A2UI Launched: Full CopilotKit support at launch!

A2UI Launched: CopilotKit has partnered with Google to deliver full support in both CopilotKit and AG-UI!

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  • Overview
  • Integrations
  • API Reference
  • Copilot Cloud
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  • Getting Started
  • Introduction to CopilotKit
  • LLM Quickstart
  • Agent Quickstart
  • Vibe Coding MCP
  • What's New
  • CopilotKit Features
  • Agentic Chat UI
  • Copilot Suggestions
  • Human in the Loop (HITL)
  • Generative UI
  • Frontend Actions
  • Backend Actions
  • Shared State
  • Premium Features
  • CopilotKit Premium
  • Fully Headless UI
  • Observability
  • Inspector
  • Agentic Protocols
  • Agentic Protocols
  • AG-UI (Agents<->Users)
  • MCP (Agents<->Tools)
  • A2A (Agents<->Agents)
  • Generative UI Specs
  • Generative UI Specs
  • A2UI
  • Open-JSON-UI
  • MCP-UI
  • Learning
  • Tutorial: AI Todo App
  • Tutorial: AI Travel App
  • Video: Research Canvas
  • Cookbook: State Machine
  • Troubleshooting
  • Error Debugging
  • Error Observability Connectors
  • Common Copilot Issues
  • Migrate to 1.10.X
  • Migrate to 1.8.2
  • Other
  • Integrations
  • ADK
  • A2A
  • Microsoft Agent Framework
  • AWS Strands
  • Direct to LLM
  • LangGraph
  • AutoGen2
  • Agno
  • CrewAI Crews
  • CrewAI Flows
  • LlamaIndex
  • Mastra
  • Open Agent Spec
  • Pydantic AI

Generative UI

Create dynamic, AI-driven user interfaces that adapt and respond to user interactions and agent state.

What is Generative UI?

Generative UI is a paradigm where user interface components are dynamically created, modified, or updated by AI agents based on context, user interactions, and application state. Instead of static, pre-defined interfaces, Generative UI creates adaptive experiences that evolve in real-time.

This approach enables AI agents to not just respond with text, but to create rich, interactive experiences tailored to each user's specific needs and context.

Choose your Integration

Generative UI can be implemented with any agentic backend, with each integration offering different approaches for creating dynamic, AI-driven interfaces.

Choose your integration to see specific implementation guides and examples.

ADK
A2A
Microsoft Agent Framework
AWS Strands
LangGraph
Agno
CrewAI Crews
CrewAI Flows
LlamaIndex
Mastra
Open Agent Spec
Pydantic AI
ADK
A2A
Microsoft Agent Framework
AWS Strands
LangGraph
Agno
CrewAI Crews
CrewAI Flows
LlamaIndex
Mastra
Open Agent Spec
Pydantic AI

Key Features

Dynamic Component Generation

AI agents create UI components in real-time based on context, user needs, and application state.

Real-time State Visualization

Interfaces update dynamically as agent state changes, providing live feedback and progress visibility.

Interactive Elements

Move beyond text responses to create forms, visualizations, buttons, and custom interactive components.

Contextual Adaptation

UI components adapt to current conversations and user context, providing exactly the right interface at the right time.

Rich Data Visualization

Charts, graphs, and visual representations that update in real-time as agents process data.

Multi-modal Interfaces

Support for various UI patterns including forms, workflows, progressive disclosure, and embedded actions.

When to Use Generative UI

Consider Generative UI when you want to:

  • Create rich user interactions beyond simple text exchange
  • Build dynamic data collection with forms that adapt to context
  • Provide real-time feedback showing agent progress and state
  • Design complex workflows that benefit from guided interfaces
  • Enable data visualization that updates based on agent analysis
  • Increase user engagement through interactive elements
  • Offer contextual actions that change based on current application state
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Human in the Loop (HITL)
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Frontend Actions

On this page

What is Generative UI?
Choose your Integration
Key Features
Dynamic Component Generation
Real-time State Visualization
Interactive Elements
Contextual Adaptation
Rich Data Visualization
Multi-modal Interfaces
When to Use Generative UI