PDF Oxide - The Fastest PDF Toolkit for Python, Rust, Go, JS/TS, C#, Java, WASM, CLI & AI

New in v0.3.54 — text-extraction fidelity pass (Hebrew / RTL visual-vs-logical detection, ToUnicode CMap fallback for bullet & ligature decode, multi-column prose reading order, reference-style two-column reading order). Java is the 8th binding (fyi.oxide:pdf-oxide:0.3.54 on Maven Central, JDK 11+, free Kotlin interop via the same JAR). Ruby, PHP, and Swift are next on the roadmap. Want another language? Open an issue and tell us.

The fastest PDF library for text extraction, image extraction, and markdown conversion. Rust core with bindings for Python, Go, JavaScript / TypeScript, C# / .NET, Java (JDK 11+, Kotlin-compatible), and WASM, plus a CLI tool and MCP server for AI assistants. 0.8ms mean per document, 5× faster than PyMuPDF, 15× faster than pypdf. 100% pass rate on 3,830 real-world PDFs. MIT licensed.

New in v0.3.24 — now available in Go, JavaScript / TypeScript, and C# / .NET, alongside the existing Python, Rust, and WASM bindings. Same Rust core, same 0.8 ms extraction speed, same 100% pass rate. See the language guides: Python · Go · JavaScript / TypeScript · C# / .NET · Java / Kotlin · WASM

Quick Start

Python

from pdf_oxide import PdfDocument

# path can be str or pathlib.Path; use with for scoped access
doc = PdfDocument("paper.pdf")
# or: with PdfDocument("paper.pdf") as doc: ...
text = doc.extract_text(0)
chars = doc.extract_chars(0)
markdown = doc.to_markdown(0, detect_headings=True)

pip install pdf_oxide

Rust

use pdf_oxide::PdfDocument;

let mut doc = PdfDocument::open("paper.pdf")?;
let text = doc.extract_text(0)?;
let images = doc.extract_images(0)?;
let markdown = doc.to_markdown(0, Default::default())?;

[dependencies]
pdf_oxide = "0.3"

CLI

pdf-oxide text document.pdf
pdf-oxide markdown document.pdf -o output.md
pdf-oxide search document.pdf "pattern"
pdf-oxide merge a.pdf b.pdf -o combined.pdf

brew install yfedoseev/tap/pdf-oxide

MCP Server (for AI assistants)

# Install
brew install yfedoseev/tap/pdf-oxide   # includes pdf-oxide-mcp

# Configure in Claude Desktop / Claude Code / Cursor
{
  "mcpServers": {
    "pdf-oxide": { "command": "crgx", "args": ["pdf_oxide_mcp@latest"] }
  }
}

Why pdf_oxide?

• Fast — 0.8ms mean per document, 5× faster than PyMuPDF, 15× faster than pypdf, 29× faster than pdfplumber
• Reliable — 100% pass rate on 3,830 test PDFs, zero panics, zero timeouts
• Complete — Text extraction, image extraction, PDF creation, and editing in one library
• Multi-platform — Rust, Python, Go, JavaScript/TypeScript, C#/.NET, Java/Kotlin, WASM, CLI, and MCP server for AI assistants
• Permissive license — MIT / Apache-2.0 — use freely in commercial and open-source projects

Performance

Benchmarked on 3,830 PDFs from three independent public test suites (veraPDF, Mozilla pdf.js, DARPA SafeDocs). Text extraction libraries only (no OCR). Single-thread, 60s timeout, no warm-up.

Python Libraries

Library	Mean	p99	Pass Rate	License
PDF Oxide	0.8ms	9ms	100%	MIT
PyMuPDF	4.6ms	28ms	99.3%	AGPL-3.0
pypdfium2	4.1ms	42ms	99.2%	Apache-2.0
pymupdf4llm	55.5ms	280ms	99.1%	AGPL-3.0
pdftext	7.3ms	82ms	99.0%	GPL-3.0
pdfminer	16.8ms	124ms	98.8%	MIT
pdfplumber	23.2ms	189ms	98.8%	MIT
markitdown	108.8ms	378ms	98.6%	MIT
pypdf	12.1ms	97ms	98.4%	BSD-3

Rust Libraries

Library	Mean	p99	Pass Rate	Text Extraction
PDF Oxide	0.8ms	9ms	100%	Built-in
oxidize_pdf	13.5ms	11ms	99.1%	Basic
unpdf	2.8ms	10ms	95.1%	Basic
pdf_extract	4.08ms	37ms	91.5%	Basic
lopdf	0.3ms	2ms	80.2%	No built-in extraction

Text Quality

99.5% text parity vs PyMuPDF and pypdfium2 across the full corpus. PDF Oxide extracts text from 7–10× more "hard" files than it misses vs any competitor.

