Large language model (LLM)-based multi-agent systems (MASs) have shown impressive performance in solving a wide range of complex problems. However, previous studies mainly focus on designing customized MAS for specific tasks, while a critical research problem remains unclear: Do LLM agent groups exhibit a form of “general intelligence” that reflects their general ability across various tasks? Researchers have found a Collective Intelligence (CI) factor in human groups that captures their general capability. Inspired by this, in this study, we aim to investigate whether an analogous CI factor also exists in LLM agent groups, which is crucial for building generalizable MAS. Motivated by human cognitive psychology experiments, we construct 108 LLM agent groups with diverse group sizes, LLM compositions, and communication topologies. We systematically evaluate these groups across a wide range of tasks and analyze their performances. Our results demonstrate that an Artificial Collective Intelligence (ACI) factor can be extracted from LLM agent groups to predict the generalization performance on new tasks. Inspired by this, we train a model to predict the ACI based on the features of MAS, and show that it can be used as a plug-in to enhance the generalization ability of MAS optimization methods.