📦 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

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

Published Docker images run as a non-root appuser. When writing generated files to a bind-mounted directory, make sure the directory is writable by the container user or pass an explicit Docker user, for example --user "$(id -u):$(id -g)".

🏃 Quick Start

Command

datamodel-codegen \
  --input schema.json \
  --input-file-type jsonschema \
  --output-model-type pydantic_v2.BaseModel \
  --preset standard-py312-20260619 \
  --output model.py

This quick start uses standard-py312-20260619 as the modern Python 3.12 baseline. Preset names include the target Python version: py312 means Python 3.12.

See CLI Reference for all options. See Presets, --preset, --input-file-type, and --output-model-type for this command.

For more schema-aware output that preserves schema-authored names, reuses models, and embeds generated documentation, use practical-py312-20260619.

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

Output (model.py)

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

from __future__ import annotations

from enum import StrEnum
from typing import Annotated

from pydantic import BaseModel, ConfigDict, Field


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


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

⚡ Speed up generation

By default, generated Python is currently formatted with black and isort. For faster generation without external formatter dependencies, add --formatters builtin for standard generated model modules. In a future version, the Black/isort dependencies will become opt-in and the default formatter will change to builtin.

If you prefer Ruff, install it with pip install 'datamodel-code-generator[ruff]' and use --formatters ruff-check ruff-format for a fast external formatter.

Custom templates can emit Python outside the standard generated model patterns covered by builtin, so custom-template output is not exhaustively validated. If --formatters builtin produces invalid or poorly formatted output with a custom template, please open an issue with a small reproducer. See Formatter Behavior for details.

See Performance Benchmarks for release benchmark data and interactive charts.

📖 Documentation

👉 datamodel-code-generator.koxudaxi.dev

• 🧰 Presets - Recommended option bundles for modern output
• 🚀 Getting Started - Installation and first model
• 🖥️ CLI Reference - All command-line options
• 🧪 Playground - Try generation in your browser
• ⚙️ pyproject.toml - Configuration file
• 🔄 CI/CD Integration - GitHub Actions, pre-commit hooks
• ✅ Conformance Dashboard - External corpus coverage signals
• 📈 Performance Benchmarks - Release benchmark tables and interactive charts
• 🧭 Architecture - Generation pipeline and synchronized component inventory
• 🚀 One-liner Usage - uvx, pipx, clipboard integration
• ❓ FAQ - Common questions

📥 Supported Input

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

📤 Supported Output

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

✅ Conformance Signals

CI exercises datamodel-code-generator against pinned external corpora for XML Schema, JSON Schema, AsyncAPI, Apache Avro, and Protocol Buffers. See the Conformance Dashboard for the generated summary of runner scripts, tox environments, CI jobs, expected corpus counts, and upstream sources.

🍳 Common Recipes

CLI option quick starts

Use these starting points when combining options; each option links to the generated CLI reference for details and examples.

• Generate a local schema file: Pin the input type and destination when the source extension is ambiguous or generated output needs a stable path. Options: --input, --input-file-type, --output.
• Target Pydantic v2 on modern Python: Set the output model family and Python/Pydantic compatibility targets together. Options: --output-model-type, --target-python-version, --target-pydantic-version.
• Use modern Python annotations: Target a recent Python version and prefer built-in collection and union syntax in generated types. Options: --target-python-version, --use-union-operator, [`--use-stan