The video tutorial demonstrates how to build a fully local agent using the Kogito V1 language model, which outperforms Llama 4, by guiding viewers through the setup process with LM Studio and Python environment preparation. It covers synchronous and asynchronous interactions with the model, including web search integration, and culminates in creating an agent class for efficient user query handling and tool management.
In this video, the presenter demonstrates how to build a fully local agent using the Kogito V1 language model (LM), which reportedly outperforms Llama 4 in various benchmarks. The focus is on utilizing the 32 billion parameter Kogito V1 model, and the tutorial guides viewers through the process of downloading and running the model locally using LM Studio. The presenter explains how to access the LM Studio download page, load models, and navigate the interface, ensuring that even beginners can follow along.
Once LM Studio is set up, the video details how to download the Kogito V1 model and prepare the Python environment for running the local LM. The presenter emphasizes the importance of using a virtual environment and provides step-by-step instructions for installing necessary packages. After confirming that the model is downloaded and the LM Studio server is running, the presenter demonstrates how to connect to the server and perform synchronous completion requests to interact with the model.
The tutorial then transitions to asynchronous completion and streaming responses, showcasing how to handle real-time data from the model. The presenter explains the differences between synchronous and asynchronous requests, highlighting the benefits of using streaming for more dynamic interactions. By providing code snippets, the video illustrates how to implement these features effectively, making it easier for viewers to understand the practical applications of the Kogito V1 model.
Next, the video delves into building tools and agents, focusing on function calling and tool usage with the local LM. The presenter introduces the SER API for web searches, guiding viewers through the process of setting up an API key and making asynchronous requests. The tutorial emphasizes the importance of structuring the data received from the API to ensure it can be effectively utilized by the LM, demonstrating how to create a clean output format for the model to process.
Finally, the presenter consolidates the previous steps into an agent class that encapsulates the entire process, allowing for efficient interaction with the local LM and the web search tool. The agent is designed to handle user queries, manage tool calls, and return responses based on the information retrieved. The video concludes by reiterating the advantages of running LMs locally, emphasizing the flexibility and control it offers users without relying on external APIs. Overall, the tutorial provides a comprehensive guide for building a local agent using the Kogito V1 model, showcasing its capabilities and potential applications.