Multiple LLM Agents perform analysis on an ongoing conversation. Next JS project with Vercel AI SDK

The video showcases a Next.js project using Vercel AI SDK for multi-agent conversation analysis, with each agent assigned specific tasks in analyzing an ongoing conversation. It also delves into the technical details of the project’s architecture, demonstrating the code structure, component interactions, and dynamic addition/removal of conversation summary components, while promoting the creator’s Patreon for access to code files, courses, and opportunities for one-on-one interactions.

The video showcases a multi-agent conversation analysis project built with Next.js and Vercel AI SDK. It involves multiple agents analyzing an ongoing conversation. The conversation is split among four agents, each assigned a specific task such as analyzing pros and cons or identifying unresolved issues. The project allows for customization of system messages for each agent, such as translating the conversation to different languages.

The main focus of the project is on Charles Babbage, a historical figure known for his work on early computer concepts. The video briefly touches on Babbage’s invention of the analytical engine, considered a precursor to modern computers. It discusses why Babbage did not build the analytical engine, hinting at potential reasons related to feasibility or lack of resources. The conversation then shifts to Babbage’s collaborations, mentioning his interaction with Michael Faraday.

The video delves into the technical details of the project, explaining how the code is structured and how different components interact. It explains the use of Next.js for building pages and APIs, as well as the integration of Vercel AI SDK for handling agent tasks. The demonstration includes a step-by-step explanation of how the chat components are created and managed, with a focus on simulating form interactions for sending messages to agents.

The project’s architecture is highlighted, showcasing the separation of components for different agents and the handling of messages between the main chat and the agents. A key feature is the dynamic addition and removal of conversation summary components based on user interactions. The video also offers insights into setting up API endpoints and managing API calls within the project.

In addition to the technical aspects, the video promotes the creator’s Patreon where viewers can access the code files and courses related to the project. The presenter emphasizes the benefits of becoming a patron, including access to code files, courses like the THX Master Class, and opportunities for one-on-one interactions. The video concludes with a call to action for viewers to explore the project further and engage with the creator’s content on Patreon.