docling.rs

docling.rs converts documents — PDF, Office, HTML, scans, audio — into Markdown, docling JSON and the other shapes an LLM or a RAG pipeline can read. It is a Rust port of IBM's docling: the same output, byte-for-byte, from one static binary. No Python, no PyTorch.

Pulling text out of a PDF is easy. Keeping its structure — headings in order, tables as tables, scanned pages read — takes layout analysis, table recognition and OCR. docling has the models for that, and a Python stack to match. docling.rs runs the same models from a single binary: PDF 4.3× faster than Python docling at under half the RAM, Office and HTML 20–60× faster, and tables that survive — cell recall 0.94 where MarkItDown gets 0.31 (numbers, method).

Install

# CLI + models (Linux x64/arm64 prebuilt; builds from source elsewhere)
curl -fsSL https://raw.githubusercontent.com/docling-project/docling.rs/master/scripts/install/install.sh | bash
docling-rs example.pdf          # Markdown on stdout

# or from crates.io; --cli puts the models where the installed binary looks
cargo install docling-cli
curl -fsSL https://raw.githubusercontent.com/docling-project/docling.rs/master/scripts/install/download_dependencies.sh | sh -s -- --cli

pip install docling-rs          # Python — drop-in for docling's and MarkItDown's APIs
npm install docling.rs          # Node.js / Bun
cargo add docling               # Rust library

# containers, models baked in (-cuda variants for NVIDIA)
docker run --rm -v "$PWD:/data" ghcr.io/docling-project/docling-rs paper.pdf
docker run -p 127.0.0.1:5001:5001 ghcr.io/docling-project/docling-rs-serve

Office, HTML, Markdown and e-mail need no models. PDF, images and audio need the ONNX models (layout, OCR, TableFormer, Whisper; under 1 GB): install.sh, --cli and the containers bring them along; with pip, run python -c "import docling_rs; docling_rs.download_models()" once; with npm or a checkout, run download_dependencies.sh where you convert (details). Windows: docs/WINDOWS.md.

▶ Try it in your browser first — the converter as wasm, OCR included; nothing leaves your device.

Use

docling-rs report.pdf > report.md               # Markdown (streamed page by page)
docling-rs report.pdf --to json                 # docling JSON; also html, text, latex, dclx, chunks, images
docling-rs --input ./docs --output ./out        # a directory tree; --jobs N in parallel
docling-rs https://example.com/paper.pdf        # URLs, data: URIs and stdin (`-`) too
docling-rs --help                               # every flag: OCR, page ranges, images, enrichment, redaction …

from docling_rs import DocumentConverter       # was: from docling.document_converter import …
doc = DocumentConverter().convert("report.pdf").document
print(doc.export_to_markdown())

import { convertFileAsync } from 'docling.rs'
const { content } = await convertFileAsync('report.pdf', { to: 'markdown' })

use docling::{DocumentConverter, SourceDocument};
let result = DocumentConverter::new().convert(SourceDocument::from_file("report.pdf")?)?;
println!("{}", result.document.export_to_markdown());

curl -F file=@report.pdf 'localhost:5001/v1/convert?to=md'   # the server; OpenAPI at /openapi.yaml

Same options, same outputs on every surface — every option in docs/OPTIONS.md.

Highlights

• From MarkItDown: pip install docling-rs, swap one import — from docling_rs.markitdown import MarkItDown — and keep its API with real layout, tables and OCR behind it (guide).
• From Python docling: from docling_rs import DocumentConverter; result.document is a genuine DoclingDocument (migration notes).
• Every docling format, and a few it lacks — the table under Status, each validated against live docling (Conformance).
• Every output docling has: Markdown (docling's, or a stricter dialect), docling JSON, HTML, plain text, DocLang, LaTeX, Pandoc AST (→ DOCX, EPUB, Typst… through pandoc), WebVTT subtitles, RAG chunks and page images (output formats).
• From any language: Rust, Python, Node.js / Bun, the browser (wasm, fully client-side), C, C++, C#, Go, Java and Swift through one C header, any HTTP client, the shell (Integration).
• A drop-in server for docling-serve: Open WebUI uses it as its Docling content-extraction engine — point Docling Server URL at http://<host>:5001 — and n8n, Dify and LangChain clients work unchanged (docling-rs serve, Open WebUI setup).
• AI agents (MCP): docling-rs mcp gives Claude Desktop, Claude Code, Cursor or VS Code MarkItDown's convert_to_markdown tool, plus convert for JSON, chunks and the rest — uvx docling-rs-mcp, or claude mcp add docling -- docling-rs mcp (MCP server).
• LangChain: pip install "docling-rs[langchain]" — langchain-docling's DoclingLoader on the Rust engine, one import to swap (details).
• AutoGen: pip install "docling-rs[autogen]" — DoclingIndexer fills any AutoGen memory with chunks that carry their pages, DoclingConvertTool lets an agent read documents (details).
• RAG: docling-rag — chunk, embed and search with a REST API and a web UI (RAG subsystem).
• Runs where you are: Linux x64 / arm64 / s390x and Windows prebuilt (CLI, wheels, npm), macOS from source, containers for amd64 and arm64; NVIDIA CUDA and TensorRT, DirectML, CoreML and WebGPU in the browser as opt-in accelerators (GPU, IBM Z).

