Score breakdown
Popularity is tracked separately. Support, ads, sponsorships, and tips never affect these signals.
Why it matters
Document extraction is still one of the most common pain points in AI stacks, and a lot of teams are stuck choosing between brittle PDF text dumps and expensive hosted parsers. This matters because it offers a well-documented local core with OCR, layout retention, and citation-friendly structure that is directly useful for ingestion and retrieval workflows.
Who should use it
Who should skip it
Skip opendataloader-project/opendataloader-pdf unless the captured evidence suggests it solves a problem you are actively working on.
About this signal
opendataloader-project/opendataloader-pdf is tracked by RepoRadar as a code repository in the Document parsing section. It was first seen on 2026-07-19 and last updated on 2026-07-19. The current verdict is 'try now' with a Gold tier and moderate setup difficulty. The standout signals for opendataloader-project/opendataloader-pdf are workflow potential (9.6) and maturity (9.1), while setup ease (7.1) trails — that balance shapes where it fits best. This page summarizes the evidence RepoRadar captured from https://github.com/opendataloader-project/opendataloader-pdf. 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 opendataloader-project/opendataloader-pdf a composite score of 8.5 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 100.0 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 parser works on user documents, so teams should review how extracted text, OCR artifacts, and images are stored or logged; Optional hybrid processing changes the privacy model compared with a strictly local parse path.