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A2UI (Agent-to-UI) is a declarative specification for generative UI. It allows agents to render structured UI components using a JSON-based schema, without requiring custom React components for every interaction.
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**Free course:** See this pattern built end-to-end in [Build Interactive Agents with Generative UI](https://www.deeplearning.ai/short-courses/build-interactive-agents-with-generative-ui/) — a free DeepLearning.AI short course taught by CopilotKit's CEO covering the full Generative UI spectrum (Controlled, Declarative, and Open-Ended).
MCP Apps are MCP servers that expose tools with associated UI resources. When the agent calls one of these tools, CopilotKit automatically fetches and renders the UI component in the chat — no additional frontend code required.
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**Free course:** See this pattern built end-to-end in [Build Interactive Agents with Generative UI](https://www.deeplearning.ai/short-courses/build-interactive-agents-with-generative-ui/) — a free DeepLearning.AI short course taught by CopilotKit's CEO covering the full Generative UI spectrum (Controlled, Declarative, and Open-Ended).
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Key benefits:
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-**Zero frontend code** — UI components are served by the MCP server
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-**Full interactivity** — Components can use HTML, CSS, and JavaScript
Open Generative UI lets the agent generate complete, sandboxed UI on the fly — HTML, CSS, and JavaScript — and stream it live into the chat. The user sees the interface build in real time: styles apply first, then HTML streams in progressively, and finally JavaScript expressions execute one by one.
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**Free course:** See this pattern built end-to-end in [Build Interactive Agents with Generative UI](https://www.deeplearning.ai/short-courses/build-interactive-agents-with-generative-ui/) — a free DeepLearning.AI short course taught by CopilotKit's CEO covering the full Generative UI spectrum (Controlled, Declarative, and Open-Ended).
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Key benefits:
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-**No predefined components** — the agent creates any UI it needs, on demand
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-**Live streaming** — HTML streams into a preview as it's generated
State rendering lets you build UI that reflects your agent's state in real-time. As your agent progresses through nodes and emits state updates, your frontend renders those changes — showing progress, drafts, or intermediate results.
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**Free course:** See this pattern built end-to-end in [Build Interactive Agents with Generative UI](https://www.deeplearning.ai/short-courses/build-interactive-agents-with-generative-ui/) — a free DeepLearning.AI short course taught by CopilotKit's CEO covering the full Generative UI spectrum (Controlled, Declarative, and Open-Ended).
Tool rendering lets you customize how your agent's backend tool calls appear in the chat. Instead of showing raw tool execution, you can render custom React components that display tool arguments, progress, and results.
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**Free course:** See this pattern built end-to-end in [Build Interactive Agents with Generative UI](https://www.deeplearning.ai/short-courses/build-interactive-agents-with-generative-ui/) — a free DeepLearning.AI short course taught by CopilotKit's CEO covering the full Generative UI spectrum (Controlled, Declarative, and Open-Ended).
Display-only generative UI lets you register React components as tools your agent can invoke. When the agent calls the tool, CopilotKit renders your component directly in the chat with the tool's arguments as props — no handler logic or user interaction required.
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**Free course:** See this pattern built end-to-end in [Build Interactive Agents with Generative UI](https://www.deeplearning.ai/short-courses/build-interactive-agents-with-generative-ui/) — a free DeepLearning.AI short course taught by CopilotKit's CEO covering the full Generative UI spectrum (Controlled, Declarative, and Open-Ended).
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**Prefer to learn by building?**[Build Interactive Agents with Generative UI](https://www.deeplearning.ai/short-courses/build-interactive-agents-with-generative-ui/) is a free DeepLearning.AI short course taught by CopilotKit's CEO. It walks through the Controlled, Declarative, and Open-Ended Generative UI patterns end-to-end with React, LangChain, and AG-UI.
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