JSON → Python Dataclass - free online tool

How to use the JSON → Python Dataclass

1. Paste your JSON object into the left panel.

2. Set the root class name in the options field.

3. Copy the generated Python dataclasses with typing imports.

What is it?

Python dataclasses (introduced in Python 3.7) provide a clean way to define data structures with type hints, automatic __init__, __repr__, and optional __eq__ methods. They're widely used in AI pipelines, Pydantic-compatible workflows, LangChain, and data processing scripts to represent structured API responses and configuration objects.

This tool generates nested dataclasses from any JSON sample, with proper Python typing including List, Optional, and Any from the typing module. Null values are typed as Optional[Any] since null can come from any nullable field. For production use with Pydantic, replace @dataclass with pydantic.BaseModel and use model_validate() for parsing.

Example

JSON Object

{"name":"Alice","age":30,"skills":["PHP","JS"],"address":{"city":"NYC"}}

↓

Python Dataclass

from dataclasses import dataclass
from typing import List, Optional, Any

@dataclass
class Address:
    city: str

@dataclass
class Root:
    name: str
    age: int
    skills: List[str]
    address: Address

For Pydantic (FastAPI, LangChain), replace @dataclass with class Root(BaseModel) — same field definitions work.

Benefits

Generate Python dataclasses with type hints from any JSON

Accurate field names and Python types from a real JSON sample — no manual class definition, no guessed types.

Bootstrap type-safe data models for AI pipeline inputs and outputs

Dataclasses with type hints enable IDE completion, mypy validation, and runtime type checking in Python AI applications.

Convert API response JSON to a usable Python class immediately

Paste the JSON, copy the dataclass, add it to your project — no manual typing of field names and their Python equivalents.

Generate nested dataclasses for complex JSON hierarchies

Nested JSON objects become properly nested dataclasses — the hierarchy is preserved exactly as it appears in the sample.

Frequently asked questions

How do I use this with Pydantic?

Replace @dataclass with class ClassName(BaseModel) from pydantic. Pydantic v2 uses model_validate(data_dict) to parse. Pydantic adds automatic validation, serialization, and JSON schema generation on top of type hints.

How are boolean types handled?

JSON true/false become Python bool. Importantly, in Python type checking, bool is a subclass of int, so you may want to ensure the type annotation is explicitly bool rather than int for boolean fields.

Can I use these with Python's json.loads()?

Dataclasses are not automatically constructed from dicts. Use dacite (pip install dacite) with dacite.from_dict(Root, json.loads(data)) for easy hydration. Or use Pydantic's model_validate() which handles this natively.