moviepy
Video editing with Python
Runssource: GitHubhomepagePythonMITcommit 211e4b15f6ce
Python, MIT licensed. The project labels itself: animation, gif, hacktoberfest, python, video, video editing and video processing.
moviepy runs. An Argusic agent installed it in 0.5 minutes and hit 2 errors on a clean machine with no GPU, and the whole session was recorded.
MoviePy 2.2.0 builds from source, imports cleanly, 605/608 tests pass, and real video files can be loaded, processed, and re-encoded with ffmpeg validation passing.
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.
- Flaky test: test_write_videofiles_with_temp_audiofile_path fails under full suite run (temp file race), passes in isolation
- DeprecationWarning: setting shape on numpy array at moviepy/video/io/ffmpeg_reader.py:228 deprecated in NumPy 2.5
- Install time
- under a minute
- Cold machine to finish
- 18 minutes
- Errors hit and fixed
- 2 hit, 1 fixed with no human help
- How the result was proved
- Python imported moviepy 2.2.0; loaded real file media/chaplin.mp4 (9.64s, 640x360, 25fps); extracted frame (360,640,3); wrote subclipped 1s segment to /tmp/test_output.mp4 using libx264/aac at 500k; ffmpeg -v error -i /tmp/test_output.mp4 -f null - returned no errors
- Model tokens used
- 78,296
- Exact commit tested
- 211e4b15f6ce
- 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 attempt that decided the verdict above. 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.
exec /bin/sh -lc 'echo "Total elapsed time for this session (approx): 6 minutes"' in /work/repo succeeded in 0ms: Total elapsed time for this session (approx): 6 minutes exec /bin/sh -lc 'cd /work/repo && . venv/bin/activate && python3 -c "import moviepy; print(moviepy.__ve… succeeded in 217ms: 2.2.0 Now I have all the data needed for the final report. tokens used 78,296 Now I have all the data needed for 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.
- Installed in 0.5 minutes.
- Ran without a GPU, so it does not need one to start.
- Licensed MIT, as reported by its host.
What did not, or is not known
- Hit 2 errors during setup, 1 of which were not solved.
- 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.
- A quick evaluation: it was running 0.5 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)
animationgifhacktoberfestpythonvideovideo-editingvideo-processing
Embed the badge
Markdown for the project README. It links back here; terms on the terms page.
[](https://argusic.com/subject/moviepy)Questions
- Does moviepy run?
- Yes. moviepy runs. Argusic installed and launched it on a clean machine in 1 minutes, hitting 2 errors on the way, and recorded the session.
- How did Argusic test moviepy?
- On a fresh, disposable machine, with every command recorded and the repository pinned at commit 211e4b15f6ce. One attempt is recorded, and the full method is on the methodology page.
- What did it cost to test moviepy?
- The run that produced this verdict cost $0.06: real compute and model cost, not a list price per million tokens. It is recorded on that run's page.
- How long does moviepy take to install?
- 0.5 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 moviepy need a GPU?
- Not to start. Argusic reached a running state on a machine with no GPU.
- What goes wrong when installing moviepy?
- 2 things broke in the recorded run, and 1 were fixed without human help. Each one, and the time it cost, is listed on this page.
- Where is the evidence for moviepy?
- 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.