JSON to Python Class Converter

What is JSON to Python conversion?

Examples

{"id":101,"name":"Ada Lovelace","email":"ada@example.com","active":true,"roles":["admin","user"]}

from dataclasses import dataclass
from typing import List


@dataclass
class Root:
    id: int
    name: str
    email: str
    active: bool
    roles: List[str]

{"repo":"pydantic","owner":{"login":"samuelcolvin","id":100}}

from dataclasses import dataclass


@dataclass
class Owner:
    login: str
    id: int


@dataclass
class Root:
    repo: str
    owner: Owner

{"login":"octocat","publicRepos":15,"createdAt":"2011-01-25"}

from pydantic import BaseModel, ConfigDict, Field


class Root(BaseModel):
    model_config = ConfigDict(populate_by_name=True)

    login: str
    public_repos: int = Field(alias="publicRepos")
    created_at: str = Field(alias="createdAt")

{"users":[{"id":1,"nick":"x"},{"id":2}]}

from typing import List, Optional, TypedDict


class User(TypedDict):
    id: int
    nick: Optional[str]


class Root(TypedDict):
    users: List[User]

How to convert JSON to Python

1. 1

   Paste your JSON

   Drop a JSON object, array, or API response into the input box. Conversion starts instantly.

2. 2

   Pick dataclass, Pydantic, or TypedDict

   Toggle the output style to match your project, and rename the root class from the default Root.

3. 3

   Copy or download

   Grab the generated Python with one click and paste it straight into your code.

Common Use Cases

Typed API clients

Turn a sample REST or GraphQL response into Pydantic models or dataclasses for a requests or httpx client without writing field definitions by hand.

Config and fixture parsing

Generate models for JSON config files, test fixtures, or webhook payloads you need to load and validate.

Fast prototyping and type hints

Paste an unfamiliar payload to instantly see its shape as Python types — a quick way to explore a new API or add TypedDict hints to legacy dict code.

How the conversion works

Structural inference

Each object becomes a named class; identical shapes are deduplicated so you get one class, not copies. Arrays of objects are merged key by key, and keys absent from some items become Optional.

Three idiomatic targets

dataclass keeps zero dependencies and preserves valid keys for Root(**data); Pydantic v2 adds Field aliases plus populate_by_name for real validation; TypedDict describes a plain dict for static type checkers, using functional syntax when a key is not an identifier.

Correct, dependency-light typing

Whole numbers map to int (arbitrary precision, no overflow), decimals and exponents to float, and null-only fields to Optional[Any]. typing imports (Optional, List, Any) are added only when actually used.

100% client-side

Parsing and generation run in your browser with no network calls, so your data stays private.

Tips for clean Python models

Match the mode to the job

Use dataclass for internal data holders, Pydantic v2 when you validate or parse untrusted input, and TypedDict when you only need static type hints over existing dicts.

Paste a representative sample

Fields are marked Optional only when a sample omits them, and null-only values fall back to Any. A filled-in, complete example yields the most precise types.

Name your root class

Set a meaningful root name (User, ApiResponse) instead of the default Root for readable, importable code.

Frequently asked questions