docling vs label-studio
Two of the top projects in Data and scraping for AI, side by side: who leads on each score part and who each one suits, then setup, license, activity and what each review found.
docling
Python library and CLI that converts PDFs, Office files and more into structured text
label-studio
Multi-user data labeling tool for audio, text, images, video and time series
Verdict
docling ranks higher, 78 to 72. Of the 5 score parts we measure for both, docling leads on 3, label-studio on 1 and 1 is even.
- Adoptiondocling leads by 32 points, 82 to 50: a wide gap.
- FreshnessEven at 100.
- Maintenancedocling leads by 13 points, 88 to 75.
- Easy to runlabel-studio leads by 33 points, 83 to 50: a wide gap.
- Agent-readydocling leads by 85 points, 85 to 0: a wide gap.
Pick docling if you are
Developers preparing documents for RAG and LLM pipelines
- Wide input coverage: PDF, Office, ODF, EPUB, email, Apple iWork, XBRL, audio, video
- Local execution suits sensitive data and air-gapped environments
Pick label-studio if you are
ML teams that need to annotate and manage training data
- Handles images, audio, text, HTML, video and time series in one instance
- Installs via pip, Docker, or a Compose stack with Nginx and PostgreSQL
| What we compare | docling | label-studio |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 82, widely used | 50, popular |
| Freshness | 100, active | 100, active |
| Maintenance | 88, healthy | 75, fair |
| Easy to run | 50, easy | 83, very easy |
| Agent-ready | 85, ready | 0, none |
| Facts from GitHub and the README | ||
| Stars | 69.2k | 28.5k |
| License | MIT (permissive) | Apache-2.0 (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Oct 2026 | Sep 2026 |
| Language | Python | TypeScript |
| Docker | Yes | Yes |
| GPU | Optional | Not needed |
| arm64 or Apple Silicon | Mentioned | Mentioned |
docling
Docling parses PDF, DOCX, PPTX, XLSX, HTML, EPUB, images, audio and other formats into a unified DoclingDocument, with PDF layout, reading order, table structure, code and formula recognition. It exports to Markdown, HTML, DocLang, DocTags and lossless JSON. It runs locally via a CLI or Python API, with OCR, VLM support (GraniteDocling), an MCP server and a docling-serve API server.
Who it is for: Developers preparing documents for RAG and LLM pipelines
Strengths
- Wide input coverage: PDF, Office, ODF, EPUB, email, Apple iWork, XBRL, audio, video
- Local execution suits sensitive data and air-gapped environments
- Integrations with LangChain, LlamaIndex, Crew AI and Haystack
- Ships a CLI, Python API, MCP server and docling-serve HTTP server
Weaknesses
- Hardware requirements (RAM, GPU) are not stated in the README
- Individual models carry their own licenses, separate from the MIT codebase
- Metadata extraction and chemistry understanding are listed as coming soon
- Requires Python 3.10 or higher; 3.9 support was dropped in 2.70.0
- GPU optional
- Docker
- Models: GraniteDocling, VLMs, OCR engines, ASR models
label-studio
Label Studio is a web-based annotation tool for preparing and correcting ML training data across images, audio, text, HTML, video and time series. Labeling interfaces are defined in a configuration language, with included templates, and results export to various model formats. It can connect to an ML backend for pre-labeling, online learning and active learning, and has a REST API for pipelines.
Who it is for: ML teams that need to annotate and manage training data
Strengths
- Handles images, audio, text, HTML, video and time series in one instance
- Installs via pip, Docker, or a Compose stack with Nginx and PostgreSQL
- ML backend SDK supports pre-labeling, online learning and active learning
- Imports from S3, GCS and files; REST API for pipeline integration
Weaknesses
- Default install stores data in SQLite; PostgreSQL needs the Compose setup
- Some features are tied to paid editions; README links a comparison page
- ML integration needs a separately run backend server
- Windows install requires manually downloading a prebuilt lxml wheel
- no GPU
- Docker + Compose
- Needs PostgreSQL (optional, Compose), Nginx (optional, Compose), MinIO (optional)
- Models: Custom ML backends via Label Studio ML SDK
- port 8080
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