How to build effective LLM-based multi-agent systems?
LLMs Working in Harmony: A Survey on the Technological Aspects of Building Effective LLM-Based Multi Agent Systems
This paper surveys technologies for building effective multi-agent systems powered by Large Language Models (LLMs). It examines how multiple LLMs can work together, focusing on system architecture (how agents are organized), memory (how they retain information), planning (how they decide what to do), and the supporting technologies/frameworks. Key points for LLM-based multi-agent systems include: various architectures like Mixture of Agents (MoA) for improved collaboration, different memory mechanisms like Vector Databases and Retrieval Augmented Generation (RAG) for knowledge access, planning strategies like ReAct for combining reasoning and action, and frameworks such as AutoGen and LangGraph for building and deploying these complex systems. The paper highlights both current capabilities and ongoing challenges in the field.