agent and foster cooperative relationships among them. Many applications of multi-agent cooperation
have successfully been built upon this foundation [27; 406; 407; 448]. Furthermore, AgentVerse [410]
constructs a versatile, multi-task-tested framework for group agents cooperation. It can assemble a
team of agents that dynamically adapt according to the task’s complexity. To promote more efficient
collaboration, researchers hope that agents can learn from successful human cooperation examples
[109]. MetaGPT [405] draws inspiration from the classic waterfall model in software development,
standardizing agents’ inputs/outputs as engineering documents. By encoding advanced human process
management experience into agent prompts, collaboration among multiple agents becomes more
structured.
However, during MetaGPT’s practical exploration, a potential threat to multi-agent cooperation has
been identified. Without setting corresponding rules, frequent interactions among multiple agents can
TheRiseandPotentialofLargeLanguageModel BasedAgents