If this tool helped you, you can buy us a coffee ☕
Automatically convert JSON data into standard Python class definitions for API responses, config files, and more.

JSON to XML & XML to JSON Converter
A two-way conversion tool for JSON and XML data structures, designed for development, testing, and data processing.

JSON to YAML & YAML to JSON Converter
Bidirectionally convert between JSON and YAML structured data formats, with support for custom output styling.

JSON to Java POJO Generator
Automatically convert JSON strings into standard Java POJO class code for API integration, data modeling, and other development scenarios.
When you need to model structured data from API responses or configuration files into usable class objects for your Python project, writing definitions by hand is tedious and error-prone. This tool parses your JSON input and automatically generates Python class definitions complete with type hints based on the nested structure and key-value pairs. Since a JSON object is essentially a collection of key-value pairs, our tool maps these directly to Python class attributes, inferring the correct data type (like str, int, list, dict, etc.) for each.
firstName or zip-code) into Python's standard snake_case naming convention (e.g., first_name, zip_code), making the code ready to use out of the box.name: str). This improves code readability and IDE support, facilitating static type checking.Q: What JSON format is required for a successful conversion?
A: The tool accepts standard JSON strings, whether formatted as an object ({}) or an array ([]). Ensure your JSON syntax is valid—for instance, strings must be enclosed in double quotes, and there should be no trailing commas.
Q: How does the tool handle JSON arrays?
A: If the top-level JSON is an array, the tool analyzes the common structure of its elements (typically objects) and generates a Python class based on that shared structure. For example, inputting [{"id": 1, "name": "a"}, {"id": 2, "name": "b"}] generates a Root class with id: int and name: str attributes.
Ensure your input JSON is valid; otherwise, it cannot be parsed. The tool's type inference relies on native JSON types (null, boolean, number, string, array, object). For arrays containing mixed types (e.g., [123, "text"]), the attribute type may be inferred broadly as Any or Union, which you might need to adjust manually to fit your business logic. Note that the generated code provides the data model definition only; it does not include JSON deserialization (like a from_dict method) or serialization logic. You may need to pair it with json.loads() or a third-party library like Pydantic for complete functionality.
For use cases requiring strict data validation and serialization, we recommend using the generated class code as a foundation for a Pydantic model. Pydantic automatically handles data parsing and validation based on type hints. For instance, by changing the generated class User: to from pydantic import BaseModel; class User(BaseModel):, you can easily convert a JSON string into a model instance using User.parse_raw(json_string).
Typical Input/Output Example: Inputting {"userName": "Alice", "age": 30, "hobbies": ["coding", "reading"]} produces a class definition with attributes like user_name: str, age: int, and hobbies: List[str].