Corpus

Suite	PDFs	Pass Rate
veraPDF (PDF/A compliance)	2,907	100%
Mozilla pdf.js	897	99.2%
SafeDocs (targeted edge cases)	26	100%
Total	3,830	100%

100% pass rate on all valid PDFs — the 7 non-passing files across the corpus are intentionally broken test fixtures (missing PDF header, fuzz-corrupted catalogs, invalid xref streams).

Features

Extract	Create	Edit
Text & Layout	Documents	Annotations
Images	Tables	Form Fields
Forms	Graphics	Bookmarks
Annotations	Templates	Links
Bookmarks	Images	Content

Python API

from pdf_oxide import PdfDocument

# Path can be str or pathlib.Path; use "with PdfDocument(...) as doc" for context manager
doc = PdfDocument("report.pdf")
print(f"Pages: {doc.page_count()}")
print(f"Version: {doc.version()}")

# 1. Scoped extraction (v0.3.14)
# Extract only from a specific area: (x, y, width, height)
header = doc.within(0, (0, 700, 612, 92)).extract_text()

# 2. Word-level extraction (v0.3.14)
words = doc.extract_words(0)
for w in words:
    print(f"{w.text} at {w.bbox}")
    # Access individual characters in the word
    # print(w.chars[0].font_name)

# Optional: override the adaptive word gap threshold (in PDF points)
words = doc.extract_words(0, word_gap_threshold=2.5)

# 3. Line-level extraction (v0.3.14)
lines = doc.extract_text_lines(0)
for line in lines:
    print(f"Line: {line.text}")

# Optional: override word and/or line gap thresholds (in PDF points)
lines = doc.extract_text_lines(0, word_gap_threshold=2.5, line_gap_threshold=4.0)

# Inspect the adaptive thresholds before overriding
params = doc.page_layout_params(0)
print(f"word gap: {params.word_gap_threshold:.1f}, line gap: {params.line_gap_threshold:.1f}")

# Use a pre-tuned extraction profile for specific document types
from pdf_oxide import ExtractionProfile
words = doc.extract_words(0, profile=ExtractionProfile.form())
lines = doc.extract_text_lines(0, profile=ExtractionProfile.academic())

# 4. Table extraction (v0.3.14)
tables = doc.extract_tables(0)
for table in tables:
    print(f"Table with {table.row_count} rows")

# 5. Traditional extraction
text = doc.extract_text(0)
chars = doc.extract_chars(0)

Form Fields

# Extract form fields
fields = doc.get_form_fields()
for f in fields:
    print(f"{f.name} ({f.field_type}) = {f.value}")

# Fill and save
doc.set_form_field_value("employee_name", "Jane Doe")
doc.set_form_field_value("wages", "85000.00")
doc.save("filled.pdf")

Rust API

use pdf_oxide::PdfDocument;

fn main() -> Result<(), Box<dyn std::error::Error>> {
    let mut doc = PdfDocument::open("paper.pdf")?;

    // Extract text
    let text = doc.extract_text(0)?;

    // Character-level extraction
    let chars = doc.extract_chars(0)?;

    // Extract images
    let images = doc.extract_images(0)?;

    // Vector graphics
    let paths = doc.extract_paths(0)?;

    Ok(())
}

Form Fields (Rust)

use pdf_oxide::editor::{DocumentEditor, EditableDocument, SaveOptions};
use pdf_oxide::editor::form_fields::FormFieldValue;

let mut editor = DocumentEditor::open("w2.pdf")?;
editor.set_form_field_value("employee_name", FormFieldValue::Text("Jane Doe".into()))?;
editor.save_with_options("filled.pdf", SaveOptions::incremental())?;

Installation

Python

pip install pdf_oxide

Wheels available for Linux, macOS, and Windows. Python 3.8–3.14.

Rust

[dependencies]
pdf_oxide = "0.3"

JavaScript/WASM

npm install pdf-oxide-wasm

const { WasmPdfDocument } = require("pdf-oxide-wasm");

CLI

brew install yfedoseev/tap/pdf-oxide    # Homebrew (macOS/Linux)
cargo install pdf_oxide_cli             # Cargo
cargo binstall pdf_oxide_cli            # Pre-built binary via cargo-binstall

MCP Server

brew install yfedoseev/tap/pdf-oxide    # Included with CLI in Homebrew
cargo install pdf_oxide_mcp             # Cargo

Other languages

• Go — go get github.com/yfedoseev/pdf_oxide/go — see go/README.md

• JavaScript / TypeScript (Node.js) — npm install pdf-oxide — see js/README.md

• C# / .NET — dotnet add package PdfOxide — see csharp/README.md

• Java / Kotlin (JDK 11+) — Maven coords fyi.oxide:pdf-oxide:0.3.55 — see java/README.md

  <dependency>
    <groupId>fyi.oxide</groupId>
    <artifactId>pdf-oxide</artifactId>
    <version>0.3.55</version>
  </dependency>
  

  // Gradle (Kotlin DSL)
  implementation("fyi.oxide:pdf-oxide:0.3.55")
  

All four share the same Rust core as the Python and WASM bindings, so everything you read in this README applies to them as well — just with each language's native naming conventions.

