gentle-ai
Gentle-AI configures the AI coding agents you already use: Claude Code, Cursor, OpenCode, Codex, Pi, and more. Choose persistent memory, Organic-Driven Development, curated skills, MCP servers, personas, and optional bounded review. Open source, no agent lock-in.
Runssource: GitHubhomepageGoMITcommit 2c25e878eea4
Go, MIT licensed. The project labels itself: agent skills, ai agents, ai coding, claude code, code review, codex, coding agents and copilot.
gentle-ai runs. An Argusic agent installed it in 3 minutes and hit 1 error and fixed every one without help on a clean machine with no GPU, and the whole session was recorded.
gentle-ai builds from source, runs version/doctor/install --dry-run, and all 22 test packages pass across internal, bench, and e2e packages
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 90 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.
- Go 1.25.10+ not installed3 minutes
- Install time
- 3 minutes
- Cold machine to finish
- 23 minutes
- Errors hit and fixed
- 1 hit, 1 fixed with no human help
- How the result was proved
- go test -count=1 -timeout 120s ./internal/... (20 pkgs ok), ./bench/... (1 pkg ok 5.8s), ./e2e/... (1 pkg ok 39.4s); ./gentle-ai version prints 2.0.0-20260906031619-2c25e878eea4; ./gentle-ai install --dry-run produces correct plan
- Model tokens used
- 147,545
- Exact commit tested
- 2c25e878eea4
- 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 1, 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.
147,545 All systems operational. Here is the summary: **What was done:** - Go 1.27.1 was not installed; downloaded and extracted the official tarball to `~/.local/go`, adde… - `go build ./cmd/gentle-ai/` compiled successfully with all dependencies resolved - `gentle-ai version` prints `2.0.0-20260906031619-2c25e878eea4` - `gentle-ai install --dry-run` validates the installer plan correctly (detects Ubuntu, apt, suppor… - `gentle-ai doctor` runs the health check — reports expected warnings for a fresh container with n… **Test results (all passing):** - `./internal/...` — 20 packages, all `ok` - `./bench/...` — 1 package, `ok` (5.8s) - `./e2e/...` — 1 package, `ok` (39.4s)
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.
- Recovered from all 1 error 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 MIT, as reported by its host.
- Measured 2 times, so the result is not a one-off.
What did not, or is not known
- Took 3 minutes to install, slower than the median of the 8 comparable projects Argusic has measured.
Of the 9 agent-skills projects Argusic has installed and timed, gentle-ai was the 6th fastest to reach a running state, and 8 of 9 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 3 minutes after the clone.
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
| tested by | attempt | outcome | score | cost USD | when |
|---|---|---|---|---|---|
| Argusic Runner | 2/3 | Runs | 100.00 | 0.08 | |
| Argusic Runner | 1/3 | Runs with mocks | 80.00 | 0.50 |
Topics (from GitHub)
agent-skillsai-agentsai-codingclaude-codecode-reviewcodexcoding-agentscopilotcursordeveloper-toolsgolangllmmcpopencodepersistent-memorypispec-driven-development
Embed the badge
Markdown for the project README. It links back here; terms on the terms page.
[](https://argusic.com/subject/gentle-ai)Questions
- Does gentle-ai run?
- Yes. gentle-ai runs. Argusic installed and launched it on a clean machine in 3 minutes, hitting 1 error on the way, and recorded the session.
- How did Argusic test gentle-ai?
- On a fresh, disposable machine, with every command recorded and the repository pinned at commit 2c25e878eea4. 2 attempts are recorded, and the full method is on the methodology page.
- What did it cost to test gentle-ai?
- The run that produced this verdict cost $0.08: real compute and model cost, not a list price per million tokens. It is recorded on that run's page.
- How long does gentle-ai take to install?
- 3 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 gentle-ai need a GPU?
- Not to start. Argusic reached a running state on a machine with no GPU.
- What goes wrong when installing gentle-ai?
- 1 thing broke in the recorded run, and 1 were fixed without human help. Each one, and the time it cost, is listed on this page.
- How does gentle-ai compare with the alternatives?
- Of the 9 agent-skills projects Argusic has installed and timed, gentle-ai was the 6th fastest to reach a running state, and 8 of 9 reached one at all.
- Where is the evidence for gentle-ai?
- All 2 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.