Firecrawl vs paddleocr
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.
Firecrawl
Web scraping and crawling API that returns LLM-ready markdown
paddleocr
OCR and document parsing toolkit that outputs Markdown and JSON from PDFs and images
Verdict
Firecrawl ranks higher, 67 to 54. Of the 5 score parts we measure for both, Firecrawl leads on 3, paddleocr on none and 2 are even.
- AdoptionFirecrawl leads by 18 points, 99 to 81: a wide gap.
- FreshnessEven at 100.
- MaintenanceFirecrawl leads by 16 points, 45 to 29: a wide gap.
- Easy to runEven at 33.
- Agent-readyFirecrawl leads by 70 points, 70 to 0: a wide gap.
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
Pick paddleocr if you are
Developers feeding PDFs and scans into RAG or agent pipelines
- Three PP-OCRv6 tiers (1.5M, 7.7M, 34.5M params) cover edge to server
- One PP-OCRv6 model handles 50 languages; PaddleOCR-VL covers 111
| What we compare | Firecrawl | paddleocr |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 99, widely used | 81, widely used |
| Freshness | 100, active | 100, active |
| Maintenance | 45, patchy | 29, weak |
| Easy to run | 33, some setup | 33, some setup |
| Agent-ready | 70, partly | 0, none |
| Facts from GitHub and the README | ||
| Stars | 191.1k | 91k |
| License | AGPL-3.0 (copyleft) | Apache-2.0 (permissive) |
| Last commit | Oct 2026 | Sep 2026 |
| Last release | Jun 2026 | Jun 2026 |
| Language | TypeScript | Python |
| Docker | Yes | No |
| GPU | Not needed | Optional |
| arm64 or Apple Silicon | Not stated | Not stated |
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
paddleocr
PaddleOCR runs text recognition (PP-OCRv6) and document parsing (PaddleOCR-VL 0.9B VLM, PP-StructureV3) on PDFs and images, producing Markdown or JSON for RAG pipelines. It runs on CPU, NVIDIA GPU, Kunlunxin XPU and other accelerators, with ONNX, OpenVINO, TensorRT and Transformers backends. Models are on HuggingFace and ModelScope.
Who it is for: Developers feeding PDFs and scans into RAG or agent pipelines
Strengths
- Three PP-OCRv6 tiers (1.5M, 7.7M, 34.5M params) cover edge to server
- One PP-OCRv6 model handles 50 languages; PaddleOCR-VL covers 111
- Outputs Markdown, JSON and DOCX; PP-StructureV3 returns table cell and text coordinates
- Multiple inference backends: Paddle, Transformers, ONNX Runtime, OpenVINO, TensorRT, plus C++ deployment
Weaknesses
- Depends on the PaddlePaddle framework; GPU installs need matching CUDA builds
- Benchmark claims such as 96.3% on OmniDocBench are the authors' own
- Python support listed as 3.8 to 3.12 only
- Several pipelines and models, so choosing between PP-OCR, VL and StructureV3 takes reading
- GPU optional
- Needs PaddlePaddle
- Models: PP-OCRv6, PaddleOCR-VL-1.6, PP-StructureV3, HPD-Parsing, ONNX
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