pdfmux

Universal PDF extraction orchestrator. Routes each page to the best backend, audits the output, re-extracts failures. 5 rule-based extractors + BYOK LLM fallback. One CLI. One API. Zero config.

PDF ──> pdfmux router ──> best extractor per page ──> audit ──> re-extract failures ──> Markdown / JSON / chunks
            |
            ├─ PyMuPDF         (digital text, 0.01s/page)
            ├─ OpenDataLoader  (complex layouts, 0.05s/page)
            ├─ RapidOCR        (scanned pages, CPU-only)
            ├─ Docling          (tables, 97.9% TEDS)
            ├─ Surya            (heavy OCR fallback)
            └─ YOUR LLM        (Gemini / Claude / GPT-4o / Ollama — BYOK via 5-line YAML)

Install

pip install pdfmux

That's it. Handles digital PDFs out of the box. Add backends for harder documents:

pip install "pdfmux[ocr]"             # RapidOCR — scanned/image pages (~200MB, CPU-only)
pip install "pdfmux[tables]"          # Docling — table-heavy docs (~500MB)
pip install "pdfmux[opendataloader]"  # OpenDataLoader — complex layouts (Java 11+)
pip install "pdfmux[llm]"            # LLM fallback — Gemini, Claude, GPT-4o, Ollama
pip install "pdfmux[all]"            # everything

Requires Python 3.11+.

Quick Start

CLI

# zero config — just works
pdfmux convert invoice.pdf
# invoice.pdf -> invoice.md (2 pages, 95% confidence, via pymupdf4llm)

# RAG-ready chunks with token limits
pdfmux convert report.pdf --chunk --max-tokens 500

# cost-aware extraction with budget cap
pdfmux convert report.pdf --mode economy --budget 0.50

# schema-guided structured extraction (5 built-in presets)
pdfmux convert invoice.pdf --schema invoice

# BYOK any LLM for hardest pages
pdfmux convert scan.pdf --llm-provider claude

# batch a directory
pdfmux convert ./docs/ -o ./output/

Python

import pdfmux

# text -> markdown
text = pdfmux.extract_text("report.pdf")

# structured data -> dict with tables, key-values, metadata
data = pdfmux.extract_json("report.pdf")

# RAG chunks -> list of dicts with token estimates
chunks = pdfmux.chunk("report.pdf", max_tokens=500)

Architecture

                           ┌─────────────────────────────┐
                           │     Segment Detector         │
                           │  text / tables / images /    │
                           │  formulas / headers per page │
                           └─────────────┬───────────────┘
                                         │
                    ┌────────────────────────────────────────┐
                    │            Router Engine                │
                    │                                        │
                    │   economy ── balanced ── premium        │
                    │   (minimize $)  (default)  (max quality)│
                    │   budget caps: --budget 0.50            │
                    └────────────────────┬───────────────────┘
                                         │
          ┌──────────┬──────────┬────────┴────────┬──────────┐
          │          │          │                  │          │
     PyMuPDF   OpenData    RapidOCR           Docling     LLM
     digital   Loader      scanned            tables    (BYOK)
     0.01s/pg  complex     CPU-only           97.9%    any provider
               layouts                        TEDS
          │     

... [View full README on GitHub](https://github.com/NameetP/pdfmux#readme)