Score breakdown
Popularity is tracked separately. Support, ads, sponsorships, and tips never affect these signals.
Why it matters
Useful for developers who need repeatable structured extraction from messy web pages and want typed outputs that can slot into automations, research flows, or downstream agent steps.
Who should use it
Who should skip it
Move on from mishushakov/llm-scraper if the licensing terms, language support, or platform requirements do not fit your project.
About this signal
mishushakov/llm-scraper is tracked by RepoRadar as a web extraction in the Developer Tools section. It was first seen on 2026-06-29 and last updated on 2026-06-29. The current verdict is 'try now' with a Gold tier and easy setup difficulty. Across RepoRadar's eight signals, mishushakov/llm-scraper is strongest on workflow potential (9.9) and practical usefulness (9.0) and weakest on momentum (7.0) — a profile worth weighing against your own priorities. This page summarizes the evidence RepoRadar has captured from captured source metadata. The score, tier, risk label, and verdict on this page are never influenced by sponsorship, ads, or tips — they reflect only the usefulness, popularity, novelty, momentum, maturity, and evidence signals described in the RepoRadar methodology.
How this item is evaluated
RepoRadar assigned mishushakov/llm-scraper a composite score of 8.4 out of 10, placing it in the Gold tier. This score combines weighted sub-signals: usefulness (35%), novelty (18%), momentum (14%), maturity (10%), open-source/build quality (7%), evidence quality (6%), workflow potential (6%), and setup ease (4%). Popularity is tracked separately at 84.0 and never affects the composite score or tier. The risk label of 'conditional' reflects inherent user-impacting hazards, not generic novelty. Items with no risk flag may still require normal code review before production use.
Putting this into practice? Read How to evaluate an AI tool before you adopt it for the checklist behind this score.
Risk explanation
The library can send extracted or rendered page content to whichever model provider you configure, so do not aim it at confidential or logged-in pages without reviewing that data path; Playwright-based extraction still inherits the target site's auth, robots, and anti-automation boundaries, so treat it as a parsing tool rather than a compliance bypass.