Whether you’re building API clients, data validation layers, or just want a clean object model, this generator transforms raw JSON into fully‑typed, nested Python classes in seconds. Choose between standard library dataclasses for simplicity, Pydantic BaseModels for runtime validation, or attrs classes for performance—then customize naming conventions, optional detection, and even add serialization helpers. No more boilerplate or tedious manual mapping.
List[T] by inspecting the first element’s type and adds default_factory=list so lists are never uninitialized.Optional[T] when values may be null or missing, assigning = None defaults for Pydantic or dataclasses.default or examples by embedding them as field defaults when provided.snake_case, camelCase, or leave them as‑is to match your codebase style.to_dict and from_dict for one‑line conversion between class instances and raw dictionaries.@dataclass, Pydantic’s BaseModel with Field validation, or @attr.s classes with auto‑attribs.2 spaces) or readable (4 spaces) indentation to fit your project’s formatting rules.{
"user_id": 42,
"active": true,
"roles": ["admin","user"],
"profile": {
"email": "u@example.com",
"score": null
}
}
from pydantic import BaseModel, Field
from typing import List, Optional, Any
class Profile(BaseModel):
email: str
score: Optional[Any] = None
class AutoGenerated(BaseModel):
user_id: int
active: bool
roles: List[str] = Field(default_factory=list)
profile: Profile
def to_dict(self) -> dict:
return self.dict()
@classmethod
def from_dict(cls, data: dict) -> 'AutoGenerated':
return cls(**data)
Use this tool to rapidly scaffold data models, automatically enforce type safety, and eliminate manual mapping errors. Ideal for generating client SDKs, setting up test fixtures, or migrating legacy JSON‑driven code to modern, maintainable Python classes—giving you confidence and saving hours of boilerplate work.