In this tutorial, we build a lightweight personal AI agent inspired by the core architecture of nanobot, while keeping every part understandable and runnable in Google Colab. We start from the provider abstraction, then move through tool registration, session memory, lifecycle hooks, skills, and an MCP-style tool server. As we progress, we do not just use an external agent framework; we recreate the core building blocks ourselves so we can clearly see how messages, tools, memory, and model responses work together within a practical agent loop.
Building the Provider Abstraction and Mock LLM
import subprocess, sys
def _pip_install(*pkgs):
try:
