Learn to build practical LLM agents using LlamaBot and Marimo notebooks. This hands-on tutorial teaches the most important lesson in agent development: start with workflows, not technology.

We'll build a complete back-office automation system through three agents: a receipt processor that extracts data from PDFs, an invoice writer that generates documents, and a coordinator that orchestrates both. This demonstrates the fundamental pattern for agent systems—map your boring workflows first, build focused agents for specific tasks, then compose them so agents can use other agents as tools.

By the end, you'll understand how to identify workflows worth automating, build agents with decision-making loops, compose agents into larger systems, and integrate them into your own work. You'll leave with working code and confidence to automate repetitive tasks.

Prerequisites: Intermediate Python, familiarity with APIs, basic LLM understanding. Participants should have Ollama and models installed beforehand (setup instructions provided).

Materials: GitHub repository with Marimo notebooks. Setup uses Pixi for dependency management.

Through the use of NetworkX's API, tutorial participants will learn about the basics of graph theory and its use in applied network science. Starting with a computationally-oriented definition of a graph and its associated methods, we will progress through the following concepts: path and structure finding, visualization, and graph storage on disk. We will also offer tutorial participants the option of one advanced topic overview, including the use of graphs alongside LLMs for knowledge retrieval, scalable alternatives to NetworkX including cuGraph, and the use of linear algebraic translation of graph problems to speed up computations.

In this tutorial, you will learn how to integrate Large Language Models (LLMs) directly into Python programs as thoughtfully-designed core components of the program rather than bolt-on additions. This hands-on session teaches design principles and practical techniques for incorporating LLM outputs into program control flow. We will use LlamaBot, an open-source Python interface to LLMs, focusing on local execution with local and efficient models.