Graft
Turbocharge Claude Code, Cursor, Codex, Gemini & every coding agent: faster, cheaper, with contextual understanding specific to your codebase.
Runssource: GitHubhomepageTypeScriptMITcommit 05760b07abc0
TypeScript, MIT licensed. The project labels itself: ai agents, anthropic, claude code, cli, code graph, codex, context engineering and cursor.
Graft runs. An Argusic agent installed it in 4.5 minutes and hit 2 errors and fixed every one without help on a clean machine with no GPU, and the whole session was recorded.
The Graft context graph engine builds, runs its full test suite with 1219 passing, and answers lexical queries against its own codebase.
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.
- Installed Node.js 18.19.1, but project requires >=202 minutes
- Test 'a process killed while holding the lock releases it' failed on Node 20 because top-level await in -e eval is not supported0.5 minutes
- Install time
- 5 minutes
- Cold machine to finish
- 11 minutes
- Errors hit and fixed
- 2 hit, 2 fixed with no human help
- How the result was proved
- npm test: 1220 tests reported (1219 pass, 0 fail, 1 skip, the skip is a Windows-only test on Linux), CLI 'graft version' printed 'graft 0.17.0', 'graft build' parsed 265/265 files into 2038 nodes
- Model tokens used
- 87,704
- Exact commit tested
- 05760b07abc0
- 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.
1. mergeGraftSettings · function [symbol]
src/claude/settings-merge.ts:L114-L153
function mergeGraftSettings( existing: Json, opts: { statusline?: boolean } = {}, ): { merged: J…
2. isGraftEntry · function [symbol]
src/hosts/config-write.ts:L42-L44
function isGraftEntry(entry: unknown): boolean
All good. Here's the final report:
**ARGUSIC_RESULT:** {"install_succeeded":true,"launch_succeeded":true,"install_minutes":4.5,"errors…
tokens used
87,704
All good. Here's the final report:
**ARGUSIC_RESULT:** {"install_succeeded":true,"launch_succeeded":true,"install_minutes":4.5,"errors…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 4.5 minutes, faster than the median of the 11 comparable projects Argusic has measured.
- 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 MIT, as reported by its host.
- Measured 2 times, so the result is not a one-off.
Of the 12 ai-agents projects Argusic has installed and timed, Graft was the 6th fastest to reach a running state, and 8 of 12 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 4.5 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
Topics (from GitHub)
ai-agentsanthropicclaude-codeclicode-graphcodexcontext-engineeringcursordeveloper-toolsgeminiknowledge-graphllmmcpmcp-serveropen-sourceopenaitree-sitter
Embed the badge
Markdown for the project README. It links back here; terms on the terms page.
[](https://argusic.com/subject/graft)Questions
- Does Graft run?
- Yes. Graft runs. Argusic installed and launched it on a clean machine in 5 minutes, hitting 2 errors on the way, and recorded the session.
- How did Argusic test Graft?
- On a fresh, disposable machine, with every command recorded and the repository pinned at commit 05760b07abc0. 2 attempts are recorded, and the full method is on the methodology page.
- What did it cost to test Graft?
- 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 Graft take to install?
- 4.5 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 Graft need a GPU?
- Not to start. Argusic reached a running state on a machine with no GPU.
- What goes wrong when installing Graft?
- 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.
- How does Graft compare with the alternatives?
- Of the 12 ai-agents projects Argusic has installed and timed, Graft was the 6th fastest to reach a running state, and 8 of 12 reached one at all.
- Where is the evidence for Graft?
- 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.