JSON to Pydantic Model Generator
Paste any JSON and generate Pydantic models with full type annotations instantly in your browser — no signup, nothing uploaded.
JSON Input
Pydantic Models
How to Generate Pydantic Models from JSON
Paste your JSON
Copy a JSON response, config file, or any JSON object and paste it into the input panel. Minified or pretty-printed JSON both work.
Set a model name
Enter a name for the top-level model class. Defaults to Root. Use something descriptive like User, ApiResponse, or OrderPayload.
Generate and copy
Click Generate Models. Copy the output or download it as a .py file with all imports included and ready to use.
What Is Pydantic?
Pydantic is a Python library for data validation using type annotations. You define a model by subclassing BaseModel and annotating each field. When you create an instance, Pydantic validates and coerces the input to match the declared types.
Pydantic is the validation layer behind FastAPI and is widely used for config management, ETL pipelines, and API client libraries. It provides automatic JSON serialization via model.model_dump_json() and deserialization via Model.model_validate(data).
Example
from __future__ import annotations
from pydantic import BaseModel
class User(BaseModel):
id: int
name: str
email: str
address: Address
class Address(BaseModel):
street: str
city: str
zip: strHow the Generator Works
Type inference
JSON strings map to str, integers to int, decimals to float, booleans to bool, and null to Optional[Any].
Nested models
Each nested JSON object generates its own named BaseModel class. The class name is derived from the JSON key in PascalCase. from __future__ import annotations enables forward references so classes can reference each other regardless of order.
Arrays of objects
When an array contains objects, all elements are merged into a single model. Keys present in only some elements get type Optional[T] = None. This handles APIs that omit fields instead of returning them as null.
Imports are generated automatically
Optional and Any from typing are only included if the generated models actually use them — no unused imports.
Use Cases
FastAPI Request & Response Models
Generate Pydantic models from your API contract or example payloads. Use them directly as FastAPI request body types and response_model declarations.
Data Validation Pipelines
Parse and validate JSON from external data sources — files, APIs, message queues — using Pydantic model_validate(). Any field type mismatch raises a ValidationError at the boundary.
Config File Schemas
Convert a JSON config file into a Pydantic Settings model. Get type-safe access to config values with automatic casting from environment variables and JSON files.
LLM Output Parsing
Define the expected shape of structured outputs from language models using Pydantic. Libraries like instructor and LangChain use Pydantic models to enforce JSON output schemas.
ETL Schema Documentation
Generate models from sample records in your data pipeline to serve as living documentation of the schema. Pydantic validation catches schema drift between pipeline stages.
Third-Party API Integration
Paste a JSON response from an external API to generate the Pydantic model for it. Use model_validate() instead of accessing dict keys directly for type-safe deserialization.
Generated Model Examples
Simple flat object
JSON input
{
"id": 42,
"name": "Alice",
"active": true
}Pydantic output
from __future__ import annotations
from pydantic import BaseModel
class Root(BaseModel):
id: int
name: str
active: boolArray with optional fields
JSON input
[
{"id":1,"name":"Alice","role":"admin"},
{"id":2,"name":"Bob"}
]Pydantic output
# Top-level JSON is an array: list[Root]
from __future__ import annotations
from typing import Optional
from pydantic import BaseModel
class Root(BaseModel):
id: int
name: str
role: Optional[str] = NoneNullable field and nested model
JSON input
{
"id": 1,
"address": {"city": "NY"},
"metadata": null
}Pydantic output
from __future__ import annotations
from typing import Any, Optional
from pydantic import BaseModel
class Root(BaseModel):
id: int
address: Address
metadata: Optional[Any] = None
class Address(BaseModel):
city: strPydantic v1 vs v2 Compatibility
The generated models use BaseModel and standard Optional/Any annotations, which are compatible with both Pydantic v1 and v2.
- •v1 usage: Parse with
Root.parse_obj(data)and serialize withinstance.json(). - •v2 usage: Parse with
Root.model_validate(data)and serialize withinstance.model_dump_json(). - •Python 3.10+ alternative: Replace
Optional[T]withT | Noneif your codebase uses the newer union syntax.
Limitations to Be Aware Of
- ⚠Null fields: A JSON null cannot reveal its real type, so it becomes
Optional[Any]. Change toOptional[str]or the real type if known. - ⚠Field names kept as-is: JSON keys are used as Python attribute names without snake_case conversion. If your JSON uses camelCase (
firstName), the field will befirstName: str. Addmodel_config = ConfigDict(alias_generator=to_camel)if you want snake_case attributes. - ⚠Single sample inference: Types are inferred from the values you provide. If a field is sometimes a string and sometimes null, provide an example with both to see the union, or manually add
Optional[str]. - ⚠No validator decorators: The generator produces basic type annotations only. Add
@field_validatororField()constraints manually where you need value validation beyond type checking.
Frequently Asked Questions
How do I convert JSON to a Pydantic model?
Paste your JSON into the input box, optionally set a root model name, and click Generate Models. The tool produces Pydantic BaseModel class definitions with type annotations that match the exact shape of your JSON, ready to use with Pydantic v1 or v2.
What is Pydantic and why should I use it?
Pydantic is a Python data validation library that uses type annotations to validate and serialize data at runtime. It is the foundation of FastAPI and widely used for config parsing, API models, and ETL pipelines. Defining Pydantic models gives you automatic validation, IDE autocomplete, and JSON serialization out of the box.
How are optional and nullable fields handled?
Fields that are null in the JSON get type Optional[Any] = None. Fields that appear in only some elements of an array are marked Optional[T] = None, where T is inferred from the elements where the field is present.
Does the generated code work with Pydantic v2?
Yes. The output uses standard Python type annotations and BaseModel, which are compatible with both Pydantic v1 and v2. In Pydantic v2, Optional[T] is equivalent to T | None and behaves identically.
How are nested objects handled?
Each nested JSON object becomes a separate Pydantic model class named in PascalCase from the JSON key. The from __future__ import annotations at the top enables forward references so class order does not matter.
Is my JSON sent to a server?
No. All model generation runs entirely in your browser using JavaScript. Your JSON data never leaves your device and is not stored anywhere.
Can I use the generated models with FastAPI?
Yes. FastAPI uses Pydantic models for request and response body validation. Paste the generated models into your FastAPI app and use them as request body types (def create_user(user: User): ...) or response models (response_model=User).
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