Skip to content

datamodel-code-generator

🚀 Generate Python data models from schema definitions in seconds.

PyPI version Conda-forge Downloads PyPI - Python Version codecov license Pydantic v1 Pydantic v2

✨ What it does

  • 📄 Converts OpenAPI 3, JSON Schema, GraphQL, and raw data (JSON/YAML/CSV) into Python models
  • 🎯 Generates Pydantic v1/v2, dataclasses, TypedDict, or msgspec output
  • 🔗 Handles complex schemas: $ref, allOf, oneOf, anyOf, enums, and nested types
  • ✅ Produces type-safe, validated code ready for your IDE and type checker

📦 Installation

uv tool install datamodel-code-generator

pip install datamodel-code-generator

uv add datamodel-code-generator

conda install -c conda-forge datamodel-code-generator

pipx install datamodel-code-generator

uvx datamodel-codegen --help

🏃 Quick Start

1️⃣ Create a schema file

pet.json
{
  "$schema": "http://json-schema.org/draft-07/schema#",
  "title": "Pet",
  "type": "object",
  "required": ["name", "species"],
  "properties": {
    "name": {
      "type": "string",
      "description": "The pet's name"
    },
    "species": {
      "type": "string",
      "enum": ["dog", "cat", "bird", "fish"]
    },
    "age": {
      "type": "integer",
      "minimum": 0,
      "description": "Age in years"
    },
    "vaccinated": {
      "type": "boolean",
      "default": false
    }
  }
}

2️⃣ Run the generator

datamodel-codegen --input pet.json --input-file-type jsonschema --output-model-type pydantic_v2.BaseModel --output model.py

3️⃣ Use your models

model.py
# generated by datamodel-codegen:
#   filename:  tutorial_pet.json

from __future__ import annotations

from enum import Enum
from typing import Optional

from pydantic import BaseModel, Field


class Species(Enum):
    dog = 'dog'
    cat = 'cat'
    bird = 'bird'
    fish = 'fish'


class Pet(BaseModel):
    name: str = Field(..., description="The pet's name")
    species: Species
    age: Optional[int] = Field(None, description='Age in years', ge=0)
    vaccinated: Optional[bool] = False

🎉 That's it! Your schema is now a fully-typed Python model.

📥 Choose Your Input

Input Type File Types Example
📘 OpenAPI 3 .yaml, .json API specifications
📋 JSON Schema .json Data validation schemas
🔷 GraphQL .graphql GraphQL type definitions
📊 JSON/YAML Data .json, .yaml Infer schema from data

📤 Choose Your Output

# 🆕 Pydantic v2 (recommended for new projects)
datamodel-codegen --output-model-type pydantic_v2.BaseModel ...

# 🔄 Pydantic v1 (default, for compatibility)
datamodel-codegen --output-model-type pydantic.BaseModel ...

# 🏗️ Python dataclasses
datamodel-codegen --output-model-type dataclasses.dataclass ...

# 📝 TypedDict (for type hints without validation)
datamodel-codegen --output-model-type typing.TypedDict ...

# ⚡ msgspec (high-performance serialization)
datamodel-codegen --output-model-type msgspec.Struct ...

See Supported Data Types for the full list.

🍳 Common Recipes

🤖 Get CLI Help from LLMs

Generate a prompt to ask LLMs about CLI options:

datamodel-codegen --generate-prompt "Best options for Pydantic v2?" | claude -p

See LLM Integration for more examples.

🌐 Generate from URL

pip install 'datamodel-code-generator[http]'
datamodel-codegen --url https://example.com/api/openapi.yaml --output model.py

⚙️ Use with pyproject.toml

pyproject.toml
[tool.datamodel-codegen]
input = "schema.yaml"
output = "src/models.py"
output-model-type = "pydantic_v2.BaseModel"

Then simply run:

datamodel-codegen

See pyproject.toml Configuration for more options.

🔄 CI/CD Integration

Validate generated models in your CI pipeline:

.github/workflows/validate-models.yml
- uses: koxudaxi/[email protected]
  with:
    input: schemas/api.yaml
    output: src/models/api.py

See CI/CD Integration for more options.

📚 Next Steps

💖 Sponsors

Astral Logo

Astral

🏢 Used by

These projects use datamodel-code-generator. See the linked examples for real-world usage.

See all dependents →