pipeshub-ai
PipesHub is an open-source platform for securely connecting enterprise knowledge to AI. Give AI agents trusted context and your team permission-aware search with verified citations across your business systems.
Runs with mockssource: GitHubhomepagePythonApache-2.0commit 1864d3d2a793
Python, Apache-2.0 licensed. The project labels itself: agent, agents, ai, docker, enterprise search, fastapi, glean and graphrag.
pipeshub-ai runs, with stand-ins for the services it depends on. An Argusic agent installed it in 14.2 minutes and hit 6 errors and fixed every one without help on a clean machine with no GPU, and the whole session was recorded.
Python dependencies installed and ~15965 of ~16880 unit tests pass TypeScript compiles with zero errors Node.js package install completes with engine warnings Node.js tests cannot run because sharp requires Node >=20.9 and ts-node loads it via the test helper chain Docker is unavailable so integration tests and full stack startup are skipped
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 56 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.
- Node.js v18.19.1 incompatible with mocha@12 and [email protected] which require Node >=20.915 minutes
- Python tests/unit/connectors/core/test_toolset_token_refresh.py: 9 tests fail
- Python tests/unit/containers/test_docling_container.py: test_logger_is_singleton fails
- Python tests/unit/sources/client/ hangs when run as a single directory
- Python tests/unit/api/ and tests/unit/services/ subdirs timeout when run together
- Docker not available cannot run integration tests
- Install time
- 14 minutes
- Cold machine to finish
- 66 minutes
- Errors hit and fixed
- 6 hit, 6 fixed with no human help
- How the result was proved
- Python pytest on ~80 percent of test directories combined ~15965 passed across 40+ individual runs TypeScript npx tsc --noEmit exited 0 npm install completed 777 packages
- Model tokens used
- 267,629
- Exact commit tested
- 1864d3d2a793
- 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.
The report has been delivered. Here's a summary of what I found: **Install** — Python venv + all 600+ dependencies installed via `uv pip install -e .` (success). No… **Tests** — Python unit tests: **~15,965 passed** across config, core, events, models, utils, servi… **Node.js tests blocked** — `[email protected]` requires Node >=20.9, and ts-node loads it via the mocha… **No Docker available** — Integration tests and full-stack verification skipped. tokens used 267,629 The report has been delivered. Here's a summary of what I found: **Install** — Python venv + all 600+ dependencies installed via `uv pip install -e .` (success). No… **Tests** — Python unit tests: **~15,965 passed** across config, core, events, models, utils, servi… **Node.js tests blocked** — `[email protected]` requires Node >=20.9, and ts-node loads it via the mocha… **No Docker available** — Integration tests and full-stack verification skipped.
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 6 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.
- Measured 2 times, so the result is not a one-off.
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 14.2 minutes to install, slower than the median of the 32 comparable projects Argusic has measured.
Of the 33 agent projects Argusic has installed and timed, pipeshub-ai was the 24th fastest to reach a running state, and 25 of 33 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 | 2/3 | Did not run | 20.00 | 0.13 | |
| Argusic Runner | 1/3 | Runs with mocks | 92.00 | 0.29 |
Topics (from GitHub)
agentagentsaidockerenterprise-searchfastapigleangraphragknowledge-graphlangchainlanggraphmcpnotionollamapythonragretrieval-augmented-generationself-hostedslackworkplace-ai
Embed the badge
Markdown for the project README. It links back here; terms on the terms page.
[](https://argusic.com/subject/pipeshub-ai)Questions
- Does pipeshub-ai run?
- pipeshub-ai runs, with mocks standing in for external services. Argusic installed and launched it on a clean machine in 14 minutes, hitting 6 errors on the way, and recorded the session.
- How did Argusic test pipeshub-ai?
- On a fresh, disposable machine, with every command recorded and the repository pinned at commit 1864d3d2a793. 2 attempts are recorded, and the full method is on the methodology page.
- What did it cost to test pipeshub-ai?
- The run that produced this verdict cost $0.29: real compute and model cost, not a list price per million tokens. It is recorded on that run's page.
- How long does pipeshub-ai take to install?
- 14.2 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 pipeshub-ai need a GPU?
- Not to start. Argusic reached a running state on a machine with no GPU.
- What goes wrong when installing pipeshub-ai?
- 6 things broke in the recorded run, and 6 were fixed without human help. Each one, and the time it cost, is listed on this page.
- How does pipeshub-ai compare with the alternatives?
- Of the 33 agent projects Argusic has installed and timed, pipeshub-ai was the 24th fastest to reach a running state, and 25 of 33 reached one at all.
- Where is the evidence for pipeshub-ai?
- All 2 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.