swirl-search
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Runssource: GitHubhomepagePythonApache-2.0commit 0506aa5638ae
Python, Apache-2.0 licensed. The project labels itself: ai search, bigquery, django, federated query, federated search, gpt, large language models and metasearch.
swirl-search runs. An Argusic agent installed it in 18 minutes and hit 2 errors and fixed every one without help on a clean machine with no GPU, and the whole session was recorded.
SWIRL Community Edition installed in a Python 3.12 virtual environment with all dependencies; Django server starts and answers requests; full test suite passes 184/184 tests.
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
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.
What went wrong, in the agent's own words
The agent's own words, worst first, never rewritten into advice.
- Python 3.12 externally-managed-environment blocks system-wide pip install1 minute
- redis-server binary not available (no root); Celery/Channels requires Redis
- Install time
- 18 minutes
- Cold machine to finish
- 11 minutes
- Errors hit and fixed
- 2 hit, 2 fixed with no human help
- How the result was proved
- pytest swirl/tests/ -q exited 0, 184 passed; manage.py runserver answers HTTP 302 on /; manage.py check shows 0 errors
- Model tokens used
- 55,690
- Exact commit tested
- 0506aa5638ae
- 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.
- 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.
In the span of about 75 minutes, I set up this project from scratch in the container and ran it. Tw… 1. **Externally managed Python environment** — Ubuntu 24.04's Python blocks system-wide pip install… 2. **No `redis-server` binary** — The Celery/Channels config defaults to `redis://localhost:6379/0`… The Galaxy UI frontend returns 404 because it's an optional Docker-based install (`install-ui.sh`).… **Verification commands and results:** | Check | Result | |---|---| | `django-admin check` | 5 warnings (no errors) — JSONField defaults, URL namespace | | `manage.py collectstatic` | 159 static files copied | | `curl http://localhost:8000/` | HTTP 302 → redirects to `/galaxy/` | | `curl http://localhost:8000/admin/` | HTTP 302 → redirects to admin login | | `pytest swirl/tests/ -q` | **184 passed, 2 warnings** |
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 18 minutes.
- Recovered from all 2 errors without a human stepping in, which says the failures are documented well enough to solve.
- Ran without a GPU, so it does not need one to start.
- Licensed Apache-2.0, 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.
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.
Run history
Topics (from GitHub)
ai-searchbigquerydjangofederated-queryfederated-searchgptlarge-language-modelsmetasearchpythonragrelevancyretrieval-augmented-generationsearchsearch-engineunified-search
Embed the badge
Markdown for the project README. It links back here; terms on the terms page.
[](https://argusic.com/subject/swirl-search)Questions
- Does swirl-search run?
- Yes. swirl-search runs. Argusic installed and launched it on a clean machine in 18 minutes, hitting 2 errors on the way, and recorded the session.
- How did Argusic test swirl-search?
- On a fresh, disposable machine, with every command recorded and the repository pinned at commit 0506aa5638ae. One attempt is recorded, and the full method is on the methodology page.
- What did it cost to test swirl-search?
- The run that produced this verdict cost $0.05: real compute and model cost, not a list price per million tokens. It is recorded on that run's page.
- How long does swirl-search take to install?
- 18 minutes in the run that decided this verdict, from a clean machine to a running state. That is one measured attempt, not an average.
- Does swirl-search need a GPU?
- Not to start. Argusic reached a running state on a machine with no GPU.
- What goes wrong when installing swirl-search?
- 2 things broke in the recorded run, and 2 were fixed without human help. Each one, and the time it cost, is listed on this page.
- Where is the evidence for swirl-search?
- 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.