Turn Your Last.fm History Into a Spotify Playlist
Last updated: July 2026.
Track Down converts any Last.fm source — top tracks, loved tracks, similar artists, a genre tag, or track radio — into a matched Spotify playlist in five steps, using only your public Last.fm username and a client-side Spotify login. No Last.fm password, no server-side account, no track limit.
What do you need before starting?
Just two things: a Last.fm username (yours, or any public profile if you're building a discovery playlist from someone else's loved tracks) and a Spotify account to connect and save the resulting playlist into. Nothing needs to be installed, and no Last.fm login or password is ever requested — only your public username.
What are the exact steps to convert Last.fm history into Spotify?
- Go to Track Down and choose a Last.fm source: Top Tracks or Loved Tracks to convert history you already have, or Similar Artists / Tag / Track Radio to build a new discovery playlist.
- Enter your Last.fm username (for Top Tracks, Loved Tracks, or Flashback) or a seed artist/tag/track (for the discovery sources) — no login to Last.fm is required, since these are public profile lookups.
- Track Down queries Last.fm's API for the matching tracks, then searches Spotify's catalog for each one and scores the candidates by artist and title similarity.
- Review the matched list — high-confidence matches are pre-selected, and anything uncertain is flagged so you can confirm or skip it manually.
- Connect your Spotify account (client-side OAuth; your token never touches Track Down's server) and save the results as a new playlist, or add them to a playlist you already have.
Which source should you pick?
Use Top Tracks or Loved Tracks to convert history you already have. Use Similar Artists, Tag, or Track Radio to build something new from one seed. Use Flashback to rebuild one specific past week.
How does the matching actually decide which Spotify track is correct?
Each "Artist — Track" pair from Last.fm is searched against Spotify's catalog, and up to five candidates are scored on artist-name overlap, track-title overlap, and character-level similarity between the input and each candidate. Matches above the confidence threshold are auto-selected; anything below it is flagged for manual review rather than guessed at silently. Full detail on the scoring formula is in How It Works.
What's the fastest workflow for someone doing this for the first time?
| Goal | Source to use | What you'll get |
|---|---|---|
| Back up my existing taste | Top Tracks or Loved Tracks | A Spotify playlist of music you already know you like |
| Find new music like one artist | Similar Artists | A playlist of artists Last.fm's community data links to that one |
| Relive a specific past week | Flashback | The actual chart from that week in your own scrobble history |
| Explore a genre | Tag | Last.fm's top tracks for that tag, matched to Spotify |
Frequently asked questions
Do I need to give Track Down my Last.fm password?
No. Last.fm sources only need your public username — Track Down reads your public profile data through Last.fm's API, the same data anyone could look up on last.fm/user/yourname. No Last.fm login is involved.
What if some tracks fail to match on Spotify?
Unmatched or low-confidence tracks are listed separately so you can review them individually rather than silently dropping them — this usually happens with tracks not available in your Spotify market or with unusual artist/title formatting in the source data.
Can I do this for tracks I loved years ago, not just recent ones?
Yes. Loved Tracks pulls your entire loved-tracks history regardless of when you loved them, and Flashback specifically rebuilds a chosen past week from your scrobble history rather than only recent activity.
Is there a limit on playlist size doing this?
Spotify's own API rate limits apply to your personal access token, not to Track Down specifically — in practice you can convert hundreds of tracks in one session, and Track Down backs off and retries automatically if a rate limit is hit.
Background on why this is worth doing at all: What Is Scrobbling? and Escape the Algorithm.