json-render-generative-ui

name	json-render-generative-ui
description	Generative UI framework that renders AI-generated JSON specs into type-safe UI components across React, Vue, Svelte, Solid, React Native, video, PDF, and email.
triggers	["use json-render to generate UI","render AI-generated components with json-render","generative UI with json-render","create a json-render catalog","stream AI UI with json-render","json-render shadcn components","build dynamic UI from AI prompts","json-render react renderer setup"]

json-render Generative UI Framework

Skill by ara.so — Daily 2026 Skills collection.

json-render is a Generative UI framework that lets AI generate dynamic interfaces from natural language prompts, constrained to a predefined component catalog. AI outputs JSON; json-render renders it safely and predictably across any platform.

Installation

# React (core)
npm install @json-render/core @json-render/react

# React + shadcn/ui (36 pre-built components)
npm install @json-render/shadcn

# React Native
npm install @json-render/core @json-render/react-native

# Vue
npm install @json-render/core @json-render/vue

# Svelte
npm install @json-render/core @json-render/svelte

# SolidJS
npm install @json-render/core @json-render/solid

# Video (Remotion)
npm install @json-render/core @json-render/remotion

# PDF
npm install @json-render/core @json-render/react-pdf

# Email
npm install @json-render/core @json-render/react-email @react-email/components @react-email/render

# 3D (React Three Fiber)
npm install @json-render/core @json-render/react-three-fiber @react-three/fiber @react-three/drei three

# OG Images / SVG / PNG
npm install @json-render/core @json-render/image

# State management adapters
npm install @json-render/zustand   # or redux, jotai, xstate

# MCP integration (Claude, ChatGPT, Cursor)
npm install @json-render/mcp

# YAML wire format
npm install @json-render/yaml

Core Concepts

Concept	Description
Catalog	Defines allowed components and actions (the guardrails for AI)
Spec	AI-generated JSON describing which components to render and with what props
Registry	Maps catalog component names to actual render implementations
Renderer	Platform-specific component that takes a spec + registry and renders UI
Actions	Named events AI can trigger (e.g. export_report, refresh_data)

Spec Format

The flat spec format uses a root key + elements map:

const spec = {
  root: "card-1",
  elements: {
    "card-1": {
      type: "Card",
      props: { title: "Dashboard" },
      children: ["metric-1", "metric-2", "button-1"],
    },
    "metric-1": {
      type: "Metric",
      props: { label: "Revenue", value: "124000", format: "currency" },
      children: [],
    },
    "metric-2": {
      type: "Metric",
      props: { label: "Growth", value: "0.18", format: "percent" },
      children: [],
    },
    "button-1": {
      type: "Button",
      props: { label: "Export Report", action: "export_report" },
      children: [],
    },
  },
};

Step 1: Define a Catalog

import { defineCatalog } from "@json-render/core";
import { schema } from "@json-render/react/schema";
import { z } from "zod";

const catalog = defineCatalog(schema, {
  components: {
    Card: {
      props: z.object({ title: z.string() }),
      description: "A card container with a title",
    },
    Metric: {
      props: z.object({
        label: z.string(),
        value: z.string(),
        format: z.enum(["currency", "percent", "number"]).nullable(),
      }),
      description: "Displays a single metric value with optional formatting",
    },
    Button: {
      props: z.object({
        label: z.string(),
        action: z.string(),
      }),
      description: "Clickable button that triggers an action",
    },
    Stack: {
      props: z.object({
        direction: z.enum([, ]).(),
        : z.().(),
      }),
      : ,
    },
  },
  : {
    : { :  },
    : { :  },
    : {
      : ,
      : z.({ : z.() }),
    },
  },
});

Step 2: Define a Registry (React)

import { defineRegistry, Renderer } from "@json-render/react";

function format(value: string, fmt: string | null): string {
  if (fmt === "currency") return `$${Number(value).toLocaleString()}`;
  if (fmt === "percent") return `${(Number(value) * 100).toFixed(1)}%`;
  return value;
}

const { registry } = defineRegistry(catalog, {
  components: {
    Card: ({ props, children }) => (
      <div className="rounded-lg border p-4 shadow-sm">
        <h3 className="text-lg font-semibold mb-3">{props.title}</h3>
        {children}
      </div>
    ),

