openllmetry
Open-source observability for your GenAI or LLM application, based on OpenTelemetry
Runssource: GitHubhomepagePythonApache-2.0commit f7082c8a99ef
Python, Apache-2.0 licensed. The project labels itself: artifical intelligence, datascience, generative ai, good first issue, good first issues, help wanted, llm and llmops.
openllmetry runs. An Argusic agent installed it in 8 minutes and hit no errors on a clean machine with no GPU, and the whole session was recorded.
The OpenLLMetry monorepo is fully operational: all 35 Python packages install, build, and pass their test suites (thousands of tests exercising real OpenTelemetry instrumentation patterns via VCR-cassette-recorded API interactions and in-memory span exporters).
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.
- Install time
- 8 minutes
- Cold machine to finish
- 12 minutes
- Errors hit and fixed
- 0 hit, 0 fixed with no human help
- How the result was proved
- npx nx run <package>:test for all 35 packages, all test suites passed without failures. SDK build produced dist/traceloop_sdk-0.62.3 wheels. SDK import and Traceloop.init() executed without crashes.
- Model tokens used
- 58,942
- Exact commit tested
- f7082c8a99ef
- 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 'export PATH="$HOME/.local/bin:$PATH" && cd /work/repo && npx nx run traceloop-sdk:buil… succeeded in 2107ms: Building wheel from source distribution... Successfully built dist/traceloop_sdk-0.62.3.tar.gz Successfully built dist/traceloop_sdk-0.62.3-py3-none-any.whl Artifacts generated at packages/traceloop-sdk/dist folder NX Successfully ran target build for project traceloop-sdk Here's my complete report. tokens used 58,942 Here's my complete 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 8 minutes.
- Nothing broke on the way: zero errors between clone and running.
- 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
- 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 8 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)
artifical-intelligencedatasciencegenerative-aigood-first-issuegood-first-issueshelp-wantedllmllmopsmetricsmlmodel-monitoringmonitoringobservabilityopen-sourceopen-telemetryopentelemetryopentelemetry-pythonpython
Embed the badge
Markdown for the project README. It links back here; terms on the terms page.
[](https://argusic.com/subject/openllmetry)Questions
- Does openllmetry run?
- Yes. openllmetry runs. Argusic installed and launched it on a clean machine in 8 minutes, hitting 0 errors on the way, and recorded the session.
- How did Argusic test openllmetry?
- On a fresh, disposable machine, with every command recorded and the repository pinned at commit f7082c8a99ef. One attempt is recorded, and the full method is on the methodology page.
- What did it cost to test openllmetry?
- 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 openllmetry take to install?
- 8 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 openllmetry need a GPU?
- Not to start. Argusic reached a running state on a machine with no GPU.
- What goes wrong when installing openllmetry?
- Nothing did, in the recorded run: zero errors between clone and running.
- Where is the evidence for openllmetry?
- 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.