Have you ever tried to get into AI agents and felt like you were just copying and pasting code without really understanding what's going on?
There are plenty of tutorials that get you from zero to a working chatbot in 10 minutes using a framework. I've gone through several of them. They work — but the moment something breaks or behaves unexpectedly, you have no idea where to start looking, because the framework is doing everything for you.
So I decided to learn it from scratch. No frameworks to start with. Just Python, a local LLM running on my laptop, and a goal to actually understand each piece before moving to the next.
This is a 9-part series documenting that journey — what I covered, what I built, and what clicked along the way.
Why from scratch?
When you build things from the ground up, something interesting happens — by the time you reach multi-agent systems, you realise it's just the same tool loop you built a few posts earlier, running on its own. Nothing feels like magic anymore, because you've already built every piece yourself.
That's the goal here. By the end, when you pick up LangChain, AutoGen, or any other framework, you'll know exactly what it's doing under the hood.
What you'll need
Just two things to follow along: Python and Ollama. Ollama lets you run LLMs locally — it's free, nothing goes to the cloud, and you can swap models with a single command. No API keys, no sign-ups, no costs. Everything runs on your laptop.
Later posts introduce a couple of extra libraries, but we'll add those only when we need them.
What's in the series
Here's what each post covers:
| Post | Topic |
|---|---|
| #0 | Introduction — this post |
| #1 | LLM Basics + Prompting |
| #2 | Tool Use & Function Calling |
| #3 | RAG (Retrieval-Augmented Generation) |
| #4 | ReAct Agents |
| #5 | Memory Systems |
| #6 | Multi-Agent Orchestration |
| #7 | MCP (Model Context Protocol) |
| #8 | Evals + Safety |
Each post has real runnable code and builds on the one before. The focus is on understanding things deeply — every concept gets a why before the how.
Post #1 starts with LLM basics and prompting — what tokens are, why temperature matters, and hands-on exercises you can run straight away. See you there. 🚀
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