I’ve been exploring the fascinating idea of enabling multiple Large Language Models (LLMs) to collaboratively discuss and arrive at a final decision, somewhat akin to the concept of Agent-to-Agent (A2A) communication. My hypothesis is that by fostering a dynamic where several LLMs can exchange insights, challenge assumptions, and synthesize their individual perspectives, we could potentially unlock significantly enhanced performance and more robust outcomes compared to relying on a single model.
For those who have ventured into this area before, I would be incredibly grateful if you could share your experiences and insights on it. Thanks in advance.
Hi @Syeed_Talha
This is a fascinating project! To help me understand better and offer more targeted advice, could you tell me What specific problem are you trying to solve or what kind of application are you aiming to build with this collaborative LLM system? (e.g., complex reasoning, creative writing, research, strategic planning?)
For starting to build, you can look into:
Google’s Agent Development Kit (ADK) and Vertex AI Agent Builder: These are designed to help you orchestrate multiple agents and are optimized for Google’s ecosystem.
Open-source frameworks like LangChain, CrewAI, and LangGraph: These provide flexible architectures for building multi-agent systems and often integrate well with various LLMs
Thank you