JSON to Pydantic Model
Generate Pydantic v2 BaseModel classes from a JSON sample. Field types are inferred (str, int, float, bool, list, datetime, EmailStr). Nested objects become nested classes. Output is ready to drop into a Python file.
How to use the JSON to Pydantic Model
Paste a JSON sample. The generator infers Python types and writes a Pydantic class for the root object plus one for each nested object. Output uses Pydantic v2 syntax (the current major version). Imports needed are listed at the top of the output.
Generating Pydantic models from JSON
Pydantic is how modern Python validates data at its edges — FastAPI request bodies, settings, parsed API responses — but writing a BaseModel by hand from a sample payload is tedious, especially getting the type hints, nested models, and optional fields right. A sample of real JSON already contains everything needed to infer that model.
This reads a JSON sample and emits Pydantic v2 classes: scalar fields typed as str, int, float, or bool, nested objects promoted to their own BaseModel, and, when detection is on, richer types like datetime, EmailStr, and HttpUrl. You can name the root class and make every field Optional. For a TypeScript equivalent that validates at runtime, the JSON to Zod generator is the counterpart.
Common use cases
- Model an API — turn a sample response into a typed Pydantic model.
- FastAPI bodies — generate request and response models from example JSON.
- Nested classes — get a separate BaseModel for each nested object.
- Rich types — infer datetime, EmailStr, and HttpUrl where they apply.
- Loosen fields — make every field Optional when the payload varies.