  Recommender models excel at providing domain-specific item recommendations by
leveraging extensive user behavior data. Despite their ability to act as
lightweight domain experts, they struggle to perform versatile tasks such as
providing explanations and engaging in conversations. On the other hand, large
language models (LLMs) represent a significant step towards artificial general
intelligence, showcasing remarkable capabilities in instruction comprehension,
commonsense reasoning, and human interaction. However, LLMs lack the knowledge
of domain-specific item catalogs and behavioral patterns, particularly in areas
that diverge from general world knowledge, such as online e-commerce.
Finetuning LLMs for each domain is neither economic nor efficient.
  In this paper, we bridge the gap between recommender models and LLMs,
combining their respective strengths to create a versatile and interactive
recommender system. We introduce an efficient framework called
\textbf{InteRecAgent}, which employs LLMs as the brain and recommender models
as tools. We first outline a minimal set of essential tools required to
transform LLMs into InteRecAgent. We then propose an efficient workflow within
InteRecAgent for task execution, incorporating key components such as memory
components, dynamic demonstration-augmented task planning, and reflection.
InteRecAgent enables traditional recommender systems, such as those ID-based
matrix factorization models, to become interactive systems with a natural
language interface through the integration of LLMs. Experimental results on
several public datasets show that InteRecAgent achieves satisfying performance
as a conversational recommender system, outperforming general-purpose LLMs. The
source code of InteRecAgent is released at https://aka.ms/recagent.
