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. First seen 2026-07-19; the source record was last checked 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.
How this item is evaluated
The opendataloader-project/opendataloader-pdf record combines a 8.5/10 composite score with separate popularity (100.0), risk (conditional), and setup (moderate) signals. See the scoring methodology for the current weights and evidence definitions.
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.