tambo README

The project's README from github.com/tambo-ai/tambo. As one markdown file: README.md.

Tambo AI

Build agents that speak your UI

The open-source generative UI toolkit for React. Connect your components—Tambo handles streaming, state management, and MCP.

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tambo-ai/tambo | Trendshift

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

Table of Contents

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.

Tambo AI Animation

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

tambo (tambo-ai/tambo)

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11.2K ·GitHub
Tambo
What is this?Tambo is an open-source toolkit for React that lets you build agents that render your own UI components ('generative UI') instead of just text replies. You register components with Zod schemas, and the agent decides which one to render and streams props into it.
Is Tambo just a React library, or does it include a backend too?It's a fullstack solution: a React SDK plus a backend that manages conversation state and runs the LLM agent loop for you. The backend can be Tambo Cloud (hosted) or self-hosted on your own infrastructure via Docker.
How do I get started with Tambo?Wrap your app with TamboProvider, register your React components with Zod schemas describing their props, and either use the hosted Tambo Cloud or self-host the backend. There are pre-built component libraries and starter templates (AI Chat with Generative UI, AI Analytics Dashboard) to fork from.
Which LLM providers can I use, and do I need my own API key?Yes, you bring your own API key for OpenAI, Anthropic, Google Gemini, Mistral, Cerebras, or any OpenAI-compatible provider. Tambo runs the conversation loop on top of whichever provider you configure.
What do I need to set up for the AI to use my components?Every component you want the agent to render must be registered with a Zod schema, which becomes the LLM's tool definition. You also must provide either a userKey (server-side) or a userToken (OAuth, client-side) to identify who owns each conversation thread.
Can I self-host Tambo instead of using Tambo Cloud?Yes. Tambo Cloud is the hosted option, but the same backend can run on your own infrastructure via Docker if you'd rather keep conversation data and orchestration in-house.
What's the difference between Generative and Interactable components?Generative components render once in response to a message, like charts or summaries. Interactable components persist and update as the user keeps refining requests, like a shopping cart or task board.
How does Tambo compare to CopilotKit or the Vercel AI SDK?Tambo's comparison table highlights that it has built-in MCP support, persistent stateful components, and automatic client-side tool execution, whereas Vercel AI SDK needs manual tool-to-component mapping and CopilotKit relies on agent frameworks like LangGraph.
What can Tambo's MCP integration connect to?Tambo supports the full MCP protocol (tools, prompts, elicitations, sampling) so you can connect to services like Linear, Slack, databases, or your own custom MCP servers.
Can the AI call functions that run in my browser?Yes, Tambo supports Local Tools: functions you define that run client-side for things like DOM manipulation, authenticated fetches, or reading React state, which the AI can call like any other tool.
What are similar projects?CopilotKit, OpenIntelligentUI, trueforge, composio, ai, LibreChat, openui, headroom
Which topics is it in?generative-ui, js, assistant, reactjs, langchain, model-context-protocol, chatbot, ui-components, docker-compose, anthropic, sdk, agents
Creator
tambo-ai
Size
289.9 MB
Stars
11.2K
Updated
CommitOct 09Releaseweb-v0.136.0Oct 09
Age
2y
Commits · 90 Days