How to build an AI agent in Google Colab without frameworks: a tutorial with memory and MCP

In Google Colab, you can build a fully-fledged AI agent — with memory, tools, and an MCP server — without importing any external frameworks. A new tutorial shows how to recreate the nanobot architecture from scratch in pure Python and understand exactly what happens "under the hood" of the agent cycle.

What is the nanobot approach

Nanobot is a minimalist approach to building AI agents: no heavy dependencies like LangChain or AutoGPT, only transparent building blocks that the developer fully controls. The tutorial authors deliberately avoid ready-made frameworks to break down the agent cycle into its component parts. The name itself alludes to the idea of small, specialized units instead of monolithic systems. When an agent is built manually, you know exactly where bottlenecks arise — in memory, in routing tool calls, or in prompt formation. Ready-made frameworks hide these details and seriously complicate debugging in non-standard situations.

What makes up an agent

The tutorial is broken down into sequential stages. Each adds a new layer of functionality on top of the previous one:

• Provider abstraction — a unified interface independent of any specific LLM: OpenAI, Anthropic, local model, or any other
• Tool registration — a mechanism that allows the agent to call external functions: search, calculator, file operations
• Session memory — storing conversation history within a single session with context length management
• Lifecycle hooks — event interception before tool calls, after model response, and on error
• Skills — reusable sets of behaviors that attach to the agent as separate modules
• MCP-style server — a local tool server inspired by Anthropic's Model Context Protocol

Each block is built from scratch — this allows you to see how messages, tool calls, and model responses connect into a single working cycle.

Why understand the fundamentals

Most developers work with agents through high-level wrappers — LangChain, CrewAI, AutoGen. This is convenient for quick starts, but creates problems when something goes wrong. When an agent "breaks" — loses context, gets stuck in a loop, or calls the wrong tool — determining the cause through multiple layers of abstraction is extremely difficult. Understanding basic mechanisms solves this problem: you see the failure point and fix it precisely, rather than restarting the entire stack hoping it "fixes itself."

"We recreate each building block to see how messages, tools, memory, and model responses come together," this is how the authors formulate the tutorial's goal.

A provider-agnostic cycle makes the agent portable: changing the LLM provider requires no business logic rewrite.

Google Colab as a platform

The entire tutorial runs in Google Colab — zero infrastructure: no need to set up a local environment, Docker, or a cloud server. Just open the notebook and run the cells in order. For learning, this is the optimal format: Colab isolates experiments, allows rapid iteration, and shows output at each step immediately. The built agent can be scaled to real LLM providers in the next notebook.

What this means

The tutorial fills a real gap between "run a ready-made framework" and "understand how agents work from the inside." If you plan to build agent products, this foundational understanding will save many hours of debugging.