DraganSr: AI UI: A2UI, OpenAI Apps SDK, MCP Apps

The app store model faces a new challenger: AI-generated UI "Apps"
Three major AI providers, Google, OpenAI, and Anthropic,
are standardizing how agents can dynamically create user interfaces on the fly.

How it works: AI applications (typically chat or search interfaces) generate structured metadata, JSON or similar formats, as part of their response to user prompts. Client-side libraries then render this metadata into native UI components. The user sees a form, chart, or interactive widget; the agent sees a simple data structure it can produce programmatically.

What's new isn't the technique, it's the standardization.

Each provider has introduced their own approach:

Provider	Standard	Approach
Google	A2UI	Custom declarative JSON → native widgets
OpenAI	Apps SDK	MCP-based apps inside ChatGPT
Anthropic + OpenAI	MCP Apps	UI extension for Model Context Protocol

This standardization means developers can build once and deploy across multiple AI hosts, while agents gain a common "vocabulary" for expressing UI intent.

The future is all about AI-generated user interfaces (says Google) - YouTube by Maximilian Schwarzmüller

Google got a new project! Okay ... that happens frequently. But it's a project that's in-line with other industry efforts to dynamically generate purpose-built UIs with AI. Kind of like new "mini app stores" that might end up being not so mini.

Introducing A2UI: An open project for agent-driven interfaces - Google Developers Blog

A2UI is an open-source project, complete with a format optimized for representing updateable, agent-generated UIs and an initial set of renderers, that allows agents to generate or populate rich user interfaces, so they can be displayed in different host applications, rendered by a range of UI frameworks such as Lit, Angular, or Flutter (with more to come). Renderers support a set of common components and/or a client advertised set of custom components which are composed into layouts. The client owns the rendering and can integrate it seamlessly into their branded UX. Orchestrator agents and remote A2A subagents can all generate UI layouts which are securely passed as messages, not as executable code.

Apps SDK

Our framework to build apps for ChatGPT.

With Apps SDK, MCP is the backbone that keeps server, model, and UI in sync. By standardising the wire format, authentication, and metadata, it lets ChatGPT reason about your app the same way it reasons about built-in tools.

MCP Apps: Extending servers with interactive user interfaces | Model Context Protocol Blog

"proposal for the MCP Apps Extension (SEP-1865) to standardize support for interactive user interfaces in the Model Context Protocol.

This extension addresses one of the most requested features from the MCP community and builds on proven work from MCP-UI and OpenAI Apps SDK - the ability for MCP servers to deliver interactive user interfaces to hosts.

MCP Apps Extension introduces a standardized pattern for declaring UI resources, linking them to tools, and enabling bidirectional communication between embedded interfaces and the host application."

