json-schema-to-pydantic-rs
Fast JSON Schema to Pydantic v2 model generation, powered by Rust.
A high-performance drop-in replacement for json-schema-to-pydantic using a Rust core via PyO3.
Performance
3-10x faster than the pure-Python original (end-to-end, including Pydantic model construction):
| Schema | Original | Rust | Speedup |
|---|---|---|---|
| Simple object (4 fields) | 445 µs | 60 µs | 7.4x |
| Nested 3 levels | 1.00 ms | 116 µs | 8.6x |
With $ref definitions |
1.22 ms | 159 µs | 7.7x |
| oneOf / anyOf / allOf / if-then-else | 2.27 ms | 707 µs | 3.2x |
| Wide object (50 fields) | 2.91 ms | 355 µs | 8.2x |
| Enums and arrays | 658 µs | 107 µs | 6.2x |
Installation
pip install json-schema-to-pydantic-rs
Requires Python 3.10+ and Pydantic v2.10.4+.
Quick start
from json_schema_to_pydantic_rs import create_model
User = create_model({
"type": "object",
"properties": {
"name": {"type": "string", "minLength": 1},
"age": {"type": "integer", "minimum": 0},
"email": {"type": "string", "format": "email"},
},
"required": ["name", "age"],
})
user = User(name="Alice", age=30, email="alice@example.com")
print(user.model_dump_json())
# {"name":"Alice","age":30,"email":"alice@example.com"}
# Round-trip
restored = User.model_validate_json('{"name":"Alice","age":30}')
API
create_model(schema, **options)
Converts a JSON Schema into a Pydantic BaseModel subclass. Accepts a dict or a JSON str. Supports the full JSON Schema spec: types, formats (uuid, date-time, email, ...), $ref/$defs, allOf/anyOf/oneOf, enum, const, additionalProperties, constraints, and nullable types.
from json_schema_to_pydantic_rs import create_model
# From a dict
Order = create_model({
"type": "object",
"properties": {
"customer": {"$ref": "#/$defs/Customer"},
"items": {"type": "array", "items": {"$ref": "#/$defs/Product"}, "minItems": 1},
},
"required": ["customer", "items"],
"$defs": {
"Customer": {
"type": "object",
"properties": {
"name": {"type": "string", "minLength": 1},
"email": {"type": "string", "format": "email"},
},
"required": ["name"],
},
"Product": {
"type": "object",
"properties": {
"name": {"type": "string"},
"price": {"type": "number", "minimum": 0},
},
"required": ["name", "price"],
},
},
})
order = Order(
customer={"name": "Alice", "email": "alice@example.com"},
items=[{"name": "Widget", "price": 9.99}],
)
print(order.customer.name) # "Alice"
print(order.items[0].price) # 9.99
# Or from a JSON string
Order = create_model('{"type": "object", "properties": {"name": {"type": "string"}}}')
Options:
create_model(
schema,
base_model_type=MyBase, # Base class for generated models
predefined_models={ # Plug your own classes for $ref paths
"#/$defs/Address": Address,
},
allow_undefined_array_items=True, # Arrays without "items" -> List[Any]
allow_undefined_type=True, # Schemas without "type" -> Any
populate_by_name=True, # Access fields by both name and alias
)
PydanticModelBuilder
For reusing config across multiple schemas:
from pydantic import BaseModel
from json_schema_to_pydantic_rs import PydanticModelBuilder
class Address(BaseModel):
street: str
city: str
builder = PydanticModelBuilder(
base_model_type=BaseModel, # custom base class
predefined_models={"#/$defs/Address": Address}, # reuse existing models
)
Model = builder.create_pydantic_model(schema)
Non-standard schema properties are preserved via json_schema_extra:
Model = create_model({
"type": "object",
"properties": {"bio": {"type": "string", "ui_widget": "textarea"}},
})
Model.model_fields["bio"].json_schema_extra # {"ui_widget": "textarea"}
Development
uv sync --dev
uv run maturin develop --release
uv run pytest
uv run python bench.py
Compatibility
- Python 3.10 - 3.14
- Pydantic v2.10.4+
- Rust edition 2024
License
MIT — see LICENSE.
Based on json-schema-to-pydantic by Richard Gyiko.
1 maintainer
Metadata
1 maintainer
Release files for json-schema-to-pydantic-rs 0.2.0
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