RRepoGEO

REPOGEO REPORT · LITE

pymupdf/pymupdf4llm

Default branch main · commit ae49fff6 · scanned 5/17/2026, 11:01:45 PM

GitHub: 1,704 stars · 209 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
28 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
2 / 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/pymupdf4llm, 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 relevant topics to the repository

    Why:

    COPY-PASTE FIX
    pdf-extraction, llm, rag, markdown, json, python, ocr, document-processing, offline, local-first
  • highreadme#2
    Emphasize local, Python library advantage in README opening

    Why:

    CURRENT
    Turn PDF and other documents into clean, LLM-ready data — in one line of code. No GPU, no Cloud, no Tokens required.
    COPY-PASTE FIX
    PyMuPDF4LLM is a **local-first Python library** that turns PDF and other documents into clean, LLM-ready data — in one line of code. **Unlike cloud services**, it requires no GPU, no Cloud, and no Tokens, making it ideal for privacy-sensitive or offline RAG pipelines.
  • mediumabout#3
    Update the repository description for clarity and keywords

    Why:

    CURRENT
    PyMuPDF4LLM
    COPY-PASTE FIX
    Local-first Python library for converting PDFs and documents into LLM-ready Markdown, JSON, and text, optimized for RAG pipelines with built-in OCR.

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
0 / 2
0% of queries surface pymupdf/pymupdf4llm
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Azure AI Document Intelligence
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Azure AI Document Intelligence · recommended 2×
  2. Google Cloud Document AI · recommended 2×
  3. AWS Textract · recommended 1×
  4. facebookresearch/nougat · recommended 1×
  5. Unstructured-IO/unstructured · recommended 1×
  • CATEGORY QUERY
    How to extract structured text from PDF documents for large language models?
    you: not recommended
    AI recommended (in order):
    1. Azure AI Document Intelligence
    2. AWS Textract
    3. Google Cloud Document AI
    4. Nougat (facebookresearch/nougat)
    5. Unstructured.io (Unstructured-IO/unstructured)
    6. PyMuPDF (pymupdf/PyMuPDF)
    7. PDFMiner.six (pdfminer/pdfminer.six)

    AI recommended 7 alternatives but never named pymupdf/pymupdf4llm. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Python library to convert scanned PDFs into clean markdown for RAG applications?
    you: not recommended
    AI recommended (in order):
    1. Nougat
    2. PaddleOCR
    3. markdownify
    4. Tesseract OCR
    5. python-markdownify
    6. Google Cloud Document AI
    7. Azure AI Document Intelligence
    8. Poppler
    9. pdf2image
    10. layoutparser

    AI recommended 10 alternatives but never named pymupdf/pymupdf4llm. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    warn

    Suggestion:

  • 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/pymupdf4llm?
    pass
    AI did not name pymupdf/pymupdf4llm — likely talking about a different project

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

  • If a team adopts pymupdf/pymupdf4llm in production, what risks or prerequisites should they evaluate first?
    pass
    AI named pymupdf/pymupdf4llm 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/pymupdf4llm solve, and who is the primary audience?
    pass
    AI named pymupdf/pymupdf4llm explicitly

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

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pymupdf/pymupdf4llm — 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