# Last.FM Spotify playlist creator with Gemini: n8n workflow

> Started by a clock. It calls services over plain HTTP rather than through connectors. It puts Gemini behind a retrieval step, so answers are drawn from...

Source: https://millionrounds.com/workflow/last-fm-spotify-playlist-creator-with-gemini (Millionrounds; every figure on the page links to its filing or article)

[Automation library](https://millionrounds.com/workflows) · [AI assistants](https://millionrounds.com/workflows?category=AI+assistants#library)

# Last.FM Spotify playlist creator with Gemini

Started by a clock. It calls services over plain HTTP rather than through connectors. It puts Gemini behind a retrieval step, so answers are drawn from documents you supply rather than from the model alone. 20 steps, 1 branch: a working afternoon to understand properly.

Medium Calls a model: Gemini Retrieval

[Download free](https://millionrounds.com/login?next=%2Fapp%2Fworkflow%2Flast-fm-spotify-playlist-creator-with-gemini)

**20 steps** Medium: a working afternoon

**1** Branch: places the flow splits

**0** Loops or waits

**0** Services by connector: it calls them over plain HTTP

## The flow

Starts it 1 Model 4 Logic 10 Service 5

The steps in the order they run, from what starts it.

- 1 **Google Gemini Chat Model** Lm Chat Google Gemini *Model*
- 2 **Schedule Trigger** Schedule trigger *Starts it*
- 3 **Simple Memory** Memory Buffer Window *Model*
- 4 **AI Agent** Agent *Model*
- 5 **Structured Output Parser** Output Parser Structured *Model*
- 6 **Date & Time** Date Time *Logic*
- 7 **Split Out the Songs** Split Out *Logic*
- 8 **Get a user's playlists** Spotify *Service*
- 9 **Search tracks by keyword** Spotify *Service*
- 10 **Collecting Playlist Names** Aggregate *Logic*
- 11 **Filtering Track Uri** Set *Logic*
- 12 **Checking If A Playlist Created Today** If *Logic*
- 13 **Add Items to the playlist** Spotify *Service*
- 14 **Create a playlist** Spotify *Service*
- 15 **Set Last.FM Username and API Key** Set *Logic*
- 16 **Get Last.FM Data** Http Request *Service*
- 17 **Split Out Tracks** Split Out *Logic*
- 18 **Splitting Artist Name, Album Name and Track Name** Set *Logic*
- 19 **Setting Up Song Details** Set *Logic*
- 20 **Collecting All Song Names to One Json** Aggregate *Logic*

## How it's built

- **Starts on:** A clock
- **Steps:** 20, with 1 branch
- **Size:** Medium: a working afternoon
- **Connects to:** Nothing by connector; it calls services over plain HTTP
- **Model:** Gemini
- **Retrieval:** Answers are drawn from documents you supply, not from the model alone

## Questions

What does it need to run?

An n8n account, and a key for Gemini. You connect your own accounts after importing it; none are included.

What starts it?

It starts on a clock.

Does it use AI?

Yes. It calls Gemini. Its answers draw on documents you give it, not on the model alone. Model calls are billed by the provider, not by n8n.

How long does it take to set up?

Plan an afternoon. 20 steps is enough that you should read each one before running it.

How do I import it?

Copy the workflow, open a new workflow in n8n and paste. The steps appear on the canvas, ready for your own accounts.

**Before you run it**

Nothing here has been tested by us. It is somebody's working automation, read and described from its own steps. Treat the connections it wants as a list of accounts it will touch, and read the 20 steps before you point it at anything that matters.

## Run it in n8n

- **Create your n8n account** Free to start. n8n is where the workflow runs. [Create an n8n account ↗](https://millionrounds.com/n8n)
- **Download the workflow** Free with a Millionrounds account. [Download free](https://millionrounds.com/login?next=%2Fapp%2Fworkflow%2Flast-fm-spotify-playlist-creator-with-gemini)
- **Paste it into n8n** Open a new workflow in n8n and paste. Then connect your own accounts.

Stored with the author's traces removed: canvas notes, instance identifiers, saved credential names and any live document they had left behind.

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Also ai assistants, with the same services where possible, closest in size first.

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