RRepoGEO

REPOGEO REPORT · LITE

pymupdf/PyMuPDF

Default branch main · commit c3b84c5e · scanned 6/21/2026, 3:31:37 AM

GitHub: 10,052 stars · 742 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
90 /100
Healthy
Category recall
2 / 2
Avg rank #2.0 when recommended
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface pymupdf/PyMuPDF, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • hightopics#1
    Add AI and LLM-related topics

    Why:

    CURRENT
    data-science, epub, extract-data, font, mupdf, ocr, pdf, pdf-documents, pymupdf, python, table-extraction, tesseract, text-processing, text-shaping, xps
    COPY-PASTE FIX
    data-science, epub, extract-data, font, mupdf, ocr, pdf, pdf-documents, pymupdf, python, table-extraction, tesseract, text-processing, text-shaping, xps, ai, machine-learning, llm
  • mediumhomepage#2
    Standardize Homepage URL to primary domain

    Why:

    CURRENT
    https://pymupdf.readthedocs.io/?utm_source=github&utm_medium=referral&utm_campaign=pymupdf_github&utm_content=about&utm_term=docs
    COPY-PASTE FIX
    https://pymupdf.io
  • lowreadme#3
    Refine README opening to highlight core differentiator upfront

    Why:

    CURRENT
    The PDF engine behind over 50 million monthly downloads, powering AI pipelines worldwide.PyMuPDF is a high-performance Python library for data extraction, analysis, conversion, rendering and manipulation of PDF (and other) documents.
    COPY-PASTE FIX
    PyMuPDF is the high-performance Python library for data extraction, analysis, conversion, rendering, and manipulation of PDF (and other) documents, powered by the lightweight, fast C engine MuPDF. It's the PDF engine behind over 50 million monthly downloads, powering AI pipelines worldwide.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
2 / 2
100% of queries surface pymupdf/PyMuPDF
Avg rank
#2.0
Lower is better. #1 = top recommendation.
Share of voice
17%
Of all named tools, what % are you?
Top rival
pdfminer/pdfminer.six
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. pdfminer/pdfminer.six · recommended 2×
  2. jsvine/pdfplumber · recommended 1×
  3. camelot-dev/camelot · recommended 1×
  4. python-pillow/Pillow · recommended 1×
  5. tesseract-ocr/tesseract · recommended 1×
  • CATEGORY QUERY
    How to efficiently extract text, images, and tables from PDF documents using Python?
    you: #3
    AI recommended (in order):
    1. pdfplumber (jsvine/pdfplumber)
    2. Camelot (camelot-dev/camelot)
    3. PyMuPDF (pymupdf/PyMuPDF) ← you
    4. pdfminer.six (pdfminer/pdfminer.six)
    5. Pillow (python-pillow/Pillow)
    6. Tesseract (tesseract-ocr/tesseract)
    7. pytesseract (madmaze/pytesseract)
    Show full AI answer
  • CATEGORY QUERY
    What's a fast Python library for converting and processing various document types like PDF or XPS?
    you: #1
    AI recommended (in order):
    1. PyMuPDF (Fitz) (pymupdf/PyMuPDF) ← you
    2. pdfminer.six (pdfminer/pdfminer.six)
    3. Apache Tika (apache/tika)
    4. Aspose.Words for Python via .NET
    5. Unstructured (Unstructured-IO/unstructured)
    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of pymupdf/PyMuPDF?
    pass
    AI named pymupdf/PyMuPDF explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts pymupdf/PyMuPDF in production, what risks or prerequisites should they evaluate first?
    pass
    AI named pymupdf/PyMuPDF explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo pymupdf/PyMuPDF solve, and who is the primary audience?
    pass
    AI named pymupdf/PyMuPDF explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

Drop this badge into the README of pymupdf/PyMuPDF. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/pymupdf/PyMuPDF.svg)](https://repogeo.com/en/r/pymupdf/PyMuPDF)
HTML
<a href="https://repogeo.com/en/r/pymupdf/PyMuPDF"><img src="https://repogeo.com/badge/pymupdf/PyMuPDF.svg" alt="RepoGEO" /></a>
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pymupdf/PyMuPDF — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite