This repository provides a framework and examples for implementing Generative UI within agentic applications. It addresses the limitation of static user interfaces by enabling AI agents to dynamically generate, select, or control UI elements at runtime, creating adaptive and interactive user experiences. The project targets developers building AI-powered applications and researchers interested in agentic AI and novel UI paradigms, offering a flexible approach to AI-driven interface design.

How It Works

The project implements Generative UI through three distinct patterns, all underpinned by the AG-UI (Agent-User Interaction Protocol) for bidirectional agent-application communication. Static Generative UI uses pre-built React components that agents select and populate with data, offering high developer control. Declarative Generative UI involves agents returning structured UI descriptions (e.g., A2UI JSONL or Open-JSON-UI JSON), which the frontend renders. Open-ended Generative UI allows agents to return complete UI surfaces, such as HTML or iframes, typically for MCP Apps, offering maximum flexibility but with potential security considerations. CopilotKit provides the tools and middleware to integrate these patterns.

Quick Start & Requirements

• Demo: Explore the Generative UI Playground at go.copilotkit.ai/gen-ui-demo.
• Documentation: Comprehensive guides are available at docs.copilotkit.ai/generative-ui.
• Prerequisites: Examples showcase React, TypeScript, and Python for agent logic. Dependencies include libraries like zod and LLM model integrations (e.g., openai/gpt-4o). Specific hardware or OS requirements are not detailed.

Highlighted Details

• Supports a spectrum of Generative UI patterns: Static, Declarative, and Open-ended.
• Integrates with agentic UI protocols including AG-UI, A2UI, and MCP Apps.
• Offers a hands-on Generative UI Playground for practical exploration.
• Provides conceptual overviews and best practices through blogs and guides.

Maintenance & Community

Contributions are welcomed via pull requests for examples, documentation improvements, or assets. Community discussions and support are available on Discord. Updates are shared via @CopilotKit. Specific details on core maintainers, sponsorships, or a public roadmap are not provided in the README.

Licensing & Compatibility

The project's license is not explicitly stated in the provided README content. This omission requires further investigation to determine compatibility for commercial use or closed-source linking.

Limitations & Caveats

The Open-ended Generative UI pattern, particularly with MCP Apps, may introduce security and performance concerns due to the rendering of arbitrary content. The README lacks explicit installation instructions beyond code examples and does not specify a project license, which could be an adoption blocker.