Build JSON Schemasfor LLM Integration
Design your data structures visually and generate Pydantic models and LLM function schemas for structured outputs.
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Tired of These Common Schema Headaches?
We've all been there. Let's fix these pain points once and for all.
Manual Schema Writing
Hours spent writing JSON schemas by hand, only to discover validation errors in production.
Schema Sync Issues
Pydantic models get out of sync with your API schemas, causing mysterious runtime errors.
LLM Integration Fails
Function calling schemas break unexpectedly, leaving your AI integrations unreliable.
There's a Better Way
Design schemas visually, validate in real-time, and export code for your LLM projects.
Visual Schema Builder
Design and validate JSON schemas with a visual interface that generates Pydantic models and LLM integration code.
Seamless LLM Integration
Build schemas that are natively compatible with function calling APIs and the latest LLM workflows.
Live Validation & Feedback
Experience real-time schema validation as you build—see errors, suggestions, and previews instantly.
See It In Action
This is a fully functional demo. Try editing fields, adding new ones, or switching tabs to see code generation in action.
Interactive Demo
Fully functional schema builder with a sample User Profile API
Schema Builder
Fields
Properties
Array Items
from enum import Enum
from typing import Any, Dict, List, Optional
# LLM client setup - replace with your preferred LLM provider
# Examples: OpenAI, Anthropic, Together, etc.
from pydantic import BaseModel, Field
class ProfileModel(BaseModel):
first_name: str = Field(description="User's first name", min_length=1)
last_name: str = Field(description="User's last name", min_length=1)
age: float | None = Field(description="User's age", default=None)
class user_profile_api(BaseModel):
user_id: str = Field(description="Unique identifier for the user", min_length=1)
email: str = Field(description="User's email address", min_length=1)
profile: ProfileModel = Field(description="User profile information")
preferences: list[str] | None = Field(description="User preferences", default=None, min_items=0)
ProfileModel.model_rebuild()
user_profile_api.model_rebuild()
# LLM function calling example
# Replace with your LLM provider's structured output method
# OpenAI: client.beta.chat.completions.parse(model="...", messages=[...], response_format=MySchema)
# Anthropic: client.messages.create(...) with structured_outputs
# Others: Check your provider's structured output documentation
# Example with OpenAI (adjust for your provider):
# completion = client.beta.chat.completions.parse(
# model="gpt-4o-mini",
# messages=[
# {"role": "system", "content": "You are a helpful assistant that generates JSON responses. Please provide your response in valid JSON format."},
# {"role": "user", "content": "Your prompt here"}
# ],
# response_format=user_profile_api,
# )
# result = completion.choices[0].message.parsed
# print(result)
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