semble
Fast and Accurate Code Search for Agents. Uses 99% fewer tokens than grep+read
Runssource: GitHubhomepagePythonMITcommit 6bbbd322fd6e
Python, MIT licensed. The project labels itself: agents, code search, embeddings, mcp, mcp server, model context protocol and retrieval.
semble 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.
semble 0.5.5 installed in a virtual environment at /work/repo/.venv; all 330 tests pass; CLI and MCP server run correctly on CPU.
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.
- Install time
- 1 minutes
- Cold machine to finish
- 2 minutes
- Errors hit and fixed
- 0 hit, 0 fixed with no human help
- How the result was proved
- uv run pytest --tb=short exited 0, 330 passed; uv run semble search "authentication flow" /work/repo --top-k 3 returned valid JSON results with file paths and scores
- Model tokens used
- 66,487
- Exact commit tested
- 6bbbd322fd6e
- 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
Watch the session
The whole run, from a clean machine, at the speed it actually happened. Nothing is cut and nothing is sped up.
A recording of attempt 2, run . The verdict above comes from a different attempt, so read this as one session and not as the current result. The terminal recording it was made from is the evidence, stored with a sha256 fingerprint; the video is a rendering of it. Nothing loads from the video host until you press play.
The end of the recording
The last lines of the terminal session that decided this verdict, exactly as they were recorded.
install Configure semble across coding agents.
uninstall Remove semble configuration from coding agents.
options:
-h, --help show this help message and exit
-V, --version show program's version number and exit
All 330 tests passed, and the CLI search returned real results. Let me record the final JSON output
exec
/bin/sh -lc "echo '{\"install_succeeded\": true, \"launch_succeeded\": true, \"install_minutes\": 1…
succeeded in 0ms:
{"install_succeeded": true, "launch_succeeded": true, "install_minutes": 1.0, "errors": [], "test_d…
tokens used
66,487Replay 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, faster than the median of the 37 comparable projects Argusic has measured.
- 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.
- Measured 3 times, so the result is not a one-off.
Of the 38 agents projects Argusic has installed and timed, semble was the 2nd fastest to reach a running state, and 28 of 38 reached one at all.
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.
Run history
Topics (from GitHub)
agentscode-searchembeddingsmcpmcp-servermodel-context-protocolretrieval
Embed the badge
Markdown for the project README. It links back here; terms on the terms page.
[](https://argusic.com/subject/semble)Questions
- Does semble run?
- Yes. semble 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 semble?
- On a fresh, disposable machine, with every command recorded and the repository pinned at commit 6bbbd322fd6e. 3 attempts are recorded, and the full method is on the methodology page.
- What did it cost to test semble?
- 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 semble 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 semble need a GPU?
- Not to start. Argusic reached a running state on a machine with no GPU.
- What goes wrong when installing semble?
- Nothing did, in the recorded run: zero errors between clone and running.
- How does semble compare with the alternatives?
- Of the 38 agents projects Argusic has installed and timed, semble was the 2nd fastest to reach a running state, and 28 of 38 reached one at all.
- Where is the evidence for semble?
- All 3 recorded runs are on this page, each linking to its full log and terminal recording, stored with a sha256 fingerprint so it cannot be quietly altered.