codex-with-chatgpt
ChatGPT thinks. Codex works. Use ChatGPT as the planning brain while keeping the Codex harness.
Runssource: GitHubTypeScriptMITcommit a9f91cd98df1
TypeScript, MIT licensed. The project labels itself: ai agents, chatgpt, codex, mcp, model context protocol and oauth.
codex-with-chatgpt runs. An Argusic agent installed it in 4.5 minutes and hit no errors on a clean machine with no GPU, and the whole session was recorded.
The project installs, builds, and passes all 164 tests; the bridge server starts on localhost port 48765 and responds to health checks with status ok.
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 73.3 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
- 5 minutes
- Cold machine to finish
- 5 minutes
- Errors hit and fixed
- 0 hit, 0 fixed with no human help
- How the result was proved
- pnpm install exited 0, pnpm build (tsc) exited 0, vitest run: 15 files passed, 164 tests passed, c2c start launched bridge on 127.0.0.1:48765, curl http://127.0.0.1:48765/health returned 200 with {"service":"c2c-bridge","version":"0.1.1","workspaceId":"ddc5e473a09b","status":"ok"}
- Model tokens used
- 67,744
- Exact commit tested
- a9f91cd98df1
- 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 3, 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 'export N_PREFIX="$HOME/.n"; export PATH="$N_PREFIX/bin:/home/runner/.local/bin:$PATH";… succeeded in 89ms: ✓ Bridge 已停止 exec /bin/sh -lc date in /work/repo succeeded in 0ms: Thu Sep 24 10:26:37 UTC 2026 All verified. Let me compile the final result. tokens used 67,744 All verified. Let me compile the final result.
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 114 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 115 ai-agents projects Argusic has installed and timed, codex-with-chatgpt was the 44th fastest to reach a running state, and 85 of 115 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.
- 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)
Embed the badge
Markdown for the project README. It links back here; terms on the terms page.
[](https://argusic.com/subject/codex-with-chatgpt)Questions
- Does codex-with-chatgpt run?
- Yes. codex-with-chatgpt runs. Argusic installed and launched it on a clean machine in 5 minutes, hitting 0 errors on the way, and recorded the session.
- How did Argusic test codex-with-chatgpt?
- On a fresh, disposable machine, with every command recorded and the repository pinned at commit a9f91cd98df1. 3 attempts are recorded, and the full method is on the methodology page.
- What did it cost to test codex-with-chatgpt?
- The run that produced this verdict cost $0.03: real compute and model cost, not a list price per million tokens. It is recorded on that run's page.
- How long does codex-with-chatgpt 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 codex-with-chatgpt need a GPU?
- Not to start. Argusic reached a running state on a machine with no GPU.
- What goes wrong when installing codex-with-chatgpt?
- Nothing did, in the recorded run: zero errors between clone and running.
- How does codex-with-chatgpt compare with the alternatives?
- Of the 115 ai-agents projects Argusic has installed and timed, codex-with-chatgpt was the 44th fastest to reach a running state, and 85 of 115 reached one at all.
- Where is the evidence for codex-with-chatgpt?
- 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.