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AI2Apps: A Visual IDE for Building LLM-based AI Agent Applications

Xin Pang, Zhucong Li, Jiaxiang Chen, Yuan Cheng, Yinghui Xu, Yuan Qi

TL;DR

AI2Apps tackles the need for a unified, end-to-end development environment for deployable LLM-based AI agents. It proposes a Visual IDE with full-cycle capabilities, combining a prototyping canvas, AI-assisted code editor, topology-aware agent debugger, deployment tools, and a management system, all harmonized through a plugin extension system. The paper demonstrates substantial efficiency gains in a case study, with the agent debugger reducing token usage by about $90\%$ and API calls by about $80\%$, and argues that AI2Apps uniquely achieves engineering-level integrity alongside full-stack visuality. By releasing open-source code and an online demo, the work aims to accelerate practical development of AIagent applications and encourage integration with LLMOps ecosystems.

Abstract

We introduce AI2Apps, a Visual Integrated Development Environment (Visual IDE) with full-cycle capabilities that accelerates developers to build deployable LLM-based AI agent Applications. This Visual IDE prioritizes both the Integrity of its development tools and the Visuality of its components, ensuring a smooth and efficient building experience.On one hand, AI2Apps integrates a comprehensive development toolkit ranging from a prototyping canvas and AI-assisted code editor to agent debugger, management system, and deployment tools all within a web-based graphical user interface. On the other hand, AI2Apps visualizes reusable front-end and back-end code as intuitive drag-and-drop components. Furthermore, a plugin system named AI2Apps Extension (AAE) is designed for Extensibility, showcasing how a new plugin with 20 components enables web agent to mimic human-like browsing behavior. Our case study demonstrates substantial efficiency improvements, with AI2Apps reducing token consumption and API calls when debugging a specific sophisticated multimodal agent by approximately 90% and 80%, respectively. The AI2Apps, including an online demo, open-source code, and a screencast video, is now publicly accessible.

AI2Apps: A Visual IDE for Building LLM-based AI Agent Applications

TL;DR

AI2Apps tackles the need for a unified, end-to-end development environment for deployable LLM-based AI agents. It proposes a Visual IDE with full-cycle capabilities, combining a prototyping canvas, AI-assisted code editor, topology-aware agent debugger, deployment tools, and a management system, all harmonized through a plugin extension system. The paper demonstrates substantial efficiency gains in a case study, with the agent debugger reducing token usage by about and API calls by about , and argues that AI2Apps uniquely achieves engineering-level integrity alongside full-stack visuality. By releasing open-source code and an online demo, the work aims to accelerate practical development of AIagent applications and encourage integration with LLMOps ecosystems.

Abstract

We introduce AI2Apps, a Visual Integrated Development Environment (Visual IDE) with full-cycle capabilities that accelerates developers to build deployable LLM-based AI agent Applications. This Visual IDE prioritizes both the Integrity of its development tools and the Visuality of its components, ensuring a smooth and efficient building experience.On one hand, AI2Apps integrates a comprehensive development toolkit ranging from a prototyping canvas and AI-assisted code editor to agent debugger, management system, and deployment tools all within a web-based graphical user interface. On the other hand, AI2Apps visualizes reusable front-end and back-end code as intuitive drag-and-drop components. Furthermore, a plugin system named AI2Apps Extension (AAE) is designed for Extensibility, showcasing how a new plugin with 20 components enables web agent to mimic human-like browsing behavior. Our case study demonstrates substantial efficiency improvements, with AI2Apps reducing token consumption and API calls when debugging a specific sophisticated multimodal agent by approximately 90% and 80%, respectively. The AI2Apps, including an online demo, open-source code, and a screencast video, is now publicly accessible.
Paper Structure (16 sections, 3 figures, 2 tables)

This paper contains 16 sections, 3 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Preliminary
  3. LLM-based AI Agent
  4. Visual IDE
  5. The AI2Apps Framework
  6. Prototyping Canvas
  7. Code Editor
  8. Agent Debugger
  9. Deployment Tool
  10. Plugin Extension System
  11. Management System
  12. Case Study and Usage
  13. Case Study
  14. Usage
  15. Comparison with Related Works
  16. ...and 1 more sections

Figures (3)

  • Figure 1: The comparison between AI2Apps and existing works on building LLM-based AI agent application is outlined, with the integrity of development tools represented on the vertical axis and the visuality of components indicated on the horizontal axis.
  • Figure 2: Architecture of AI2Apps. The left and right sides display screenshots. (a) Prototyping Canvas utilizes built-in components for designing topology diagrams. (b) Code Editor utilizes AI assistance to continue programming the code generated in real-time by the Prototyping Canvas. (c) Agent Debugger pinpoints issues and optimizes agent performance. (d) Deployment Tool releases deployable apps. (e) Plugin Extension System introduces new components. (f) Management System supports the operating environment and resource scheduling.
  • Figure 3: Screenshot of our usage assistant built by AI2Apps.