OpenAPI to Pydantic Converter — Generate Python...

Generate clean Python Pydantic v1 and v2 BaseModel classes directly from OpenAPI 3.x and Swagger 2.0 specifications in YAML or JSON. Supports type hints, optional nulls, field aliases, and enum types.

JavaScript is required to use this interactive tool. Please enable JavaScript in your browser, or use our OpenAPI to Pydantic API for programmatic access.

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OpenAPI to Pydantic is also known as: swagger to pydantic, openapi to python, openapi to pydantic v2, swagger to pydantic models, openapi pydantic generator.

How to OpenAPI to Pydantic Online

Paste your OpenAPI 3.x or Swagger 2.0 specification in YAML or JSON format into the editor.
Select your target Pydantic version: Pydantic v2 (recommended for modern FastAPI) or Pydantic v1.
Toggle Field Aliases if your API spec uses camelCase or kebab-case keys that should map to Python snake_case attributes.
Optionally set a class name prefix (e.g., Api) to namespace generated models.
Copy the generated Python code or download models.py directly for use in your FastAPI, Celery, or Django projects.

OpenAPI to Pydantic Features

Full OpenAPI 3.x & Swagger 2.0 Support: Seamlessly parses components.schemas and legacy definitions blocks in both YAML and JSON.
Pydantic v2 & v1 Native: Generates modern BaseModel classes with ConfigDict, model_config, and Field validation.
Automatic Pythonic Naming: Converts camelCase and hyphenated properties to PEP 8 snake_case with Field(alias="...") mapping.
Nested Object & Array Handling: Automatically extracts inline schemas into clean referenced child models.
Type Hints & Nullability: Accurately maps JSON Schema types to Python typing: str, int, float, bool, List[T], Dict[str, Any], and Optional[T].
Zero Telemetry Guarantee: Conversion runs 100% in your browser using client-side JavaScript. Private internal API contracts are never uploaded.

Pre-built Automation Pipelines

Chain OpenAPI to Pydantic with other utilities in a multi-step visual workflow.

beginner

encoding

Base64 Decode → JSON Format

Decode a Base64 string and pretty-print the JSON inside it.

Flow:

InputBase64 Encode/DecodeJSON FormatterOutput

4 nodes

beginner

data-transformation

CSV → JSON → YAML

Convert CSV data to JSON, then to YAML format.

Flow:

InputCSV to JSONYAML ConverterOutput

4 nodes

intermediate

development

JSON Format → TypeScript Schema

Format JSON and generate TypeScript/Zod schema from it.

Flow:

InputJSON FormatterJSON to TypeScript & Schema GeneratorOutput

4 nodes

Example Input & Output

Sample Reference

Interactive Example: OpenAPI to Pydantic in Action

Show example

Sample Input (text)

openapi: 3.0.0
info:
  title: Petstore API
  version: 1.0.0
components:
  schemas:
    Pet:
      type: object
      required:
        - id
        - name
      properties:
        id:
          type: integer
          format: int64
        name:
          type: string
        tag:
          type: string
        status:
          type: string
          enum: [available, pending, sold]
    Order:
      type: object
      required:
        - id
        - petId
      properties:
        id:
          type: integer
        petId:
          type: integer
        quantity:
          type: integer
        shipDate:
          type: string
          format: date-time
        complete:
          type: boolean

Sample Output

[Processed output for OpenAPI to Pydantic]
Input transformed successfully using openapi, swagger, pydantic.

What happened:

Convert OpenAPI 3 & Swagger specs (YAML/JSON) to Python Pydantic models

All Converters

Convert YAML to Python Pydantic Models

Free, 100% client-side private YAML to Python Pydantic Models converter. Transform syntax instantly in your browser with zero data logging, strict AST validation, and CLI support.

