Tambo AI: Generative UI Toolkit for React
Build AI agents that speak your UI — register components, stream props, and let the agent decide what to render.
- Source repo
- tambo-ai/tambo
- Stars
- ★ 11k
- Last updated
- 9d ago
- License
- MIT
- Primary language
- TypeScript
- FA score
- 42/100 · Major gaps
At a glance
- How it runs
- Works with
- Universal · cross-platformOpenAI API · Claude API
- Cost
- Free tier plus a paid hosted plan
- Setup effort
- Medium · a few setup steps
- You'll need
- Typical use
- A dashboard builder needs to let users ask for charts and have them rendered dynamically from natural language.
- Not a fit if
- Frontend-only teams that don't want a backend service
- Teams not using TypeScript or Zod schemas
- Teams that need fully offline, no-cloud operation
- Source review
- 42/100 · Major gaps 2 safety controls not found
What does this agent do, and when should you use it?
Tambo is an open-source generative UI toolkit for React that lets you build agents capable of rendering UI dynamically. Developers register components with Zod schemas; the agent selects the right component and streams its props in real-time, enabling interactive and stateful interfaces. It includes a complete React SDK and backend, handling streaming, state management, MCP integration, and local tool execution. Tambo supports major LLM providers like OpenAI, Anthropic, Gemini, and Mistral, and offers both a hosted cloud service and a self-hostable Docker backend. The toolkit also includes a pre-built component library and templates for quick prototyping.
Tambo provides a React SDK and backend to build generative UI agents. You define components using APIs like withInteractable and Zod schemas, wrap your app in TamboProvider, and the agent runs the LLM loop to select components and stream props to them, allowing users to interact live. It integrates with MCP servers (e.g., filesystem, Linear, Slack) for tool access, supports local browser-based tools (e.g., DOM manipulation, authenticated fetches), and offers context helpers and user authentication. The CLI command npm create tambo-app scaffolds a new project. Tambo cloud handles conversation state and agent orchestration, while self-hosted runs the same backend via Docker.
- A dashboard builder needs to let users ask for charts and have them rendered dynamically from natural language.
- An e-commerce team wants an interactive shopping cart that users can update through conversation.
- A productivity app wants to connect to external tools like Slack or Linear via MCP for automated actions.
- A developer prototyping an AI feature wants to add a streaming chat without building the LLM loop from scratch.
- An enterprise needing data privacy prefers self-hosting the backend on their own infrastructure.
How do you install or deploy this agent?
Install via npm:
npm create tambo-app my-tambo-app
cd my-tambo-app
npm run devThis command auto-initializes git and Tambo setup. Requires Node.js and npm. For self-hosted, you'll also need Docker.
How do you use this agent?
- Wrap your app with TamboProvider, passing an API key and a userKey or userToken.
- Define components as a TamboComponent array (or use withInteractable for stateful ones) and pass them to the provider.
- Use the useTambo() hook to get messages and streaming state, and useTamboThreadInput() to handle user input.
- Optionally configure MCP servers via mcpServers prop, or define local tools via tools prop.
- Run your app; the agent will render components in response to user messages.
- For production, either use Tambo Cloud or deploy the backend yourself with Docker.
What are this agent's strengths and limitations?
- Built-in agent and streaming infrastructure, saving development time.
- Supports multiple LLM providers (OpenAI, Anthropic, Gemini, Mistral) for flexibility.
- Offers both cloud-hosted and self-hosted deployment options.
- First-class MCP integration and client-side tool execution.
- MIT license, allowing modification and internal use.
- Requires a backend (Tambo Cloud or self-hosted Docker), adding deployment complexity.
- The generative UI model requires learning new concepts and component schemas.
- Zod schemas might be restrictive for teams not using TypeScript.
- Provider switching is possible but requires configuration changes and potential rework.
- Documentation is extensive but may require time to fully understand all features.
How does this agent compare with similar options?
Compared to Vercel AI SDK, Tambo offers automatic component selection and persistent stateful components, while Vercel AI SDK focuses on streaming abstractions and manual tool mapping. Compared to CopilotKit, Tambo provides built-in MCP and client-side tools, while CopilotKit relies on agent frameworks like LangGraph. Compared to Assistant UI, Tambo is geared toward full app UI control, whereas Assistant UI is more chat-focused. See README for a feature comparison table.
Key facts side by side with the most closely related agents.
| Agent | Source review | Form / cost | Stars | Updated | Language | Full support on |
|---|---|---|---|---|---|---|
| Tambo AI: Generative UI Toolkit for React This agent | 42 · Major gaps | Library / SDKFreemium | ★ 11k | 9d ago | TypeScript | OpenAI API · Claude API |
| ZhikunCode | 56 · Major gaps | Self-hosted serviceFree + model costs | ★ 525 | 1d ago | Java | OpenAI API · Claude API |
| Mirafold | 89 · Good | CLIFreemium | ★ 18 | 1mo ago | TypeScript | Codex · Claude Code |
| Aster | 77 · Good | CLIFree + model costs | ★ 129 | today | Rust | OpenAI API |
How does FollowAgents rate this agent?
