value to a field with frozen=True, or to delete such a field:

Python

from pydantic import BaseModel, Field, ValidationError


class Model(BaseModel):
    x: str = Field('test', frozen=True)


model = Model()

try:
    model.x = 'test1'
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'frozen_field'

try:
    del model.x
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'frozen_field'

`frozen_instance`

This error is raised when frozen is set in the configuration and you attempt to delete or assign a new value to any of the fields:

Python

from pydantic import BaseModel, ConfigDict, ValidationError


class Model(BaseModel):
    x: int

    model_config = ConfigDict(frozen=True)


m = Model(x=1)

try:
    m.x = 2
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'frozen_instance'

try:
    del m.x
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'frozen_instance'

`frozen_set_type`

This error is raised when the input value's type is not valid for a frozenset field:

Python

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    x: frozenset


try:
    model = Model(x='test')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'frozen_set_type'

`get_attribute_error`

This error is raised when model_config['from_attributes'] == True and an error is raised while reading the attributes:

Python

from pydantic import BaseModel, ConfigDict, ValidationError


class Foobar:
    def __init__(self):
        self.x = 1

    @property
    def y(self):
        raise RuntimeError('intentional error')


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

    model_config = ConfigDict(from_attributes=True)


try:
    Model.model_validate(Foobar())
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'get_attribute_error'

`greater_than`

This error is raised when the value is not greater than the field's gt constraint:

Python

from pydantic import BaseModel, Field, ValidationError


class Model(BaseModel):
    x: int = Field(gt=10)


try:
    Model(x=10)
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'greater_than'

`greater_than_equal`

This error is raised when the value is not greater than or equal to the field's ge constraint:

Python

from pydantic import BaseModel, Field, ValidationError


class Model(BaseModel):
    x: int = Field(ge=10)


try:
    Model(x=9)
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'greater_than_equal'

`int_from_float`

This error is raised when you provide a float value for an int field:

Python

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    x: int


try:
    Model(x=0.5)
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'int_from_float'

`int_parsing`

This error is raised when the value can't be parsed as int:

Python

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    x: int


try:
    Model(x='test')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'int_parsing'

`int_parsing_size`

This error is raised when attempting to parse a python or JSON value from a string outside the maximum range that Python str to int parsing permits:

Python

import json

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    x: int


# from Python
assert Model(x='1' * 4_300).x == int('1' * 4_300)  # OK

too_long = '1' * 4_301
try:
    Model(x=too_long)
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'int_parsing_size'

# from JSON
try:
    Model.model_validate_json(json.dumps({'x': too_long}))
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'int_parsing_size'

`int_type`

This error is raised when the input value's type is not valid for an int field:

Python

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    x: int


try:
    Model(x=None)
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'int_type'

`invalid_key`

This error is raised when attempting to validate a dict that has a key that is not an instance of str:

Python

from pydantic import BaseModel, ConfigDict, ValidationError


class Model(BaseModel):
    x: int

    model_config = ConfigDict(extra='allow')


try:
    Model.model_validate({'x': 1, b'y': 2})
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'invalid_key'

`is_instance_of`

This error is raised when the input value is not an instance of the expected type:

Python

from pydantic import BaseModel, ConfigDict, ValidationError


class Nested:
    x: str


class Model(BaseModel):
    y: Nested

    model_config = ConfigDict(arbitrary_types_allowed=True)


try:
    Model(y='test')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'is_instance_of'

`is_subclass_of`

This error is raised when the input value is not a subclass of the expected type:

Python

from pydantic import BaseModel, ValidationError


class Nested:
    x: str


class Model(BaseModel):
    y: type[Nested]


try:
    Model(y='test')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'is_subclass_of'

`iterable_type`

This error is raised when the input value is not valid as an Iterable:

Python

from collections.abc import Iterable

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    y: Iterable[str]


try:
    Model(y=123)
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'iterable_type'

`iteration_error`

This error is raised when an error occurs during iteration:

