Multi-Agent LLM Plugin

Improving the way complex queries are handled by using a multi-agent AI system to deliver quicker, more accurate responses.

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Inspiration

The inspiration behind the Multi-Agent LLM System came from the limitations we noticed in existing chatbot technologies like ChatGPT and Gemini when handling complex, multi-step queries. Users often need to break down their questions or simplify them into smaller sections just to get accurate responses, which can lead to a fragmented and inefficient experience. We wanted to create a system that could automate the handling of these complex queries, offering a more seamless and efficient way to deliver answers without relying on multiple plugins.

What it does

The Multi-Agent LLM System addresses the challenge of complex, multi-step queries by distributing tasks among specialized AI agents. Each agent focuses on a specific part of the query. The system combines the strengths of multiple AI models—such as ChatGPT, Gemini, and Ollama—to work together to provide faster and more comprehensive answers in a smooth and integrated manner.

How we built it

  • ChatGPT acts as the main supervisor, managing the query process and directing tasks to the specialized agents.
  • Gemini and Ollama handle the query-related tasks. ## Challenges We Ran Into
  • New to Plugin Development: As newcomers to plugin development, we had to learn a lot about how plugins interact and integrate with each other.
  • Integrating Three AI Models: We found it challenging to integrate three different AI models (ChatGPT, Gemini, and Ollama) to work seamlessly together.
  • Context and Instruction Integration: We struggled to find a solution for integrating the context and instructions for Gemini and Ollama after ChatGPT determines the tasks for each. Ensuring that each AI agent receives the right context and direction to perform their task effectively without confusion or loss of information was a key hurdle. ## Accomplishments that we're proud of
  • While the plugin is not fully functional yet, we are proud of the logical foundation we established in the code. We did our best to follow a coherent and structured approach, which we believe will provide a strong basis for further development.
  • We gained valuable experience in plugin integration and multi-agent system design, learning how to coordinate the behavior of different AI models. ## What We Learned We learned the basics of plugin building in Hexabot and how to create a chatbot using different blocks, gaining insights into plugin development and task delegation in multi-agent systems. ## What's Next for Multi-Agent LLM Plugin
  • Make it functional: The first priority is to ensure the plugin is fully operational.
  • User customization: In the future, we plan to allow users to choose different AI agents based on their needs.
  • Code improvement: We will continue optimizing and refining the code to enhance performance and efficiency.

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