    Metric: ({ props }) => (
      <div className="flex flex-col">
        < =>{props.label}
        
          {format(props.value, props.format)}
        
      
    ),

    :  (
      
    ),

    :  (
      
    ),
  },
});

Step 3: Render the Spec

import { Renderer } from "@json-render/react";

function Dashboard({ spec, onAction }) {
  return (
    <Renderer
      spec={spec}
      registry={registry}
      onAction={(action, payload) => {
        console.log("Action triggered:", action, payload);
        onAction?.(action, payload);
      }}
    />
  );
}

Generating Specs with AI (Vercel AI SDK)

import { generateObject } from "ai";
import { openai } from "@ai-sdk/openai";
import { getCatalogSchema, getCatalogPrompt } from "@json-render/core";

async function generateDashboard(userPrompt: string) {
  const { object: spec } = await generateObject({
    model: openai("gpt-4o"),
    schema: getCatalogSchema(catalog),
    system: getCatalogPrompt(catalog),
    prompt: userPrompt,
  });

  return spec;
}

// Usage
const spec = await generateDashboard(
  "Create a sales dashboard showing revenue, conversion rate, and an export button"
);

Streaming Specs

import { streamObject } from "ai";
import { openai } from "@ai-sdk/openai";
import { getCatalogSchema, getCatalogPrompt, parseSpecStream } from "@json-render/core";
import { Renderer } from "@json-render/react";
import { useState, useEffect } from "react";

function StreamingDashboard({ prompt }: { prompt: string }) {
  const [spec, setSpec] = useState(null);

  useEffect(() => {
    async function stream() {
      const { partialObjectStream } = await streamObject({
        model: openai("gpt-4o"),
        schema: getCatalogSchema(catalog),
        system: getCatalogPrompt(catalog),
        prompt,
      });

      for await (const partial of partialObjectStream) {
        setSpec(partial); // Renderer handles partial specs gracefully
      }
    }
    stream();
  }, [prompt]);

  if (!spec) return ;
   ;
}

Using Pre-built shadcn/ui Components

import { defineCatalog } from "@json-render/core";
import { schema } from "@json-render/react/schema";
import { defineRegistry, Renderer } from "@json-render/react";
import { shadcnComponentDefinitions } from "@json-render/shadcn/catalog";
import { shadcnComponents } from "@json-render/shadcn";

// Pick any of the 36 available shadcn components
const catalog = defineCatalog(schema, {
  components: {
    Card: shadcnComponentDefinitions.Card,
    Stack: shadcnComponentDefinitions.Stack,
    Heading: shadcnComponentDefinitions.Heading,
    Text: shadcnComponentDefinitions.Text,
    Button: shadcnComponentDefinitions.Button,
    Badge: shadcnComponentDefinitions.Badge,
    Table: shadcnComponentDefinitions.Table,
    Chart: shadcnComponentDefinitions.Chart,
    Input: shadcnComponentDefinitions.Input,
    Select: shadcnComponentDefinitions.Select,
  },
  actions: {
    submit: { description: "Submit a form" },
    export: { description:  },
  },
});

 { registry } = (catalog, {
  : {
    : shadcnComponents.,
    : shadcnComponents.,
    : shadcnComponents.,
    : shadcnComponents.,
    : shadcnComponents.,
    : shadcnComponents.,
    : shadcnComponents.,
    : shadcnComponents.,
    : shadcnComponents.,
    : shadcnComponents.,
  },
});

 () {
   ;
}

Vue Renderer

import { h, defineComponent } from "vue";
import { defineCatalog } from "@json-render/core";
import { schema } from "@json-render/vue/schema";
import { defineRegistry, Renderer } from "@json-render/vue";
import { z } from "zod";

const catalog = defineCatalog(schema, {
  components: {
    Card: {
      props: z.object({ title: z.string() }),
      description: "Card container",
    },
    Button: {
      props: z.object({ label: z.string() }),
      description: "Button",
    },
  },
  actions: {
    click: { description: "Button clicked" },
  },
});

const { registry } = defineRegistry(catalog, {
  components: {
    Card: ({ props, children }) =>
      h("div", { class: "card" }, [
        h("h3", null, props.),
        children,
      ]),
    : 
      (, { :  () }, props.),
  },
});

