pezzo
🕹️ Open-source, developer-first LLMOps platform designed to streamline prompt design, version management, instant delivery, collaboration, troubleshooting, observability and more.
Runs with mockssource: GitHubhomepageTypeScriptApache-2.0commit 0787e3c037ad
TypeScript, Apache-2.0 licensed. The project labels itself: ai, devtools, gpt 3, gpt 4, hacktoberfest, javascript, langchain and llm.
pezzo runs, with stand-ins for the services it depends on. An Argusic agent installed it in 7 minutes and hit 2 errors and fixed every one without help on a clean machine with no GPU, and the whole session was recorded.
Pezzo server built successfully and started on port 3000, returning HTTP 200 with status OK and version 0.8.0 from /api/healthz. All nx targets (build, test) pass. Mock infrastructure for ClickHouse, Redis, Supertokens, and KMS was stood up to allow the server to initialize without Docker.
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 92 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.
- No Docker in container, so PostgreSQL/ClickHouse/Redis/Supertokens infra not available for true end-to-end startup
- Prisma cannot connect to PostgreSQL on port 5432 (no Postgres running)
- Install time
- 7 minutes
- Cold machine to finish
- 20 minutes
- Errors hit and fixed
- 2 hit, 2 fixed with no human help
- How the result was proved
- curl -s http://localhost:3000/api/healthz returned {"status":"OK","version":"0.8.0"}
- Model tokens used
- 104,323
- Exact commit tested
- 0787e3c037ad
- 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
- Behaviour against real services
- this run was verified against stand-in services only
The end of the recording
The last lines of the terminal session that decided this verdict, exactly as they were recorded.
No tests found, exiting with code 0 > NX Successfully ran target test for project common All builds and tests pass (no tests exist in the repo). Let me now summarize the results. exec /bin/sh -lc '# Kill mock infra pkill -f "python3.*mock_infra" 2>/dev/null echo "Cleaned up"' in /work/repo exited 143 in 0ms: Let me compile the final report. tokens used 104,323 Let me compile 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
- Recovered from all 2 errors without a human stepping in, which says the failures are documented well enough to solve.
- Licensed Apache-2.0, as reported by its host.
What did not, or is not known
- Only reached a running state against stand-in services, so nothing here proves it works against the real ones.
- Took 7 minutes to install, slower than the median of the 69 comparable projects Argusic has measured.
- Measured once so far. A single attempt shows it can run, not that it runs reliably.
Of the 70 ai projects Argusic has installed and timed, pezzo was the 39th fastest to reach a running state, and 52 of 70 reached one at all.
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
| tested by | attempt | outcome | score | cost USD | when |
|---|---|---|---|---|---|
| Argusic Runner | 1/3 | Runs with mocks | 92.00 | 0.10 |
Topics (from GitHub)
aidevtoolsgpt-3gpt-4hacktoberfestjavascriptlangchainllmllmopsmonitoringnestjsnodejsobservabilityopenaiplatformpromptprompt-engineeringprompt-managementpythontypescript
Embed the badge
Markdown for the project README. It links back here; terms on the terms page.
[](https://argusic.com/subject/pezzo)Questions
- Does pezzo run?
- pezzo runs, with mocks standing in for external services. Argusic installed and launched it on a clean machine in 7 minutes, hitting 2 errors on the way, and recorded the session.
- How did Argusic test pezzo?
- On a fresh, disposable machine, with every command recorded and the repository pinned at commit 0787e3c037ad. One attempt is recorded, and the full method is on the methodology page.
- What did it cost to test pezzo?
- The run that produced this verdict cost $0.10: real compute and model cost, not a list price per million tokens. It is recorded on that run's page.
- How long does pezzo 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 pezzo need a GPU?
- Not to start. Argusic reached a running state on a machine with no GPU.
- What goes wrong when installing pezzo?
- 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 pezzo compare with the alternatives?
- Of the 70 ai projects Argusic has installed and timed, pezzo was the 39th fastest to reach a running state, and 52 of 70 reached one at all.
- Where is the evidence for pezzo?
- 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.