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Tambo 1.0 is here! Read the announcement: Introducing Tambo: Generative UI for React

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Table of Contents

• What is Tambo?
• Get Started
• How It Works
• Features
• How Tambo Compares
• Community
• License

What is Tambo?

Tambo is a React toolkit for building agents that render UI (also known as generative UI).

Register your components with Zod schemas. The agent picks the right one and streams the props so users can interact with them. "Show me sales by region" renders your <Chart>. "Add a task" updates your <TaskBoard>.

Get started in 5 minutes →

https://github.com/user-attachments/assets/8381d607-b878-4823-8b24-ecb8053bef23

What's Included

Tambo is a fullstack solution for adding generative UI to your app. You get a React SDK plus a backend that handles conversation state and agent execution.

1. Agent included — Tambo runs the LLM conversation loop for you. Bring your own API key (OpenAI, Anthropic, Gemini, Mistral, or any OpenAI-compatible provider). Works with agent frameworks like LangChain and Mastra, but they're not required.

2. Streaming infrastructure — Props stream to your components as the LLM generates them. Cancellation, error recovery, and reconnection are handled for you.

3. Tambo Cloud or self-host — Cloud is a hosted backend that manages conversation state and agent orchestration. Self-hosted runs the same backend on your infrastructure via Docker.

Most software is built around a one-size-fits-all mental model. We built Tambo to help developers build software that adapts to users.

Get Started

npm create tambo-app my-tambo-app  # auto-initializes git + tambo setup
cd my-tambo-app
npm run dev

Tambo Cloud is a hosted backend, free to get started with plenty of credits to start building. Self-hosted runs on your own infrastructure.

Check out the pre-built component library for agent and generative UI primitives:

https://github.com/user-attachments/assets/6cbc103b-9cc7-40f5-9746-12e04c976dff

Or fork a template:

Template	Description
AI Chat with Generative UI	Chat interface with dynamic component generation
AI Analytics Dashboard	Analytics dashboard with AI-powered visualization

How It Works

Tell the AI which components it can use. Zod schemas define the props. These schemas become LLM tool definitions—the agent calls them like functions and Tambo renders the result.

Generative Components

Render once in response to a message. Charts, summaries, data visualizations.

https://github.com/user-attachments/assets/3bd340e7-e226-4151-ae40-aab9b3660d8b

const components: TamboComponent[] = [
  {
    name: "Graph",
    description: "Displays data as charts using Recharts library",
    component: Graph,
    propsSchema: z.object({
      data: z.array(z.object({ name: z.string(), value: z.number() })),
      type: z.enum(["line", "bar", "pie"]),
    }),
  },
];

Interactable Components

Persist and update as users refine requests. Shopping carts, spreadsheets, task boards.

https://github.com/user-attachments/assets/12d957cd-97f1-488e-911f-0ff900ef4062

const InteractableNote = withInteractable(Note, {
  componentName: "Note",
  description: "A note supporting title, content, and color modifications",
  propsSchema: z.object({
    title: z.string(),
    content: z.string(),
    color: z.enum(["white", "yellow", "blue", "green"]).optional(),
  }),
});

Docs: generative components, interactable components

The Provider

Wrap your app with TamboProvider. You must provide either userKey or userToken to identify the thread owner.

<TamboProvider
  apiKey={process.env.NEXT_PUBLIC_TAMBO_API_KEY!}
  userKey={currentUserId}
  components={components}
>
  <Chat />
  <InteractableNote id="note-1" title="My Note" content="Start writing..." />
</TamboProvider>

Use userKey for server-side or trusted environments. Use userToken (OAuth access token) for client-side apps where the token contains the user identity. See User Authentication for details.

Docs: provider options

Hooks

useTambo() is the primary hook — it gives you messages, streaming state, and thread management. useTamboThreadInput() handles user input and message submission.

const { messages, isStreaming } = useTambo();
const { value, setValue, submit, isPending } = useTamboThreadInput();

Docs: threads and messages, streaming status, full tutorial

Features

MCP Integrations

Connect to Linear, Slack, databases, or your own MCP servers. Tambo supports the full MCP protocol: tools, prompts, elicitations, and sampling.

import { MCPTransport } from "@tambo-ai/react/mcp";

const mcpServers = [
  {
    name: "filesystem",
    url: "http://localhost:8261/mcp",
    transport: MCPTransport.HTTP,
  },
];

<TamboProvider
  apiKey={process.env.NEXT_PUBLIC_TAMBO_API_KEY!}
  userKey={currentUserId}
  components={components}
  mcpServers={mcpServers}
>
  <App />
</TamboProvider>;

https://github.com/user-attachments/assets/c7a13915-8fed-4758-be1b-30a60fad0cda

Docs: MCP integration

Local Tools

Sometimes you need functions that run in the browser. DOM manipulation, authenticated fetches, accessing React state. Define them as tools and the AI can call them.

const tools: TamboTool[] = [
  {
    name: "getWeather",
    description: "Fetches weather for a location",
    tool: async (params: { location: string }) =>
      fetch(`/api/weather?q=${encodeURIComponent(params.location)}`).then((r) =>
        r.json(),
      ),
    inputSchema: z.object({
      location: z.string(),
    }),
    outputSchema: z.object({
      temperature: z.number(),
      condition: z.string(),
      location: z.string(),
    }),
  },
];

<TamboProvider
  apiKey={process.env.NEXT_PUBLIC_TAMBO_API_KEY!}
  userKey={currentUserId}
  tools={tools}
  components={components}
>
  <App />
</TamboProvider>;

Docs: local tools

Context, Auth, and Suggestions

Additional context lets you pass metadata to give the AI better responses. User state, app settings, current page. User authentication passes tokens from your auth provider. Suggestions generates prompts users can click based on what they're doing.

<TamboProvider
  apiKey={process.env.NEXT_PUBLIC_TAMBO_API_KEY!}
  userToken={userToken}
  contextHelpers={{
    selectedItems: () => ({
      key: "selectedItems",
      value: selectedItems.map((i) => i.name).join(", "),
    }),
    currentPage: () => ({ key: "page", value: window.location.pathname }),
  }}
/>

const { suggestions, accept } = useTamboSuggestions({ maxSuggestions: 3 });

suggestions.map((s) => (
  <button key={s.id} onClick={() => accept(s)}>
    {s.title}
  </button>
));

Docs: additional context, user authentication, suggestions

Supported LLM Providers

OpenAI, Anthropic, Cerebras, Google Gemini, Mistral, and any OpenAI-compatible provider. Full list. Missing one? Let us know.

How Tambo Compares

Feature	Tambo	Vercel AI SDK	CopilotKit	Assistant UI
Component selection	AI decides which components to render	Manual tool-to-component mapping	Via agent frameworks (LangGraph)	Chat-focused tool UI
MCP integration	Built-in	Experimental (v4.2+)	Recently added	Requires AI SDK v5
Persistent stateful components	Yes	No	Shared state patterns	No
Client-side tool execution	Declarative, automatic	Manual via onToolCall	Agent-side only	No
Self-hostable	MIT (SDK + backend)	Apache 2.0 (SDK only)	MIT	MIT
Hosted option	Tambo Cloud	No	CopilotKit Cloud	Assistant Cloud
Best for	Full app UI control	Streaming and tool abstractions	Multi-agent workflows	Chat interfaces

Community

Join the Discord to chat with other developers and the core team.

Interested in contributing? Read the Contributing Guide.

Join the conversation on Twitter and follow @tambo_ai.

License

MIT unless otherwise noted. Some workspaces (like apps/api) are Apache-2.0.

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For AI/LLM agents: docs.tambo.co/llms.txt