Build a Nanobot-Style AI Agent in Google Colab

This tutorial guides you through building a lightweight personal AI agent inspired by the core architecture of nanobot, 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 recreate the core building blocks ourselves to clearly see how messages, tools, memory, and model responses work together within a practical agent loop.

We set up the environment, install optional dependencies, and prepare the imports needed for the full tutorial. We define a provider abstraction that allows the agent to work with either a real OpenAI-compatible model or a deterministic mock provider. We also build the normalized response structures so the rest of the agent loop can work independently of the backend model.

We create a tool system that allows ordinary Python functions to become callable agent tools. We use type hints and docstrings to automatically generate JSON-style tool schemas, which makes the framework easier to extend. We also add practical offline tools such as a calculator, a time lookup tool, a Python execution tool, a web search stub, and token-budgeted memory.

We implement the lifecycle hooks, skill structure, MCP-style server adapter, and the main agent loop. We use hooks to observe or modify the agent’s behavior without changing the core runtime. We then run the central loop where the model receives messages, requests tools when needed, consumes tool results, and finally returns a plain-text answer.

We wrap the lower-level agent in a Nanobot-style interface that feels more like a real SDK. We add support for registering tools, loading skills, connecting MCP-style servers, and running the bot with session-specific memory. We also define several demo functions that show basic chat, tool calling, multi-step execution, memory, skills, MCP tools, and hooks in action.

We add simple long-term key-value memory tools to store and recall facts. We define the main execution function that creates the bot, registers custom tools, and runs every demo from start to finish. We complete the tutorial by showing how the rebuilt nanobot-style architecture connects to the real nanobot package for future extension.

The ability to build and customize AI agents like this has significant implications for developers and businesses looking to integrate AI into their applications. By providing a clear understanding of how the different components of an AI agent work together, this tutorial empowers readers to create their own agents tailored to specific use cases. As AI technology continues to evolve, the demand for skilled developers who can design and implement AI solutions will only grow.