Why each dimension lost points
Evidence shows: README describes user key/token authentication, but least privilege is not explicit; no user confirmation mechanism; data flow transparency is limited, only mentions context helpers; sensitive data handling not detailed; dependency security has overrides and pinned versions, but no vulnerability scanning evidence; external effects include MCP and local tools, but permission controls not explicit; no rollback mechanism; source attribution only via license and copyright, publisher unverified. Deductions: lack of user confirmation, rollback, and detailed data handling.
Evidence shows: README and code examples are consistent, but package.json description is inconsistent with README (describes Hydra AI); dependency availability not verified, but CI configures tests and build; failure messages not explicit in docs. Deductions: description inconsistency, insufficient failure handling documentation.
Evidence shows: README provides multiple scenarios (chat, dashboard) and templates; capability boundaries defined via components and tools; trigger precision not explicit; environment fit includes cloud and self-hosted. Deductions: trigger precision and boundary documentation insufficient.
Evidence shows: information architecture clear with TOC and doc links; install notes simple; naming stable but package.json description inconsistent; examples and FAQ have examples but no FAQ; known limitations not explicit; license MIT; versioning and changelog not provided; maintenance responsibility not explicit. Deductions: missing changelog and known limitations.
Evidence shows: output usability via component rendering and streaming; marginal value via generative UI and MCP integration; cost-benefit not detailed. Deductions: insufficient cost-benefit analysis.
Evidence shows: README claims have some doc links, but no specific evidence; cross-source verification limited; facts and inferences not clearly separated. Deductions: lack of verifiable evidence and separation.
- Not found in source: confirmation before actingTurn on (or add) a confirmation step before it acts, and try it in a sandbox or test environment before real data.
- Not found in source: rollback or recovery pathBack up first, or work on a git branch or snapshot, so its changes can be undone.
- Publisher identity unverified; evaluate with caution.
- package.json description inconsistent with README, may affect trust.
- No user confirmation mechanism, potential risk of automatic actions.
- Dependency security has overrides but no vulnerability scanning evidence.
Recent releases
Latest entries from the project's GitHub Releases. Notes are quoted from upstream as written, not rewritten or tested by FollowAgents.
-
web-v0.136.0 web: v0.136.0
Release notes excerpt
[0.136.0](https://github.com/tambo-ai/tambo/compare/web-v0.135.1...web-v0.136.0) (2026-10-08) Features * cloud: prepare Tambo Cloud shutdown ([#3027](https://github.com/tambo-ai/tambo/issues/3027)) ([0bd450b](https://github.com/tambo-ai/tambo/commit/0bd450b607e5bf0f78ebf17279b75ab7fff773ef)) * web: gate new signups behind DISABLE_NEW_SIGNUPS ([#3031](https://github.com/tambo-ai/tambo/issues/3031)) ([524d917](https://github.com/tambo-ai/tambo/commit/524d917e279cc936108a4724a26d9b862e13b241)) Bug Fixes …View on GitHub ↗ -
react-v1.4.0 react: v1.4.0
Release notes excerpt
[1.4.0](https://github.com/tambo-ai/tambo/compare/react-v1.3.0...react-v1.4.0) (2026-10-08) Features * cloud: prepare Tambo Cloud shutdown ([#3027](https://github.com/tambo-ai/tambo/issues/3027)) ([0bd450b](https://github.com/tambo-ai/tambo/commit/0bd450b607e5bf0f78ebf17279b75ab7fff773ef)) Bug Fixes * deps: update ag-ui to v0.0.57 ([#2966](https://github.com/tambo-ai/tambo/issues/2966)) ([4e14c18](https://github.com/tambo-ai/tambo/commit/4e14c18d562957394e3d0575262908dd71c8fec8)) …View on GitHub ↗ -
client-v1.3.0 client: v1.3.0
Release notes excerpt
[1.3.0](https://github.com/tambo-ai/tambo/compare/client-v1.2.0...client-v1.3.0) (2026-10-08) Features * cloud: prepare Tambo Cloud shutdown ([#3027](https://github.com/tambo-ai/tambo/issues/3027)) ([0bd450b](https://github.com/tambo-ai/tambo/commit/0bd450b607e5bf0f78ebf17279b75ab7fff773ef)) Bug Fixes * client: prevent orphaned ephemeral messages persisting across runs ([#2995](https://github.com/tambo-ai/tambo/issues/2995)) ([d24882e](https://github.com/tambo-ai/tambo/commit/d24882e2b581a564a7bb4f2727feb15e3737452d)) …View on GitHub ↗