RRepoGEO

REPOGEO REPORT · LITE

pymupdf/pymupdf4llm

Default branch main · commit 288fe552 · scanned 6/29/2026, 4:31:50 AM

GitHub: 1,891 stars · 229 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

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

OVERALL DIRECTION
  • highreadme#1
    Reposition README's opening to highlight LLM-specific differentiation

    Why:

    CURRENT
    **Turn PDF and other documents into clean, LLM-ready data — in one line of code. No GPU, no Cloud, no Tokens required.**

PyMuPDF4LLM is a lightweight extension for PyMuPDF that converts documents into structured Markdown, JSON, and plain text optimised for RAG pipelines, vector embeddings, and LLM ingestion.
    COPY-PASTE FIX
    **PyMuPDF4LLM: The fastest, most accurate Python library for turning PDFs and other documents into clean, LLM-ready data — in one line of code. No GPU, no Cloud, no Tokens required.**

Built as a lightweight extension for PyMuPDF, PyMuPDF4LLM is specifically engineered to convert documents into structured Markdown, JSON, and plain text, perfectly optimized for RAG pipelines, vector embeddings, and direct LLM ingestion. Unlike generic PDF parsers or even PyMuPDF itself, PyMuPDF4LLM focuses on semantic extraction and chunking to preserve document context for AI models.
  • mediumabout#2
    Update the 'About' description for clarity and LLM focus

    Why:

    CURRENT
    PyMuPDF4LLM
    COPY-PASTE FIX
    Extract clean, LLM-ready structured data (Markdown, JSON, text) from PDFs and documents using PyMuPDF4LLM, optimized for RAG and AI ingestion.

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
LayoutParser
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LayoutParser · recommended 2×
  2. PyMuPDF · recommended 2×
  3. Adobe Acrobat Pro DC · recommended 1×
  4. Nougat · recommended 1×
  5. PDFplumber · recommended 1×
  • CATEGORY QUERY
    How to efficiently extract structured text from complex PDF documents for LLM ingestion?
    you: not recommended
    AI recommended (in order):
    1. Adobe Acrobat Pro DC
    2. LayoutParser
    3. Nougat
    4. PDFplumber
    5. Azure AI Document Intelligence
    6. Google Cloud Document AI
    7. PyMuPDF

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

    Show full AI answer
  • CATEGORY QUERY
    What are the best Python libraries for robust PDF content extraction to prepare data for AI models?
    you: not recommended
    AI recommended (in order):
    1. LayoutParser
    2. pdfminer.six
    3. PyMuPDF
    4. Camelot
    5. pdfplumber
    6. Tesseract
    7. Unstructured

    AI recommended 7 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