React Native Renderer

import { defineCatalog } from "@json-render/core";
import { schema } from "@json-render/react-native/schema";
import {
  standardComponentDefinitions,
  standardActionDefinitions,
} from "@json-render/react-native/catalog";
import { defineRegistry, Renderer } from "@json-render/react-native";

// 25+ standard mobile components out of the box
const catalog = defineCatalog(schema, {
  components: { ...standardComponentDefinitions },
  actions: standardActionDefinitions,
});

const { registry } = defineRegistry(catalog, {
  components: {}, // use all standard implementations
});

export function AIScreen({ spec }) {
  return <Renderer spec={spec} registry={registry} />;
}

PDF Generation

import { renderToBuffer } from "@json-render/react-pdf";

const invoiceSpec = {
  root: "doc",
  elements: {
    doc: {
      type: "Document",
      props: { title: "Invoice #1234" },
      children: ["page-1"],
    },
    "page-1": {
      type: "Page",
      props: { size: "A4" },
      children: ["heading-1", "table-1"],
    },
    "heading-1": {
      type: "Heading",
      props: { text: "Invoice #1234", level: "h1" },
      children: [],
    },
    "table-1": {
      type: "Table",
      props: {
        columns: [
          { header: "Item", width: "60%" },
          { header: "Amount", width: "40%", align: "right" },
        ],
        rows: [
          ["Widget A", "$10.00"],
          ["Widget B", "$25.00"],
          [, ],
        ],
      },
      : [],
    },
  },
};


 buffer =  (invoiceSpec);


   () {
   buffer =  (invoiceSpec);
    (buffer, {
    : { :  },
  });
}

Email Generation

import { renderToHtml } from "@json-render/react-email";
import { schema, standardComponentDefinitions } from "@json-render/react-email";
import { defineCatalog } from "@json-render/core";

const catalog = defineCatalog(schema, {
  components: standardComponentDefinitions,
});

const emailSpec = {
  root: "html-1",
  elements: {
    "html-1": {
      type: "Html",
      props: { lang: "en" },
      children: ["head-1", "body-1"],
    },
    "head-1": { type: "Head", props: {}, children: [] },
    "body-1": {
      type: "Body",
      props: { style: { backgroundColor: "#f6f9fc" } },
      children: ["container-1"],
    },
    "container-1": {
      type: "Container",
      props: { style: { maxWidth: "600px", margin: "0 auto" } },
      children: ["heading-1", "text-1", ],
    },
    : {
      : ,
      : { :  },
      : [],
    },
    : {
      : ,
      : { :  },
      : [],
    },
    : {
      : ,
      : { : , :  },
      : [],
    },
  },
};

 html =  (emailSpec);

MCP Integration (Claude, ChatGPT, Cursor)

import { createMCPServer } from "@json-render/mcp";

const server = createMCPServer({
  catalog,
  name: "my-ui-server",
  version: "1.0.0",
});

server.start();

State Management Integration

import { create } from "zustand";
import { createZustandAdapter } from "@json-render/zustand";

const useStore = create((set) => ({
  data: {},
  setData: (data) => set({ data }),
}));

const stateStore = createZustandAdapter(useStore);

// Pass to Renderer for action handling with state
<Renderer spec={spec} registry={registry} stateStore={stateStore} />;

YAML Wire Format

import { parseYAML, toYAML } from "@json-render/yaml";

// AI can output YAML instead of JSON (often more token-efficient)
const yamlSpec = `
root: card-1
elements:
  card-1:
    type: Card
    props:
      title: Hello World
    children: [button-1]
  button-1:
    type: Button
    props:
      label: Click Me
    children: []
`;

const spec = parseYAML(yamlSpec);