Python

from pydantic import BaseModel, ValidationError


def gen():
    yield 1
    raise RuntimeError('error')


class Model(BaseModel):
    x: list[int]


try:
    Model(x=gen())
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'iteration_error'

`json_invalid`

This error is raised when the input value is not a valid JSON string:

Python

from pydantic import BaseModel, Json, ValidationError


class Model(BaseModel):
    x: Json


try:
    Model(x='test')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'json_invalid'

`json_type`

This error is raised when the input value is of a type that cannot be parsed as JSON:

Python

from pydantic import BaseModel, Json, ValidationError


class Model(BaseModel):
    x: Json


try:
    Model(x=None)
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'json_type'

`less_than`

This error is raised when the input value is not less than the field's lt constraint:

Python

from pydantic import BaseModel, Field, ValidationError


class Model(BaseModel):
    x: int = Field(lt=10)


try:
    Model(x=10)
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'less_than'

`less_than_equal`

This error is raised when the input value is not less than or equal to the field's le constraint:

Python

from pydantic import BaseModel, Field, ValidationError


class Model(BaseModel):
    x: int = Field(le=10)


try:
    Model(x=11)
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'less_than_equal'

`list_type`

This error is raised when the input value's type is not valid for a list field:

Python

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    x: list[int]


try:
    Model(x=1)
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'list_type'

`literal_error`

This error is raised when the input value is not one of the expected literal values:

Python

from typing import Literal

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    x: Literal['a', 'b']


Model(x='a')  # OK

try:
    Model(x='c')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'literal_error'

`mapping_type`

This error is raised when a problem occurs during validation due to a failure in a call to the methods from the Mapping protocol, such as .items():

Python

from collections.abc import Mapping

from pydantic import BaseModel, ValidationError


class BadMapping(Mapping):
    def items(self):
        raise ValueError()

    def __iter__(self):
        raise ValueError()

    def __getitem__(self, key):
        raise ValueError()

    def __len__(self):
        return 1


class Model(BaseModel):
    x: dict[str, str]


try:
    Model(x=BadMapping())
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'mapping_type'

`missing`

This error is raised when there are required fields missing from the input value:

Python

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    x: str


try:
    Model()
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'missing'

`missing_argument`

This error is raised when a required positional-or-keyword argument is not passed to a function decorated with validate_call:

Python

from pydantic import ValidationError, validate_call


@validate_call
def foo(a: int):
    return a


try:
    foo()
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'missing_argument'

`missing_keyword_only_argument`

This error is raised when a required keyword-only argument is not passed to a function decorated with validate_call:

Python

from pydantic import ValidationError, validate_call


@validate_call
def foo(*, a: int):
    return a


try:
    foo()
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'missing_keyword_only_argument'

`missing_positional_only_argument`

This error is raised when a required positional-only argument is not passed to a function decorated with validate_call:

Python

from pydantic import ValidationError, validate_call


@validate_call
def foo(a: int, /):
    return a


try:
    foo()
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'missing_positional_only_argument'

`missing_sentinel_error`

This error is raised when the MISSING sentinel is the only value allowed, and wasn't provided during validation:

Python

from pydantic import MISSING, BaseModel, ValidationError


class Model(BaseModel):
    f: MISSING


try:
    Model(f=1)
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'missing_sentinel_error'

`model_attributes_type`

This error is raised when the input value is not a valid dictionary, model instance, or instance that fields can be extracted from:

Python

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    a: int
    b: int


# simply validating a dict
print(Model.model_validate({'a': 1, 'b': 2}))
#> a=1 b=2


class CustomObj:
    def __init__(self, a, b):
        self.a = a
        self.b = b


# using from attributes to extract fields from an objects
print(Model.model_validate(CustomObj(3, 4), from_attributes=True))
#> a=3 b=4

try:
    Model.model_validate('not an object', from_attributes=True)
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'model_attributes_type'

`model_type`

This error is raised when the input to a model is not an instance of the model or dict:

