AIGCDesign: Open-Source Cross-Platform AI Component Solution for Frontend Development

AIGC (Artificial Intelligence Generated Content) is defined as AI‑generated text, images, audio, and video, leveraging deep learning models to learn patterns from large datasets and produce original content.

The Open‑AI component solution combines traditional frontend component library features with LLM capabilities, streaming display, and flexible extensibility through standard protocols, lifecycle management, and plug‑in modules, enabling rapid multi‑scenario, multi‑stack, and multi‑device integration.

Since the release of ChatGPT in November 2022, AI has transformed user experiences with AI assistants, recommendation engines, and intelligent agents, prompting the need for scalable, reusable component abstractions to meet the surge in AI‑driven business demands.

Research identified six major AIGC component libraries, evaluating them on extensibility, component coverage, platform support, and framework compatibility, summarized in a comparative table.

The proposed architecture consists of three layers: a core layer providing AI platform integration APIs, a container layer handling multi‑platform, multi‑framework rendering and LLM interaction, and a component layer offering base, business, and custom UI components.

Implementation details include native base components (e.g., toast, voice interaction), native container components for event handling and API requests, and JDHybrid extensions for H5‑native communication.

Lifecycle hooks such as beforeLaunch , onLaunch , onSubmit , onLLMResult , and onChatUpdate allow developers to inject custom logic at each stage of the AI chat flow.

Configuration can be minimal—simply import AiContainer , provide the Yanshi platform apiKey and model endpoint aiPath —or fully customized via lifecycle events and component properties for model requests, UI rendering, and interaction extensions.

Web multi‑framework support is achieved through a UMD bundle built with React, which can be loaded via a script tag and rendered into any DOM node, enabling framework‑agnostic integration.

Multiple real‑world cases demonstrate the solution’s adaptability across MP, PC, and app platforms, including AI‑driven store assistants, supply‑chain helpers, and home‑appliance chatbots.

Future directions focus on enhancing core capabilities (configurability, multi‑endpoint support, OCR/ASR/TTS integration) and expanding B2B/B2C scenario coverage with highly configurable interaction components.