Large Language Model based Multi-Agents: A Survey of Progress and
  Challenges
Taicheng Guo Xiuying Chen Yaqi Wang Ruidi Chang Shichao Pei Nitesh V. Chawla Olaf Wiest Xiangliang Zhang
Large Language Models (LLMs) have achieved remarkable success across a wide
array of tasks. Due to the impressive planning and reasoning abilities of LLMs,
they have been used as autonomous agents to do many tasks automatically.
Recently, based on the development of using one LLM as a single planning or
decision-making agent, LLM-based multi-agent systems have achieved considerable
progress in complex problem-solving and world simulation. To provide the
community with an overview of this dynamic field, we present this survey to
offer an in-depth discussion on the essential aspects of multi-agent systems
based on LLMs, as well as the challenges. Our goal is for readers to gain
substantial insights on the following questions: What domains and environments
do LLM-based multi-agents simulate? How are these agents profiled and how do
they communicate? What mechanisms contribute to the growth of agents'
capacities? For those interested in delving into this field of study, we also
summarize the commonly used datasets or benchmarks for them to have convenient
access. To keep researchers updated on the latest studies, we maintain an
open-source GitHub repository, dedicated to outlining the research on LLM-based
multi-agent systems.