Python

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    a: int
    b: int


# simply validating a dict
m = Model.model_validate({'a': 1, 'b': 2})
print(m)
#> a=1 b=2

# validating an existing model instance
print(Model.model_validate(m))
#> a=1 b=2

try:
    Model.model_validate('not an object')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'model_type'

`multiple_argument_values`

This error is raised when you provide multiple values for a single argument while calling a function decorated with validate_call:

Python

from pydantic import ValidationError, validate_call


@validate_call
def foo(a: int):
    return a


try:
    foo(1, a=2)
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'multiple_argument_values'

`multiple_of`

This error is raised when the input is not a multiple of a field's multiple_of constraint:

Python

from pydantic import BaseModel, Field, ValidationError


class Model(BaseModel):
    x: int = Field(multiple_of=5)


try:
    Model(x=1)
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'multiple_of'

`named_tuple_type`

This error is raised when the input value is not valid for a named tuple field:

Python

from typing import NamedTuple

from pydantic import BaseModel, ValidationError


class Point(NamedTuple):
    x: int
    y: int


class Model(BaseModel):
    p: Point


try:
    Model(p='invalid')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'named_tuple_type'

`needs_python_object`

This type of error is raised when validation is attempted from a format that cannot be converted to a Python object. For example, we cannot check isinstance or issubclass from JSON:

Python

import json

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    bm: type[BaseModel]


try:
    Model.model_validate_json(json.dumps({'bm': 'not a basemodel class'}))
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'needs_python_object'

`no_such_attribute`

This error is raised when validate_assignment=True in the config, and you attempt to assign a value to an attribute that is not an existing field:

Python

from pydantic import ConfigDict, ValidationError, dataclasses


@dataclasses.dataclass(config=ConfigDict(validate_assignment=True))
class MyDataclass:
    x: int


m = MyDataclass(x=1)
try:
    m.y = 10
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'no_such_attribute'

`none_required`

This error is raised when the input value is not None for a field that requires None:

Python

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    x: None


try:
    Model(x=1)
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'none_required'

You may encounter this error when there is a naming collision in your model between a field name and its type. More specifically, this error is likely to be thrown when the default value of that field is None.

For example, the following would yield the none_required validation error since the field int is set to a default value of None and has the exact same name as its type, which causes problems with validation.

Python

from pydantic import BaseModel


class M1(BaseModel):
    int: int | None = None


m = M1(int=123)  # errors

`ordered_dict_type`

This error is raised when the input value's type is not valid for an OrderedDict field:

Python

from collections import OrderedDict

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    x: OrderedDict[str, int]


try:
    Model(x='test')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'ordered_dict_type'

`recursion_loop`

This error is raised when a cyclic reference is detected:

Python

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    x: list['Model']


d = {'x': []}
d['x'].append(d)
try:
    Model(**d)
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'recursion_loop'

`set_item_not_hashable`

This error is raised when an unhashable value is validated against a set or a frozenset:

Python

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    x: set[object]


class Unhashable:
    __hash__ = None


try:
    Model(x=[{'a': 'b'}, Unhashable()])
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'set_item_not_hashable'
    print(repr(exc.errors()[1]['type']))
    #> 'set_item_not_hashable'

`set_type`

This error is raised when the value type is not valid for a set field:

Python

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    x: set[int]


try:
    Model(x='test')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'set_type'

`string_not_ascii`

This error is raised when the input string contains non-ASCII characters:

Python

from typing import Annotated

from pydantic import BaseModel, StringConstraints, ValidationError


class Model(BaseModel):
    v: Annotated[str, StringConstraints(ascii_only=True)]


try:
    Model(v='caf\u00e9')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'string_not_ascii'

`string_pattern_mismatch`

This error is raised when the input value doesn't match the field's pattern constraint:

Python

from pydantic import BaseModel, Field, ValidationError


class Model(BaseModel):
    x: str = Field(pattern='test')


try:
    Model(x='1')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'string_pattern_mismatch'

