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JSON to Pydantic Converter

Paste a JSON sample, get production-ready Pydantic code. Runs entirely in your browser.

Runs locally in your browser. Nothing you paste is uploaded to a conversion API.
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Examples:

Typical user object from a /me API response

InputJSON

Tip: paste one JSON value, a JSON array, or NDJSON samples to infer optional fields.

OutputPydantic
…

About this converter

FastAPI users hit this every day: receive a JSON sample (from a webhook, a fixture, a partner API) and need a typed model. The result drops straight into a request_model parameter and you get OpenAPI docs for free.

Why convert JSON to Pydantic

  • • Get parsing, validation, and serialization in one Python class.
  • • Power FastAPI request/response models with zero extra boilerplate.
  • • Replace ad-hoc dict access with typed attributes that IDEs understand.

How to use

  1. Paste your JSON on the left panel, or pick one of the sample tabs above.
  2. The converter infers field names, optionality, and types automatically.
  3. Copy the generated Pydantic on the right and drop it straight into your codebase.

Common pitfalls

  • • Pydantic is strict about types by default. If your real data sometimes contains unexpected fields, you may want to set `model_config = ConfigDict(extra='ignore')`.
  • • Optional fields get `Optional[X] = None` only when the sample shows null. Real-world APIs often have more optional fields than a single sample reveals.
  • • Datetime strings stay as `str` — switch to `datetime` if you want Pydantic to parse them.

FAQ

Is this json to pydantic converter free?
Yes. It is fully free, no signup, and runs entirely in your browser. We do not store your input.
Does it work with nested objects and arrays?
Yes. Nested objects produce separate named types, and arrays infer the element type from the first non-null sample.
What about optional / nullable fields?
Fields whose value is null in the sample (or marked optional in JSON Schema / Prisma / GraphQL) are marked optional/nullable in the output. For real APIs, you may want to widen optionality manually after generation.
Can I generate Pydantic from multiple JSON samples?
Today the tool processes a single sample. For more aggressive inference across multiple shapes, run the converter on the union/merge of your samples or open an issue.
Is the source code available?
Yes — the entire project is open source. See the GitHub link in the footer.

Next steps with Pydantic

Once you have the generated code, here are the most common ways to use it in a real project.

Use directly in FastAPI

Pass the model as a function parameter — FastAPI parses JSON, validates, and gives you a typed object automatically.

@app.post("/users")
async def create_user(user: User):
    return {"id": user.id, "name": user.name}
Round-trip JSON

Use .model_dump_json() to serialize and User.model_validate_json(s) to parse — no third-party JSON library needed.

Make a field strict

By default Pydantic coerces (e.g. '1' → 1). Use Field(..., strict=True) on a field, or model_config = ConfigDict(strict=True) on the class.

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