open-edit
Open-source, agent-driven editing pipeline: create subtitles, motion graphics, slides, edit and render videos.
Runssource: GitHubTypeScriptApache-2.0commit dd7913b0bb12
TypeScript, Apache-2.0 licensed. The project labels itself: ai agents, captions, claude code, ffmpeg, macos, subtitles, transcription and video.
open-edit runs. An Argusic agent installed it in 7 minutes and hit 3 errors and fixed every one without help on a clean machine with no GPU, and the whole session was recorded.
CLI builds and runs (version 0.0.0-dev), 967/967 unit tests pass, help/version/command-parsing verified working on linux-x64; rendering requires macOS arm64 or Windows x64 which this platform does not provide
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.
- Node v18.19.1 requires >=20; installing Node v20.18.11 minute
- pnpm not on PATH; project requires [email protected]1 minute
- CLI dist not built (no cli/dist/)1 minute
- Install time
- 7 minutes
- Cold machine to finish
- 7 minutes
- Errors hit and fixed
- 3 hit, 3 fixed with no human help
- How the result was proved
- node --import tsx --test $(find cli/tests -name '*.test.ts' | sort): 967 passed, 0 failed, 4 skipped, 0 cancelled (47s)
- Model tokens used
- 53,620
- Exact commit tested
- dd7913b0bb12
- 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.
exec /bin/sh -lc 'echo "Start: $(( $(date -d '"'2026-10-07 04:20:00' +%s) ))\" echo \"Now: "'$(date +%s)" echo "Elapsed: $(( $(date +%s) - $(date -d '"'2026-10-07 04:20:00' +%s) )) seconds\"" in /work/repo succeeded in 0ms: Start: 1791346800 Now: 1791347174 Elapsed: 374 seconds Here's the final report: tokens used 53,620 Here's the final 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.
- Recovered from all 3 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
- Took 7 minutes to install, slower than the median of the 46 comparable projects Argusic has measured.
- Measured once so far. A single attempt shows it can run, not that it runs reliably.
Of the 47 ai-agents projects Argusic has installed and timed, open-edit was the 28th fastest to reach a running state, and 35 of 47 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 7 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-agentscaptionsclaude-codeffmpegmacossubtitlestranscriptionvideovideo-editing
Embed the badge
Markdown for the project README. It links back here; terms on the terms page.
[](https://argusic.com/subject/open-edit)Questions
- Does open-edit run?
- Yes. open-edit runs. Argusic installed and launched it on a clean machine in 7 minutes, hitting 3 errors on the way, and recorded the session.
- How did Argusic test open-edit?
- On a fresh, disposable machine, with every command recorded and the repository pinned at commit dd7913b0bb12. One attempt is recorded, and the full method is on the methodology page.
- What did it cost to test open-edit?
- 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 open-edit take to install?
- 7 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 open-edit need a GPU?
- Not to start. Argusic reached a running state on a machine with no GPU.
- What goes wrong when installing open-edit?
- 3 things broke in the recorded run, and 3 were fixed without human help. Each one, and the time it cost, is listed on this page.
- How does open-edit compare with the alternatives?
- Of the 47 ai-agents projects Argusic has installed and timed, open-edit was the 28th fastest to reach a running state, and 35 of 47 reached one at all.
- Where is the evidence for open-edit?
- 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.