This is one of the easiest ways to understand what an AI agent actually is. Instead of only chatting, the workflow can choose a tool, execute it, and use memory to keep context across turns.
What it does
- Receives messages through a chat trigger
- Uses Google Gemini to interpret the intent
- Selects and calls the right tool
- Keeps short-term memory for follow-up questions
- Returns the result back into the chat flow
Why it is useful
Beginners often hear the word “agent” without seeing a simple implementation. This workflow makes the concept concrete: an LLM plus tools plus memory.
It is useful as a starting point for:
- learning how tool-calling works
- building a demo for your team
- prototyping a more capable assistant later
Prerequisites
- n8n, cloud or self-hosted
- Google AI Studio API key
How to use
- Import the template and add your Google AI API key.
- Optionally edit the system message to set the persona and tone.
- Test with a simple question like “What’s the weather in Paris?”
- Add more tools, such as Gmail or Google Calendar, once the base flow works.
Caveats
- It is a starter workflow, not a production agent
- Tool quality matters more than model choice once the workflow grows
- You still need monitoring and guardrails for real-world use
Bottom line
If you want to understand agents by building one, this is a strong place to start. It is small enough to learn quickly but real enough to show the core pattern. an overseas service; mainland China access typically requires an international network.
- The free tier is enough to start; watch model quotas and cost at scale.