Docling vs pdf-inspector
Which should you use? A side-by-side look at what each one does, who it suits, what it costs, how hard it is to set up, and where each falls short.
Open source · MIT
Python library that converts PDFs and office files into structured text for AI use.
Open source · MIT
Rust library and CLI that checks whether a PDF has real text and converts it to Markdown locally.
The short answer
Choose Docling if you need…
- Python developers building retrieval or RAG pipelines over PDFs and office files
- Teams that need tables and reading order preserved when extracting document content
- Users who need local processing for sensitive or air-gapped documents
- People who want a command-line tool to convert a document to Markdown
Choose pdf-inspector if you need…
- Developers building pipelines that process many PDFs and need to route scanned ones to OCR
- Teams converting reports, papers, invoices, or legal PDFs into Markdown locally
- Projects that want to avoid paid OCR services for text-based documents
- Browser apps that need PDF text extraction without a server round trip
Side-by-side
| Docling | pdf-inspector | |
|---|---|---|
| Type | Library | Library |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 70k | 19.8k |
| Setup difficulty | Beginner | Intermediate |
| How you get it | Install with a package manager | Install with a package manager |
| Main language | Python | Rust |
| Last code update | 5 days ago | 14 days ago |
What each one is
Docling
Docling parses many document formats, including PDF, Word, PowerPoint, Excel, HTML and email, and turns them into a structured representation. It exports that content to formats such as Markdown, HTML and JSON. People use it to prepare documents for generative AI and retrieval pipelines, and it can run locally for sensitive data.
pdf-inspector
pdf-inspector reads PDF files and first decides whether each one is text-based or scanned, which tells you if it needs OCR (optical character recognition). For text-based PDFs it extracts the text with position information and converts it to clean Markdown without OCR. It is a Rust library with bindings for Python, Node.js, and browser WebAssembly, plus command-line tools.
Best for
Docling
- Python developers building retrieval or RAG pipelines over PDFs and office files
- Teams that need tables and reading order preserved when extracting document content
- Users who need local processing for sensitive or air-gapped documents
- People who want a command-line tool to convert a document to Markdown
pdf-inspector
- Developers building pipelines that process many PDFs and need to route scanned ones to OCR
- Teams converting reports, papers, invoices, or legal PDFs into Markdown locally
- Projects that want to avoid paid OCR services for text-based documents
- Browser apps that need PDF text extraction without a server round trip
Strengths
Docling
- Supports a wide range of input formats in one library, per the README feature list
- Can run locally, which the README highlights for sensitive data and air-gapped setups
- Includes a CLI, a Python API, an MCP server and an API server option
pdf-inspector
- Fast local processing; the README reports text-based PDFs handled in under 200ms and a benchmark run with the fastest total time among compared local engines
- Clear output types (TextBased, Scanned, ImageBased, Mixed) with a confidence score and per-page OCR routing
- Available as a Rust crate, Python and Node packages, a browser WebAssembly build, and CLI tools under the MIT license
Not ideal for
Docling
- Users who need a no-code desktop app, since the README centers on pip install and CLI or Python use
- Projects that need metadata extraction such as title and authors, which the README lists as coming soon
- Chemistry-heavy work, since molecular structure understanding is also listed as coming soon
pdf-inspector
- People who only need a one-off conversion and do not want to install a toolchain, since no prebuilt binaries are listed for the latest release
- Users with mostly scanned PDFs who want OCR as the main feature, because OCR requires separately installed PDFium and ONNX Runtime libraries
- Anyone who needs guaranteed output on unusual layouts, since the README benchmark is a single corpus evaluation
Trade-offs
Docling
- Advanced features such as VLM pipelines and audio or video parsing may need extra setup or models; the README does not detail requirements for each
- Some features are newer, as shown in the What's new section, so behavior may change between releases
pdf-inspector
- Requires Rust tooling for the CLI and Rust library; the latest release lists no binaries
- Selective OCR needs extra external libraries (PDFium and ONNX Runtime) installed separately
- The benchmark is produced by the project's own team and covers only a 200-PDF corpus with OCR disabled
Getting started
pdf-inspector
Python package
pip install pdf-inspectorNode.js package
npm install @firecrawl/pdf-inspectorFrequently asked questions
What is the difference between Docling and pdf-inspector?
Docling: Python library that converts PDFs and office files into structured text for AI use. pdf-inspector: Rust library and CLI that checks whether a PDF has real text and converts it to Markdown locally. Docling suits python developers building retrieval or rag pipelines over pdfs and office files, while pdf-inspector suits developers building pipelines that process many pdfs and need to route scanned ones to ocr.
Are Docling and pdf-inspector free?
Docling: Free and open source. pdf-inspector: Free and open source.
Which is easier to set up, Docling or pdf-inspector?
Docling is easier to get started with (rated beginner); pdf-inspector is rated intermediate and usually needs more setup.
Is Docling or pdf-inspector more popular?
Docling has more GitHub stars (70k) than pdf-inspector (19.8k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to Docling and pdf-inspector?
Other options people compare them with include MarkItDown, Rich, Showdown, remark, Turndown.
Keep comparing
Built from our catalogue entries for both products: open-source details come from each project's README (AI-assisted, refreshed weekly) and GitHub data; AI-tool details are editorial. Spotted something wrong? Let us know from either product's page.