  LLM-based agents have gained considerable attention for their decision-making
skills and ability to handle complex tasks. Recognizing the current gap in
leveraging agent capabilities for multi-agent collaboration in recommendation
systems, we introduce MACRec, a novel framework designed to enhance
recommendation systems through multi-agent collaboration. Unlike existing work
on using agents for user/item simulation, we aim to deploy multi-agents to
tackle recommendation tasks directly. In our framework, recommendation tasks
are addressed through the collaborative efforts of various specialized agents,
including Manager, User/Item Analyst, Reflector, Searcher, and Task
Interpreter, with different working flows. Furthermore, we provide application
examples of how developers can easily use MACRec on various recommendation
tasks, including rating prediction, sequential recommendation, conversational
recommendation, and explanation generation of recommendation results. The
framework and demonstration video are publicly available at
https://github.com/wzf2000/MACRec.