`string_too_long`

This error is raised when the input value is a string whose length is greater than the field's max_length constraint:

Python

from pydantic import BaseModel, Field, ValidationError


class Model(BaseModel):
    x: str = Field(max_length=3)


try:
    Model(x='test')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'string_too_long'

`string_too_short`

This error is raised when the input value is a string whose length is less than the field's min_length constraint:

Python

from pydantic import BaseModel, Field, ValidationError


class Model(BaseModel):
    x: str = Field(min_length=3)


try:
    Model(x='t')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'string_too_short'

`string_type`

This error is raised when the input value's type is not valid for a str field:

Python

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    x: str


try:
    Model(x=1)
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'string_type'

This error is also raised for strict fields when the input value is not an instance of str.

`string_unicode`

This error is raised when the value cannot be parsed as a Unicode string:

Python

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    x: str


try:
    Model(x=b'\x81')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'string_unicode'

`time_delta_parsing`

This error is raised when the input value is a string that cannot be parsed for a timedelta field:

Python

from datetime import timedelta

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    x: timedelta


try:
    Model(x='t')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'time_delta_parsing'

`time_delta_type`

This error is raised when the input value's type is not valid for a timedelta field:

Python

from datetime import timedelta

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    x: timedelta


try:
    Model(x=None)
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'time_delta_type'

This error is also raised for strict fields when the input value is not an instance of timedelta.

`time_parsing`

This error is raised when the input value is a string that cannot be parsed for a time field:

Python

from datetime import time

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    x: time


try:
    Model(x='25:20:30.400')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'time_parsing'

`time_type`

This error is raised when the value type is not valid for a time field:

Python

from datetime import time

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    x: time


try:
    Model(x=None)
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'time_type'

This error is also raised for strict fields when the input value is not an instance of time.

`timezone_aware`

This error is raised when the datetime value provided for a timezone-aware datetime field doesn't have timezone information:

Python

from datetime import datetime

from pydantic import AwareDatetime, BaseModel, ValidationError


class Model(BaseModel):
    x: AwareDatetime


try:
    Model(x=datetime.now())
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'timezone_aware'

`timezone_naive`

This error is raised when the datetime value provided for a timezone-naive datetime field has timezone info:

Python

from datetime import datetime, timezone

from pydantic import BaseModel, NaiveDatetime, ValidationError


class Model(BaseModel):
    x: NaiveDatetime


try:
    Model(x=datetime.now(tz=timezone.utc))
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'timezone_naive'

`too_long`

This error is raised when the input value's length is greater than the field's max_length constraint:

Python

from pydantic import BaseModel, Field, ValidationError


class Model(BaseModel):
    x: list[int] = Field(max_length=3)


try:
    Model(x=[1, 2, 3, 4])
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'too_long'

`too_short`

This error is raised when the value length is less than the field's min_length constraint:

Python

from pydantic import BaseModel, Field, ValidationError


class Model(BaseModel):
    x: list[int] = Field(min_length=3)


try:
    Model(x=[1, 2])
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'too_short'

`tuple_type`

This error is raised when the input value's type is not valid for a tuple field:

Python

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    x: tuple[int]


try:
    Model(x=None)
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'tuple_type'

This error is also raised for strict fields when the input value is not an instance of tuple.

`unexpected_keyword_argument`

This error is raised when you provide a value by keyword for a positional-only argument while calling a function decorated with validate_call:

Python

from pydantic import ValidationError, validate_call


@validate_call
def foo(a: int, /):
    return a


try:
    foo(a=2)
except ValidationError as exc:
    print(repr(exc.errors()[1]['type']))
    #> 'unexpected_keyword_argument'

It is also raised when using pydantic.dataclasses and extra=forbid:

Python

from pydantic import TypeAdapter, ValidationError
from pydantic.dataclasses import dataclass


@dataclass(config={'extra': 'forbid'})
class Foo:
    bar: int


try:
    TypeAdapter(Foo).validate_python({'bar': 1, 'foobar': 2})
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'unexpected_keyword_argument'

