CoSearchAgent: A Lightweight Collaborative Search Agent with Large
  Language Models
Peiyuan Gong Jiamian Li Jiaxin Mao
Collaborative search supports multiple users working together to accomplish a
specific search task. Research has found that designing lightweight
collaborative search plugins within instant messaging platforms aligns better
with users' collaborative habits. However, due to the complexity of multi-user
interaction scenarios, it is challenging to implement a fully functioning
lightweight collaborative search system. Therefore, previous studies on
lightweight collaborative search had to rely on the Wizard of Oz paradigm. In
recent years, large language models (LLMs) have been demonstrated to interact
naturally with users and achieve complex information-seeking tasks through
LLM-based agents. Hence, to better support the research in collaborative
search, in this demo, we propose CoSearchAgent, a lightweight collaborative
search agent powered by LLMs. CoSearchAgent is designed as a Slack plugin that
can support collaborative search during multi-party conversations on this
platform. Equipped with the capacity to understand the queries and context in
multi-user conversations and the ability to search the Web for relevant
information via APIs, CoSearchAgent can respond to user queries with answers
grounded on the relevant search results. It can also ask clarifying questions
when the information needs are unclear. The proposed CoSearchAgent is highly
flexible and would be useful for supporting further research on collaborative
search. The code and demo video are accessible.