Summary by Gemini

Recent developments in late 2024 and 2025 have crystallized a new paradigm often called "AI UI" or Generative UI. This is the shift from static, pre-built user interfaces to dynamic interfaces generated on-the-fly by AI agents to match a user's specific intent.
The three terms you listed—A2UI, OpenAI Apps SDK, and MCP Apps—are the competing and complementary standards emerging to build this future.
1. The Big Picture: What is AI UI?Traditionally, apps have fixed buttons and menus. If you want to book a flight, you navigate a static form. In AI UI, an AI agent (like ChatGPT or Claude) determines what you need and generates the interface in real-time.
Old Way: You scroll through a list of 50 tools to find the "Calendar" button.
AI UI Way: You say "Schedule a meeting," and the AI generates a date-picker widget right in the chat.
2. A2UI (Agent-to-User Interface)"The Native, Declarative Standard"
Origin: An open-source project initiated by Google.
Core Concept: A2UI allows an AI agent to send a "blueprint" (a JSON file) of a UI to your device, which your device then builds using its own native components (like native iOS buttons or Android sliders).
How it works:The agent does not send code (like HTML or JavaScript) to be executed.
Instead, it sends a secure list of components (e.g., type: "button", label: "Book Now").
The client application (your phone or browser) renders this safely.
Why it matters: It is highly secure because no arbitrary code is running. It also ensures the UI looks perfectly consistent with the app you are using (e.g., a dark-mode iOS app will render a dark-mode iOS button).
3. OpenAI Apps SDK"Apps Inside ChatGPT"
Origin: OpenAI (built on top of the MCP standard).
Core Concept: A framework that lets developers build mini-applications that run directly inside the ChatGPT interface.
How it works:It extends the Model Context Protocol (see below) to include UI capabilities.
Developers define "widgets" (interactive cards, charts, dashboards) that the AI can choose to display during a conversation.
Unlike A2UI's strictly native approach, this often leverages web technologies to render rich, app-like experiences inside the chat window.
Why it matters: It turns ChatGPT from a chatbot into a platform/OS. Instead of just talking, you can use a "Spotify App" or "Jira App" inside the chat to perform complex tasks with visual feedback.
4. MCP Apps (Model Context Protocol Apps)"The Universal Standard for UI-as-a-Resource"
Origin: An extension of the Model Context Protocol (MCP), originally championed by Anthropic and widely adopted by the industry.
Core Concept: MCP provided a standard way for AI models to connect to data (like reading a database). MCP Apps adds a standard way for those tools to return User Interfaces as a resource.
How it works:It treats a UI like a file or a web page. A tool can return a ui:// URI, which the AI client (like Claude Desktop or an IDE) fetches and displays.
It typically uses sandboxed technologies (like iframes) to show these interfaces securely.
Why it matters: It solves the "fragmentation" problem. A developer can build one "MCP App" (e.g., a stock ticker tool) and it should theoretically work in Claude, ChatGPT, Cursor, or any other AI client that supports the standard.
Summary ComparisonFeatureA2UIOpenAI Apps SDKMCP Apps
Primary PhilosophyNative First: Sends a JSON "blueprint"; client builds the UI.Platform First: Builds apps specifically for the ChatGPT ecosystem.Resource First: Treats UI as a fetchable resource (like a webpage).
RenderingUses the host app's native widgets (Swift, Kotlin, Flutter components).Renders web-based widgets/cards inline within the chat.Often uses sandboxed web content (HTML/iframes) or standard templates.
SecurityHigh (No executable code sent).High (Managed by OpenAI's sandbox).High (Sandboxed isolation).
Best For...Mobile apps and deeply integrated agents where "look and feel" matters.Developers wanting to sell/distribute tools to ChatGPT's massive user base.Tool builders who want their tool to work across any AI model (Claude, OpenAI, etc.).

Feature	A2UI	OpenAI Apps SDK	MCP Apps
Primary Philosophy	Native First: Sends a JSON "blueprint"; client builds the UI.	Platform First: Builds apps specifically for the ChatGPT ecosystem.	Resource First: Treats UI as a fetchable resource (like a webpage).
Rendering	Uses the host app's native widgets (Swift, Kotlin, Flutter components).	Renders web-based widgets/cards inline within the chat.	Often uses sandboxed web content (HTML/iframes) or standard templates.
Security	High (No executable code sent).	High (Managed by OpenAI's sandbox).	High (Sandboxed isolation).
Best For...	Mobile apps and deeply integrated agents where "look and feel" matters.	Developers wanting to sell/distribute tools to ChatGPT's massive user base.	Tool builders who want their tool to work across any AI model (Claude, OpenAI, etc.).