CLI

22 commands for PDF processing directly from your terminal:

pdf-oxide text report.pdf                      # Extract text
pdf-oxide markdown report.pdf -o report.md     # Convert to Markdown
pdf-oxide html report.pdf -o report.html       # Convert to HTML
pdf-oxide info report.pdf                      # Show metadata
pdf-oxide search report.pdf "neural.?network"  # Search (regex)
pdf-oxide images report.pdf -o ./images/       # Extract images
pdf-oxide merge a.pdf b.pdf -o combined.pdf    # Merge PDFs
pdf-oxide split report.pdf -o ./pages/         # Split into pages
pdf-oxide watermark doc.pdf "DRAFT"            # Add watermark
pdf-oxide forms w2.pdf --fill "name=Jane"      # Fill form fields

Run pdf-oxide with no arguments for interactive REPL mode. Use --pages 1-5 to process specific pages, --json for machine-readable output.

MCP Server

pdf-oxide-mcp lets AI assistants (Claude, Cursor, etc.) extract content from PDFs locally via the Model Context Protocol.

Add to your MCP client configuration:

{
  "mcpServers": {
    "pdf-oxide": { "command": "crgx", "args": ["pdf_oxide_mcp@latest"] }
  }
}

The server exposes an extract tool that supports text, markdown, and HTML output formats with optional page ranges and image extraction. All processing runs locally — no files leave your machine.

Building from Source

# Clone and build
git clone https://github.com/yfedoseev/pdf_oxide
cd pdf_oxide
cargo build --release

# Run tests
cargo test

# Build Python bindings
maturin develop

# Build the shared library for Go, JS/TS, and C# bindings
cargo build --release --lib
# Output: target/release/libpdf_oxide.{so,dylib} or pdf_oxide.dll

Documentation

• Full Documentation — Complete documentation site
• Getting Started (Rust) — Rust guide
• Getting Started (Python) — Python guide
• Getting Started (Go) — Go guide
• Getting Started (JavaScript / TypeScript) — Node.js guide
• Getting Started (C# / .NET) — .NET guide
• Getting Started (WASM) — Browser and Node.js WASM guide
• API Docs — Full Rust API reference
• Performance Benchmarks — Full benchmark methodology and results

Use Cases

• RAG / LLM pipelines — Convert PDFs to clean Markdown for retrieval-augmented generation with LangChain, LlamaIndex, or any framework
• AI assistants — Give Claude, Cursor, or any MCP-compatible tool direct PDF access via the MCP server
• Document processing at scale — Extract text, images, and metadata from thousands of PDFs in seconds
• Data extraction — Pull structured data from forms, tables, and layouts
• Academic research — Parse papers, extract citations, and process large corpora
• PDF generation — Create invoices, reports, certificates, and templated documents programmatically
• PyMuPDF alternative — MIT licensed, 5× faster, no AGPL restrictions

Why I built this

I needed PyMuPDF's speed without its AGPL license, and I needed it in more than one language. Nothing existed that ticked all three boxes — fast, MIT, multi-language — so I wrote it. The Rust core is what does the real work; the bindings for Python, Go, JS/TS, C#, and WASM are thin shells around the same code, so a bug fix in one lands in all of them. It now passes 100% of the veraPDF + Mozilla pdf.js + DARPA SafeDocs test corpora (3,830 PDFs) on every platform I've tested.

If it's useful to you, a star on GitHub genuinely helps. If something's broken or missing, open an issue — I read all of them.

— Yury

License

Dual-licensed under MIT or Apache-2.0 at your option. Unlike AGPL-licensed alternatives, pdf_oxide can be used freely in any project — commercial or open-source — with no copyleft restrictions.

Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

cargo build && cargo test && cargo fmt && cargo clippy -- -D warnings

Citation

@software{pdf_oxide,
  title = {PDF Oxide: Fast PDF Toolkit for Rust, Python, Go, JavaScript, and C#},
  author = {Yury Fedoseev},
  year = {2025},
  url = {https://github.com/yfedoseev/pdf_oxide}
}

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Rust + Python + Go + JS/TS + C# + WASM + CLI + MCP | MIT/Apache-2.0 | 100% pass rate on 3,830 PDFs | 0.8ms mean | 5× faster than the industry leaders