datamodel-code-generator 0.59.0

Datamodel Code Generator

datamodel-code-generator

🚀 Generate Python data models from schema definitions in seconds.

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

✨ What it does

• 📄 Converts OpenAPI 3, AsyncAPI, JSON Schema, Apache Avro, XML Schema, Protocol Buffers/gRPC, GraphQL, 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

---

📖 Documentation

👉 datamodel-code-generator.koxudaxi.dev

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

---

📦 Installation

uv tool install datamodel-code-generator

Other installation methods

pip:

pip install datamodel-code-generator

uv (add to project):

uv add datamodel-code-generator

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

---

🏃 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

---

📥 Supported Input

• OpenAPI 3 (YAML/JSON)
• AsyncAPI (YAML/JSON)
• JSON Schema
• Apache Avro schema (AVSC)
• XML Schema (XSD)
• Protocol Buffers / gRPC (.proto)
• 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

---

🍳 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.

---

💖 Sponsors

Astral

---

🏢 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 →

---

🔗 Related Projects

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

---

🤝 Contributing

See Development & Contributing for how to get started!

📄 License

MIT License - see LICENSE for details.