sonobarr

Music discovery tool that integrates with Lidarr and provides recommendations using Last.fm

Runssource: GitHubPythonMITcommit b09f065315d9

Python, MIT licensed. The project labels itself: artificial intelligence, docker compose, lidarr companion, music discovery and self hosted.

sonobarr runs. An Argusic agent installed it in 1 minute and hit no errors on a clean machine with no GPU, and the whole session was recorded.

Python virtual environment created, all pip dependencies installed, and 81/81 pytest suite passes; the Flask application boots, serves the login page, and runs without errors.

What the agent ended up with on a clean machine, in its own words. How this is measured

Measured by Argusic on a fresh machine every time. Every number links to its evidence. Argusic Score 100 of 100 (the mean of this project's run scores).

At a glance

verdict
Runs
Argusic Score
100 / 100
cost of the verifying run
$0.01 (measured)
recorded runs
1
last tested
stars
383
forks
4
open issues
8
watchers
3
size
16 MB
created
last push

Subject data from GitHub, linked at the top of this page, refreshed . Test data by Argusic (CC BY 4.0); every number links to a run page with the full log, the recording, and their sha256 hashes.

What was measured

From the run that decided this verdict. Every line is a recorded value, not an estimate.

Install time
1 minutes
Cold machine to finish
4 minutes
Errors hit and fixed
0 hit, 0 fixed with no human help
Model tokens used
29,045
Exact commit tested
b09f065315d9
Container image
sha256:cdd920bce783

What was not measured

The other half, so you can see exactly how far this evidence goes. A gap is printed as a gap and never filled in.

How the result was proved
runs before 2026-09-07 did not record the command used to check the result
Peak GPU memory
this ran on a CPU machine, so there was no GPU to sample
Behaviour over a long run
each attempt uses the smallest documented settings and a fixed time budget, so this measures whether it runs, not how it holds up under load

The end of the recording

The last lines of the terminal session that decided this verdict, exactly as they were recorded.

    resp2 = c.get('/discover')
    print(f'GET /discover status: {resp2.status_code}')
\" 2>&1 | grep -v 'INFO\\|WARNING'" in /work/repo
 succeeded in 1263ms:
GET /login status: 200
GET /discover status: 404
Login page serves fine. The `/discover` 404 is because it requires auth context. The app is verifie…
Now for my summary report:
tokens used
29,045
Login page serves fine. The `/discover` 404 is because it requires auth context. The app is verifie…
Now for my summary report:

Replay the whole session, every command from a clean machine to this point.

How it was tested

One agent, one container, no human help. It installs the project the way its own documentation says to, runs it, and fixes what breaks. Everything below is recorded as it happened: the terminal session, the log and the exact commit. The full method.

Strengths and limits

Measured facts, not opinions. How this is written.

What went well

  • Reached a running state on a clean machine, with the session recorded.
  • Installed in 1 minute.
  • Nothing broke on the way: zero errors between clone and running.
  • Ran without a GPU, so it does not need one to start.
  • Licensed MIT, as reported by its host.

What did not, or is not known

  • Measured once so far. A single attempt shows it can run, not that it runs reliably.

What it is a reasonable choice for

  • Trying it on a laptop or a small server: it reached a running state without a GPU.
  • A quick evaluation: it was running 1 minutes after the clone.
  • Following its own README: the documented path worked with nothing to fix.

Also tested, in the same area

Every one of these was installed and run by Argusic on a clean machine. Nothing appears here that was not tested.

  • twinny verified to run · TypeScript · 2 shared topics
  • matrixhub verified to run · Go · 2 shared topics
  • harbor tested, not verified · Python · 2 shared topics

Run history

tested byattemptoutcomescorecost USDwhen
Argusic Runner1/3Runs100.000.01

Topics (from GitHub)

artificial-intelligencedocker-composelidarr-companionmusic-discoveryself-hosted

Embed the badge

Markdown for the project README. It links back here; terms on the terms page.

[![Tested by Argusic](https://argusic.com/badge/sonobarr.svg)](https://argusic.com/subject/sonobarr)

Questions

Does sonobarr run?
Yes. sonobarr runs. Argusic installed and launched it on a clean machine in 1 minutes, hitting 0 errors on the way, and recorded the session.
How did Argusic test sonobarr?
On a fresh, disposable machine, with every command recorded and the repository pinned at commit b09f065315d9. One attempt is recorded, and the full method is on the methodology page.
What did it cost to test sonobarr?
The run that produced this verdict cost $0.01: real compute and model cost, not a list price per million tokens. It is recorded on that run's page.
How long does sonobarr take to install?
1 minute in the run that decided this verdict, from a clean machine to a running state. That is one measured attempt, not an average.
Does sonobarr need a GPU?
Not to start. Argusic reached a running state on a machine with no GPU.
What goes wrong when installing sonobarr?
Nothing did, in the recorded run: zero errors between clone and running.
Where is the evidence for sonobarr?
The recorded run is on this page, each linking to its full log and terminal recording, stored with a sha256 fingerprint so it cannot be quietly altered.

Discussion