ggui

<p align="center"> <picture> <source media="(prefers-color-scheme: dark)" srcset=".github/logo-dark.svg" /> <img src=".github/logo-light.svg" alt="ggui — generative graphical user interface" width="480" /> </picture> </p> <p align="center"><strong>ggui</strong> is the universal MCP-UI protocol — a runtime-negotiated data contract between AI agents and human users.</p> <p align="center"> <a href="https://docs.ggui.ai">Docs</a> · <a href="https://github.com/ggui-ai/agentic-app-templates">Template repos</a> · <a href="https://github.com/ggui-ai/ggui/releases">Releases</a> </p> > 🚧 **Active development — iterating on `v0.1.0` release candidates.** APIs are converging; pin exact versions (see badges below) and watch [Releases](https://github.com/ggui-ai/ggui/releases) for the next RC and the v0.1.0 final. --- Agents describe what they need in natural language; ggui generates ephemeral, interactive interfaces over MCP. No frontend code, no React templates, no custom components — agents talk, users see UI. This repo is the **open protocol + reference runtime**. Self-host with `ggui serve`; pair against any MCP-aware agent runtime (Claude Desktop, Claude Code, claude.ai, Cursor, ChatGPT desktop, Goose, your own). Zero account required, zero managed infrastructure required, zero cloud dependency. --- ## Quick start — pick your path ### 1. Build an agentic app from a template _(recommended for new apps)_ The fastest path to **ship an agent end-to-end**. One command scaffolds a complete pnpm monorepo — chat UI + agent loop + a sample MCP server — pinned to your agent SDK; one more runs the whole thing. ```bash npx @ggui-ai/create-agentic-app --agent claude-agent-sdk my-app # or: --agent openai-agents-sdk | --agent google-adk cd my-app && pnpm install cp .env.example .env.local # add your LLM API key pnpm dev # starts ggui + MCP servers + agent + web, then opens the app ``` `pnpm dev` brings all four services up together and opens **`http://localhost:6890`** once it's ready — so you never have to guess which port to visit (server logs are hidden by default; `pnpm dev --verbose` streams them). The full loop runs locally: you type → the agent calls domain tools and renders a React UI → you click in that UI → the agent reacts. Each template subdir at [github.com/ggui-ai/agentic-app-templates](https://github.com/ggui-ai/agentic-app-templates) is a complete project with its own README + `CLAUDE.md` and a `/bootstrap` Claude Code command that walks you through customisation: the system prompt, your own MCP servers (drop a folder under `servers/mcps/` — it's auto-started by `pnpm dev` and auto-registered with the agent), blueprints, and gadgets. ### 2. Self-host the OSS MCP server + test from claude.ai For **testing the ggui protocol against a real chat host**. Localhost won't work from claude.ai — you need a public HTTPS URL, which [cloudflared](https://developers.cloudflare.com/cloudflare-one/connections/connect-networks/) provides for free. ```bash # terminal 1 — boot the OSS MCP server npm install -g @ggui-ai/cli ANTHROPIC_API_KEY=sk-… ggui serve --mcp-only # http://127.0.0.1:6781/mcp # terminal 2 — expose it to the public internet (no Cloudflare account needed) cloudflared tunnel --url http://127.0.0.1:6781 # prints https://<random>.trycloudflare.com ``` Then in **claude.ai → Settings → Connectors → Add custom connector**, paste `https://<random>.trycloudflare.com/mcp`. Ask Claude to render any UI; the server generates the component and serves it back as a rich rendered card inside the chat. Install cloudflared via your package manager: `brew install cloudflared` (macOS), `apt install cloudflared` (Debian), or grab a binary from [cloudflare.com/products/tunnel](https://www.cloudflare.com/products/tunnel/). ### 3. Use the hosted ggui.ai cloud — `mcp.ggui.ai` _(deploying soon)_ For **production**, sign up at [ggui.ai](https://ggui.ai) → create an app → get a managed MCP URL (form: `https://mcp.ggui.ai/<app-id>/mcp`). Paste into your chat host's connector settings — no self-hosting, no tunnel, no key management. 