This tutorial provides a step-by-step guide to building a lightweight personal AI agent inspired by the nanobot architecture in Google Colab. The approach focuses on recreating core components—such as provider abstractions, tool registration, session memory, and lifecycle hooks—rather than relying on heavy external frameworks. Key features include a tool registry for Python functions, token-budgeted memory management, and an MCP-style tool server for external capabilities. The guide includes a complete Python implementation that supports both live OpenAI-compatible models and a deterministic mock provider for offline testing.
Main topics covered:
- Provider abstraction for multi-model compatibility
- Automated tool schema generation using decorators
- Session-specific memory with token budgeting
- Lifecycle hooks for auditing and timing
- Dynamic skill loading and MCP server connection
Main topics covered:
- Provider abstraction for multi-model compatibility
- Automated tool schema generation using decorators
- Session-specific memory with token budgeting
- Lifecycle hooks for auditing and timing
- Dynamic skill loading and MCP server connection