Full Next.js App Router Example

// app/dashboard/page.tsx
import { generateObject } from "ai";
import { openai } from "@ai-sdk/openai";
import { getCatalogSchema, getCatalogPrompt } from "@json-render/core";
import { DashboardRenderer } from "./DashboardRenderer";
import { catalog } from "@/lib/catalog";

export default async function DashboardPage({
  searchParams,
}: {
  searchParams: { q?: string };
}) {
  const prompt = searchParams.q ?? "Show me a sales overview dashboard";

  const { object: spec } = await generateObject({
    model: openai("gpt-4o"),
    schema: getCatalogSchema(catalog),
    system: getCatalogPrompt(catalog),
    prompt,
  });

  return <DashboardRenderer spec={spec} />;
}

// app/dashboard/DashboardRenderer.tsx
"use client";
import { Renderer } from "@json-render/react";
import { registry } from "@/lib/registry";
import { useRouter } from "next/navigation";

export function DashboardRenderer({ spec }) {
  const router = useRouter();

  return (
    <Renderer
      spec={spec}
      registry={registry}
      onAction={(action, payload) => {
        switch (action) {
          case "navigate":
            router.push(payload.path);
            break;
          case "export_report":
            window.open("/api/export", "_blank");
            break;
          case "refresh_data":
            router.refresh();
            break;
        }
      }}
    />
  );
}

Common Patterns

Conditional Component Availability

// Restrict catalog based on user role
function getCatalogForRole(role: "admin" | "viewer") {
  const base = { Card, Stack, Heading, Text, Metric };
  const adminOnly = role === "admin" ? { Button, Form, Table } : {};
  const adminActions = role === "admin"
    ? { export: { description: "Export data" } }
    : {};

  return defineCatalog(schema, {
    components: { ...base, ...adminOnly },
    actions: adminOnly ? adminActions : {},
  });
}

Dynamic Props with Runtime Data

// Components can fetch their own data
const { registry } = defineRegistry(catalog, {
  components: {
    LiveMetric: ({ props }) => {
      const { data } = useSWR(`/api/metrics/${props.metricId}`);
      return (
        <div>
          <span>{props.label}</span>
          <span>{data?.value ?? "..."}</span>
        </div>
      );
    },
  },
});

Type-Safe Action Handling

import { type ActionHandler } from "@json-render/core";

const handleAction: ActionHandler<typeof catalog> = (action, payload) => {
  // action and payload are fully typed based on your catalog definition
  if (action === "navigate") {
    router.push(payload.path); // payload.path is typed as string
  }
};

Troubleshooting

Problem	Cause	Fix
AI generates unknown component type	Component not in catalog	Add component to defineCatalog or update AI prompt
Props validation error	AI hallucinated a prop	Tighten Zod schema, add .strict() or .describe() hints
Renderer shows nothing	root key doesn't match an elements key	Check spec structure; root must reference a valid element ID
Partial spec renders incorrectly	Streaming not handled	Use parseSpecStream utility or check for null elements before render
Actions not firing	onAction not passed to Renderer	Pass onAction prop to <Renderer>
shadcn components unstyled	Missing Tailwind config	Ensure @json-render/shadcn paths are in tailwind.config.js content array
TypeScript errors in registry	Catalog/registry mismatch	Ensure defineRegistry(catalog, ...) uses the same catalog instance

Environment Variables

# For AI generation (use your preferred provider)
OPENAI_API_KEY=your_key_here
ANTHROPIC_API_KEY=your_key_here

# For MCP server
MCP_SERVER_PORT=3001

Key API Reference

// Core
defineCatalog(schema, { components, actions })  // Define guardrails
getCatalogSchema(catalog)                        // Get Zod schema for AI
getCatalogPrompt(catalog)                        // Get system prompt for AI

// React
defineRegistry(catalog, { components })          // Create typed registry
<Renderer spec={spec} registry={registry} onAction={fn} />

// Core utilities
parseSpecStream(stream)    // Parse streaming partial specs
toYAML(spec)              // Convert spec to YAML
parseYAML(yaml)           // Parse YAML spec to JSON

// PDF
renderToBuffer(spec)       // → Buffer
renderToStream(spec)       // → ReadableStream

// Email
renderToHtml(spec)         // → HTML string
renderToText(spec)         // → plain text string