Firecrawl vs langextract
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
langextract
Python library that extracts source-grounded structured data from text using LLMs
Verdict
Firecrawl ranks higher, 67 to 57. Of the 5 score parts we measure for both, Firecrawl leads on 2, langextract on 1 and 2 are even.
- AdoptionFirecrawl leads by 60 points, 99 to 39: a wide gap.
- FreshnessEven at 100.
- Maintenancelangextract leads by 37 points, 82 to 45: 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 langextract if you are
Python developers extracting structured data from long documents
- Each extraction is grounded to a character span; ungrounded results get char_interval = None
- Chunking, parallel workers and multiple passes for long documents (max_workers, extraction_passes)
| What we compare | Firecrawl | langextract |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 99, widely used | 39, known |
| Freshness | 100, active | 100, active |
| Maintenance | 45, patchy | 82, healthy |
| Easy to run | 33, some setup | 33, some setup |
| Agent-ready | 70, partly | 0, none |
| Facts from GitHub and the README | ||
| Stars | 191.3k | 38.9k |
| License | AGPL-3.0 (copyleft) | Apache-2.0 (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Jun 2026 | Oct 2026 |
| Language | TypeScript | Python |
| Docker | Yes | Yes |
| 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
langextract
LangExtract is a Python library that takes a prompt and few-shot examples, runs an LLM over unstructured text, and returns structured extractions mapped to exact character spans in the source. Long documents are handled by chunking, parallel workers and multiple passes. Results save to JSONL and render as an interactive HTML review page. Backends include Gemini, OpenAI and local Ollama models.
Who it is for: Python developers extracting structured data from long documents
Strengths
- Each extraction is grounded to a character span; ungrounded results get char_interval = None
- Chunking, parallel workers and multiple passes for long documents (max_workers, extraction_passes)
- Gemini, OpenAI and Ollama supported, plus a plugin system for custom providers
- Generates a self-contained interactive HTML view of extractions in context
Weaknesses
- Output schema is driven by few-shot examples; poor examples cause alignment warnings and weaker results
- Ollama provider does not support output_schema
- Controlled generation is documented mainly for Gemini; cloud models need an API key
- Library only, with no server or UI beyond the HTML visualization
- GPU optional
- Docker
- Needs Gemini, OpenAI or Vertex AI API key for cloud models, Ollama for local models
- Models: Gemini, OpenAI, Ollama, Vertex AI, OpenAI-compatible endpoints
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