datamodel-code-generator

🚀 Generate Python data models from schema definitions in seconds.

🧪 Try it in your browser: Playground

Note

Playground privacy: generation runs locally in your browser with Pyodide. Schemas and options are not sent to a backend. Shared repro URLs encode them in the URL fragment (#state=...), which browsers do not send to the server; the full URL can still be stored in your browser history or wherever you share it.

📣 💼 Maintainer update: Open to opportunities. 🔗 koxudaxi.dev

✨ 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

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📖 Documentation

👉 datamodel-code-generator.koxudaxi.dev

• 🖥️ CLI Reference - All command-line options
• 🧪 Playground - Try generation in your browser
• ⚙️ pyproject.toml - Configuration file
• 🔄 CI/CD Integration - GitHub Actions, pre-commit hooks
• 🚀 One-liner Usage - uvx, pipx, clipboard integration
• ❓ FAQ - Common questions

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Coding agent skill

This repository includes an experimental Agent Skill that teaches compatible coding agents to run datamodel-codegen when generating Python models from OpenAPI, AsyncAPI, JSON Schema, GraphQL, JSON/YAML/CSV sample data, MCP tool schemas, Protocol Buffers, XML Schema, Apache Avro, or existing Python model objects.

See Coding Agent Skill for detailed guidance and troubleshooting.

Install the directory for your agent:

# Codex, project-local
mkdir -p .agents/skills
cp -R skills/datamodel-code-generator .agents/skills/datamodel-code-generator

# Claude Code, project-local
mkdir -p .claude/skills
cp -R skills/datamodel-code-generator .claude/skills/datamodel-code-generator

For a personal install, copy the same directory to $HOME/.agents/skills/datamodel-code-generator/ for Codex or ~/.claude/skills/datamodel-code-generator/ for Claude Code.

Check your agent's current documentation for exact search paths.

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📦 Installation

Recommended for standalone CLI use:

uv tool install datamodel-code-generator

For projects that should pin the generator version, add it as a development dependency instead:

uv add --dev datamodel-code-generator

Other installation methods

pip:

pip install datamodel-code-generator

uv (run without adding to project):

uv run --with datamodel-code-generator datamodel-codegen --help

conda:

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

With HTTP support (for resolving remote $ref):

pip install 'datamodel-code-generator[http]'

With GraphQL support:

pip install 'datamodel-code-generator[graphql]'

With Protocol Buffers support:

pip install 'datamodel-code-generator[protobuf]'

Docker:

docker pull koxudaxi/datamodel-code-generator

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🏃 Quick Start

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

📄 schema.json (input)

{
  "$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
    }
  }
}

🐍 model.py (output)

# generated by datamodel-codegen:
#   filename:  schema.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

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📥 Supported Input

• OpenAPI 3 (YAML/JSON)
• AsyncAPI (YAML/JSON)
• JSON Schema
• Apache Avro schema (AVSC)
• XML Schema (XSD)
• Protocol Buffers / gRPC (.proto)
• MCP tool schemas
• JSON / YAML / CSV data
• GraphQL schema
• Python types (Pydantic, dataclass, TypedDict) via --input-model
• Python dictionary

📤 Supported Output

• pydantic v2 BaseModel
• pydantic v2 dataclass
• dataclasses
• TypedDict
• msgspec Struct

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🍳 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@0.44.0
  with:
    input: schemas/api.yaml
    output: src/models/api.py

See CI/CD Integration for more options.

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💖 Sponsors

Astral

OpenAI

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🏢 Projects that use datamodel-code-generator

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 →

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🔗 Related Projects

• fastapi-code-generator - Generate FastAPI app from OpenAPI
• pydantic-pycharm-plugin - PyCharm plugin for Pydantic

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🤝 Contributing

See Development & Contributing for how to get started!

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👤 Maintainer

Koudai Aono (@koxudaxi)

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📄 License

MIT License - see LICENSE for details.