Multi-Agent Collaboration Mechanisms: A Survey of LLMs

TL;DR

This survey reviews the development of multi-agent systems based on large language models, highlighting collaboration mechanisms, applications, and future research directions for more intelligent collective AI solutions.

Contribution

It introduces an extensible framework for characterizing collaboration in LLM-based multi-agent systems and reviews existing methodologies and applications across various domains.

Findings

Framework characterizes collaboration mechanisms based on key dimensions.

MAS applications span diverse domains like networks, Industry 5.0, and social settings.

Identifies open challenges and future research directions.

Abstract

With recent advances in Large Language Models (LLMs), Agentic AI has become phenomenal in real-world applications, moving toward multiple LLM-based agents to perceive, learn, reason, and act collaboratively. These LLM-based Multi-Agent Systems (MASs) enable groups of intelligent agents to coordinate and solve complex tasks collectively at scale, transitioning from isolated models to collaboration-centric approaches. This work provides an extensive survey of the collaborative aspect of MASs and introduces an extensible framework to guide future research. Our framework characterizes collaboration mechanisms based on key dimensions: actors (agents involved), types (e.g., cooperation, competition, or coopetition), structures (e.g., peer-to-peer, centralized, or distributed), strategies (e.g., role-based or model-based), and coordination protocols. Through a review of existing methodologies, our findings serve as a foundation for demystifying and advancing LLM-based MASs toward more intelligent and collaborative solutions for complex, real-world use cases. In addition, various applications of MASs across diverse domains, including 5G/6G networks, Industry 5.0, question answering, and social and cultural settings, are also investigated, demonstrating their wider adoption and broader impacts. Finally, we identify key lessons learned, open challenges, and potential research directions of MASs towards artificial collective intelligence.