`unexpected_positional_argument`

This error is raised when you provide a positional value for a keyword-only argument while calling a function decorated with validate_call:

Python

from pydantic import ValidationError, validate_call


@validate_call
def foo(*, a: int):
    return a


try:
    foo(2)
except ValidationError as exc:
    print(repr(exc.errors()[1]['type']))
    #> 'unexpected_positional_argument'

`union_tag_invalid`

This error is raised when the input's discriminator is not one of the expected values:

Python

from typing import Literal

from pydantic import BaseModel, Field, ValidationError


class BlackCat(BaseModel):
    pet_type: Literal['blackcat']


class WhiteCat(BaseModel):
    pet_type: Literal['whitecat']


class Model(BaseModel):
    cat: BlackCat | WhiteCat = Field(discriminator='pet_type')


try:
    Model(cat={'pet_type': 'dog'})
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'union_tag_invalid'

`union_tag_not_found`

This error is raised when it is not possible to extract a discriminator value from the input:

Python

from typing import Literal

from pydantic import BaseModel, Field, ValidationError


class BlackCat(BaseModel):
    pet_type: Literal['blackcat']


class WhiteCat(BaseModel):
    pet_type: Literal['whitecat']


class Model(BaseModel):
    cat: BlackCat | WhiteCat = Field(discriminator='pet_type')


try:
    Model(cat={'name': 'blackcat'})
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'union_tag_not_found'

`url_parsing`

This error is raised when the input value cannot be parsed as a URL:

Python

from pydantic import AnyUrl, BaseModel, ValidationError


class Model(BaseModel):
    x: AnyUrl


try:
    Model(x='test')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'url_parsing'

`url_scheme`

This error is raised when the URL scheme is not valid for the URL type of the field:

Python

from pydantic import BaseModel, HttpUrl, ValidationError


class Model(BaseModel):
    x: HttpUrl


try:
    Model(x='ftp://example.com')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'url_scheme'

`url_syntax_violation`

This error is raised when the URL syntax is not valid:

Python

from pydantic import BaseModel, Field, HttpUrl, ValidationError


class Model(BaseModel):
    x: HttpUrl = Field(strict=True)


try:
    Model(x='http:////example.com')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'url_syntax_violation'

`url_too_long`

This error is raised when the URL length is greater than 2083:

Python

from pydantic import BaseModel, HttpUrl, ValidationError


class Model(BaseModel):
    x: HttpUrl


try:
    Model(x='x' * 2084)
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'url_too_long'

`url_type`

This error is raised when the input value's type is not valid for a URL field:

Python

from pydantic import BaseModel, HttpUrl, ValidationError


class Model(BaseModel):
    x: HttpUrl


try:
    Model(x=None)
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'url_type'

`uuid_parsing`

This error is raised when the input value's type is not valid for a UUID field:

Python

from uuid import UUID

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    u: UUID


try:
    Model(u='12345678-124-1234-1234-567812345678')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'uuid_parsing'

`uuid_type`

This error is raised when the input value's type is not valid instance for a UUID field (str, bytes or UUID):

Python

from uuid import UUID

from pydantic import BaseModel, ValidationError


class Model(BaseModel):
    u: UUID


try:
    Model(u=1234567812412341234567812345678)
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'uuid_type'

`uuid_version`

This error is raised when the input value's type is not match UUID version:

Python

from pydantic import UUID5, BaseModel, ValidationError


class Model(BaseModel):
    u: UUID5


try:
    Model(u='a6cc5730-2261-11ee-9c43-2eb5a363657c')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'uuid_version'

`value_error`

This error is raised when a ValueError is raised during validation:

Python

from pydantic import BaseModel, ValidationError, field_validator


class Model(BaseModel):
    x: str

    @field_validator('x')
    @classmethod
    def repeat_b(cls, v):
        raise ValueError()


try:
    Model(x='test')
except ValidationError as exc:
    print(repr(exc.errors()[0]['type']))
    #> 'value_error'