📁 Base Options¶

📋 Options¶

Option	Description
`--emit-model-metadata`	Write a separate JSON map from source schema references to t...
`--encoding`	Specify character encoding for input and output files.
`--external-ref-mapping`	Map external `$ref` files to Python packages.
`--input`	Specify the input schema file path.
`--input-file-type`	Specify the input file type for code generation.
`--input-model`	Import a Python type or dict schema from a module.
`--input-model-ref-strategy`	Strategy for referenced types when using --input-model.
`--output`	Specify the destination path for generated Python code.
`--preset`	Apply an immutable built-in option preset.
`--schema-version`	Schema version to use for parsing.
`--schema-version-mode`	Schema version validation mode.
`--url`	Fetch schema from URL with custom HTTP headers.

`--emit-model-metadata`¶

Write a separate JSON map from source schema references to the final generated models, modules, fields, and type hints.

Usage

datamodel-codegen --input schema.json --emit-model-metadata model-map.json --module-split-mode single --disable-timestamp # (1)!

--emit-model-metadata - the option documented here

Examples

Input Schema:

{
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "title": "Root title",
  "type": "object",
  "properties": {
    "method": {
      "const": "thread/started"
    },
    "payload": {
      "$ref": "#/$defs/MetadataPayload"
    }
  },
  "required": ["method", "payload"],
  "$defs": {
    "MetadataPayload": {
      "title": "Payload title",
      "type": "object",
      "properties": {
        "id": {
          "type": "integer"
        }
      },
      "required": ["id"]
    }
  }
}

Output:

# generated by datamodel-codegen:
#   filename:  model_metadata.json

from __future__ import annotations

from typing import Literal

from pydantic import BaseModel

from . import metadata_payload


class RootTitle(BaseModel):
    method: Literal['thread/started']
    payload: metadata_payload.MetadataPayload

Generated metadata (model-map.json):

{
  "version": 1,
  "models": [
    {
      "class_name": "RootTitle",
      "name": "RootTitle",
      "module": "root_title",
      "source_ref": "model_metadata.json#",
      "source_path": [],
      "title": "Root title",
      "fields": [
        {
          "name": "method",
          "alias": "method",
          "original_name": "method",
          "type": "Literal['thread/started']",
          "required": true
        },
        {
          "name": "payload",
          "alias": "payload",
          "original_name": "payload",
          "type": "metadata_payload.MetadataPayload",
          "required": true
        }
      ]
    },
    {
      "class_name": "MetadataPayload",
      "name": "MetadataPayload",
      "module": "metadata_payload",
      "source_ref": "model_metadata.json#/$defs/MetadataPayload",
      "source_path": [
        "$defs",
        "MetadataPayload"
      ],
      "title": "Payload title",
      "fields": [
        {
          "name": "id",
          "alias": "id",
          "original_name": "id",
          "type": "int",
          "required": true
        }
      ]
    }
  ]
}

`--encoding`¶

Specify character encoding for input and output files.

The --encoding flag sets the character encoding used when reading the schema file and writing the generated Python code. This is useful for schemas containing non-ASCII characters (e.g., Japanese, Chinese). Default is UTF-8, which is the standard encoding for JSON and most modern text files.

Usage

datamodel-codegen --input schema.json --encoding utf-8 # (1)!

--encoding - the option documented here

Examples

Input Schema:

{
  "$schema": "http://json-schema.org/draft-07/schema#",
  "title": "日本語Model",
  "description": "モデルの説明文",
  "type": "object",
  "properties": {
    "名前": {
      "type": "string",
      "description": "ユーザー名"
    },
    "年齢": {
      "type": "integer"
    }
  }
}

Output:

# generated by datamodel-codegen:
#   filename:  encoding_test.json
#   timestamp: 2019-07-26T00:00:00+00:00

from __future__ import annotations

from pydantic import BaseModel, Field


class 日本語Model(BaseModel):
    名前: str | None = Field(None, description='ユーザー名')
    年齢: int | None = None

`--external-ref-mapping`¶

Map external $ref files to Python packages.

Use --external-ref-mapping FILE_PATH=PYTHON_PACKAGE to import referenced models from an existing package, instead of generating duplicate classes from external schema files.

Usage

datamodel-codegen --input schema.json --input-file-type openapi --external-ref-mapping common.yaml=mypackage.shared.models # (1)!

