AI Agent tutorial from the basics to building multi-agent teams

We’ve been building OSS dev tools for over 7 years. From that experience, we’ve seen that tutorials which combine key concepts with hands-on code examples are the most effective way to understand the why and how of agent development.

Tutorial: https://voltagent.dev/tutorial/introduction/

GitHub Repo: https://github.com/voltagent/voltagent

Tutorial Source Code: https://github.com/VoltAgent/voltagent/tree/main/website/src/pages/tutorial

What we implemented:

1 – The Chatbot Problem

Why most chatbots are limited and what makes AI agents fundamentally different.

2 – Tools: Give Your Agent Superpowers

Let your agent do real work: call APIs, send emails, query databases, and more.

3 – Memory: Remember Every Conversation

Persist conversations so your agent builds context over time.

4 – MCP: Connect to Everything

Using MCP to integrate GitHub, Slack, databases, etc.

5 – Subagents: Build Agent Teams

Create specialized agents that collaborate to handle complex tasks.

It’s all built using VoltAgent, our TypeScript-first open-source AI agent framework.(I'm maintainer) It handles routing, memory, observability, and tool execution, so you can focus on logic and behavior.

Although the tutorial uses VoltAgent, the core ideas tools, memory, coordination are framework-agnostic. So even if you’re using another framework or building from scratch, the steps should still be useful.

We’d love your feedback, especially from folks building agent systems. If you notice anything unclear or incomplete, feel free to open an issue or PR. It’s all part of the open-source repo.