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YAML to Python Converter

Paste a YAML document and get Python classes — @dataclass, pydantic, TypedDict, or attrs — with typed fields, nested classes, and snake_case naming.

Last updated: May 27, 2026

Client-Side Processing
Input Data Stays on Device
Instant Local Execution
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What is YAML to Python Converter?

Loading a YAML config into a typed Python class lets the type checker catch bugs the YAML parser cannot. The four mainstream styles — stdlib `@dataclass`, third-party `pydantic` (runtime validation), `TypedDict` (pure type hint), and `attrs` (`@attrs.define`) — each fit a different use case. Pydantic for API I/O and validation, dataclass for value objects, TypedDict for typing existing dict-based code.

This tool parses YAML to JSON internally, then reuses the JSON-to-Python generator. The result is the same as running YAML to JSON and then JSON to Python, but you skip the intermediate step.

How to Use YAML to Python Converter

1

Paste a YAML document into the input panel.

2

Pick a style: @dataclass, pydantic, TypedDict, or @attrs.define.

3

Pick a Python version: 3.10+ for modern syntax or 3.9 for typing-module imports.

4

Toggle "Make all fields Optional" if any key may be missing.

5

Copy the generated Python code into your project.

Common Use Cases

  • Generating pydantic models for a FastAPI service that loads YAML configs.
  • Typing a Kubernetes manifest or Helm values file in a Python tool.
  • Producing dataclasses for a CLI that reads YAML.
  • Translating a YAML schema into Python type definitions.

Example Input and Output

A small YAML mapping becomes a @dataclass.

YAML input
name: Alice
age: 30
active: true
Generated @dataclass
from dataclasses import dataclass


@dataclass
class Root:
    name: str
    age: int
    active: bool

Privacy

All YAML parsing and Python generation happen in your browser. No content is sent to a server.

YAML coverage

The internal YAML parser handles the common subset (mappings, sequences, scalars, multi-line strings). Anchors, aliases, and explicit tags may not be supported.

Frequently Asked Questions

@dataclass vs pydantic?

Pydantic when you need runtime validation, JSON/YAML parsing, and OpenAPI integration. @dataclass when you just need a typed container.

How are YAML scalars typed?

true/false become bool, integers become int, decimals become float, strings become str, null becomes Any. Nested mappings produce nested classes.

Does this send my YAML anywhere?

No. All parsing happens locally in your browser.

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