Schema Validation

The Basics

The simplest way to validate an instance under a given schema is to use the validate() function.

jsonschema.validate(instance, schema, cls=None, *args, **kwargs)[source]

Validate an instance under the given schema.

>>> validate([2, 3, 4], {"maxItems": 2})
Traceback (most recent call last):
ValidationError: [2, 3, 4] is too long

validate() will first verify that the provided schema is itself valid, since not doing so can lead to less obvious error messages and fail in less obvious or consistent ways.

If you know you have a valid schema already, especially if you intend to validate multiple instances with the same schema, you likely would prefer using the Validator.validate method directly on a specific validator (e.g. Draft7Validator.validate).

  • instance – The instance to validate

  • schema – The schema to validate with

  • cls (Validator) – The class that will be used to validate the instance.

If the cls argument is not provided, two things will happen in accordance with the specification. First, if the schema has a $schema property containing a known meta-schema 1 then the proper validator will be used. The specification recommends that all schemas contain $schema properties for this reason. If no $schema property is found, the default validator class is the latest released draft.

Any other provided positional and keyword arguments will be passed on when instantiating the cls.




known by a validator registered with jsonschema.validators.validates


For information on creating JSON schemas to validate your data, there is a good introduction to JSON Schema fundamentals underway at Understanding JSON Schema

The Validator Protocol

jsonschema defines a protocol that all validator classes should adhere to.

class jsonschema.protocols.Validator(*args, **kwargs)[source]

The protocol to which all validator classes should adhere.

  • schema – the schema that the validator object will validate with. It is assumed to be valid, and providing an invalid schema can lead to undefined behavior. See Validator.check_schema to validate a schema first.

  • resolver – an instance of jsonschema.RefResolver that will be used to resolve $ref properties (JSON references). If unprovided, one will be created.

  • format_checker – an instance of jsonschema.FormatChecker whose jsonschema.FormatChecker.conforms method will be called to check and see if instances conform to each format property present in the schema. If unprovided, no validation will be done for format. Certain formats require additional packages to be installed (ipv5, uri, color, date-time). The required packages can be found at the bottom of this page.

META_SCHEMA: ClassVar[dict]

An object representing the validator’s meta schema (the schema that describes valid schemas in the given version).

TYPE_CHECKER: ClassVar[jsonschema.TypeChecker]

A jsonschema.TypeChecker that will be used when validating type properties in JSON schemas.

VALIDATORS: ClassVar[dict]

A mapping of validator names (strs) to functions that validate the validator property with that name. For more information see Creating or Extending Validator Classes.

classmethod check_schema(schema: dict) None[source]

Validate the given schema against the validator’s META_SCHEMA.


jsonschema.exceptions.SchemaError if the schema is invalid

evolve(**kwargs) Validator[source]

Create a new validator like this one, but with given changes.

Preserves all other attributes, so can be used to e.g. create a validator with a different schema but with the same $ref resolution behavior.

>>> validator = Draft202012Validator({})
>>> validator.evolve(schema={"type": "number"})
Draft202012Validator(schema={'type': 'number'}, format_checker=None)
is_type(instance: Any, type: str) bool[source]

Check if the instance is of the given (JSON Schema) type.

Return type



jsonschema.exceptions.UnknownType if type is not a known type.

is_valid(instance: dict) bool[source]

Check if the instance is valid under the current schema.

Return type


>>> schema = {"maxItems" : 2}
>>> Draft3Validator(schema).is_valid([2, 3, 4])
iter_errors(instance: dict) Iterator[jsonschema.exceptions.ValidationError][source]

Lazily yield each of the validation errors in the given instance.

Return type

an of jsonschema.exceptions.ValidationErrors

>>> schema = {
...     "type" : "array",
...     "items" : {"enum" : [1, 2, 3]},
...     "maxItems" : 2,
... }
>>> v = Draft3Validator(schema)
>>> for error in sorted(v.iter_errors([2, 3, 4]), key=str):
...     print(error.message)
4 is not one of [1, 2, 3]
[2, 3, 4] is too long
schema: dict | bool

The schema that was passed in when initializing the object.

validate(instance: dict) None[source]

Check if the instance is valid under the current schema.


jsonschema.exceptions.ValidationError if the instance is invalid

>>> schema = {"maxItems" : 2}
>>> Draft3Validator(schema).validate([2, 3, 4])
Traceback (most recent call last):
ValidationError: [2, 3, 4] is too long

All of the versioned validators that are included with jsonschema adhere to the protocol, and implementers of validator classes that extend or complement the ones included should adhere to it as well. For more information see Creating or Extending Validator Classes.

