How to use JSON to Pydantic models
- Paste one representative JSON object. Include the nested objects and consistent array items that your application needs.
- Select Convert to infer primitive types and nested model classes. Unsupported mixed array shapes produce an error.
- Review aliases, required fields, null-only types, and list[Any] annotations. Adjust the model to match your real contract.
- Copy the Python code. Use Python 3.10 or later and Pydantic 2 in your own project; this browser does not execute the generated code.
An example is not a complete schema
A single JSON object shows observed values. It cannot establish which fields may be absent, whether a string is an email address, a numeric range, or all valid variants of a response. This converter therefore makes every observed field required and adds no inferred email, date, URL, or range validator.
Strings infer str, booleans infer bool, parsed whole numbers infer int, and other finite numbers infer float. A null-only example infers None rather than guessing a missing type. Empty arrays infer list[Any]. Review those choices using your application documentation and more examples.
Nested models and safe field aliases
Nested objects become BaseModel subclasses. Arrays are supported when every observed item has the same inferred type or object shape. Mixed types, inconsistent object keys, or different nested shapes are rejected rather than silently choosing one example.
Python keywords, unsupported identifier characters, collisions, and reserved model names receive safe field identifiers. Field aliases retain the original JSON keys. When serializing with Pydantic, model_dump(by_alias=True) requests the JSON aliases. Generated models use extra='forbid'; change that deliberately if your contract permits additional fields.
Limits and local processing
Generation accepts a root object, up to 200,000 UTF-16 units, up to 20 recursive value levels, and 500 observed object fields. The shared JSON reader also rejects duplicate keys, unsafe integer values, and non-finite numbers. Decimal precision follows JavaScript Number.
The browser creates code text without importing Python, installing Pydantic, or validating future records. Input is processed in the page with no conversion-server request or saved history. The generated code is not a proof that your API has a stable schema.
Which format do you need?
| Your next task | Input or choice | Output or result |
|---|---|---|
| Start from a known example | JSON shows the values you observed. | Python classes describe a reviewed starting shape. |
| Decide whether a field is optional | One example cannot establish omission rules. | Every observed field is required until you revise it. |
| Support mixed array variants | Examples may contain incompatible item shapes. | This subset rejects them; write the union deliberately. |
Questions about this tool
Does the tool emit JSON Schema?
It emits Python Pydantic 2 model classes. Your Python project can generate JSON Schema from a reviewed model; the browser does not run that step.
Are generated fields optional?
No. Every observed field is required. A null-only example infers the None type without a default value, so it remains required in Pydantic 2.
What happens to invalid Python field names?
The converter creates safe identifiers and uses Field aliases to preserve the original JSON keys. Use model_dump(by_alias=True) when you want aliases in serialized output.
Why did a mixed array fail?
The supported subset requires every array item to share one inferred type and object shape. Review variants and write an explicit union when your contract needs one.
Is the generated code tested against my API?
No. The converter reads one example and creates a draft. It does not call the API, execute Python, or verify unseen records.