Local LLM – AI Agents With CrewAI

In this video, viewers are shown how to set up a free local agentic workflow using CrewAI and Ollama. The tutorial, presented by Analytics Camp, outlines a five-step process to automate tasks using AI agents. First, the video demonstrates installing Ollama to use the Mistral model, known for its contextual precision. Next, viewers are guided through setting up a virtual environment with Python 3.12 and configuring it to work with Ollama. The third step involves installing CrewAI and necessary tools. The tutorial then explains how to create a project directory and set up two files: a model file to specify the LLM parameters and a shell script to pull and execute the model. The final step involves creating AI agents and defining their tasks using Python. The tutorial emphasizes the refinement method, where multiple AI agents complement and refine each other’s responses. The video includes detailed instructions on configuring environment variables, creating and executing script files, and using CrewAI functionalities to build and deploy AI agents. The tutorial concludes with a demonstration of the agents’ performance, showcasing their ability to perform tasks and produce accurate, refined outputs. The straightforward setup is highlighted for its versatility, allowing users to create multiple agents for various tasks, enhancing productivity and accuracy.