OpenUI is a full-stack, renderer-agnostic Generative UI framework built around a compact, streaming-first language. It offers official React support with built-in component libraries and ready-to-use chat interfaces, plus community-supported integrations for other frameworks. OpenUI Lang uses up to 67% fewer tokens than JSON.

Important: OpenUI has no official cryptocurrency, token, or coin. Any asset using the OpenUI name is unaffiliated with this project and is not endorsed by its maintainers.

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What is OpenUI

At the center of OpenUI is OpenUI Lang: a compact, streaming-first language for model-generated UI. Instead of treating model output as only text, OpenUI lets you define components, generate prompt instructions from that component library, and render structured UI as the model streams.

Core capabilities:

• OpenUI Lang - A compact language for structured UI generation designed for streaming output.
• Built-in component libraries - Charts, forms, tables, layouts, and more, ready to use or extend.
• Prompt generation from your component library - Generate model instructions directly from the components you allow.
• Streaming renderer - Parse and render model output progressively in React as tokens arrive.
• Chat and app surfaces - Use the same foundation for assistants, copilots, and broader interactive product flows.

Quick Start

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

This is the fastest way to start with OpenUI. The scaffolded app gives you an end-to-end starting point with streaming, built-in UI, and OpenUI Lang support.

What this gives you:

• OpenUI Lang support - Start with structured UI generation built into the app flow.
• Library-driven prompts - Generate instructions from your allowed component set.
• Streaming support - Update the UI progressively as output arrives.
• Working app foundation - Start from a ready-to-run example instead of wiring everything manually.

How it works

Your components define what the model can generate.

flowchart LR
    A["Component Library"] --> B["System Prompt"]
    B --> C["LLM"]
    C --> D["OpenUI Lang Stream"]
    D --> E["Renderer"]
    E --> F["Live UI"]

1. Define or reuse a component library.
2. Generate a system prompt from that library.
3. Send that prompt to your model.
4. Stream OpenUI Lang output back to the client.
5. Render the output progressively with Renderer.

Try it yourself in the Playground: generate UI live with the default component library.

Packages

Common starting points:

# React app with OpenUI rendering and prebuilt components
npm install @openuidev/react-lang @openuidev/react-ui

# Framework-agnostic backend or Edge prompt generation
npm install @openuidev/lang-core

# LangChain/LangGraph agent and server integration
npm install @openuidev/langchain @langchain/langgraph

# Vue or Svelte runtime
npm install @openuidev/vue-lang
npm install @openuidev/svelte-lang

Why OpenUI Lang

OpenUI Lang is designed for model-generated UI that needs to be both structured and streamable.

• Streaming output - Emit UI incrementally as tokens arrive.
• Token efficiency - Up to 67% fewer tokens than equivalent JSON (see benchmarks).
• Controlled rendering - Restrict output to the components you define and register.
• Typed component contracts - Define component props and structure up front with Zod schemas.

Token efficiency benchmarks

Measured with tiktoken (GPT-5 encoder). OpenUI Lang vs two JSON-based streaming formats across seven UI scenarios:

Full methodology and reproduction steps in benchmarks/.

Documentation

Detailed documentation is available at openui.com.

Repository structure

openui/
├── packages/
│   ├── react-lang/       # Core runtime (parser, renderer, prompt generation)
│   ├── react-headless/   # Headless chat state & streaming adapters
│   ├── react-ui/         # Prebuilt chat layouts & component libraries
│   ├── react-email/      # React Email component library for generated emails
│   ├── lang-core/        # Framework-agnostic parser, prompt, and runtime layer
│   ├── langchain/        # LangChain/LangGraph streaming integration
│   ├── vue-lang/         # Vue runtime bindings for OpenUI Lang
│   ├── svelte-lang/      # Svelte runtime bindings for OpenUI Lang
│   ├── browser-bundle/   # Script-tag bundle for CDN / iframe / no-build embeds
│   └── openui-cli/       # CLI for scaffolding & prompt generation
├── skills/
│   └── openui/           # Claude Code skill for AI-assisted development
├── examples/             # Capability and integration reference implementations
│   ├── agent-frameworks/
│   ├── app-frameworks/
│   ├── design-systems/
│   ├── harnesses/
│   └── miscellaneous/
├── docs/                 # Documentation site (openui.com)
└── benchmarks/           # Token efficiency benchmarks

Good places to start:

• openui.com for the full docs
• Quickstart to scaffold a working app
• examples/README.md to find a focused reference implementation
• CONTRIBUTING.md if you want to contribute

Community

• Discord - Ask questions, share what you're building
• GitHub Issues - Report bugs or request features

How OpenUI compares

For more details, refer to the official OpenUI Lang comparison documentation.

Adopters

A list of organizations and projects using OpenUI is maintained in ADOPTERS.md. If you're using OpenUI, please consider adding your organization; it helps the project gain momentum and helps other adopters find peers using OpenUI in similar contexts.

Contributing

Contributions are welcome. See CONTRIBUTING.md for contribution guidelines and ways to get involved.

Agent Skill

OpenUI ships an Agent Skill so AI coding assistants (Claude Code, Codex, Cursor, Copilot, etc.) can help you scaffold, build, and debug Generative UI apps using OpenUI Lang.

The skill is maintained in the thesysdev/skills repository.

Install

# With the skills CLI (works across all agents)
npx skills add thesysdev/skills --skill openui

The skill covers component library design, OpenUI Lang syntax, system prompt generation, the Renderer, SDK packages, and debugging malformed LLM output.

License

This project is available under the terms described in LICENSE.