AI Agents from Scratch, build an agent step by step with a local LLM (github.com)

A new repository has been launched to provide a hands-on, local-first approach to understanding AI agents by guiding users through the step-by-step construction of a simple agent using a local large language model (LLM). Without relying on complex frameworks or cloud APIs, this resource offers a transparent look at how AI agents operate, emphasizing core concepts like loops, state, and constraints. It comprises ten lessons, each progressively adding new capabilities such as decision-making logic, memory management, and planning techniques, culminating in an interactive learning experience that allows coders to develop a robust agent from scratch. This initiative is significant for the AI/ML community as it demystifies the process of building AI agents, making it accessible for developers who may feel lost in existing frameworks like LangChain. The repository emphasizes an educational focus over rapid implementation, promoting a deeper understanding of the underlying principles rather than just providing a ready-to-use solution. With a clear structure designed to build knowledge gradually, it caters particularly to learners and educators seeking a solid foundation in agent development while avoiding the overwhelming complexity often associated with the field.