JSON to Python
Generate clean, typed Python @dataclass and Pydantic v2 BaseModel classes with nested models and type annotations.
- Runs in your browser
- Free, no signup
from __future__ import annotations
from dataclasses import dataclass, field
from typing import Optional, List, Any, Union
@dataclass
class Profile:
bio: str
reputation: int
@dataclass
class Root:
id_val: int # JSON key: "id"
name: str
email: str
is_admin: bool
roles: List[str]
profile: Profile
Your files are processed locally in your browser and are not uploaded to our server.
Writing Python data models for complex JSON responses from REST APIs, microservices, or database queries is repetitive and prone to type inaccuracies. This tool parses your JSON and instantly generates idiomatic, strongly typed Python data classes using modern type annotations.
Choose between the Python standard library dataclasses module (@dataclass) or Pydantic v2 (BaseModel) with automatic field aliases. Nested JSON objects are automatically extracted into distinct, reusable classes, snake_case naming conventions are applied to fields, and null values become Optional[T]. All code generation executes directly inside your browser for complete data privacy.
How to use this tool
- Paste your JSON payload into the left editor or click "Load Sample" to load a demonstration object.
- Choose your preferred model style: Standard Dataclass (@dataclass) or Pydantic v2 (BaseModel).
- Specify your desired root class name (such as Root, UserResponse, or EventPayload).
- Click "Copy Code" to copy the generated Python models to your clipboard, or click "Download .py" to save a ready-to-import file.
Modern Python 3.10+ Type Annotations
The generator produces clean type annotations conforming to modern Python conventions. Integers become int, decimals become float, strings become str, and lists become List[T].
Fields that contain null in the JSON sample are typed as Optional[T] (or Union[T, None]) to ensure static type checkers like mypy and pyright catch potential None-attribute errors before runtime.
Standard Dataclasses vs Pydantic v2 Models
When you select Dataclass mode, the generator uses Python's built-in @dataclass decorator with zero third-party dependencies, perfect for lightweight scripts and standard library applications.
In Pydantic mode, classes inherit from pydantic.BaseModel and use Field(alias="...") for properties that differ from snake_case identifiers. This ensures full compatibility with FastAPI, data validation pipelines, and serialization workflows.
Recursive Nested Model Extraction
Deeply nested JSON objects are extracted into standalone classes defined in correct topological order so that child models are declared before parent models reference them.
Class names are automatically converted to PascalCase, and property names are formatted in idiomatic Python snake_case while preserving original JSON keys in comments or Pydantic field aliases.
Frequently asked questions
Can I use the generated code with FastAPI?
Yes. Select the "Pydantic v2 (BaseModel)" option in the toolbar. The generated models inherit from BaseModel and can be used directly as FastAPI request bodies or response models.
How are reserved Python keywords handled?
If a JSON key matches a reserved Python keyword (such as "class", "from", "import", or "def"), the generator appends an underscore (e.g. "class_val") and adds a comment or alias indicating the original JSON key.
Are my JSON payloads uploaded to any external server?
No. Parsing, AST traversal, and Python code generation happen entirely inside your web browser. Nothing is ever sent across the network.