llm-scraper
Turn any webpage into structured data using LLMs
Runs with mockssource: GitHubTypeScriptMITcommit 2b43d999a17e
TypeScript, MIT licensed. The project labels itself: ai, artificial intelligence, browser, browser automation, gpt, gpt 4, langchain and llama.
llm-scraper runs, with stand-ins for the services it depends on. An Argusic agent installed it in 0.5 minutes and hit 2 errors and fixed every one without help on a clean machine with no GPU, and the whole session was recorded.
The project installs and builds without errors; 8/8 test cases pass with a mocked OpenAI Responses API endpoint.
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.
- tests call real OpenAI API and fail without valid credentials8 minutes
- vitest 4.x requires Node 20+ (container has Node 18.19.1)2 minutes
- Install time
- under a minute
- Cold machine to finish
- 24 minutes
- Errors hit and fixed
- 2 hit, 2 fixed with no human help
- How the result was proved
- cd /work/repo && OPENAI_API_KEY=test-key OPENAI_BASE_URL=http://localhost:7899 npx vitest run exited 0, 8 passed, 0 failed; npx tsc -p tsconfig.json exited 0
- Model tokens used
- 153,083
- Exact commit tested
- 2b43d999a17e
- 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.
**Errors found and fixed:** 1. **Vitest 4.x requires Node 20+** — container has Node 18.19.1. Fixed by downgrading to `vitest@^… 2. **Tests require valid Argusic API keys** — the test suite calls the real `@ai-sdk/openai` provid… **Verification:** `OPENAI_API_KEY=test-key OPENAI_BASE_URL=http://localhost:7899 npx vitest run` ex… tokens used 153,083 The task is complete. Here's a concise summary of what happened: **Install & Build** — `npm install` succeeded (engine warnings only for Node 18, harmless). `npx ts… **Errors found and fixed:** 1. **Vitest 4.x requires Node 20+** — container has Node 18.19.1. Fixed by downgrading to `vitest@^… 2. **Tests require valid Argusic API keys** — the test suite calls the real `@ai-sdk/openai` provid… **Verification:** `OPENAI_API_KEY=test-key OPENAI_BASE_URL=http://localhost:7899 npx vitest run` ex…
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
- Installed in 0.5 minutes, faster than the median of the 58 comparable projects Argusic has measured.
- 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.
- Measured once so far. A single attempt shows it can run, not that it runs reliably.
Of the 59 ai projects Argusic has installed and timed, llm-scraper was the 6th fastest to reach a running state, and 45 of 59 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 | 0.11 |
Topics (from GitHub)
aiartificial-intelligencebrowserbrowser-automationgptgpt-4langchainllamallmopenaiplaywrightpuppeteerscraper
Embed the badge
Markdown for the project README. It links back here; terms on the terms page.
[](https://argusic.com/subject/llm-scraper)Questions
- Does llm-scraper run?
- llm-scraper runs, with mocks standing in for external services. Argusic installed and launched it on a clean machine in 1 minutes, hitting 2 errors on the way, and recorded the session.
- How did Argusic test llm-scraper?
- On a fresh, disposable machine, with every command recorded and the repository pinned at commit 2b43d999a17e. One attempt is recorded, and the full method is on the methodology page.
- What did it cost to test llm-scraper?
- The run that produced this verdict cost $0.11: real compute and model cost, not a list price per million tokens. It is recorded on that run's page.
- How long does llm-scraper take to install?
- 0.5 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 llm-scraper need a GPU?
- Not to start. Argusic reached a running state on a machine with no GPU.
- What goes wrong when installing llm-scraper?
- 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 llm-scraper compare with the alternatives?
- Of the 59 ai projects Argusic has installed and timed, llm-scraper was the 6th fastest to reach a running state, and 45 of 59 reached one at all.
- Where is the evidence for llm-scraper?
- 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.