Summary by Claude

These appear to be emerging concepts in the AI interface/application space. Let me break down what I know and search for current information:Now I have enough context. Let me explain these three concepts:
A2UI (Agent-to-User Interface)A2UI is a declarative UI protocol for agent-driven interfaces created by Google (just announced 3 days ago, v0.8 preview). The core problem it solves: how can AI agents safely send rich UIs across trust boundaries? Instead of text-only responses or risky code execution, A2UI lets agents send declarative component descriptions that clients render using their own native widgets.
Key characteristics:
Security: No iframes or arbitrary code execution. The client renders with its own UI framework, inheriting app styling, accessibility, and performance.
The agent sends a description of the component tree and data model, and the client application maps those to its native widgets—web components, Flutter widgets, React, SwiftUI, etc.
LLM-Friendly: Flat component list with ID references that's easy to generate incrementally, correct mistakes, and stream.
Think: agent says "render a booking form with these fields" in JSON, your app renders it natively in your design system.
OpenAI Apps SDKOpenAI's Apps SDK enables developers to build apps that live inside ChatGPT. Apps appear in conversation naturally and include interactive interfaces you can use right in the chat.
What it does:
The Apps SDK builds on the Model Context Protocol (MCP)—so it's MCP-based under the hood
With the Apps SDK, you get the full stack: connect your data, trigger actions, render a fully interactive UI
Pilot partners include Booking.com, Canva, Coursera, Figma, Expedia, Spotify, Zillow
This is OpenAI's play for distribution—you build once, and your app reaches ChatGPT's 800M weekly users.
Related: OpenAI also released AgentKit, which includes Agent Builder (visual canvas for multi-agent workflows), ChatKit (embeddable chat UI toolkit), and Connector Registry. The Agents SDK (separate from Apps SDK) is their lightweight framework for building agentic AI apps with primitives like Agents, Handoffs, Guardrails, and Sessions.
MCP AppsThe MCP Apps Extension (SEP-1865) standardizes support for interactive user interfaces in the Model Context Protocol. It introduces a pattern for declaring UI resources, linking them to tools, and enabling bidirectional communication between embedded interfaces and the host application.
The problem it addresses: Currently, MCP servers are limited to exchanging text and structured data with hosts. While this works well for many use cases, it creates friction when tools need to present visual information or gather complex user input.
How it works:
UI templates are resources with the ui:// URI scheme, referenced in tool metadata
The initial spec supports only text/html content rendered in sandboxed iframes with restricted permissions
All UI-to-host communication goes through loggable JSON-RPC
The SEP was authored by MCP Core Maintainers at OpenAI and Anthropic, together with the MCP-UI creators.
How They RelateA2UIOpenAI Apps SDKMCP Apps
WhoGoogleOpenAIAnthropic + OpenAI joint
ScopeProtocol for agent-generated UIFull app platform in ChatGPTUI extension for MCP
RenderingNative widgets (no iframe)Full interactive UISandboxed iframes
TransportWorks with A2A, AG-UIBuilt on MCPPart of MCP spec
A2UI and AG-UI/CopilotKit work well together—A2UI is a generative UI specification while AG-UI is the agent↔user interaction protocol connecting frontend to backend.
These are all part of the emerging "agentic UI" layer—the recognition that agents need to go beyond text responses to deliver rich, interactive experiences. Given your work with MCP clients/servers, the MCP Apps extension is probably most directly relevant to you.

	A2UI	OpenAI Apps SDK	MCP Apps
Who	Google	OpenAI	Anthropic + OpenAI joint
Scope	Protocol for agent-generated UI	Full app platform in ChatGPT	UI extension for MCP
Rendering	Native widgets (no iframe)	Full interactive UI	Sandboxed iframes
Transport	Works with A2A, AG-UI	Built on MCP	Part of MCP spec

Visual Overview by Claude

AI UI Protocols: A2UI, OpenAI Apps SDK, and MCP AppsA comprehensive guide to the emerging standards for agent-driven user interfaces.
Executive SummaryAs AI agents move beyond text-only responses, three major approaches have emerged for delivering rich, interactive UIs:
ProtocolCreatorKey ApproachRendering
A2UIGoogleDeclarative JSON → Native widgetsNo iframe, native rendering
OpenAI Apps SDKOpenAIFull-stack apps in ChatGPTBuilt on MCP
MCP AppsAnthropic + OpenAIUI extension for MCPSandboxed iframes
How They Fit Together

Protocol	Creator	Key Approach	Rendering
A2UI	Google	Declarative JSON → Native widgets	No iframe, native rendering
OpenAI Apps SDK	OpenAI	Full-stack apps in ChatGPT	Built on MCP
MCP Apps	Anthropic + OpenAI	UI extension for MCP	Sandboxed iframes

A2UI (Agent-to-User Interface)

What It Is

A2UI is a declarative UI protocol for agent-driven interfaces, created by Google and released in December 2024 (v0.8 preview). It solves a fundamental problem: how can AI agents safely send rich UIs across trust boundaries?

