NameetP/pdfmux

**Self-healing PDF extraction that flags the pages it can't read instead of dropping them — and now certifies any extractor's output for silent drops.** Open-source LlamaParse alternative for RAG pipelines, MCP server for Claude Desktop, LangChain + LlamaIndex loaders.
> pdfmux extracts PDFs and checks its own work — and now certifies any extractor's, telling you which pages it silently dropped. Free, MIT. Patent-pending method. pip install pdfmux.
**Two jobs, one tool:**
- **Self-healing extraction.** The only PDF extractor that audits its own output. Catches blank pages, scrambled columns, broken tables — re-extracts them with a stronger backend, and flags what it still can't read instead of silently dropping it. So your LLM gets clean data, not silent garbage. Routes each page to the best of 7 built-in extraction backends + BYOK LLM fallback (Gemini / Claude / GPT-4o / Ollama). One CLI. One API. Zero config.
- **Certify Anything — new in v1.8.1.** pdfmux verify audits *any* extraction engine's output against the source PDF — Reducto, Mistral OCR, LlamaParse, Docling, your in-house parser — and tells you which pages it silently dropped. Free, MIT, patent-clean.
Install
That handles digital PDFs. **For any real-world batch, install pdfmux[ocr] too** — almost every directory of PDFs has at least one scan, and without OCR those pages return empty text:
Other backends, by document type:
Requires Python 3.11+.
Quick Start
CLI
Python
For batch processing, use batch_extract() — not a subprocess.run(['pdfmux', ...]) loop. Same pipeline, no per-file process spawn, handles non-ASCII filenames:
bash
Certify pdfmux's own extraction of a document
pdfmux verify --source report.pdf --engine pdfmux
Certify ANOTHER engine's output (JSON / Markdown / text)
pdfmux verify --source report.pdf --extracted reducto.json --engine-name reducto
Batch a whole directory — the "M pages silently dropped across N docs" report
pdfmux verify --source ./pdfs/ --extracted ./engine-outputs/ -o certification.json
CI gate: exit non-zero unless the overall verdict is PASS
pdfmux verify --source report.pdf --extracted out.json --strict
pdfmux verify — report.pdf · engine: reducto
FAIL confidence 71% · coverage 68%
reducto: FAIL; 3 page(s) SILENTLY DROPPED (pages 7, 12, 31); overall
confidence 71%, coverage 68% across 40 page(s).
❌ 3 page(s) SILENTLY DROPPED: 7, 12, 31
python
from pdfmux import verify_extraction, verify_batch
Single document → a CertificationManifest
manifest = verify_extraction("report.pdf", "reducto.json", engine="reducto")
print(manifest.verdict) # "PASS" | "REVIEW" | "FAIL"
print(manifest.silent_drops) # e.g. (7, 12, 31) — 1-indexed page numbers
print(manifest.coverage) # 0.0–1.0
Many documents → a BatchCertification ("M pages dropped across N docs")
batch = verify_batch([("a.pdf", "a.json"), ("b.pdf", "b.json")], engine="llamaparse")
print(batch.total_silent_drops, "pages dropped across", batch.doc_count, "docs")
bash
pdfmux verify-manifest manifest.json # free, MIT, offline — no account
┌─────────────────────────────┐
│ 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
│ │ │ │ │
└──────────┴──────────┴────────┬────────┴──────────┘
│
┌────────────────────────────────────────┐
│ Quality Auditor │
│ │
│ 4-signal dynamic confidence scoring │
│ per-page: good / bad / empty │
│ if bad -> re-extract with next backend│
└────────────────────┬───────────────────┘
│
┌────────────────────────────────────────┐