datamodel-code-generator¶

🚀 Generate Python data models from schema definitions in seconds.

✨ What it does¶

Pick any one of the supported inputs and pick the Python model style you want as output. --input-model path/to/file.py:ClassName can even retarget an existing Pydantic, dataclass, or TypedDict class defined in another Python file to a different output type.

• 📄 Converts OpenAPI 3, AsyncAPI, JSON Schema, Apache Avro, XML Schema, Protocol Buffers/gRPC, GraphQL, MCP tool schemas, and raw data (JSON/YAML/CSV) into Python models
• 🐍 Generates from existing Python types (Pydantic, dataclass, TypedDict) via --input-model
• 🎯 Generates Pydantic v2, Pydantic v2 dataclass, 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

🧪 Try It In Your Browser¶

Generate models in your browser without installing anything.

Open Playground

📦 Installation¶

uv tool install datamodel-code-generator

pip install datamodel-code-generator

uv add --dev datamodel-code-generator

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

pipx install datamodel-code-generator

uvx datamodel-codegen --help

Use uv tool install when you want datamodel-codegen available as a standalone CLI. Use uv add --dev when a project or CI workflow should pin the generator version in its lockfile.

🏃 Quick Start¶

1️⃣ Create a schema file¶

{
  "$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¶

# 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¶

📤 Choose Your Output¶

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

[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:

- uses: koxudaxi/datamodel-code-generator@0.60.0
  with:
    input: schemas/api.yaml
    output: src/models/api.py

See CI/CD Integration for more options.

📚 Next Steps¶

• 🖥️ CLI Reference - All command-line options with examples
• ⚙️ pyproject.toml Configuration - Configure via pyproject.toml
• 🚀 One-liner Usage - uvx, pipx, clipboard integration
• 🔄 CI/CD Integration - GitHub Actions and CI validation
• 🎨 Custom Templates - Customize generated code with Jinja2
• 🖌️ Code Formatting - Configure black, isort, and ruff
• ❓ FAQ - Common questions and troubleshooting

💖 Sponsors¶

Astral

🏢 Used by¶

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

• PostHog/posthog - Generate models via npm run
• airbytehq/airbyte - Generate Python, Java/Kotlin, and Typescript protocol models
• apache/iceberg - Generate Python code
• open-metadata/OpenMetadata - datamodel_generation.py
• openai/codex - Python SDK dev dependency
• vllm-project/vllm - Test dependency for model tests
• stanfordnlp/dspy - Generate Pydantic models from JSON Schema for reliability tests
• topoteretes/cognee - Runtime generation of graph data models from JSON Schema
• e2b-dev/E2B - Generate MCP server TypedDict models via Makefile
• apache/airflow - Generate OpenAPI datamodels for airflow-ctl and task-sdk via pyproject codegen config
• browser-use/browser-use - Eval dependency
• firebase/genkit - Generate core typing models from JSON Schema
• open-telemetry/opentelemetry-python - Generate SDK configuration dataclasses from JSON Schema
• DataDog/integrations-core - Config models
• argoproj-labs/hera - Makefile
• tensorzero/tensorzero - Generate Python dataclasses from JSON Schema in the schema generation pipeline
• IBM/compliance-trestle - Building models from OSCAL schemas

See all dependents →