pydantic-core

Common errors

Warnings

Install

Install compatibility verified last tested: 2026-05-12

Imports

• SchemaValidator
  wrong

  from pydantic_core._pydantic_core import SchemaValidator

  correct

  from pydantic_core import SchemaValidator

  Always import from the top-level pydantic_core package, not from the private _pydantic_core Rust extension module directly

• SchemaSerializer

  from pydantic_core import SchemaSerializer

  Python wrapper around the Rust serialization logic; use for custom standalone serializers

• ValidationError

  from pydantic_core import ValidationError

  Raised by SchemaValidator on invalid input; also re-exported from pydantic

• core_schema

  from pydantic_core import core_schema

  Module containing all schema builder functions (str_schema, int_schema, typed_dict_schema, etc.) used to construct CoreSchema dicts

• PydanticCustomError

  from pydantic_core import PydanticCustomError

  Raise inside custom validators to emit structured validation errors with a custom error type and message template

• PydanticSerializationUnexpectedValue

  from pydantic_core import PydanticSerializationUnexpectedValue

  Raise inside custom field_serializer functions when the runtime value type does not match the declared type

• CoreSchema (type alias)

  from pydantic_core import CoreSchema

  TypedDict type alias for the CoreSchema dict structure; useful for type annotations in __get_pydantic_core_schema__ signatures

Quickstart verified last tested: 2026-04-23

Directly construct and use a SchemaValidator with core_schema helpers. Most users should use the higher-level pydantic.BaseModel instead; this API is for library authors or performance-critical custom validation pipelines.

from pydantic_core import SchemaValidator, ValidationError, core_schema

# Build a schema using the core_schema helpers
schema = core_schema.typed_dict_schema(
    {
        'name': core_schema.typed_dict_field(core_schema.str_schema()),
        'age': core_schema.typed_dict_field(
            core_schema.int_schema(ge=18)
        ),
        'is_developer': core_schema.typed_dict_field(
            core_schema.with_default_schema(
                core_schema.bool_schema(), default=True
            ),
            required=False,
        ),
    }
)

v = SchemaValidator(schema)

# Validate a Python dict
result = v.validate_python({'name': 'Alice', 'age': 30})
print(result)  # {'name': 'Alice', 'age': 30, 'is_developer': True}

# Validate JSON bytes directly (faster than json.loads + validate_python)
result_json = v.validate_json('{"name": "Bob", "age": 25}')
print(result_json)

# Catch validation errors
try:
    v.validate_python({'name': 'Eve', 'age': 15})
except ValidationError as e:
    print(e)
    # 1 validation error for typed-dict
    # age
    #   Input should be greater than or equal to 18 [type=greater_than_equal, ...]