Type Checking

To handle JSON Schema’s type property, a Validator uses an associated TypeChecker. The type checker provides an immutable mapping between names of types and functions that can test if an instance is of that type. The defaults are suitable for most users - each of the versioned validators that are included with jsonschema have a TypeChecker that can correctly handle their respective versions.

See also

Validating With Additional Types

For an example of providing a custom type check.

class jsonschema.TypeChecker(type_checkers: Mapping[str, Callable[[TypeChecker, Any], bool]] = pmap({}))[source]

A type property checker.

A TypeChecker performs type checking for a Validator. Type checks to perform are updated using TypeChecker.redefine or TypeChecker.redefine_many and removed via TypeChecker.remove. Each of these return a new TypeChecker object.


type_checkers (dict) – The initial mapping of types to their checking functions.

is_type(instance, type)[source]

Check if the instance is of the appropriate type.

  • instance (object) – The instance to check

  • type (str) – The name of the type that is expected.


Whether it conformed.

Return type



jsonschema.exceptions.UndefinedTypeCheck – if type is unknown to this object.

redefine(type, fn)[source]

Produce a new checker with the given type redefined.

  • type (str) – The name of the type to check.

  • fn ( – A function taking exactly two parameters - the type checker calling the function and the instance to check. The function should return true if instance is of this type and false otherwise.


A new TypeChecker instance.


Produce a new checker with the given types redefined.


definitions (dict) – A dictionary mapping types to their checking functions.


A new TypeChecker instance.


Produce a new checker with the given types forgotten.


types (Iterable) – the names of the types to remove.


A new TypeChecker instance


jsonschema.exceptions.UndefinedTypeCheck – if any given type is unknown to this object

exception jsonschema.exceptions.UndefinedTypeCheck(type)[source]

A type checker was asked to check a type it did not have registered.

Raised when trying to remove a type check that is not known to this TypeChecker, or when calling jsonschema.TypeChecker.is_type directly.

Validating With Additional Types

Occasionally it can be useful to provide additional or alternate types when validating the JSON Schema’s type property.

jsonschema tries to strike a balance between performance in the common case and generality. For instance, JSON Schema defines a number type, which can be validated with a schema such as {"type" : "number"}. By default, this will accept instances of Python numbers.Number. This includes in particular ints and floats, along with decimal.Decimal objects, complex numbers etc. For integer and object, however, rather than checking for numbers.Integral and, jsonschema simply checks for int and dict, since the more general instance checks can introduce significant slowdown, especially given how common validating these types are.

If you do want the generality, or just want to add a few specific additional types as being acceptable for a validator object, then you should update an existing TypeChecker or create a new one. You may then create a new Validator via jsonschema.validators.extend.

class MyInteger(object):

def is_my_int(checker, instance):
    return (
        Draft3Validator.TYPE_CHECKER.is_type(instance, "number") or
        isinstance(instance, MyInteger)

type_checker = Draft3Validator.TYPE_CHECKER.redefine("number", is_my_int)

CustomValidator = extend(Draft3Validator, type_checker=type_checker)
validator = CustomValidator(schema={"type" : "number"})
exception jsonschema.exceptions.UnknownType(type, instance, schema)[source]

A validator was asked to validate an instance against an unknown type.

Versioned Validators

jsonschema ships with validator classes for various versions of the JSON Schema specification. For details on the methods and attributes that each validator class provides see the Validator protocol, which each included validator class implements.

class jsonschema.Draft202012Validator(schema, resolver=None, format_checker=None)
class jsonschema.Draft201909Validator(schema, resolver=None, format_checker=None)
class jsonschema.Draft7Validator(schema, resolver=None, format_checker=None)
class jsonschema.Draft6Validator(schema, resolver=None, format_checker=None)
class jsonschema.Draft4Validator(schema, resolver=None, format_checker=None)
class jsonschema.Draft3Validator(schema, resolver=None, format_checker=None)

For example, if you wanted to validate a schema you created against the Draft 7 meta-schema, you could use:

from jsonschema import Draft7Validator

schema = {
    "$schema": "",

    "type": "object",
    "properties": {
        "name": {"type": "string"},
        "email": {"type": "string"},
    "required": ["email"]

Validating Formats

JSON Schema defines the format property which can be used to check if primitive types (strings, numbers, booleans) conform to well-defined formats. By default, no validation is enforced, but optionally, validation can be enabled by hooking in a format-checking object into an Validator.