Instead of text-only responses or risky code execution, A2UI lets agents send declarative component descriptions that clients render using their own native widgets. It's like having agents speak a universal UI language.

Key Characteristics

Security First: No arbitrary code execution. The client maintains a catalog of trusted, pre-approved UI components, and the agent can only request components from that catalog.
Native Feel: No iframes. The client renders with its own UI framework, inheriting app styling, accessibility, and performance.
Portability: One agent response works everywhere. The same JSON renders on web (Lit/Angular/React), mobile (Flutter/SwiftUI/Jetpack Compose), and desktop.
LLM-Friendly: Flat component list with ID references that's easy to generate incrementally, correct mistakes, and stream.
Framework-Agnostic: Agent sends abstract component tree; client maps to native widgets.

Architecture

Message Flow Example

Example Payload

{
  "components": [
    {
      "id": "card-1",
      "type": "Card",
      "children": ["header-1", "form-1", "button-1"]
    },
    {
      "id": "header-1", 
      "type": "Text",
      "content": "Book a Table"
    },
    {
      "id": "form-1",
      "type": "TextField",
      "label": "Party Size",
      "inputType": "number"
    },
    {
      "id": "button-1",
      "type": "Button",
      "text": "Confirm Booking",
      "action": "submit"
    }
  ]
}

When to Use A2UI

✅ Good for:

Agent-generated UI across trust boundaries
Multi-agent systems with standard protocol
Cross-platform apps (one agent, many renderers)
Security-critical applications (declarative data, no code execution)
Brand consistency (client controls styling)

❌ Not ideal for:

Static websites (use HTML/CSS)
Simple text-only chat (use Markdown)
Remote widgets not integrated with client (use iframes/MCP Apps)

Resources

Website: a2ui.org
GitHub: github.com/google/A2UI
Status: v0.8 Public Preview, Apache 2.0 licensed

OpenAI Apps SDK

What It Is

The OpenAI Apps SDK enables developers to build apps that live inside ChatGPT. These apps appear naturally in conversation—you can discover them when ChatGPT suggests one, or call them by name. Apps respond to natural language and include interactive interfaces you can use right in the chat.

The Apps SDK is built on the Model Context Protocol (MCP), making it an open standard that others can implement.

Key Characteristics

Distribution: Reach ChatGPT's 800M+ weekly users
Contextual Discovery: Apps appear when relevant to the conversation
Interactive UI: Full interfaces rendered in chat, not just text
MCP-Based: Built on the open Model Context Protocol
Full Stack: Connect data, trigger actions, render UI

Architecture

Message Flow Example

Related: AgentKit & Agents SDKOpenAI offers additional tools for building agents:
AgentKit includes:
Agent Builder: Visual canvas for creating and versioning multi-agent workflows
ChatKit: Toolkit for embedding customizable chat-based agent experiences
Connector Registry: Central admin panel for managing data sources across workspaces
Agents SDK (separate from Apps SDK):
Lightweight framework for building agentic AI apps
Core primitives: Agents, Handoffs, Guardrails, Sessions
Built-in tracing for visualization and debugging
Available in Python and TypeScript
Launch PartnersBooking.com
Canva
Coursera
Figma
Expedia
Spotify
Zillow
ResourcesApps SDK Docs: platform.openai.com/docs
Agents SDK: openai.github.io/openai-agents-python
AgentKit: openai.com/index/introducing-agentkit
MCP AppsWhat It IsMCP Apps is a proposed extension (SEP-1865) to the Model Context Protocol that standardizes support for interactive user interfaces. It was authored jointly by MCP Core Maintainers at OpenAI and Anthropic, along with the MCP-UI community.
The extension addresses a key limitation: MCP servers are currently limited to exchanging text and structured data. When tools need to present visual information or gather complex user input, this creates friction.
Key CharacteristicsUI Templates as Resources: Templates use the ui:// URI scheme
Tool Metadata Linking: Tools reference their UI via _meta.ui/resourceUri
Sandboxed Rendering: HTML content runs in sandboxed iframes with restricted permissions
Bidirectional Communication: UI-to-host messaging via JSON-RPC over postMessage
Security Layers: Predeclared templates, auditable messages, user consent for tool calls
Architecture

