Large Language Model based Multi-Agents: A Survey of Progress and Challenges

By Taicheng Guo, Xiuying Chen, Yaqi Wang, Ruidi Chang, Shichao Pei, Nitesh V. Chawla, Olaf Wiest, Xiangliang Zhang. International Joint Conference on Artificial Intelligence, 2024

1,039 citations recorded by the source corpus.

Large Language Models (LLMs) have achieved remarkable success across a wide array of tasks. Due to their notable capabilities in planning and reasoning, LLMs have been utilized as autonomous agents for the automatic execution of various tasks. Recently, LLM-based agent systems have rapidly evolved from single-agent planning or decision-making to operating as multi-agent systems, enhancing their ability in complex problem-solving and world simulation. To offer an overview of this dynamic field, we present this survey to offer an in-depth discussion on the essential aspects and challenges of LLM-based multi-agent (LLM-MA) systems. Our objective is to provide readers with an in-depth understanding of these key points: the domains and settings where LLM-MA systems operate or simulate; the profiling and communication methods of these agents; and the means by which these agents develop their skills. For those interested in delving into this field, we also summarize the commonly used datasets or benchmarks. To keep researchers updated on the latest studies, we maintain an open-source GitHub repository (github.com/taichengguo/LLM_MultiAgents_Survey_Papers), dedicated to outlining the research of LLM-MA research.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/8f070e301979732e0dd73f6aa6170309cf73aa7d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Marinka Zitnik
2. Jure Leskovec

Citations in the corpus

• Why Do Multi-Agent LLM Systems Fail? (2025, neutral): “MASs are structured to coordinate efforts, enabling task decomposition, performance parallelization, context isolation, specialized model ensembling, and diverse reasoning discussions (He et al., 2024b; Mandi et al., 2023; Zhang et al., 2024; Du et al., 2023; Park et al., 2023a; Guo et al., 2024a).”
• A Survey on the Memory Mechanism of Large Language Model-based Agents (2024, neutral): “It designs a web search strategy for acquiring related external knowledge.”
• Empowering Biomedical Discovery with AI Agents (2024, positive): “…aspirational, advances in agent-based AI pave the way to the development of AI agents as conversable systems capable of reflective learning and reasoning that coordinate large language models (LLMs), machine learning (ML) tools, experimental platforms, or even combinations of them [1– 4] (Figure 1).”
• Harnessing the Wisdom of LLM Crowds through Complementarity-Driven Iterative Collaboration (2026, unknown): “Conversely, MAS frameworks orchestrate collaboration through the role-playing of agents and the use of either predefined or automatically generated team workflows (Li et al. 2024, Guo et al. 2024).”
• CodeARC: Benchmarking Reasoning Capabilities of LLM Agents for Inductive Program Synthesis (2025, unknown): “Recent advances in Large Language Models (LLMs) have led to the emergence of autonomous agents capable of decision-making, multi-step planning, tool use, and iterative self-improvement through interaction and feedback (Chen et al., 2023; Liu et al., 2023e; 2024b; Guo et al., 2024; Xi et al., 2025).”