Multi-Agent Collaboration: Harnessing the Power of Intelligent LLM
  Agents
Yashar Talebirad Amirhossein Nadiri
In this paper, we present a novel framework for enhancing the capabilities of
large language models (LLMs) by leveraging the power of multi-agent systems.
Our framework introduces a collaborative environment where multiple intelligent
agent components, each with distinctive attributes and roles, work together to
handle complex tasks more efficiently and effectively. We demonstrate the
practicality and versatility of our framework through case studies in
artificial general intelligence (AGI), specifically focusing on the Auto-GPT
and BabyAGI models. We also examine the "Gorilla" model, which integrates
external APIs into the LLM. Our framework addresses limitations and challenges
such as looping issues, security risks, scalability, system evaluation, and
ethical considerations. By modeling various domains such as courtroom
simulations and software development scenarios, we showcase the potential
applications and benefits of our proposed multi-agent system. Our framework
provides an avenue for advancing the capabilities and performance of LLMs
through collaboration and knowledge exchange among intelligent agents.