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BlocksArtifacts
JSON Render Data Table
JSON Render Data Table
AI-generated data tables using json-render. Describe columns, rows, sorting, and filters — stream specs into an interactive DataTable with toolbar satellites.
Use this when you are building a prompt-driven data grid with search, filter, pagination, and bulk action toolbars generated from a json-render spec.
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Problems solved
Technical challenges this implementation handles out of the box.
- Generate sortable, filterable tables from natural language
- Stream json-render specs into a live DataTable preview
- Compose toolbars with search, filters, pagination, and bulk actions
Use cases
Products and workflows this pattern is designed to support.
- Admin and CRM table demos
- Analytics reporting prototypes
- Dataset explorers
- Prompt-driven data grid builders
Compare with
Similar patterns, and when each one is the better fit.
JSON Render ReactUse this when you want to define your own small component catalog and render streamed JSON specs with the base @json-render React renderer.
JSON Render Three.jsUse this when users describe a 3D scene in plain language and need it streamed as a live React Three Fiber render they can orbit right away.
JSON Render ShadcnUse this when prompts should render only from a fixed catalog of shadcn components provided by the @json-render/shadcn package.
JSON Render ImageUse this when users describe a social card or marketing graphic in plain language and need a downloadable SVG or PNG image as the result.
Setup
Requirements, wiring steps, and what this pattern adds to your project.
Getting started
Pick how you want to pull this pattern in. Then wire env vars and routes the same way.
- 1
Download → Next.js app
Use Download → Next.js app in the preview toolbar. You get a zip scaffold (Pro downloads rewrite preview `/view/.../api/*` paths to `/api/*` automatically).
- 2
Extract the zip and install dependencies
Unzip the archive, then run `pnpm install` (or npm / yarn) in the project folder.
- 3
Fill .env.local
The download includes .env.local placeholders when needed. Add real values for: AI_GATEWAY_API_KEY.
- 4
Customize the agent and tool files
Adapt prompts, tools, and stop conditions in the generated project for your product.
- 5
Run the app and open the pattern route
Run `pnpm dev`, open the setup/checklist page if present, then navigate to the generated pattern route and verify responses.
Environment variables
AI_GATEWAY_API_KEYGet key
Capabilities
AI SDK APIs
streamTextgatewayProviders
Claude
External services
Upstash
Files added
15 files
app/page.tsxapp/layout.tsxapp/api/json-render-table/route.tscomponents/json-render-demo.tsxcomponents/empty-state.tsxcomponents/data-table.tsxcomponents/table-interaction-context.tsxcomponents/data-table-schema.tsxcomponents/satellites.tsxlib/catalog.tslib/registry.tsxlib/table-components.tsxlib/rate-limit.tslib/request-utils.tslib/json-render-table-debug.ts
Dependencies
15 total
npm packages
10AISDK@json-render/core@json-render/reactUpstashlucide-reactmotionNext.jsReactsonnerZod
Registry components
buttonemptyinputscroll-areatable
Critical files
4 files
lib/catalog.tsdefineCatalog with tableComponentDefinitions from data-table-schema; getTablePrompt() feeds the streaming API system prompt.
lib/registry.tsxdefineRegistry wiring DataTable and satellite components through lib/table-components adapters for json-render ComponentContext.
lib/table-components.tsxComponentFn adapters mapping { props, emit } to flat props and onAction for the table UI implementations.
json-render-table/route.tsStreaming POST handler: rate limit, getTablePrompt(), streamText via gateway("anthropic/claude-sonnet-4.6").
Requirements
- Complexity
- Intermediate
- Setup time
- ~6 minutes
- Node
- 24+
- Memory
- 256MB
Upstash Redis