Developed with Claude Code and TENET (minimalistic AI-driven development framework).

Status

Every format in docling's pipeline converts — plus a few docling lacks — and a file that fails under its extension is checked by content and converted as what it really is, with a warning.

Every extension, with what each backend does: docs/MIGRATION.md.

Any ffmpeg ≥ 4.x on PATH works; docling.rs shells out to the binary and parses its output, so no dev headers/libraries are needed.

• Debian/Ubuntu: sudo apt-get install ffmpeg
• Fedora: sudo dnf install ffmpeg-free (or ffmpeg from RPM Fusion)
• Alpine: apk add ffmpeg
• macOS: brew install ffmpeg
• Windows: winget install ffmpeg (or choco install ffmpeg, or scoop install ffmpeg). Installing from a downloaded zip (gyan.dev / BtbN) also works — either add the extracted bin\ folder to PATH, or skip PATH entirely and point DOCLING_FFMPEG at the exe: set DOCLING_FFMPEG=C:\tools\ffmpeg\bin\ffmpeg.exe

Check with ffmpeg -version. DOCLING_FFMPEG overrides the binary used on any OS; the docling-rs-serve Docker image ships ffmpeg preinstalled.

Conformance with Microsoft MarkItDown

MarkItDown converts the same kinds of documents to Markdown for LLMs. Measured side by side — MarkItDown 0.1.8 (markitdown[all]) and docling.rs 2.16.0 (release build), 4 vCPU, no GPU — on this repository's test corpus and on MarkItDown's own OCR test set (scripts/conformance/markitdown_compare.py):

Accuracy is scored against Python docling's groundtruth, which favours docling.rs by construction; the timings do not depend on it. Text drawn into images (MarkItDown's own OCR test set) reads at 1.00 with docling.rs' local OCR, against 0.00–0.33 for MarkItDown without an LLM. Method, caveats and the full OCR comparison: docs/MARKITDOWN.md.

Conformance with Python docling

Every declarative format is compared against the live library — Markdown byte for byte, JSON structurally (every key and value but origin/version and re-encoded image bytes). Latest sweep, docling 2.129.0 (scripts/conformance/full_conformance.py; ✅ all = every file matches):

¹ 1706.03762: one tabular's cells carry consistent row_span/col_span where upstream writes the span into the offsets only; PDF figures carry no image payload (upstream renders them with pypdfium2).

² xlsx_02_sample_sales_data: 20 date cells print by docling PR #4628's number-format rules (#634), which the released docling does not apply yet.

The residuals, with the exact files: docs/MIGRATION.md. The PDF/image pipeline is pinned by a deterministic snapshot baseline and scored against docling's groundtruth in docs/PDF_CONFORMANCE.md.

Performance

One fixture per format through published Python docling and the Rust release binary (scripts/test/performance.sh); docling ÷ docling.rs, so bigger means Rust wins by more.

† A 4 KB CSV converts in microseconds, so Rust's own process startup dominates its number; end to end it is 870× faster.

Declarative formats hold a few MB against docling's ~750 MB (it imports torch even for them) and finish 300–870× sooner end to end; the PDF peaks at 0.37 GB against 2.4 GB and converts 28.5× faster end to end. Where the time goes, and what was measured not to help: docs/PDF_CONFORMANCE.md.

Integration — using docling.rs from your language

One engine, several front doors, the same options and outputs everywhere.

Rust when you're already in Cargo; the CLI for scripts and batch jobs; serve when several services share one warm pipeline; Node / Python in-process; wasm when the document must not leave the user's machine; FFI for anything that can call C.

