snake_case fieldsOptional nullsDetect dates

JSON Input

Pydantic Output (.py)

What is JSON to Pydantic with Validators Generator?

Pydantic is the most widely used data validation library in the Python ecosystem, powering FastAPI, Django Ninja, and countless internal APIs. While basic Pydantic generators produce simple type annotations, real-world applications need input validation — email format checks, URL scheme enforcement, phone number patterns, string length bounds, and numeric range constraints. This tool goes beyond basic type inference by inspecting your JSON data and generating production-ready Pydantic v2 BaseModel classes with Field() constraints and @field_validator decorators. It auto-detects email addresses, URLs, and phone numbers from string values and generates corresponding regex validators that raise descriptive ValueError messages. The snake_case conversion option adds Field(alias="...") declarations so your models accept camelCase JSON while using idiomatic Python attribute names. Nullable fields use the modern T | None union syntax, and date detection converts ISO 8601 strings to Python date or datetime types with the appropriate imports.

How to Use

Paste your JSON object or array into the input area
Configure basic options: root class name, snake_case conversion, null handling, date detection
Click "Validators" to expand validator options for email, URL, phone, string, number, and list constraints
Enable auto-detection for emails, URLs, or phone numbers based on your data patterns
Set constraint ranges for strings (min/max length), numbers (min/max, positive only), and lists (min/max items)
Click "Generate" to produce the Pydantic code with validators, then copy into your project

Why Use This Tool?

Automatic type inference from JSON values with no manual annotation required

Field constraints using Field() with min_length, max_length, gt, ge, lt, and le

Auto-generated @field_validator decorators for emails, URLs, and phone numbers with regex patterns

Modern Python 3.10+ syntax with built-in generics (list[str] instead of List[str])

Nested model generation with correct class ordering (dependencies before dependents)

Field alias support for camelCase to snake_case conversion with ConfigDict(populate_by_name=True)

Pydantic v2 compatible with ConfigDict, field_validator, and modern type union syntax

Tips & Best Practices

Use realistic sample data for accurate type inference and validator auto-detection
Enable snake_case for idiomatic Python field names — the alias preserves JSON compatibility
Email validator uses regex pattern matching for common email formats
URL validator checks for http:// or https:// prefix — adjust the pattern for other schemes
Phone validator matches international format with optional country code prefix
Set number constraints to enforce business rules (e.g., age > 0, score <= 100)
List constraints help validate array sizes (e.g., tags must have 1-10 items)

Frequently Asked Questions

How are JSON types mapped to Python?

JSON strings become str (or date/datetime if ISO 8601 format is detected), integers become int, floats become float, booleans become bool, null becomes T | None using the modern union syntax, arrays become list[T] with inferred element types, and nested objects become separate BaseModel classes.

When should I avoid using this generator?

Skip this tool if you need advanced Pydantic features that cannot be inferred from a single JSON sample, such as discriminated unions (Annotated[Union[...], Field(discriminator="type")]), computed fields, custom serializers, or root models. Also avoid it when your JSON has self-referential structures, since Python classes cannot forward-reference themselves in a way that this generator handles.

What validators are generated?

Email fields get @field_validator with regex validation. URL fields get a validator checking http:// or https:// prefix. Phone fields get a validator matching international phone format. All validators raise ValueError with descriptive messages when validation fails.

What are Field constraints?

Field constraints are validation rules applied directly in the Field() declaration. Strings can have min_length and max_length. Numbers can have gt (greater than), ge (greater or equal), lt (less than), le (less or equal). Lists can have min_length and max_length for item count. These are checked automatically during model instantiation.

Why use snake_case conversion?

Python convention uses snake_case for variable and attribute names, while JSON often uses camelCase. This option converts field names and adds Field(alias="...") so the original JSON keys still work during deserialization. ConfigDict(populate_by_name=True) lets you instantiate models using either the Python name or the alias.

Is my data sent to a server?

No. All type inference, constraint detection, and code generation happens entirely in your browser. Your JSON data is never transmitted to any server, so it is safe to paste payloads containing personal data, API keys, or other sensitive values.

Real-world Examples

Generating validated API request models for FastAPI

When building a FastAPI endpoint, paste a sample request body to generate a Pydantic model with email validation, string length constraints, and positive number checks. The generated model enforces input rules at the API boundary before your business logic runs.

Input

{
  "name": "Alice Johnson",
  "email": "alice@example.com",
  "age": 28,
  "website": "https://example.com",
  "tags": ["developer", "python"]
}

Output

from pydantic import BaseModel, Field, ConfigDict, field_validator
import re

class Root(BaseModel):
    name: str = Field(..., min_length=1)
    email: str
    age: int = Field(..., gt=0)
    website: str
    tags: list[str] = Field(..., min_length=1)

    @field_validator('email')
    @classmethod
    def validate_email(cls, v):
        if v is not None and not re.match(r'^[\w\.-]+@[\w\.-]+\.\w+$', v):
            raise ValueError('Invalid email format')
        return v

    @field_validator('website')
    @classmethod
    def validate_website(cls, v):
        if v is not None and not v.startswith(('http://', 'https://')):
            raise ValueError('URL must start with http:// or https://')
        return v

Creating configuration models with date detection and optional fields

Application configuration files often mix required and optional fields with date timestamps. This tool detects ISO 8601 date strings and generates proper date/datetime types, while null values become Optional fields with None defaults.