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flaser381/spotilyze

Analysis updated 2026-05-18

★ 11TypeScriptAudience · generalComplexity · 3/5LicenseSetup · moderate

TLDR

A local, privacy-first tool that turns your Spotify export into years of listening patterns, charts, and an optional AI personality read.

Mindmap

mindmap
  root((spotilyze))
    What it does
      Detect taste shifts
      Sound profile charts
      AI personality read
    Privacy
      Fully local processing
      No uploads by default
      AGPL open source
    Tech
      TypeScript backend
      Vue.js dashboard
      Docker setup
    Features
      Phase detection
      Genre evolution
      HTML export sharing
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Code map

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What do people build with it?

USE CASE 1

Explore how your music taste shifted across life phases like moves, breakups, or new social circles

USE CASE 2

Generate a shareable offline HTML profile of your listening history to compare with a friend

USE CASE 3

Get an AI-written personality read or roast based on your Spotify data, running locally

USE CASE 4

See when during the day you actually listen and which artists you quietly burned out on

What is it built with?

TypeScriptVue.jsBunDocker

How does it compare?

flaser381/spotilyzeanousss007/ng-blatuiblockedpath/pi-xai-oauth
Stars111111
LanguageTypeScriptTypeScriptTypeScript
Setup difficultymoderatemoderateeasy
Complexity3/53/52/5
Audiencegeneraldeveloperdeveloper

Figures from each repo's GitHub metadata at analysis time.

How do you get it running?

Difficulty · moderate Time to first run · 30min

Requires Docker Desktop, first run builds the app (a few minutes). Spotify Extended Streaming History can take several days to arrive from Spotify.

Free to use and modify, but if you build a hosted service on it you must release your changes under the same license.

In plain English

Spotilyze is a local-first tool that analyzes your Spotify listening history and turns it into charts, patterns, and optional written personality reads. You export your data from Spotify, drop the file into Spotilyze, and it runs all its analysis on your own machine. Nothing is uploaded anywhere unless you choose to use a cloud AI service for the written commentary, in which case only a summary is sent to the provider you pick. The core of what Spotilyze does is look for patterns across years of listening. It detects periods where your taste shifted, which often line up with real-life changes: a move, a breakup, or a new social circle. It plots each track on a three-axis sound profile measuring arousal, mood, and depth, so you can watch how your sonic character drifted over time. It also surfaces things like which artists you quietly stopped playing, when during the day you actually listen, and which songs you played on repeat before abandoning. On top of the pattern analysis, Spotilyze can generate a written read of your listening history if you point it at a local AI model or provide your own API key. These reads come in different modes: a personality summary, an ad-profile demo showing what could be inferred from your data, a dating read, a roast, or recommendations. The README is clear that these outputs are guesses rather than validated science. The sound-profile is built on ideas from music-and-psychology research, but it has not been peer-reviewed or validated against clinical data. You can run Spotilyze using Docker, which is the recommended approach for non-coders: download the folder, run one command, and open a browser at localhost. For developers, it also runs with Bun directly. The frontend is built in Vue.js, the backend handles data processing in TypeScript, and the whole thing can export to a single self-contained HTML file you can share offline with a friend. The project is AGPL-3.0 licensed, which means any hosted service built on it must stay open-source. It is a hobby project that its author describes as AI-assisted in implementation.

Copy-paste prompts

Prompt 1
I have my Spotify Extended Streaming History export. Walk me through what Spotilyze's change-point detection does and how it finds where my taste shifted over the years.
Prompt 2
I want to run Spotilyze with a local LLM instead of an API key. How does the optional AI write-up work and what does it actually send to the model?
Prompt 3
Show me how Spotilyze computes the AVD (Arousal, Valence, Depth) sound-profile from a Spotify listening history export.
Prompt 4
I want to build a similar listening history analyzer in TypeScript with Vue.js and Bun. How is Spotilyze's data pipeline structured from the raw export file to the dashboard?

Frequently asked questions

What is spotilyze?

A local, privacy-first tool that turns your Spotify export into years of listening patterns, charts, and an optional AI personality read.

What language is spotilyze written in?

Mainly TypeScript. The stack also includes TypeScript, Vue.js, Bun.

What license does spotilyze use?

Free to use and modify, but if you build a hosted service on it you must release your changes under the same license.

How hard is spotilyze to set up?

Setup difficulty is rated moderate, with roughly 30min to a first successful run.

Who is spotilyze for?

Mainly general.

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