In recent years, large language model (LLM)–based agents have garnered widespread attention across various fields. Their impressive capabilities, such as natural language communication,21,23 instruction following,26,28 and task execution,22,38 have the potential to expand both the format of information carriers and the way in which information is exchanged. LLM-based agents can now evolve into domain experts, becoming novel information carriers with domain-specific knowledge.1,28 For example, a Travel Agent can retain travel-related information within its parameters. LLM-based agents are also showcasing a new form of information exchange, facilitating more intuitive and natural interactions with users through dialogue and task execution.24,34 Figure 1 shows an example of these capabilities, in which users engage in dialogue with a Travel Agent to obtain information and complete their travel plans.

The new kind of Agent-oriented information system, exemplified by GPTs, urges us to inspect the information system infrastructure to support Agent-level information processing and to adapt to the characteristics of Large Language Model (LLM)-based Agents, such as interactivity. In this work, we envisage the prospect of the recommender system on LLM-based Agent platforms and introduce a novel recommendation paradigm called Rec4Agentverse, comprised of Agent Items and Agent Recommender. Rec4Agentverse emphasizes the collaboration between Agent Items and Agent Recommender, thereby promoting personalized information services and enhancing the exchange of information beyond the traditional user-recommender feedback loop. Additionally, we prospect the evolution of Rec4Agentverse and conceptualize it into three stages based on the enhancement of the interaction and information exchange among Agent Items, Agent Recommender, and the user. A preliminary study involving several cases of Rec4Agentverse validates its significant potential for application. Lastly, we discuss potential issues and promising directions for future research.