>>> validate("", {"format" : "ipv4"})
>>> validate(
...     instance="-12",
...     schema={"format" : "ipv4"},
...     format_checker=draft7_format_checker,
... )
Traceback (most recent call last):
ValidationError: "-12" is not a "ipv4"
class jsonschema.FormatChecker(formats=None)[source]

A format property checker.

JSON Schema does not mandate that the format property actually do any validation. If validation is desired however, instances of this class can be hooked into validators to enable format validation.

FormatChecker objects always return True when asked about formats that they do not know how to validate.

To check a custom format using a function that takes an instance and returns a bool, use the FormatChecker.checks or FormatChecker.cls_checks decorators.


formats (Iterable) – The known formats to validate. This argument can be used to limit which formats will be used during validation.


A mapping of currently known formats to tuple of functions that validate them and errors that should be caught. New checkers can be added and removed either per-instance or globally for all checkers using the FormatChecker.checks or FormatChecker.cls_checks decorators respectively.

classmethod cls_checks(format, raises=())

Register a decorated function as globally validating a new format.

Any instance created after this function is called will pick up the supplied checker.

  • format (str) – the format that the decorated function will check

  • raises (Exception) – the exception(s) raised by the decorated function when an invalid instance is found. The exception object will be accessible as the jsonschema.exceptions.ValidationError.cause attribute of the resulting validation error.

check(instance, format)[source]

Check whether the instance conforms to the given format.

  • instance (any primitive type, i.e. str, number, bool) – The instance to check

  • format (str) – The format that instance should conform to


FormatError – if the instance does not conform to format

checks(format, raises=())[source]

Register a decorated function as validating a new format.

  • format (str) – The format that the decorated function will check.

  • raises (Exception) –

    The exception(s) raised by the decorated function when an invalid instance is found.

    The exception object will be accessible as the jsonschema.exceptions.ValidationError.cause attribute of the resulting validation error.

conforms(instance, format)[source]

Check whether the instance conforms to the given format.

  • instance (any primitive type, i.e. str, number, bool) – The instance to check

  • format (str) – The format that instance should conform to


whether it conformed

Return type


exception jsonschema.FormatError(message, cause=None)[source]

Validating a format failed.

There are a number of default checkers that FormatCheckers know how to validate. Their names can be viewed by inspecting the FormatChecker.checkers attribute. Certain checkers will only be available if an appropriate package is available for use. The easiest way to ensure you have what is needed is to install jsonschema using the format or format_nongpl setuptools extra – i.e.

$ pip install jsonschema[format]

which will install all of the below dependencies for all formats.

Or if you want to install MIT-license compatible dependencies only:

$ pip install jsonschema[format_nongpl]

The non-GPL extra is intended to not install any direct dependencies that are GPL (but that of course end-users should do their own verification). At the moment, it supports all the available checkers except for iri and iri-reference.

The more specific list of available checkers, along with their requirement (if any,) are listed below.


If the following packages are not installed when using a checker that requires it, validation will succeed without throwing an error, as specified by the JSON Schema specification.




requires webcolors



requires rfc3339-validator


requires isoduration



requires fqdn


requires idna



OS must have socket.inet_pton function


requires rfc3987


requires rfc3987


requires jsonpointer



requires jsonpointer


requires rfc3339-validator


requires rfc3987 or rfc3986-validator


requires rfc3987 or rfc3986-validator


requires uri-template


Since in most cases “validating” an email address is an attempt instead to confirm that mail sent to it will deliver to a recipient, and that that recipient is the correct one the email is intended for, and since many valid email addresses are in many places incorrectly rejected, and many invalid email addresses are in many places incorrectly accepted, the email format validator only provides a sanity check, not full rfc5322 validation.

The same applies to the idn-email format.