In this hands-on tutorial, we build a lightweight personal AI agent inspired by the core architecture of nanobot, a compact and modular AI agent framework. Every component is designed to be understandable and fully runnable in Google Colab—no external agent frameworks required. We start with a provider abstraction, then progressively implement tool registration, session memory, lifecycle hooks, custom skills, and an MCP-style tool server.
As we build from scratch, we gain a clear, inside-out understanding of how messages, tools, memory, and model responses interact within a practical agent loop. By the end, you'll have a working, customizable AI agent that you can extend for your own projects—all from a free Colab notebook.
Building the Provider Abstraction and Mock LLM
We begin by defining a generic provider interface that abstracts the underlying language model. This abstraction allows the agent to work with different LLMs—whether Google Gemini, Groq, a local model, or a mock for testing—without modifying the core loop. In practice, you can swap providers by implementing a small set of methods: generate(), stream(), and getmodelinfo().
To validate the architecture, we build a mock LLM that returns predefined responses. This mock is invaluable for testing tool calls, memory behavior, and skill execution without incurring API costs or relying on external services. It also makes the tutorial self-contained and reproducible.
Tool Registration and Function Calling
Tools are the agent's interface to the outside world. We implement a simple tool registry that accepts Python functions with typed signatures and automatically converts them into JSON schemas compatible with function-calling APIs (e.g., OpenAI, Gemini). Each tool includes a name, description, parameter definitions, and a callable handler.
During each turn, the agent generates a structured tool call request. The execution engine dispatches the call to the appropriate handler, captures the result, and feeds it back into the conversation context. This round-trip pattern is the core of the agent's ability to act—whether it's querying a database, making an API call, or performing a computation.
Session Memory and Context Management
A stateless agent is of little use in extended conversations. We implement a session manager that stores message history, tool call results, and user metadata across interactions. The memory module supports both short-term (in-context) and long-term (persistent) storage strategies.
To keep token usage efficient, we include a configurable message window that discards old history beyond a certain limit. We also introduce a summarization hook that optionally compresses past exchanges into a compact summary, preserving key facts without bloating the prompt.
Lifecycle Hooks and Custom Skills
Lifecycle hooks allow developers to inject custom behavior at key points in the agent's execution: before model inference, after tool completion, or when errors occur. These hooks make the agent observable and extensible—for example, logging, monitoring, or dynamically adjusting tool availability.
Skills are reusable, composable units of capability that bundle one or more tools with associated prompts and validation logic. We design a simple skill interface that registers tools, defines trigger phrases, and can optionally override the default system prompt. Skills can be loaded, unloaded, or chained together dynamically, enabling the agent to grow its abilities over time.
MCP-Style Tool Server
The Model Context Protocol (MCP) is an emerging standard for exposing tools and data sources to LLMs in a uniform way. We implement a lightweight MCP-style server within the Colab notebook that listens for tool invocations from the agent over local HTTP or WebSocket.
This server registers the same tools as the in-process registry but exposes them via a standardized interface, making it easy to connect external clients, other agents, or even a frontend UI. The MCP server communicates using JSON-RPC messages, and we include a simple client that the agent uses to discover and call remote tools.
Bringing It All Together: The Agent Loop
With all components in place, we assemble the final agent loop:
- Initialize provider, memory, tool registry, and MCP server.
- Load skills and register their tools.
- Enter conversation loop:
- Receive user input.
- Run pre-hooks (e.g., logging, guardrails).
- Query the LLM (or mock) with current context.
- If tool call is requested: execute, capture result, append to history.
- If response is text: return to user.
- Run post-hooks (e.g., summarization, memory cleanup).
- Persist session state for later resumption.
The loop is deliberately simple but powerful—each component is a pluggable module that you can replace, upgrade, or extend.
Try It in Google Colab
The complete code for this nanobot-style agent is available in a Colab notebook. It runs entirely in the free tier and requires no API keys unless you choose to connect a real LLM provider. The notebook is fully commented, with cell-by-cell explanations of each architectural decision.
In 2026, building your own AI agent from scratch is more accessible than ever—and more valuable. Understanding the internals of tool calling, memory management, and provider abstraction gives you the flexibility to build customized agents that no off-the-shelf framework can match. This tutorial equips you with both the knowledge and the code to do just that.
via MarkTechPost