Crawl4AI 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.
Crawl4AI
Python web crawler that converts pages to LLM-ready Markdown and JSON
langextract
Python library that extracts source-grounded structured data from text using LLMs
Verdict
Crawl4AI ranks higher, 74 to 57. Of the 5 score parts we measure for both, Crawl4AI leads on 3, langextract on none and 2 are even.
- AdoptionCrawl4AI leads by 35 points, 74 to 39: a wide gap.
- FreshnessEven at 100.
- MaintenanceCrawl4AI leads by 1 point, 83 to 82: close to even.
- Easy to runCrawl4AI leads by 34 points, 67 to 33: a wide gap.
- Agent-readyEven at 0.
Pick Crawl4AI if you are
Developers feeding web content into RAG, agents and data pipelines
- Schema-based CSS/XPath extraction runs without any LLM call
- Fit Markdown filters (pruning, BM25, LLM) strip menus and boilerplate
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 | Crawl4AI | langextract |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 74, popular | 39, known |
| Freshness | 100, active | 100, active |
| Maintenance | 83, healthy | 82, healthy |
| Easy to run | 67, easy | 33, some setup |
| Agent-ready | 0, none | 0, none |
| Facts from GitHub and the README | ||
| Stars | 85.5k | 38.9k |
| License | Apache-2.0 (permissive) | Apache-2.0 (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Sep 2026 | Oct 2026 |
| Language | Python | Python |
| Docker | Yes | Yes |
| GPU | Not needed | Optional |
| arm64 or Apple Silicon | Mentioned | Not stated |
Crawl4AI
Crawl4AI is a Python library, CLI (`crwl`) and Docker REST server that drives Playwright browsers (Chromium, Firefox, WebKit) to turn web pages into clean or filtered Markdown. It extracts structured data with CSS/XPath/regex schemas without an LLM, or through any LiteLLM-supported provider into a typed JSON schema. It also supports deep crawls, adaptive crawling and batch fetching. A hosted Crawl4AI Cloud with search and answer endpoints is sold separately.
Who it is for: Developers feeding web content into RAG, agents and data pipelines
Strengths
- Schema-based CSS/XPath extraction runs without any LLM call
- Fit Markdown filters (pruning, BM25, LLM) strip menus and boilerplate
- Persistent profiles, CDP remote browsers, rotating proxies and stealth mode
- Docker server exposes /md, /crawl, /screenshot, /pdf, plus MCP, dashboard and playground
Weaknesses
- Search, SERP and answer endpoints exist only in the paid hosted cloud
- Bot-wall handling when self-hosted depends on your own proxies and settings
- Needs a Playwright browser install via crawl4ai-setup
- README asks for attribution when you use it
- no GPU
- Docker + Compose
- Needs Playwright browsers (Chromium), Docker (optional), LLM provider API key (optional)
- Models: LiteLLM providers, OpenAI, Ollama
- port 11235
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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