Title: Beyond Text: Implementing Multimodal Large Language Model-Powered Multi-Agent Systems Using a No-Code Platform Show Chinese title

Title: 超越文本：使用无代码平台实现多模态大型语言模型驱动的多智能体系统

Abstract: This study proposes the design and implementation of a multimodal LLM-based Multi-Agent System (MAS) leveraging a No-Code platform to address the practical constraints and significant entry barriers associated with AI adoption in enterprises. Advanced AI technologies, such as Large Language Models (LLMs), often pose challenges due to their technical complexity and high implementation costs, making them difficult for many organizations to adopt. To overcome these limitations, this research develops a No-Code-based Multi-Agent System designed to enable users without programming knowledge to easily build and manage AI systems. The study examines various use cases to validate the applicability of AI in business processes, including code generation from image-based notes, Advanced RAG-based question-answering systems, text-based image generation, and video generation using images and prompts. These systems lower the barriers to AI adoption, empowering not only professional developers but also general users to harness AI for significantly improved productivity and efficiency. By demonstrating the scalability and accessibility of No-Code platforms, this study advances the democratization of AI technologies within enterprises and validates the practical applicability of Multi-Agent Systems, ultimately contributing to the widespread adoption of AI across various industries. Show Chinese abstract

Abstract: 本研究提出了基于多模态大语言模型（LLM）的多智能体系统（MAS）的设计与实现，利用无代码平台来解决企业在采用人工智能时面临的实际限制和显著的入门障碍。 先进的AI技术，如大语言模型（LLMs），由于其技术复杂性和高昂的实施成本，常常带来挑战，使得许多组织难以采用。 为了克服这些限制，本研究开发了一个基于无代码的多智能体系统，旨在使没有编程知识的用户能够轻松构建和管理AI系统。 该研究考察了各种使用案例，以验证AI在业务流程中的适用性，包括从基于图像的笔记生成代码、基于高级检索增强生成（RAG）的问答系统、基于文本的图像生成以及使用图像和提示生成视频。 这些系统降低了AI采用的门槛，不仅使专业开发人员，也使普通用户能够利用AI显著提高生产力和效率。 通过展示无代码平台的可扩展性和易用性，本研究推动了企业内部AI技术的民主化，并验证了多智能体系统的实际适用性，最终促进了AI在各行业的广泛应用。