docling vs Firecrawl
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
Firecrawl
Web scraping and crawling API that returns LLM-ready markdown
Verdict
docling ranks higher, 70 to 67. Of the 5 score parts we measure for both, docling leads on 3, Firecrawl on 1 and 1 is even.
- AdoptionFirecrawl leads by 47 points, 99 to 52: a wide gap.
- FreshnessEven at 100.
- Maintenancedocling leads by 36 points, 81 to 45: a wide gap.
- Easy to rundocling leads by 17 points, 50 to 33: a wide gap.
- Agent-readydocling leads by 15 points, 85 to 70: 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 Firecrawl if you are
Teams feeding web content to RAG pipelines and agents
- Seven SDKs plus CLI and MCP server; SDKs poll async crawl jobs automatically
- Crawl, map and batch-scrape endpoints return job IDs for large sites
| What we compare | docling | Firecrawl |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 52, popular | 99, widely used |
| Freshness | 100, active | 100, active |
| Maintenance | 81, healthy | 45, patchy |
| Easy to run | 50, easy | 33, some setup |
| Agent-ready | 85, ready | 70, partly |
| Facts from GitHub and the README | ||
| Stars | 68.8k | 191.1k |
| License | MIT (permissive) | AGPL-3.0 (copyleft) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Oct 2026 | Jun 2026 |
| Language | Python | TypeScript |
| Docker | Yes | Yes |
| GPU | Optional | Not needed |
| arm64 or Apple Silicon | Mentioned | Not stated |
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
Firecrawl
API that turns URLs into markdown, HTML, screenshots or schema-based JSON, with endpoints for search, scrape, crawl, map, batch scrape, page interaction and a prompt-driven agent. Handles JS-rendered pages and parses hosted PDFs and DOCX. SDKs for Python, Node, Go, Java, Elixir, Rust and Ruby plus an MCP server and CLI, for teams feeding web content to RAG pipelines and agents.
Who it is for: Teams feeding web content to RAG pipelines and agents
Strengths
- Seven SDKs plus CLI and MCP server; SDKs poll async crawl jobs automatically
- Crawl, map and batch-scrape endpoints return job IDs for large sites
- Scrape supports actions (click, scroll, write, wait) before extraction
- Compose file at the repo root for self-hosting
Weaknesses
- README is written around the hosted API and keys; self-hosting lives in separate docs
- AGPL-3.0 license; network use of a modified version triggers source obligations
- Agent endpoint runs the hosted spark-2 model, not a local LLM
- Proxy rotation and anti-bot handling are hosted-service features
- no GPU
- Compose
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