Message Flow Example

Example: Registering a UI Template

// Server registers UI resource
{
  uri: "ui://charts/bar-chart",
  name: "Bar Chart Viewer",
  mimeType: "text/html+mcp"
}

// Tool references it in metadata
{
  name: "visualize_data_as_bar_chart",
  description: "Plots data as a bar chart",
  inputSchema: {
    type: "object",
    properties: {
      series: { type: "array", items: { ... } }
    }
  },
  _meta: {
    "ui/resourceUri": "ui://charts/bar-chart"
  }
}

Security Model

MCP Apps addresses security through multiple layers:

Iframe Sandboxing: All UI content runs in sandboxed iframes with restricted permissions
Predeclared Templates: Hosts can review HTML content before rendering
Auditable Messages: All UI-to-host communication goes through loggable JSON-RPC
User Consent: Hosts can require explicit approval for UI-initiated tool calls

The initial specification supports only text/html content. External URLs, remote DOM, and native widgets are explicitly deferred to future iterations.

Resources

Proposal: blog.modelcontextprotocol.io/posts/2025-11-21-mcp-apps
MCP Spec: modelcontextprotocol.io
Status: SEP-1865 proposal, under development

Comparison: When to Use What

Scenario	Recommended	Why
Building apps for ChatGPT users	OpenAI Apps SDK	Direct integration, 800M user reach
Cross-platform native UI	A2UI	One JSON → web, mobile, desktop
Adding UI to existing MCP server	MCP Apps	Standard extension, works with Claude/ChatGPT
Security-critical enterprise	A2UI	No code execution, declarative only
Quick prototypes	MCP Apps	HTML templates, familiar web tech
Multi-agent orchestration	A2UI + A2A	Designed for agent-to-agent communication
React/frontend focus	AG-UI + CopilotKit	State sync, A2UI compatible

The Bigger Picture

These protocols represent the industry's recognition that agents need to go beyond text. The fragmentation you see (three different approaches from Google, OpenAI, and Anthropic+OpenAI) is typical of an emerging standard—expect consolidation over time.

Key trends to watch:

MCP as the transport layer: Both OpenAI Apps SDK and MCP Apps build on MCP
A2UI for native rendering: Google's approach may win for cross-platform native apps
Interoperability: A2UI and AG-UI are already compatible; more bridges will emerge
Security patterns: All three prioritize security differently (no-code vs sandbox vs consent)

The Agentic AI Foundation (launched by Linux Foundation with Anthropic, Block, OpenAI, Amazon, Google, Microsoft, Cloudflare, and Bloomberg) will likely drive standardization of these protocols.

Quick Reference

A2UI

Agent → Declarative JSON → Client Renderer → Native Widgets

OpenAI Apps SDK

User → ChatGPT → MCP → Your Server → Interactive UI in Chat

MCP Apps

Tool → ui:// Template → Sandboxed iframe → postMessage ↔ Host

Last updated: December 2024

To be continued...

Wednesday, December 17, 2025

AI UI: A2UI, OpenAI Apps SDK, MCP Apps

Summary by Gemini

1. The Big Picture: What is AI UI?

2. A2UI (Agent-to-User Interface)

3. OpenAI Apps SDK

4. MCP Apps (Model Context Protocol Apps)

Summary Comparison

Summary by Claude

A2UI (Agent-to-User Interface)

OpenAI Apps SDK

MCP Apps

How They Relate

Visual Overview by Claude

AI UI Protocols: A2UI, OpenAI Apps SDK, and MCP Apps

Executive Summary

How They Fit Together

A2UI (Agent-to-User Interface)

What It Is

Key Characteristics

Architecture

Message Flow Example

Example Payload

When to Use A2UI

Resources

OpenAI Apps SDK

What It Is

Key Characteristics

Architecture

Message Flow Example

Related: AgentKit & Agents SDK

Launch Partners

Resources

MCP Apps

What It Is

Key Characteristics

Architecture

Message Flow Example

Example: Registering a UI Template

Security Model

Resources

Comparison: When to Use What

The Bigger Picture

Quick Reference

A2UI

OpenAI Apps SDK

MCP Apps

No comments:

Post a Comment