Firecrawl vs Jina Reader
Two of the top data and scraping for ai, side by side: score, setup, license, activity and what each review found.
Firecrawl
Web scraping and crawling API that returns LLM-ready markdown
Jina Reader
Converts any URL or search query into LLM-friendly markdown
| What we compare | Firecrawl | Jina Reader |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 99, widely used | 20, niche |
| Freshness | 100, active | 81, active |
| Maintenance | 45, patchy | 15, weak |
| Easy to run | 33, some setup | 50, easy |
| Agent-ready | 70, partly | 40, minimal |
| Facts from GitHub and the README | ||
| Stars | 190.3k | 12.1k |
| License | AGPL-3.0 (copyleft) | Apache-2.0 (permissive) |
| Last commit | Oct 2026 | May 2026 |
| Last release | Jun 2026 | None published |
| Language | Not stated | Not stated |
| Docker | Yes | Yes |
| GPU | Not needed | Not needed |
| 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
Jina Reader
Open-source branch of the service behind r.jina.ai and s.jina.ai: fetches a page with headless Chrome or curl-impersonate, parses PDFs and Office files, and returns markdown, text, HTML, screenshots or JSON controlled by request headers (engine, timeout, token limits). The ghcr.io image bundles Chrome, LibreOffice and CJK fonts, serves HTTP/1.1 on 8081 and h2c on 8080, and runs stateless or with S3-compatible caching.
Who it is for: Developers feeding web pages and documents to LLMs
Strengths
- Prebuilt image with Chrome, LibreOffice and CJK fonts; stateless by default
- Fine-grained headers: x-respond-timing, x-max-tokens, x-token-budget, x-target-selector
- Optional VLM captions for images without alt text
- Semantic markdown chunking by heading or block level
Weaknesses
- Hosted proxy pool, rate limiting and MongoDB storage layer are not in the OSS branch
- Needs non-redistributable assets (MaxMind GeoLite2, Source Han Sans) fetched at build
- Last commit May 2026; the SaaS resync was April 2026
- Default h2c port 8080 needs --http2-prior-knowledge from curl; use 8081 otherwise
- no GPU
- Docker + Compose
- Needs Headless Chrome and LibreOffice (bundled in image), S3-compatible bucket (optional cache), VLM endpoint for image captions (optional)
- port 8081