openui

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Build generative UI apps with OpenUI and OpenUI Lang — the token-efficient open standard for LLM-generated interfaces. Use when mentioning OpenUI, @openuidev, generative UI, streaming UI from LLMs, component libraries for AI, or replacing json-render/A2UI. Covers scaffolding, defineComponent, system prompts, the Renderer, and debugging OpenUI Lang output.

5installs

Sourcethesysdev/openui

Added on2026-03-24

NPX Install

npx skill4agent add thesysdev/openui openui

SKILL.md Content

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OpenUI — The Open Standard for Generative UI

OpenUI is a full-stack Generative UI framework by Thesys. At its center is OpenUI Lang: a compact, line-oriented language designed for LLMs to generate user interfaces, up to 67% more token-efficient than JSON-based alternatives.

Instead of treating LLM output as only text/markdown, OpenUI lets you define a component library, auto-generate a system prompt from it, and render structured UI progressively as the model streams.

Core Architecture

OpenUI has four building blocks that form a pipeline:

Library — Components defined with Zod schemas + React renderers via
```
defineComponent
```
. This is the contract between app and AI: it constrains what the LLM can generate.
Prompt Generator —
```
library.prompt()
```
converts the library into a system prompt with syntax rules, component signatures, and streaming guidelines.
Parser — Parses OpenUI Lang line-by-line (streaming-compatible) into a typed element tree. Validates against the library's JSON Schema.
Renderer — The
```
<Renderer />
```
React component maps parsed elements to your React components, rendering progressively as the stream arrives.

Component Library → System Prompt → LLM → OpenUI Lang Stream → Parser → Renderer → Live UI

OpenUI Lang Overview

OpenUI Lang is a compact, declarative, line-oriented DSL. The LLM generates this instead of JSON or markdown.

Syntax Rules (Critical)

One statement per line:
```
identifier = Expression
```
Root entry point: The first statement MUST assign to the identifier
```
root
```
.
Top-down generation: Write Layout → Components → Data for best streaming performance.
Positional arguments: Arguments map to component props by position, determined by key order in the Zod schema.
Forward references (hoisting): An identifier can be referenced before it's defined — the renderer shows a skeleton/placeholder until the definition arrives.

Example:

root = Stack([header, stats])
header = TextContent("Q4 Dashboard", "large-heavy")
stats = Grid([s1, s2])
s1 = StatCard("Revenue", "$1.2M", "up")
s2 = StatCard("Users", "450k", "flat")

Documentation

For comprehensive reference, fetch the full documentation:

https://www.openui.com/llms-full.txt

For a topic index (page titles and descriptions only):

https://www.openui.com/llms.txt

When you need detail on a specific topic, fetch the relevant page:

Topic	URL
Quickstart & scaffolding	https://www.openui.com/docs/openui-lang/quickstart
Defining components	https://www.openui.com/docs/openui-lang/defining-components
System prompts	https://www.openui.com/docs/openui-lang/system-prompts
Renderer	https://www.openui.com/docs/openui-lang/renderer
Language specification	https://www.openui.com/docs/openui-lang/specification
Interactivity	https://www.openui.com/docs/openui-lang/interactivity
Built-in component library	https://www.openui.com/docs/openui-lang/standard-library

SDK Packages

Package	Purpose	When to use
`@openuidev/react-lang`	Core: defineComponent, createLibrary, Renderer, parser	Every OpenUI project
`@openuidev/react-headless`	Chat state: ChatProvider, hooks, streaming adapters (OpenAI, AG-UI)	Custom chat UI
`@openuidev/react-ui`	Prebuilt layouts (Copilot, FullScreen, BottomTray) + built-in libraries	Fast path to working chat

Scaffolding

bash

npx @openuidev/cli@latest create --name my-genui-app
cd my-genui-app
echo "OPENAI_API_KEY=sk-your-key-here" > .env
npm run dev

Framework Integration

OpenUI works with any LLM framework. The scaffolded app uses Next.js with the OpenAI SDK. Integration patterns exist for: Vercel AI SDK, LangChain, CrewAI, OpenAI Agents SDK, Anthropic Agents SDK, Google ADK, and any framework that produces a text stream.

The core integration point is always the same: send the system prompt (from

library.prompt()

) to your LLM, then feed the streamed text into

<Renderer />