PyMuPDF

// Overview

PyMuPDF is a Python library for reading, extracting, and rendering content from PDFs and other document formats. It is built on top of MuPDF, a small C engine, and exposes both low-level access to document internals and higher-level helper methods. It installs with a single pip command and has no mandatory external dependencies.

It is aimed at developers who need to pull text, tables, images, or layout metadata out of documents, or who need to render, annotate, redact, merge, and split PDFs. Beyond plain text, it can return per-span detail like font, size, color, and bounding boxes, which is useful when document structure matters.

Within parsing and ingestion for RAG pipelines, PyMuPDF handles the step of turning raw PDFs into usable data. The companion pymupdf4llm package converts documents to structure-aware Markdown that you can pass straight to an LLM or a vector store.

// What it does

• Text extraction as plain text or a rich dict with font, size, color, and bounding-box metadata
• Table detection with find_tables(), exporting to Markdown or a Pandas DataFrame
• Page rendering to images at any DPI, plus embedded image extraction
• Tesseract-based OCR for scanned pages, with configurable language
• Annotation, redaction, form filling, and PDF editing (merge, split, reorder pages)
• LLM-ready Markdown and JSON output via the pymupdf4llm extra

// Getting started

Install the package with pip and open a document to start extracting content. No external dependencies are required for the core library.

pip install pymupdf

import pymupdf

doc = pymupdf.open("document.pdf")
for page in doc:
    print(page.get_text())

import pymupdf

doc = pymupdf.open("spreadsheet.pdf")
page = doc[0]

tables = page.find_tables()
for table in tables:
    print(table.to_markdown())
    df = table.to_pandas()

pip install pymupdf4llm

# then in Python:
# import pymupdf4llm
# md = pymupdf4llm.to_markdown("report.pdf")

Commands and code are distilled from the project's own documentation — always check the official repo for the latest.

// When to use it

• Ingesting PDFs into a RAG pipeline by converting them to clean Markdown for chunking and embedding
• Pulling structured tables out of reports or spreadsheets-as-PDF into Pandas for analysis
• Extracting text with layout metadata (font, size, position) when document structure matters
• Rendering pages to images, OCRing scanned documents, or redacting sensitive content before sharing

// How PyMuPDF compares

PyMuPDF alongside other open-source parsing & ingestion tools AI/TLDR tracks, ranked by GitHub stars.