Build a Nanobot-style AI agent in Google Colab from scratch
A hands-on tutorial published this week walks developers through building a lightweight AI agent directly in Google Colab, eschewing heavy frameworks in favor of clear, runnable code. The focus is on recreating core components—provider abstraction, tool registration, session memory, lifecycle hooks, skills, and an MCP-style server—so each piece can be inspected and modified.
From provider abstraction to real-time interaction
The guide begins by defining a base provider class that standardizes how messages, tools, and model responses interact within a single agent loop. Instead of locking into a single vendor, it uses an OpenAI-compatible provider that works with a range of gateways—OpenRouter, DeepSeek, Together, vLLM, LM Studio, Ollama’s /v1 endpoint, and others—so users can swap models without rewriting the agent logic. The provider class handles tool calling, token usage tracking, and response normalization, ensuring consistent behavior across different backends.
Session memory and lifecycle hooks in practice
Next, the tutorial layers on session memory and lifecycle hooks, two features that turn a stateless chat into a persistent assistant. Session memory is implemented as a simple in-memory store that preserves conversation context between calls, while lifecycle hooks let developers inject code at key points—before a tool runs, after a response is generated, or when an error occurs. These hooks make it easier to add logging, validation, or custom logic without cluttering the main agent loop.
Skills and MCP servers in the same notebook
Finally, the guide demonstrates how to register “skills” as modular Python functions and expose them through an MCP-style server running inside Colab. This allows the agent to call local utilities—file operations, web searches, code execution—while keeping the notebook self-contained. The MCP server pattern also opens the door to future extensions, letting developers plug in new tools without altering the core agent architecture.
Source: MarkTechPost. AI-assisted editorial synthesis — TechnoExpress.