datamodel-code-generator
Datamodel Code Generator
Decision gist · record as of 2026-08-14
Yes. Active maintenance, no known vulnerabilities, low install friction, and permissive MIT license make it a safe choice. The tool solves a real problem—keeping Python models in sync with external schemas—and supports a wide range of input formats. Install as a dev dependency or standalone CLI depending on your workflow.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later; optional extras (http, graphql, protobuf, ruff) needed for specific schema types or formatters.
- Low friction: pure Python wheel with nine runtime dependencies (argcomplete, black, genson, inflect, isort, jinja2, pydantic, pyyaml, tomli).
- Active maintenance—released 2 days ago with 4002 GitHub stars and commits as recent as 2026-08-14.
License · maintenance · safety
MIT (permissive) — MIT license (permissive) allows commercial and private use with minimal restrictions; attribution required but no copyleft obligations.
last release 2026-08-12 (2 days) · last repo commit 2026-08-14 · 4,002 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 17,555,589 downloads/mo, #1,113 on PyPI
Alternatives
Verify before relying
pip install datamodel-code-generator
datamodel-codegen \
--input schema.json \
--input-file-type jsonschema \
--output-model-type pydantic_v2.BaseModel \
--output model.py- Performance characteristics for very large or deeply nested schemas
- Completeness of schema feature support across all input formats (e.g., edge cases in $ref resolution)
- Whether the experimental httpx2 backend is production-ready
What it is and what it does
Datamodel-code-generator is a CLI tool and library that converts schema definitions into type-safe Python model code. It reads from OpenAPI 3, AsyncAPI, JSON Schema, Apache Avro, XML Schema, Protocol Buffers, GraphQL, MCP tool schemas, and raw data (JSON/YAML/CSV), then outputs Pydantic v2, Pydantic v2 dataclass, dataclass, TypedDict, or msgspec models. The generated code handles complex schema features like $ref, allOf, oneOf, anyOf, enums, and nested types.
It's designed for developers who need to keep Python models in sync with external schema definitions or who want to avoid hand-writing boilerplate. The tool supports both standalone CLI use and integration as a development dependency in projects. It can also retarget existing Python types (Pydantic, dataclass, TypedDict) to different output formats via the --input-model flag. Output is formatted by default with black and isort, though faster builtin or ruff formatters are available.
Use it for
- Generate Pydantic models from OpenAPI specs to keep API clients and servers synchronized with schema changes.
- Convert JSON Schema definitions into dataclasses for configuration file parsing with type safety.
- Transform Protocol Buffer definitions into Python models for gRPC service development.
- Generate TypedDict or msgspec models from raw JSON/YAML/CSV data for rapid prototyping.
- Retarget existing Pydantic models to dataclass output for environments that don't use Pydantic.
- Automate model generation in CI/CD pipelines to enforce schema-driven code generation.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Active maintenance, no known vulnerabilities, low install friction, and permissive MIT license make it a safe choice. The tool solves a real problem—keeping Python models in sync with external schemas—and supports a wide range of input formats. Install as a dev dependency or standalone CLI depending on your workflow.
Install
datamodel-code-generator on PyPI
Before you install
Low friction: pure Python wheel with nine runtime dependencies (argcomplete, black, genson, inflect, isort, jinja2, pydantic, pyyaml, tomli). Active maintenance—released 2 days ago with 4002 GitHub stars and commits as recent as 2026-08-14.
Requires Python 3.10 or later; optional extras (http, graphql, protobuf, ruff) needed for specific schema types or formatters.
License in practice
MIT license (permissive) allows commercial and private use with minimal restrictions; attribution required but no copyleft obligations.
Quickstart
pip install datamodel-code-generator
datamodel-codegen \
--input schema.json \
--input-file-type jsonschema \
--output-model-type pydantic_v2.BaseModel \
--output model.pyVerify before relying
- Performance characteristics for very large or deeply nested schemas
- Completeness of schema feature support across all input formats (e.g., edge cases in $ref resolution)
- Whether the experimental httpx2 backend is production-ready
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 9 packagesargcompleteblackgensoninflectisortjinja2pydanticpyyamltomli |
| Maintenance | Actively maintained 2 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 17,555,589 / month, #1,113 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaLicense :: OSI Approved :: MIT LicenseNatural Language :: EnglishProgramming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Programming Language :: Python :: Implementation :: CPython |
Evidence: datamodel_code_generator-0.72.4-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “xml schema code gen”
- datamodel-code-generatorGenerates Python data models (Pydantic, dataclasses, TypedDict,…
- gllm-inference-binaryProvides inference components and utilities for generative AI…
- xsdataxsdata generates Python dataclasses from XML schemas, WSDL, DTD, and…
Give your agent the search over MCP, or paste the wish link into any chat.
More Build Tools packages
Provides reusable utilities for Python packaging interoperability, including version handling, specifiers, markers, requirements, tags, and metadata parsing according to standards like PEP 440 and PEP 425.
Wraps any iterable to display a real-time progress bar in the terminal or Jupyter notebook, showing iteration count, elapsed time, and estimated time remaining.
pip is the standard installer for Python packages, enabling you to download and install packages from the Python Package Index and other indexes into your Python environment.
Hatchling is a standards-compliant Python build backend that handles packaging, metadata, and distribution of Python projects when configured in a project's pyproject.toml file.
Generates Python gRPC service stubs and message classes from Protocol Buffer definitions, enabling developers to build gRPC clients and servers.
pre-commit is a framework for installing and running git hooks written in any language before commits are made, automating code quality and validation checks across multi-language projects.
Install it if your team needs consistent, automated validation at commit time.
See also dataclasses-avroschema · py-avro-schema · jsonschema-gentypes · dataclasses-jsonschema · graphql-query · apischema · polyfactory · adaptix · graphene-pydantic · pydantic-avro