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JSON to Types

Generate TypeScript interfaces and Python type declarations from JSON payloads.

Documentation & User Guide

JSON to Types (TypeScript / Python) Converter Guide

TypeScript and Python Pydantic models require explicit type declarations to validate API request schemas. When building integrations, manually translating JSON API responses into type interfaces is tedious and prone to typos. Our JSON to Types converter analyzes sample JSON data, detects data types (and nested objects/arrays), and automatically generates clean TypeScript interfaces or Python classes (Pydantic / dataclasses).

⚙️ Key Features

  • Converts raw JSON data into TypeScript interfaces or Python Pydantic models.
  • Supports unquoted keys, single quotes, and template variables (e.g. `{{VAR}}`) in JSON inputs.
  • Merges heterogeneous arrays into union types (e.g. `(string | number)[]`).
  • Deduplicates identical nested objects, renaming and reusing sub-interfaces.

📖 How to Use

  1. Paste your sample JSON payload into the input editor panel.
  2. Select your output target: TypeScript interfaces, Python Pydantic v2 models, or Python `@dataclass` classes.
  3. The generated code will appear in the output window.
  4. Copy the resulting types and paste them into your project files.
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Privacy & Security:

All parsing and code generation algorithms run client-side. Your JSON structures and field names are never transmitted over the internet.

Frequently Asked Questions (FAQ)

How does the tool handle missing or null keys?

Null keys or values with varying structures across array elements are inferred as optional fields (e.g., `key?: string` in TypeScript or `Optional[str]` in Python).

What is Pydantic and why use it?

Pydantic is a library for data parsing and validation in Python. It enforces type hints at runtime, throwing clean errors if input data does not match defined schemas. This is the standard for modern APIs built with FastAPI.

Can I use malformed JSON as input?

Yes, our parser is designed to be relaxed. It automatically normalizes common issues like unquoted keys, single quotes, trailing commas, and template placeholders before parsing.