pipeshub-ai
The open-source context layer for AI agents. PipesHub turns your company's knowledge (Slack, Drive, Jira, GitHub, Microsoft 365 and 50+ connectors) into a permission-aware workspace that agents can search, grep, navigate and cite. MCP, SDKs and built-in agents. Self-hosted.
Runs with mockssource: GitHubhomepagePythonApache-2.0commit 1864d3d2a793
Python, Apache-2.0 licensed. The project labels itself: agentic rag, ai, ai agents, claude code, context engineering, context layer, deep research and enterprise search.
pipeshub-ai runs, with stand-ins for the services it depends on. An Argusic agent installed it in 60 minutes and hit 4 errors and fixed every one without help on a clean machine with no GPU, and the whole session was recorded.
286 Python unit tests pass, 9633 JavaScript unit tests pass (4 pending). Both suites execute against mocked external services without real infrastructure dependencies.
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 68 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.
- pytest-timeout 2.4.0 incompatible with pytest 9.x — hangs on test collection of multiple files8 minutes
- Node.js v18 available but project requires v22.15.05 minutes
- sharp native module not compiled for platform2 minutes
- No Docker in container
- Install time
- 60 minutes
- Cold machine to finish
- 62 minutes
- Errors hit and fixed
- 4 hit, 4 fixed with no human help
- How the result was proved
- python -m pytest (top-level files), 286 passed in 65s; npm test (node22), 9633 passing, 4 pending in 2m
- Model tokens used
- 953,372
- 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.
exec
/bin/sh -lc "echo '{\"install_succeeded\": true, \"launch_succeeded\": true, \"install_minutes\": 6…
succeeded in 0ms:
{"install_succeeded": true, "launch_succeeded": true, "install_minutes": 60, "errors": [{"msg": "py…
exec
/bin/sh -lc "# Validate JSON is well-formed
echo '{\"install_succeeded\": true, \"launch_succeeded\": true, \"install_minutes\": 60, \"errors\"…
succeeded in 0ms:
JSON valid
2026-09-25T05:17:55.044406Z ERROR codex_core::session: failed to record rollout items: thread 01a0d…
tokens used
953,372Replay 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 4 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 3 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 60 minutes to install, slower than the median of the 136 comparable projects Argusic has measured.
Of the 137 agent projects Argusic has installed and timed, pipeshub-ai was the 136th fastest to reach a running state, and 89 of 137 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 | 3/3 | Runs with mocks | 92.00 | 0.25 | |
| 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)
agentic-ragaiai-agentsclaude-codecontext-engineeringcontext-layerdeep-researchenterprise-searchgleangraphraghybrid-searchknowledge-graphllmmcpmcp-serverollamapythonragself-hostedworkplace-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 60 minutes, hitting 4 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. 3 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 $0.25: 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?
- 60 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?
- 4 things broke in the recorded run, and 4 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 137 agent projects Argusic has installed and timed, pipeshub-ai was the 136th fastest to reach a running state, and 89 of 137 reached one at all.
- Where is the evidence for pipeshub-ai?
- 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.