json-schema-to-pydantic
A Python library for automatically generating Pydantic v2 models from JSON Schema definitions
Decision gist · record as of 2026-08-14
Yes. The package is actively maintained, has no known vulnerabilities, low install friction, and solves a real problem—eliminating boilerplate when you already have a JSON Schema. It's especially valuable if you work with OpenAPI specs or schema-heavy APIs. The MIT license is permissive. The single dependency on pydantic is a strength, not a weakness, since pydantic is ubiquitous.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Low friction: pure Python wheel with a single runtime dependency on pydantic.
- Active maintenance with recent commits and no known vulnerabilities.
License · maintenance · safety
MIT (permissive) — MIT license permits commercial and private use with minimal restrictions—suitable for most projects.
last release 2026-03-09 (158 days) · last repo commit 2026-03-09 · 46 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 2,105,875 downloads/mo, #3,293 on PyPI
Alternatives
Verify before relying
pip install json-schema-to-pydantic
from json_schema_to_pydantic import create_model
schema = {
"title": "User",
"type": "object",
"properties": {
"name": {"type": "string"},
"email": {"type": "string", "format": "email"}
},
"required": ["name", "email"]
}
UserModel = create_model(schema)
user = UserModel(name="John", email="john@example.com")- Performance characteristics with very large or deeply nested schemas
- Completeness of JSON Schema draft version support (draft 7, 2020-12, etc.)
- Behavior when predefined_models or predefined_refs contain conflicting definitions
What it is and what it does
json-schema-to-pydantic bridges JSON Schema and Pydantic by generating type-safe Pydantic v2 models directly from schema definitions. It handles the complexity of schema features—$ref resolution with circular reference detection, combiners like allOf/anyOf/oneOf, format validations (email, uri, uuid, date-time), and edge cases like underscore-prefixed fields common in OpenAPI specs—so you don't have to write model classes by hand.
The library exposes a simple `create_model()` function for straightforward cases and a lower-level `PydanticModelBuilder` for advanced scenarios where you need to inject predefined models or type aliases. It supports relaxed validation modes for schemas that omit type information, and it generates models with full type hints. The package requires Python 3.9+ and depends only on pydantic.
Use it for
- Convert OpenAPI/Swagger specs to Pydantic models for API client or server validation
- Automatically generate data models from JSON Schema files in data pipelines
- Build form validators from schema definitions without manual model coding
- Validate incoming JSON payloads against a schema-derived Pydantic model
- Integrate schema-driven APIs with Python codebases that use Pydantic for type safety
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
The package is actively maintained, has no known vulnerabilities, low install friction, and solves a real problem—eliminating boilerplate when you already have a JSON Schema. It's especially valuable if you work with OpenAPI specs or schema-heavy APIs. The MIT license is permissive. The single dependency on pydantic is a strength, not a weakness, since pydantic is ubiquitous.
Install
json-schema-to-pydantic on PyPI
Before you install
Low friction: pure Python wheel with a single runtime dependency on pydantic. Active maintenance with recent commits and no known vulnerabilities.
License in practice
MIT license permits commercial and private use with minimal restrictions—suitable for most projects.
Quickstart
pip install json-schema-to-pydantic
from json_schema_to_pydantic import create_model
schema = {
"title": "User",
"type": "object",
"properties": {
"name": {"type": "string"},
"email": {"type": "string", "format": "email"}
},
"required": ["name", "email"]
}
UserModel = create_model(schema)
user = UserModel(name="John", email="john@example.com")Verify before relying
- Performance characteristics with very large or deeply nested schemas
- Completeness of JSON Schema draft version support (draft 7, 2020-12, etc.)
- Behavior when predefined_models or predefined_refs contain conflicting definitions
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.9 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 1 packagepydantic |
| Maintenance | Actively maintained 158 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 2,105,875 / month, #3,293 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersLicense :: OSI Approved :: MIT LicenseProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Topic :: Software Development :: Libraries :: Python ModulesTyping :: Typed |
Evidence: json_schema_to_pydantic-0.4.11-py3-none-any.whl
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See also jsonschema-pydantic-converter · jambo · django-pydantic-field · redis-om · turnkey-sdk-types · flask-pydantic-spec · dydantic · jsonschema-pydantic · openapi-schema-pydantic · openapi-pydantic