Convert online

Frequently Asked Questions

How do I generate Pydantic models from OpenAPI in FastAPI?: FastAPI natively uses Pydantic for request and response validation. By pasting your OpenAPI specification into this tool, you generate the exact Pydantic classes (models.py) you need to type request bodies, path responses, and query parameters in your FastAPI route handlers.
Does this generator support Pydantic v2 syntax?: Yes. Pydantic v2 is the default output format. It uses modern Python typing and Pydantic v2 constructs, but you can toggle Pydantic v1 compatibility at any time.
Can I convert YAML OpenAPI specs without converting to JSON first?: Yes. The parser natively handles YAML and JSON specifications without requiring external pre-conversion.
Are my private API schemas or tokens sent to any server?: No. DevFlow runs the entire parsing and code emission pipeline client-side in the browser. No schemas or data ever leave your computer.

Developer Reference & Learning Hubs

Developer Guides

In-depth technical tutorials, debugging guides, and best practices.

Technical Glossary

Authoritative definitions for RFC standards, formats, and protocols.

AI Models Directory

Verified per-token API pricing, rate cards, and benchmarks across LLMs.

from __future__ import annotations from typing import Optional from pydantic import BaseModel, Field, ConfigDict from datetime import datetime class Pet(BaseModel): id: float name: str tag: Optional[str] = None status: Optional[str] = None model_config = ConfigDict(populate_by_name=True) class Order(BaseModel): id: float pet_id: float = Field(alias="petId") quantity: Optional[float] = None ship_date: Optional[datetime] = Field(default=None, alias="shipDate") complete: Optional[bool] = None model_config = ConfigDict(populate_by_name=True)

How to OpenAPI to Pydantic Online

Paste your OpenAPI 3.x or Swagger 2.0 specification in YAML or JSON format into the editor.

Select your target Pydantic version: Pydantic v2 (recommended for modern FastAPI) or Pydantic v1.

Toggle Field Aliases if your API spec uses camelCase or kebab-case keys that should map to Python snake_case attributes.

Optionally set a class name prefix (e.g., Api) to namespace generated models.

Copy the generated Python code or download models.py directly for use in your FastAPI, Celery, or Django projects.

OpenAPI to Pydantic Features

Full OpenAPI 3.x & Swagger 2.0 Support: Seamlessly parses components.schemas and legacy definitions blocks in both YAML and JSON.

Pydantic v2 & v1 Native: Generates modern BaseModel classes with ConfigDict, model_config, and Field validation.

Automatic Pythonic Naming: Converts camelCase and hyphenated properties to PEP 8 snake_case with Field(alias="...") mapping.

Nested Object & Array Handling: Automatically extracts inline schemas into clean referenced child models.

Type Hints & Nullability: Accurately maps JSON Schema types to Python typing: str, int, float, bool, List[T], Dict[str, Any], and Optional[T].

Zero Telemetry Guarantee: Conversion runs 100% in your browser using client-side JavaScript. Private internal API contracts are never uploaded.

openapi: 3.0.0 info: title: Petstore API version: 1.0.0 components: schemas: Pet: type: object required: - id - name properties: id: type: integer format: int64 name: type: string tag: type: string status: type: string enum: [available, pending, sold] Order: type: object required: - id - petId properties: id: type: integer petId: type: integer quantity: type: integer shipDate: type: string format: date-time complete: type: boolean

Frequently Asked Questions

How do I generate Pydantic models from OpenAPI in FastAPI?

FastAPI natively uses Pydantic for request and response validation. By pasting your OpenAPI specification into this tool, you generate the exact Pydantic classes (models.py) you need to type request bodies, path responses, and query parameters in your FastAPI route handlers.

Does this generator support Pydantic v2 syntax?

Yes. Pydantic v2 is the default output format. It uses modern Python typing and Pydantic v2 constructs, but you can toggle Pydantic v1 compatibility at any time.

Can I convert YAML OpenAPI specs without converting to JSON first?

Yes. The parser natively handles YAML and JSON specifications without requiring external pre-conversion.

Are my private API schemas or tokens sent to any server?

No. DevFlow runs the entire parsing and code emission pipeline client-side in the browser. No schemas or data ever leave your computer.