--external-ref-mapping - the option documented here

Examples

Input Schema:

openapi: "3.0.3"
info:
  title: API
  version: "1.0.0"
paths: {}
components:
  schemas:
    UserResponse:
      type: object
      properties:
        user:
          $ref: "common.yaml#/components/schemas/User"
        request_id:
          type: string
      required:
        - user
        - request_id
    ErrorResponse:
      type: object
      properties:
        error:
          $ref: "common.yaml#/components/schemas/Error"
        timestamp:
          type: string
          format: date-time
      required:
        - error
        - timestamp

Output:

# generated by datamodel-codegen:
#   filename:  api.yaml
#   timestamp: 2019-07-26T00:00:00+00:00

from __future__ import annotations

from mypackage.shared.models import Error, User
from pydantic import AwareDatetime, BaseModel


class UserResponse(BaseModel):
    user: User
    request_id: str


class ErrorResponse(BaseModel):
    error: Error
    timestamp: AwareDatetime

`--input`¶

Specify the input schema file path.

The --input flag specifies the path to the schema file (JSON Schema, OpenAPI, GraphQL, etc.). Multiple input files can be specified to merge schemas. Required unless using --url to fetch schema from a URL.

Usage

datamodel-codegen --input schema.json --input pet_simple.json --output output.py # (1)!

--input - the option documented here

Examples

Input Schema:

{
  "$schema": "http://json-schema.org/draft-07/schema#",
  "title": "Pet",
  "type": "object",
  "properties": {
    "id": {
      "type": "integer"
    },
    "name": {
      "type": "string"
    },
    "tag": {
      "type": "string"
    }
  }
}

Output:

# generated by datamodel-codegen:
#   filename:  pet_simple.json
#   timestamp: 2019-07-26T00:00:00+00:00

from __future__ import annotations

from pydantic import BaseModel


class Pet(BaseModel):
    id: int | None = None
    name: str | None = None
    tag: str | None = None

`--input-file-type`¶

Specify the input file type for code generation.

The --input-file-type flag explicitly sets the input format.

Important distinction:

Use jsonschema, openapi, asyncapi, graphql, mcp-tools, xmlschema, protobuf, or avro for schema definition files
Use json, yaml, or csv for raw sample data to automatically infer a schema

For example, if you have a JSON Schema written in YAML format, use --input-file-type jsonschema, not --input-file-type yaml. The yaml type treats the file as raw data and infers a schema from it.

Usage

datamodel-codegen --input schema.json --input-file-type json # (1)!

--input-file-type - the option documented here

Examples

JSONYAML

Input Schema:

{
  "Pet": {
    "name": "dog",
    "age": 2
  }
}

Output:

# generated by datamodel-codegen:
#   filename:  pet.json
#   timestamp: 2019-07-26T00:00:00+00:00

from __future__ import annotations

from pydantic import BaseModel, Field


class Pet(BaseModel):
    name: str
    age: int


class Model(BaseModel):
    Pet_1: Pet = Field(..., alias='Pet')

Input Schema:

Pet:
  name: cat
  age: 3

Output:

# generated by datamodel-codegen:
#   filename:  pet.yaml
#   timestamp: 2019-07-26T00:00:00+00:00

from __future__ import annotations

from pydantic import BaseModel, Field


class Pet(BaseModel):
    name: str
    age: int


class Model(BaseModel):
    Pet_1: Pet = Field(..., alias='Pet')

`--input-model`¶

Import a Python type or dict schema from a module.

Use the format module:Object or path/to/file.py:Object to specify the type.

Usage

datamodel-codegen --input schema.json --input-model mymodule:MyModel # (1)!

--input-model - the option documented here

Examples

Output:

`--input-model-ref-strategy`¶

Strategy for referenced types when using --input-model.

The --input-model-ref-strategy option determines whether to regenerate or import referenced types. Use regenerate-all (default) to regenerate all types, reuse-foreign to import types from different families (like enums when generating dataclasses) while regenerating same-family types, or reuse-all to import all referenced types directly.

Usage

datamodel-codegen --input schema.json --input-model-ref-strategy reuse-foreign # (1)!

--input-model-ref-strategy - the option documented here

Examples

Output:

`--output`¶

Specify the destination path for generated Python code.

The --output flag specifies where to write the generated Python code. It can be either a file path (single-file output) or a directory path (multi-file output for modular schemas). If omitted, the generated code is written to stdout.

Usage

datamodel-codegen --input schema.json --input pet_simple.json --output output.py # (1)!

