JSON Schema Generator

Generate a strict JSON Schema for LLM structured output. Create validation schemas for AI responses with support for required fields, enums, and nested objects.

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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": {...}
        }
    },
)

About JSON Schema Generator

JSON Schema is a declarative format that describes the structure of JSON data — which fields are required, what types they have, and how nested objects are shaped. It plays a central role in LLM structured output: providers like OpenAI and Anthropic accept a JSON Schema in their response format or function-calling APIs to force the model to return valid, predictable JSON. This generator builds such a schema from simple field definitions.

Use it whenever you need reliable JSON back from a language model — for example, turning free-form responses into typed data your code can parse without error handling. A few notes: keep the schema strict but minimal (overly complex schemas can hurt model compliance), mark genuinely optional fields as optional, and test your schema with the built-in validator before deploying it in production.