pymupdf4llm-c 1.6.0

C-backed PDF to structured JSON extractor.

PyMuPDF4LLM-C

A "blazingly-fast" PDF extractor in C using MuPDF, inspired by pymupdf4llm. I took many of its heuristics and approach but rewrote it in C, then bound it to Python so it's easy to use.

Most extractors give you raw text (fast but useless) or full-on OCR/ML. This is a middle ground.

Outputs JSON for every block: text, type, bounding box, font metrics, tables. You get the raw data to process however you need.

speed: ~300 pages/second on CPU. 1 million pages in ~55 minutes.

AMD Ryzen 7 4800H (8 cores, 6 used), ~1600-page, table & text heavy document.

Capabilities/comparisons to others tools here.

Primarily intended for use with Python bindings.

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Installation

pip install pymupdf4llm-c

You can prefix this with whatever tools you use, like uv, poetry, etc.

There are wheels for Python 3.9–3.14 (inclusive of minor versions) on macOS (ARM/x64) and all modern Linux distributions.

To build from source, see BUILD.md.

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Capabilities

Tool	Speed (pps)	Tables	Images (Figures)	OCR (Y/N)	JSON Output	Best For
pymupdf4llm-C	~300	Yes	No (WIP)	N	Yes (structured)	RAG, high volume
pymupdf4llm	~10	Yes	Yes (but not ML to get contents)	N	Markdown	General extraction
pymupdf (alone)	~250	No	No, not by itself, requires more effort I believe	N	No (text only)	basic text extraction
marker	~0.5-1	Yes	Yes (contents with ML?)	Y (optional?)	Markdown	Maximum fidelity
docling	~2-5	Yes	Yes	Y	JSON	Document intelligence
PaddleOCR	~20-50	Yes	Yes	Y	Text	Scanned documents

Trade-off: speed and control vs automatic extraction. Marker and Docling give higher fidelity if you have time.

what it handles well

• millions of pages, fast
• custom parsing logic; you own the rules
• document archives, chunking strategies, any structured extraction
• CPU only; no expensive inference
• iterating on parsing logic without waiting hours

what it doesn't handle

• scanned or image-heavy PDFs (no OCR)
• 99%+ accuracy on edge cases; trades precision for speed
• figures or image extraction

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Usage

basic

from pymupdf4llm_c import to_json

result = to_json("example.pdf", output="example.json")
print(f"Extracted to: {result.path}")

You can omit the output field; it defaults to <file>.json

collect all pages in memory

result = to_json("report.pdf", output="report.json")
pages = result.collect()

# Access pages as objects with markdown conversion
for page in pages:
    print(page.markdown)
    
# Access individual blocks
for block in pages[0]:
    print(f"{block.type}: {block.text if hasattr(block, 'text') else ''}")

This still saves it to result.path; it just allows you to load it into memory. If you don't want to write to disk at all, consider providing a special path.

This is only for smaller PDFs. For larger ones, this may result in crashes due to loading everything into RAM. See below for a solution.

stream pages (memory-efficient)

result = to_json("large.pdf", output="large.json")

# Iterate one page at a time without loading everything
for page in result:
    for block in page:
        print(f"Block type: {block.type}")

convert to markdown

result = to_json("document.pdf", output="document.json")
pages = result.collect()

# Full document as markdown
full_markdown = pages.markdown

# Single page as markdown
page_markdown = pages[0].markdown

# Single block as markdown
block_markdown = pages[0][0].markdown

.markdown is a property, not a function

command-line

python -m pymupdf4llm_c.main input.pdf [output_dir]

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Output structure

Each page is a JSON array of blocks. Every block has:

• type: block type (text, heading, paragraph, list, table, code)
• bbox: [x0, y0, x1, y1] bounding box coordinates
• font_size: font size in points (average for multi-span blocks)
• length: character count
• spans: array of styled text spans with style flags (bold, italic, mono-space, etc.)

Note that a span represents a logical group of styling. in most blocks, it is likely that there is only one span.