--output - the option documented here

Examples

Input Schema:

{
  "$schema": "http://json-schema.org/draft-07/schema#",
  "title": "Pet",
  "type": "object",
  "properties": {
    "id": {
      "type": "integer"
    },
    "name": {
      "type": "string"
    },
    "tag": {
      "type": "string"
    }
  }
}

Output:

# generated by datamodel-codegen:
#   filename:  pet_simple.json
#   timestamp: 2019-07-26T00:00:00+00:00

from __future__ import annotations

from pydantic import BaseModel


class Pet(BaseModel):
    id: int | None = None
    name: str | None = None
    tag: str | None = None

`--preset`¶

Apply an immutable built-in option preset.

The standard-py312-20260619 preset enables the recommended modern Python output style for new projects. The preset name pins generated Python syntax and backports.

Related: --target-python-version

See also: pyproject.toml Configuration

Usage

datamodel-codegen --input schema.json --preset standard-py312-20260619 # (1)!

--preset - the option documented here

Examples

Input Schema:

{
  "$schema": "http://json-schema.org/draft-07/schema#",
  "title": "Person",
  "type": "object",
  "properties": {
    "firstName": {
      "type": "string",
      "description": "The person's first name."
    },
    "lastName": {
      "type": ["string", "null"],
      "description": "The person's last name."
    },
    "age": {
      "description": "Age in years which must be equal to or greater than zero.",
      "type": "integer",
      "minimum": 0
    },
    "friends": {
      "type": "array"
    },
    "comment": {
      "type": "null"
    }
  }
}

Output:

# generated by datamodel-codegen:
#   filename:  person.json

from __future__ import annotations

from typing import Annotated, Any

from pydantic import BaseModel, ConfigDict, Field


class Person(BaseModel):
    model_config = ConfigDict(
        populate_by_name=True,
    )
    first_name: Annotated[
        str | None, Field(alias='firstName', description="The person's first name.")
    ] = None
    last_name: Annotated[
        str | None, Field(alias='lastName', description="The person's last name.")
    ] = None
    age: Annotated[
        int | None,
        Field(
            description='Age in years which must be equal to or greater than zero.',
            ge=0,
        ),
    ] = None
    friends: list[Any] | None = None
    comment: Annotated[None, Field(None)] = None

`--schema-version`¶

Schema version to use for parsing.

The --schema-version option specifies the schema version to use instead of auto-detection. Valid values depend on input type: JsonSchema (draft-04, draft-06, draft-07, 2019-09, 2020-12) or OpenAPI (3.0, 3.1, 3.2). Default is 'auto' (detected from $schema or openapi field).

Usage

datamodel-codegen --input schema.json --schema-version draft-07 # (1)!

--schema-version - the option documented here

Examples

OpenAPIJSON Schema

Input Schema:

openapi: "3.0.0"
info:
  version: 1.0.0
  title: Swagger Petstore
  license:
    name: MIT
servers:
  - url: http://petstore.swagger.io/v1
paths:
  /pets:
    get:
      summary: List all pets
      operationId: listPets
      tags:
        - pets
      parameters:
        - name: limit
          in: query
          description: How many items to return at one time (max 100)
          required: false
          schema:
            type: integer
            format: int32
      responses:
        '200':
          description: A paged array of pets
          headers:
            x-next:
              description: A link to the next page of responses
              schema:
                type: string
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/Pets"
        default:
          description: unexpected error
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/Error"
                x-amazon-apigateway-integration:
                  uri:
                    Fn::Sub: arn:aws:apigateway:${AWS::Region}:lambda:path/2015-03-31/functions/${PythonVersionFunction.Arn}/invocations
                  passthroughBehavior: when_no_templates
                  httpMethod: POST
                  type: aws_proxy
    post:
      summary: Create a pet
      operationId: createPets
      tags:
        - pets
      responses:
        '201':
          description: Null response
        default:
          description: unexpected error
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/Error"
                x-amazon-apigateway-integration:
                  uri:
                    Fn::Sub: arn:aws:apigateway:${AWS::Region}:lambda:path/2015-03-31/functions/${PythonVersionFunction.Arn}/invocations
                  passthroughBehavior: when_no_templates
                  httpMethod: POST
                  type: aws_proxy
  /pets/{petId}:
    get:
      summary: Info for a specific pet
      operationId: showPetById
      tags:
        - pets
      parameters:
        - name: petId
          in: path
          required: true
          description: The id of the pet to retrieve
          schema:
            type: string
      responses:
        '200':
          description: Expected response to a valid request
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/Pets"
        default:
          description: unexpected error
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/Error"
    x-amazon-apigateway-integration:
      uri:
        Fn::Sub: arn:aws:apigateway:${AWS::Region}:lambda:path/2015-03-31/functions/${PythonVersionFunction.Arn}/invocations
      passthroughBehavior: when_no_templates
      httpMethod: POST
      type: aws_proxy
components:
  schemas:
    Pet:
      required:
        - id
        - name
      properties:
        id:
          type: integer
          format: int64
          default: 1
        name:
          type: string
        tag:
          type: string
    Pets:
      type: array
      items:
        $ref: "#/components/schemas/Pet"
    Users:
      type: array
      items:
        required:
          - id
          - name
        properties:
          id:
            type: integer
            format: int64
          name:
            type: string
          tag:
            type: string
    Id:
      type: string
    Rules:
      type: array
      items:
        type: string
    Error:
      description: error result
      required:
        - code
        - message
      properties:
        code:
          type: integer
          format: int32
        message:
          type: string
    apis:
      type: array
      items:
        type: object
        properties:
          apiKey:
            type: string
            description: To be used as a dataset parameter value
          apiVersionNumber:
            type: string
            description: To be used as a version parameter value
          apiUrl:
            type: string
            format: uri
            description: "The URL describing the dataset's fields"
          apiDocumentationUrl:
            type: string
            format: uri
            description: A URL to the API console for each API
    Event:
      type: object
      description: Event object
      properties:
        name:
          type: string
    Result:
        type: object
        properties:
          event:
            $ref: '#/components/schemas/Event'

