Agentic AI with GraphRAG and MCP tools use

Learn to create a self-reflective agent with open source tools and your choice of foundation model (local, OpenAI, Claude, Gemini) based on hardware and preferences. The example we will use is of creating a chatbot to ask questions about your grandmother’s handwritten recipes. An ETL (extract, transform, and load) pipeline will interpret the recipes using a multimodal LLM and store them in a database, both graph and vector databases. And, then we will create a CrewAI chatbot agent that will query this database using a GraphRAG (Graph-enabled Retrieval Augmented Generation) when asked questions about the recipes and will use MCP (Model Context Protocol) tools to perform additional tasks with the recipes. During this experience, we will learn about functional Agentic AI patterns, frameworks, evaluation/testing methods, and other considerations. We will also learn about ETL, embeddings, APIs, and different foundation models.