🚧 _The hosted endpoint is deploying — coming in a follow-up rc. Use path 1 or 2 in the meantime._ --- ## The `ggui` CLI `@ggui-ai/cli` ships the `ggui` binary — the single entrypoint for every OSS workflow. Five verbs cover the full lifecycle: | Verb | What it does | | ---------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `ggui serve` | Boot the OSS MCP server (`/mcp`), session viewer (`/r/<shortCode>`), pairing endpoints, and live-channel WebSocket. `--mcp-only` skips agent supervision — fastest first-run. `--port`, `--host` adjust binding. | | `ggui dev` | Local UI registry + compile-on-demand dev hub for iterating on a `ggui.json` project. Optional tunnel, agent supervision, browser auto-open. Run `ggui --help` for the full flag list. | | `ggui blueprint` | Author + publish + install cached UI templates — `create`, `publish`, `install`. Blueprints make a known screen cheap, fast, and visually consistent by matching before falling back to full LLM generation. | | `ggui gadget` | Author + publish + install client-side libraries (maps, charts, camera, clipboard, anything) wrapped as ggui hooks/components so the generator can use them — `create`, `publish`, `install`. | | `ggui theme` | Validate and inspect `ggui.json#theme` DTCG documents — `ggui theme validate <path>`. Catches schema errors before they reach the runtime. | Plus auth verbs for the hosted path: `ggui login` / `ggui logout` / `ggui whoami` / `ggui keys`. Run `ggui --help` for the top-level overview, or `ggui <verb> --help` for per-command flags. Full CLI reference: [`@ggui-ai/cli` README](./packages/ggui-cli/README.md). --- ## Runnable examples [`samples/`](https://github.com/ggui-ai/ggui/tree/main/samples) holds end-to-end examples you can clone: - [`samples/gguis/`](https://github.com/ggui-ai/ggui/tree/main/samples/gguis) — ready-to-run project configs (`default`, `leaflet-demo`, `mapbox-demo`, `canvas-demo`) showing how a `ggui.json` is shaped. - [`samples/agents/`](https://github.com/ggui-ai/ggui/tree/main/samples/agents) — reference agents per SDK (Claude Agent SDK, OpenAI Agents SDK, Google ADK) talking to ggui as an MCP server. These same samples are what the template repo's `/bootstrap` fetches. - [`samples/gadgets/`](https://github.com/ggui-ai/ggui/tree/main/samples/gadgets) — example component / hook gadgets for the marketplace. - [`samples/mcp-servers/`](https://github.com/ggui-ai/ggui/tree/main/samples/mcp-servers) — minimal domain MCP servers (e.g. a todo server) you can pair against. ### Honest scope today - ✅ Local server, viewer, cookie-authenticated WebSocket subscribe → ack all work end-to-end. - ✅ `ggui_render` mints shortCodes and lands on the same-origin viewer. - ✅ Component-code generation is wired on the OSS path via `createUiGenerator()` from `@ggui-ai/ui-gen` (the same harness the hosted runtime uses). `ggui_render` returns `codeReady: false` only when no BYOK credentials resolve (no `ANTHROPIC_API_KEY` / `OPENAI_API_KEY` / etc.); supply a key to get full generation locally. - 🔒 Default auth is dev-mode (any non-empty bearer → `builder`). Swap in a real `AuthAdapter` via `createGguiServer({ auth })` before exposing beyond `127.0.0.1`. ## How it works ``` ┌─────────┐ MCP Tools ┌──────────┐ WebSocket ┌──────────┐ │ Your │ ────────────────→ │ ggui │ ────────────────→ │ User's │ │ Agent │ ggui_render │ server │ real-time UI │ browser │ │ │ ggui_update │ │ updates │ │ │ │ ←──────────────── │ │ ←──────────────── │ │ │ │ user events │ │ clicks, forms │ │ └─────────┘ └──────────┘ └──────────┘ ``` Your agent uses MCP tools to push UIs and receive user events. The protocol is defined by `@ggui-ai/protocol`; the reference server lives in `@ggui-ai/mcp-server`; embedding primitives ship in `@ggui-ai/react`. ### MCP tools (primary surface) | Tool | Description | | ---------------- | --------------------------------------------------------- | | `ggui_render` | Render a UI for the user (natural-language prompt + data) | | `ggui_update` | Update props on an existing UI (no regeneration, ~200ms) | | `ggui_handshake` | Initial session bootstrap | | `ggui_consume` | Long-poll for user gestures (clicks, form submits) | Plus a blueprint family (`ggui_search_blueprints`, `ggui_render_blueprint`, `ggui_list_featured_blueprints`, …) for catalogue lookups. Full reference: [MCP Protocol Reference](https://docs.ggui.ai/api/mcp-protocol/). ### Zero agent code (MCP