plandex
Open source AI coding agent. Designed for large projects and real world tasks.
Runssource: GitHubhomepageGoMITcommit e2d772072efa
Go, MIT licensed. The project labels itself: ai, ai agents, ai developer tools, ai tools, cli, command line, developer tools and git.
plandex runs. An Argusic agent installed it in 30 minutes and hit 5 errors and fixed every one without help on a clean machine with no GPU, and the whole session was recorded.
Plandex server starts on port 8099, connects to local PostgreSQL, runs migrations, responds 200 on /health, and all unit tests pass.
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 98.7 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.
- PostgreSQL not installed5 minutes
- uuid-ossp extension dependencies missing5 minutes
- Go not installed3 minutes
- Python modules uvicorn/fastapi missing2 minutes
- Migration dirty state after first uuid-ossp failure1 minute
- Install time
- 30 minutes
- Cold machine to finish
- 30 minutes
- Errors hit and fixed
- 5 hit, 5 fixed with no human help
- How the result was proved
- go test -count=1 ./syntax/ ./types/ ./utils/ ./model/parse/ ./model/plan/ returned PASS for all; curl http://localhost:8099/health returned 200 HTTP status
- Model tokens used
- 181,437
- Exact commit tested
- e2d772072efa
- 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.
tokens used 181,437 Argusic complete. Here is the report: **Install & build**: CLI downloaded from GitHub releases (v2.2.1) to `~/.local/bin`. Server and CLI… **Errors encountered and fixed**: - No Go in container → installed Go 1.23.3 amd64 tarball - No PostgreSQL → built PG 14.13 from source into `~/pg/` - Missing `uvicorn` and `fastapi` → installed via `pip3 install --break-system-packages` - Migration required `uuid-ossp` PostgreSQL extension (depends on system `libuuid`/`ossp-uuid` not … - First migration attempt left dirty state → dropped and recreated the database **Server**: Started on port 8099, connected to PostgreSQL, ran all migrations, registered routes. H… **Tests**: All 5 server test packages passed — `syntax/`, `types/`, `utils/`, `model/parse/`, `mode…
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 5 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 3 times, so the result is not a one-off.
What did not, or is not known
- Took 30 minutes to install, slower than the median of the 36 comparable projects Argusic has measured.
Of the 37 ai projects Argusic has installed and timed, plandex was the 37th fastest to reach a running state, and 30 of 37 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.
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)
aiai-agentsai-developer-toolsai-toolsclicommand-linedeveloper-toolsgitgolanggpt-4llmopenaipolyglot-programmingterminalterminal-basedterminal-ui
Embed the badge
Markdown for the project README. It links back here; terms on the terms page.
[](https://argusic.com/subject/plandex)Questions
- Does plandex run?
- Yes. plandex runs. Argusic installed and launched it on a clean machine in 30 minutes, hitting 5 errors on the way, and recorded the session.
- How did Argusic test plandex?
- On a fresh, disposable machine, with every command recorded and the repository pinned at commit e2d772072efa. 3 attempts are recorded, and the full method is on the methodology page.
- What did it cost to test plandex?
- The run that produced this verdict cost $0.19: real compute and model cost, not a list price per million tokens. It is recorded on that run's page.
- How long does plandex take to install?
- 30 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 plandex need a GPU?
- Not to start. Argusic reached a running state on a machine with no GPU.
- What goes wrong when installing plandex?
- 5 things broke in the recorded run, and 5 were fixed without human help. Each one, and the time it cost, is listed on this page.
- How does plandex compare with the alternatives?
- Of the 37 ai projects Argusic has installed and timed, plandex was the 37th fastest to reach a running state, and 30 of 37 reached one at all.
- Where is the evidence for plandex?
- 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.