Rust API

use docling::{DocumentConverter, SourceDocument};

let converter = DocumentConverter::new();
let result = converter.convert(SourceDocument::from_file("input.md")?)?;

println!("{}", result.document.export_to_markdown()); // Markdown
println!("{}", result.document.export_to_json());     // docling DoclingDocument JSON

Every conversion knob is one serializable struct, docling::ConvertOptions, filled the same way by every surface (docs/OPTIONS.md). The library guide covers the output formats, error handling, chunking, image extraction, streaming, OCR, the PDF switches and the remote VLM pipeline.

HTTP conversion API — docling-rs serve

docling-serve exposes the converter over HTTP with one warm PDF/image pipeline shared across requests (repeat PDFs skip the model load), streamed Markdown, batches, ZIP inputs, async jobs, an interactive test form at GET / and an OpenAPI 3.1 description at GET /openapi.yaml. It also serves Python docling-serve's routes, so Open WebUI, n8n, Dify and LangChain clients work unchanged.

docker run -p 127.0.0.1:5001:5001 ghcr.io/docling-project/docling-rs-serve
curl -F file=@paper.pdf localhost:5001/v1/convert                # Markdown
curl -F file=@report.docx 'localhost:5001/v1/convert?to=json'    # docling JSON
curl -F file=@a.pdf -F file=@b.docx localhost:5001/v1/convert    # a batch

Request options, server flags, async jobs, the docling-serve routes and the Open WebUI setup: crates/docling-serve/README.md; containers and sizing: docs/DEPLOYMENT.md.

MCP server — docling-rs mcp

docling-rs mcp is a Model Context Protocol server in the default docling-rs binary and image. Agents get MarkItDown's convert_to_markdown(uri) tool — same name and contract, so a markitdown-mcp setup switches by changing the command — plus convert(uri, to, options) and list_input_formats().

{
  "mcpServers": {
    "docling": { "command": "docling-rs", "args": ["mcp", "--root", "/Users/me/Documents"] }
  }
}

claude mcp add docling -- docling-rs mcp --root ~/Documents     # Claude Code
uvx docling-rs-mcp --root ~/Documents                           # the same server from PyPI

Local reads stay under --root; URLs need --allow-url-fetch. The PyPI launcher, Docker, Streamable HTTP (--http), the security model and memory behaviour: crates/docling-cli/README.md.

RAG subsystem

crates/docling-rag turns documents into chunks, embeds them (Ollama, Gemini or a local ONNX model), stores them (SQLite + sqlite-vec or PostgreSQL + pgvector) and searches them — hybrid, multi-query and HyDE retrieval, an evaluation harness, a REST API and a built-in search UI.

In the browser — docling-wasm

The declarative converters and the PDF text layer compile to WebAssembly: npm i docling.rs-wasm, then convert(bytes, filename, to) runs fully client-side — no server, a ~3.4 MB gzipped module, optional in-browser OCR. Details and the demo page: crates/docling-wasm.

Node.js, Python and C

• Node.js / Bun — docling.rs on npm, a prebuilt N-API addon: sync and async conversion, streaming, chunking, ZIP batches, a warm Pipeline.
• Python — docling-rs on PyPI (docling-rs-cuda for NVIDIA): docling's DocumentConverter with a genuine docling-core DoclingDocument, MarkItDown's API, and the LangChain and AutoGen integrations.
• C ABI — docling-ffi: one library and one docling.h for C, C++, C#/.NET, Go, Java, Swift and Zig, prebuilt in every release.

Deploy in a container

Compose files (standalone, Caddy TLS, CUDA), environment variables, sizing and observability: docs/DEPLOYMENT.md.

Getting the ML models

The PDF/image pipeline needs ONNX models — RT-DETR layout, PP-OCR, and TableFormer — and audio needs Whisper. download_dependencies.sh fetches them from this repository's releases into ./.models, where every surface looks (--cli: beside a cargo-installed binary instead):

scripts/install/download_dependencies.sh                       # from a checkout
curl -fsSL https://raw.githubusercontent.com/docling-project/docling.rs/master/scripts/install/download_dependencies.sh | sh         # anywhere

Every asset and option (ASR presets, enrichment models, NER for PII redaction, INT8, fonts), GPU execution providers and the environment overrides: docs/MODELS.md. IBM Z and other targets without a prebuilt ONNX Runtime: docs/IBMZ.md.

Contributing

Bug reports and pull requests are welcome — see CONTRIBUTING.md for the build and test commands, the conformance workflow, and the conventions a change is expected to follow.

License

MIT, matching upstream docling.