Recent advancements in Large Language Models (LLMs) have led to the active development of multi-agent systems (MAS) for collaborative problem-solving. This paper provides a comprehensive review of MAS structures and applications, focusing on two recent frameworks-CodeAgent and MetaGPT-that leverage LLMs in complex software engineering tasks. Through detailed examination of their design and workflows, we identify the strengths and limitations of each system and propose future directions to improve the adaptability and scalability of MAS frameworks while addressing their reliability challenges.
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Acknowledgement
This research was supported by the MSIT(Ministry of Science and ICT), Korea, under the ICAN(ICT Challenge and Advanced Network of HRD) program(No. IITP-2022-RS-2022-00156310) supervised by the IITP(Institute of Information & Communications Technology Planning & Evaluation)