config only) If your agent runtime supports MCP natively, skip the SDK entirely. Add `ggui serve` as an MCP server: ```json { "mcpServers": { "ggui": { "url": "http://127.0.0.1:6781/mcp", "headers": { "Authorization": "Bearer dev" } } } } ``` The runtime's native tool-calling loop discovers `ggui_render`, `ggui_update`, `ggui_consume`, and the blueprint catalogue tools directly. Working examples per framework: [Claude](https://docs.ggui.ai/examples/claude-agent/), [OpenAI](https://docs.ggui.ai/examples/openai-agent/), [Gemini](https://docs.ggui.ai/examples/gemini-agent/), [generic MCP](https://docs.ggui.ai/examples/generic-mcp/). ## Embedding UIs `<McpAppIframe>` is the canonical consumer primitive. It takes an MCP Apps resource and mounts the ggui render inside a same-origin iframe. The iframe owns the WebSocket lifecycle, renderer bundle, and render mount — host code does not touch `Render` / WebSocket / renderer internals. ```tsx import { McpAppIframe, type ProtocolError } from "@ggui-ai/react"; import { useEffect, useState } from "react"; function App({ renderId }: { renderId: string }) { const [resource, setResource] = useState<{ uri: string; mimeType: string; text: string } | null>( null ); useEffect(() => { // Fetch the render-resource envelope from your MCP host. On the // OSS path the renderer route at /r/<shortCode> embeds the // bootstrap inline, so a resource with just `{ uri }` is enough. fetchRenderResource(renderId).then((r) => setResource(r.contents[0])); }, [renderId]); if (!resource) return <p>Loading…</p>; return <McpAppIframe resource={resource} onError={(err: ProtocolError) => console.error(err)} />; } ``` Implementer references for the full protocol: [Architecture overview](https://docs.ggui.ai/architecture/overview/), [MCP Apps support](https://docs.ggui.ai/api/mcp-apps/), [WebSocket protocol](https://docs.ggui.ai/api/websocket-protocol/). For non-React frameworks, embed the viewer directly: ```html <iframe src="http://127.0.0.1:6781/r/{shortCode}" width="100%" height="600"></iframe> ``` ## Packages Consumer-facing surface — what you `npm install`: | Package | Purpose | npm | | ------------------------------------------------------- | ------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------- | | [`@ggui-ai/cli`](./packages/ggui-cli) | The `ggui` binary — `serve`, `dev`, `blueprint`, `gadget`, `theme` | [](https://npmjs.com/package/@ggui-ai/cli) | | [`@ggui-ai/mcp-server`](./packages/mcp-server) | Reference OSS server (programmatic embedding) | [](https://npmjs.com/package/@ggui-ai/mcp-server) | | [`@ggui-ai/react`](./packages/ggui-react) | React embedding — `<McpAppIframe>` + shells | [](https://npmjs.com/package/@ggui-ai/react) | | [`@ggui-ai/react-native`](./packages/ggui-react-native) | React Native embedding — WebView-backed renderer | [](https://npmjs.com/package/@ggui-ai/react-native) | | [`@ggui-ai/protocol`](./packages/protocol) | Wire types (events, sessions, WebSocket, MCP envelopes) | [](https://npmjs.com/package/@ggui-ai/protocol) | | [`@ggui-ai/gadgets`](./packages/gadgets) | Author wrappers for 3rd-party libs (Leaflet, Mapbox, …) | [](https://npmjs.com/package/@ggui-ai/gadgets) | Plus 27 supporting packages under [`packages/`](./packages) spanning the runtime (`@ggui-ai/mcp-server-core`, `@ggui-ai/mcp-server-handlers`, `@ggui-ai/ui-gen`, `@ggui-ai/negotiator`), authoring (`@ggui-ai/project-config`, `@ggui-ai/ui-registry`), registry (`@ggui-ai/registry-core`, `@ggui-ai/registry-server`), and dev tooling (`@ggui-ai/dev-stack`, `@ggui-ai/agent-runtime`, `@ggui-ai/console`). See each subdirectory for details. ## Hosted providers Self-hosting is the primary path. For managed infrastructure (no server to run, no LLM key to wire, hosted dashboards), the first-party hosted endpoint at **`mcp.ggui.ai`** is deploying — see [path 3](#3-use-the-hosted-gguiai-cloud--mcpgguiai-deploying-soon) above. [Guuey](https://guuey.com) hosts an upgraded experience built on top of the protocol. The protocol is identical on all paths — you can move between self-hosted and hosted without rewriting anything against this SDK. ## Contributing See [CONTRIBUTING.md](./CONTRIBUTING.md). Issues + PRs welcome. ## License Apache 2.0 — see [LICENSE](./LICENSE).