  Multi-agent collaboration with Large Language Models (LLMs) demonstrates
proficiency in basic tasks, yet its efficiency in more complex scenarios
remains unexplored. In gaming environments, these agents often face situations
without established coordination protocols, requiring them to make intelligent
inferences about teammates from limited data. This problem motivates the area
of ad hoc teamwork, in which an agent may potentially cooperate with a variety
of teammates to achieve a shared goal. Our study focuses on the ad hoc teamwork
problem where the agent operates in an environment driven by natural language.
Our findings reveal the potential of LLM agents in team collaboration,
highlighting issues related to hallucinations in communication. To address this
issue, we develop CodeAct, a general agent that equips LLM with enhanced memory
and code-driven reasoning, enabling the repurposing of partial information for
rapid adaptation to new teammates.
