Noria
An agentic extractor for messaging platforms. Noria receives messages & URLs via chat; scrapes web data to evaluate the content against custom scoring matrices (e.g. jobs, scholarships); and sends a structured summary with a calculated match score directly to the assigned messenger number.
Runs with mockssource: GitHubhomepageJavaScriptMITcommit 68a46c1e4402
JavaScript, MIT licensed. The project labels itself: ai agent, automation, data extraction, event driven architecture, gemini api, jina ai, llm pipeline and puppeteer stealth.
Noria runs, with stand-ins for the services it depends on. An Argusic agent installed it in 10 minutes and hit 2 errors and fixed every one without help on a clean machine with no GPU, and the whole session was recorded.
npm install completes, all 232 unit tests pass across 17 suites with zero lint errors, the Node.js backend boots registering all domain/LLM/scraper/sender/listener plugins and loading the scholarships and jobs pipeline configs, and the Streamlit Control Center UI starts and serves on port 8501.
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.
- pip3 install -r requirements.txt failed: externally-managed-environment (PEP 668) prevents system-wide pip install on Ubuntu 24.042 minutes
- npm install failed: puppeteer postinstall tried to download Chrome but unzip is missing from the container (required by @puppeteer/browsers extraction)1 minute
- Install time
- 10 minutes
- Cold machine to finish
- 11 minutes
- Errors hit and fixed
- 2 hit, 2 fixed with no human help
- How the result was proved
- npm test: 17 suites passed, 232 tests passed (0 failures); npm run lint: zero errors; node src/index.js: boots, registers all 7 plugin categories and 2 pipeline configs; streamlit run app.py: Uvicorn started on 0.0.0.0:8501
- Model tokens used
- 222,100
- Exact commit tested
- 68a46c1e4402
- 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.
Test Suites: 17 passed, 17 total Tests: 232 passed, 232 total Here's my report. All 232 tests pass, ESLint is clean, the Node.js backend boots and registers all plugins, and the S… ``` ``` tokens used 222,100 Here's my report. All 232 tests pass, ESLint is clean, the Node.js backend boots and registers all plugins, and the S… ``` ```
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 MIT, 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 10 minutes to install, slower than the median of the 38 comparable projects Argusic has measured.
- Measured once so far. A single attempt shows it can run, not that it runs reliably.
Of the 39 ai-agent projects Argusic has installed and timed, Noria was the 23rd fastest to reach a running state, and 21 of 39 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 | 0.04 |
Topics (from GitHub)
ai-agentautomationdata-extractionevent-driven-architecturegemini-apijina-aillm-pipelinepuppeteer-stealthsemantic-analysisstructured-outputsweb-scrapingwhatsapp-bot
Embed the badge
Markdown for the project README. It links back here; terms on the terms page.
[](https://argusic.com/subject/noria)Questions
- Does Noria run?
- Noria runs, with mocks standing in for external services. Argusic installed and launched it on a clean machine in 10 minutes, hitting 2 errors on the way, and recorded the session.
- How did Argusic test Noria?
- On a fresh, disposable machine, with every command recorded and the repository pinned at commit 68a46c1e4402. One attempt is recorded, and the full method is on the methodology page.
- What did it cost to test Noria?
- The run that produced this verdict cost $0.04: real compute and model cost, not a list price per million tokens. It is recorded on that run's page.
- How long does Noria take to install?
- 10 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 Noria need a GPU?
- Not to start. Argusic reached a running state on a machine with no GPU.
- What goes wrong when installing Noria?
- 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 Noria compare with the alternatives?
- Of the 39 ai-agent projects Argusic has installed and timed, Noria was the 23rd fastest to reach a running state, and 21 of 39 reached one at all.
- Where is the evidence for Noria?
- 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.