import BaseLayout from '@versal/ui/BaseLayout'; const title = 'JSON Schema Generator'; const description = 'Generate a strict JSON Schema for LLM structured output. Create validation schemas for AI responses with support for required fields, enums, and nested objects.'

{title}

{description}

Step 1 — Define fields

Add fields that the LLM should return. Each field becomes a JSON Schema property.

Step 2 — Preview & generate

Schema ID

Description

Generated JSON Schema

—

Validate against schema

How it works

Add fields with names, types, and optional descriptions.
Choose types: string, number, boolean, array, object, or enum.
Mark fields as required or optional.
Generate a JSON Schema Draft-2020-12 compatible schema.
Use with OpenAI response_format or Anthropic structured output.

LLM integration example

OpenAI (Python)

from openai import OpenAI
import json

client = OpenAI()

completion = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Get user info for alice"}],
    response_format={"type": "json_schema", "json_schema": {
        "name": "user_schema",
        "schema": {...}
    }},
)
result = json.loads(completion.choices[0].message.content)

Anthropic (Python)

import anthropic

client = anthropic.Anthropic()

message = client.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Get user info for alice"}],
    response_format={
        "type": "json_schema",
        "json_schema": {
            "name": "user_schema",
            "schema": {...}
        }
    },
)