Block types

Not real JSON; just to demonstrate output. (psuedo).

text/paragraph/code blocks:

{
  "type": "text",
  "bbox": [72.03, 132.66, 542.7, 352.22],
  "font_size": 12.0,
  "length": 1145,
  "lines": 14,
  "spans": [
    {
      "text": "Block content here...",
      "font_size": 12.0,
      "bold": false,
      "italic": false,
      "monospace": false,
      "strikeout": false,
      "superscript": false,
      "subscript": false,
      "link": false,
      "uri": false
    }
  ]
}

headings:

{
  "type": "heading",
  "bbox": [111.80, 187.53, 509.10, 217.56],
  "font_size": 32.0,
  "length": 25,
  "level": 1,
  "spans": [
    {
      "text": "Heading Text",
      // all styling flags (as seen in the above)
    }
  ]
}

lists:

{
  "type": "list",
  "bbox": [40.44, 199.44, 107.01, 345.78],
  "font_size": 11.04,
  "length": 89,
  "spans": [],
  "items": [
    {
      "spans": [
        {
          "text": "First item",
		  // all styling flags.
        }
      ],
      "list_type": "bulleted",
      "indent": 0,
      "prefix": false
    },
    {
      "spans": [
        {
          "text": "Second item",
		  // all styling flags.
        }
      ],
      "list_type": "numbered",
      "indent": 0,
      "prefix": "1."
    }
  ]
}

tables:

{
  "type": "table",
  "bbox": [72.0, 220.0, 523.5, 400.0],
  "font_size": 12.0,
  "length": 256,
  "row_count": 3,
  "col_count": 2,
  "cell_count": 2,
  "spans": [],
  "rows": [
    {
      "bbox": [72.0, 220.0, 523.5, 250.0],
      "cells": [
        {
          "bbox": [72.0, 220.0, 297.75, 250.0],
          "spans": [
            {
              "text": "Header A",
              // all styling flags.
            }
          ]
        },
        {
          "bbox": [297.75, 220.0, 523.5, 250.0],
          "spans": [
            {
              "text": "Header B",
              // all styling flags.
            }
          ]
        }
      ]
    }
  ]
}

Span fields

all text spans contain:

• text: span content
• font_size: size in points
• bold, italic, monospace, strikeout, superscript, subscript: boolean style flags
• link: boolean indicating if span contains a hyperlink
• uri: URI string if linked, otherwise false

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FAQ

why not marker/docling?
if you have time and need maximum accuracy, use those. this is for when you're processing millions of pages or iterating on extraction logic quickly.

how do i use bounding boxes for semantic chunking?
large y-gaps indicate topic breaks. font size changes show sections. indentation shows hierarchy. you write the logic using the metadata.

will this handle my complex PDF?
optimized for well-formed digital PDFs. scanned documents, complex table structures, and image-heavy layouts won't extract as well as ML tools.

commercial use?
only under AGPL-v3 or with a license from Artifex (MuPDF's creators). see LICENSE

Any trade-offs due to the speed gains; you must have lost some fidelity from pymupdf4llm? If we're talking trade-offs in comparison to PyMuPDF4LLM:

Not as much as you'd think.

The reason for PyMuPDF4LLM being so slow wasn't due to its quality. It was an inefficient code-base. O(n^2) algorithms, raw numbers in Python, pretty much just unoptimized code and a bad language for lots of maths.

This isn't a trade-off of the project itself, but there may still be minor cases where I haven't 100% copied the heuristics.

If we're talking about trade-offs in comparison to tools like Paddle, Marker & Docling:

It does not do any fancy ML. It's just some basic geometric maths. Therefore it won't handle:

• scanned pages; no OCR
• & complex tables or tables without some form of edges

why did you build this? Dumb reason. I was building a RAG project with my dad (I'm 15). He did not care about speed at all. But I just got bored of waiting for chunking the PDFs every time I made a minor change. I couldn't find anything with even 50% of the quality that would be faster. And anyway, my chunks were trash. So it was either: raw text, or ML, and I didn't want either of them.

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Licensing and Links

licensing

TL;DR: use it all you want in OSS software. if you buy license for MUPDF from Artifex, you are excluded from all AGPL requirements.

• derived work of mupdf.
• inspired by pymupdf4llm; i have used it as a reference

AGPL v3. commercial use requires license from Artifex.

modifications and enhancements specific to this library are 2026 Adit Bajaj.

see LICENSE for the legal stuff.

links

• repo: github.com/intercepted16/pymupdf4llm-C
• pypi: pymupdf4llm-C

feedback welcome.