Output:

Error: File not found: openapi/api.py

Input Schema:

{
  "$schema": "http://json-schema.org/draft-07/schema",
  "type": "object",
  "properties": {"s": {"type": ["string"]}},
  "required": ["s"]
}

Output:

# generated by datamodel-codegen:
#   filename:  simple_string.json

from __future__ import annotations

from pydantic import BaseModel


class Model(BaseModel):
    s: str

`--schema-version-mode`¶

Schema version validation mode.

The --schema-version-mode option controls how schema version validation is performed. 'lenient' (default): accept all features regardless of version. 'strict': warn on features outside the declared/detected version.

Usage

datamodel-codegen --input schema.json --schema-version-mode lenient # (1)!

--schema-version-mode - the option documented here

Examples

Input Schema:

{
  "$schema": "http://json-schema.org/draft-07/schema",
  "type": "object",
  "properties": {"s": {"type": ["string"]}},
  "required": ["s"]
}

Output:

# generated by datamodel-codegen:
#   filename:  simple_string.json

from __future__ import annotations

from pydantic import BaseModel


class Model(BaseModel):
    s: str

`--url`¶

Fetch schema from URL with custom HTTP headers.

The --url flag specifies a remote URL to fetch the schema from instead of a local file. The --http-headers flag adds custom HTTP headers to the request, useful for authentication (e.g., Bearer tokens) or custom API requirements. Format: HeaderName:HeaderValue.

Usage

datamodel-codegen --input schema.json --url https://api.example.com/schema.json --http-headers "Authorization:Bearer token" # (1)!

--url - the option documented here

Examples

Input Schema:

{
  "$schema": "http://json-schema.org/draft-07/schema#",
  "title": "Pet",
  "type": "object",
  "properties": {
    "id": {
      "type": "integer"
    },
    "name": {
      "type": "string"
    },
    "tag": {
      "type": "string"
    }
  }
}

Output:

# generated by datamodel-codegen:
#   filename:  https://api.example.com/schema.json
#   timestamp: 2019-07-26T00:00:00+00:00

from __future__ import annotations

from pydantic import BaseModel


class Pet(BaseModel):
    id: int | None = None
    name: str | None = None
    tag: str | None = None

📁 Base Options¶

📋 Options¶

--emit-model-metadata¶

--encoding¶

--external-ref-mapping¶

--input¶

--input-file-type¶

--input-model¶

--input-model-ref-strategy¶

--output¶

--preset¶

--schema-version¶

--schema-version-mode¶

--url¶

`--emit-model-metadata`¶

`--encoding`¶

`--external-ref-mapping`¶

`--input`¶

`--input-file-type`¶

`--input-model`¶

`--input-model-ref-strategy`¶

`--output`¶

`--preset`¶

`--schema-version`¶

`--schema-version-mode`¶

`--url`¶