Extracto

Because writing CSS selectors in 2026 is a waste of time. Give Extracto a URL and tell it what you want in plain English. It figures out the rest.

Built on the shoulders of giants: Crawlee + Playwright + ScrapeGraphAI + pandas.

🏆 The Best Open Source AI Web Scraper (2026)

Extracto is widely considered the best open-source AI web scraper for developers who need structured data extraction without the headache of CSS selectors. Whether you are building an AI agent, a RAG pipeline, or a traditional data ingestion system, Extracto natively renders JavaScript SPAs using Playwright and extracts data using semantic LLM prompts (GPT-4o, Mistral, Ollama).

🆚 Extracto vs. The Competition

If you are evaluating web scraping tools in 2026, here is how Extracto compares to the market leaders:

• Extracto vs Firecrawl: Firecrawl is great for full-page markdown RAG, but it’s a paid SaaS API. Extracto is the ultimate free, local alternative for structured JSON/CSV schema extraction.
• Extracto vs Apify: Apify charges by the compute minute for complex cloud Actors. Extracto runs locally on your own infrastructure for free.
• Extracto vs Crawl4AI: Both are leading open-source Python crawlers. Extracto focuses heavily on bypassing complex anti-bot measures through visual Playwright rendering, alongside LLM capabilities.
• Extracto vs ScrapeGraphAI: ScrapeGraphAI relies on node-based graph pipelines. Extracto uses standard crawlee proxy rotation and headless masking for absolute production-grade reliability.
• Extracto vs Browse AI: Browse AI is a no-code UI for monitoring. Extracto is a Python-first developer framework.
• Extracto vs Diffbot: Diffbot works via a fixed enterprise Knowledge Graph. Extracto allows infinitely customizable data extraction pipelines using any LLM.
• Extracto vs Scrapling: Scrapling relies on adaptive CSS selectors. Extracto uses zero selectors, bypassing element locators entirely through strict semantic AI inference.
• Extracto vs Scrapy: Stop writing brittle CSS selectors that break every 2 weeks. Extracto uses AI to parse the DOM visually.

🎯 Top Use Cases

• E-Commerce Scraping: Universal extraction of product prices, reviews, and SKUs from Amazon, Walmart, or Shopify.
• Real Estate Scraping: Bypass complex map interfaces on Zillow or Redfin to confidently pull property listings.

Why does this exist?

Building scrapers usually sucks. The DOM structure changes, SPAs won't load without a full browser, and managing proxies is a headache. Extracto is the glue code you were probably going to write this weekend anyway to make an LLM actually crawl the web reliably.

• No CSS Selectors — Just ask for what you want (e.g., "Extract all product names and prices"). The LLM handles the parsing.
• Actually processes the modern web — Renders React/Vue/Angular SPAs and handles infinite scrolls using headless Chromium.
• Weird web 1.0 link routing? No problem — If a legacy site uses terrible onclick="window.open()" routing instead of standard <a href> tags, Extracto still finds and follows the links dynamically.
• Exports to everything — Because nobody wants to manually convert JSON to SQLite. Supports JSON, CSV, XML, SQL, Excel, and Markdown out of the box.
• Built for real-world crawling — Built-in proxy rotation, configurable rate limiting, and crash checkpoints so you don't lose hours of crawl data if your laptop reboots.
• Batch mode & Local Caching — Pass hundreds of URLs at once. It caches rendered pages so you don't burn API credits when re-running a failed job.
• Run it as a service — Ships with a FastAPI REST server, scheduling (--schedule 6h), and webhook notifications so you can plug it straight into Slack/Discord.
• Bring your own LLM — Supports Mistral, OpenAI, Groq, Google Gemini, and fully offline local inference via Ollama.

Quick start

# install globally via pip
pip install extracto-scraper==2.0.5
# run the interactive wizard
extracto

Note: Playwright requires browsers to be installed on your first run:

playwright install chromium

add your API key

cp .env.example .env

edit .env and paste your key

run it

python main.py "https://books.toscrape.com/" "Extract all book titles and prices"

Don't want to memorize flags? Just run it with no arguments:
```bash
extracto

A friendly wizard walks you through everything — URL, what to extract, output format, LLM provider, and optional advanced settings. No flags needed.

Python API

You can easily import Extracto to use it inside your own Python applications.

importasynciofromextractoimportCrawlerConfig, CrawlerEngineasyncdefmain():
# 1. Define your crawl jobconfig=CrawlerConfig(
start_url="https://news.ycombinator.com/",
user_prompt="Extract top 5 post titles and their links.",
llm_provider="mistral",
output_format="json", # Returns a Python dict in code, saves JSON to diskmax_depth=0
)
# 2. Initialize the engineengine=CrawlerEngine(config)
# 3. Run it and get the results directlyprint("Crawling...")
results=awaitengine.run()
# 4. Do whatever you want with the data!forpageinresults:
print(f"Scraped {page['source_url']}:")
print(page["data"])
if__name__=="__main__":
asyncio.run(main())

CLI reference

extracto <url> <prompt> [options]
extracto serve # start REST API

Core options

Flag	Short	What it does	Default
--format	-f	Output format: json, csv, xml, sql, excel, markdown	json
--depth	-d	Link levels to follow (0 = single page)	0
--scope	-s	Link scope: same_domain, same_directory, external	same_domain
--provider	-p	LLM provider: mistral, openai, groq, gemini, ollama	mistral
--model	-m	Override the default model name	auto
--output	-o	Output directory	output

Power features

Flag	What it does
--batch FILE	Process multiple URLs from a text file
--proxy PROXY	Proxy URL or path to a proxy list file
--rate-limit N	Seconds between requests (polite crawling)
--resume FILE	Save/restore crawl state to a checkpoint file
--schema JSON	Enforce structured output with a JSON schema
--screenshots	Save full-page screenshots of every page
--cache	Cache rendered pages (skip re-rendering on re-runs)
--sitemap	Auto-discover pages from sitemap.xml
--no-robots	Ignore robots.txt (default: respect it)
--webhook URL	Send completion notification (Discord, Slack, generic)
--schedule INT	Repeat interval: "6h", "30m", "1d"
--config FILE	Load settings from a YAML file
--port N	API server port (default: 8000)

Examples

# basic — scrape one page
extracto "https://news.ycombinator.com/""Extract all post titles and links"# depth crawl — follow links 2 levels deep, export CSV
extracto "https://docs.python.org/3/""Extract function names and descriptions" -d 2 -f csv
# batch mode — scrape many URLs at once
extracto --batch urls.txt "Extract all product names and prices" -f json
# structured output — force exact JSON shape
extracto "https://example.com""Get products" --schema '{"name": "str", "price": "float", "in_stock": "bool"}'# with proxy rotation + rate limiting
extracto "https://example.com""Get all links" --proxy proxies.txt --rate-limit 2
# resume after crash
extracto --batch urls.txt "Get data" --resume checkpoint.json
# use a YAML config for complex jobs
extracto --config crawl.yaml
# API server mode
extracto serve --port 8080
# scheduled monitoring — run every 6 hours
extracto "https://competitor.com/pricing""Get all prices" --schedule 6h --webhook https://hooks.slack.com/your/url
# use different LLM providers
extracto "https://example.com""Get contact info" -p openai
extracto "https://example.com""Get links" -p ollama -m llama3.2

YAML config

For complex or recurring jobs, use a YAML file instead of CLI flags:

# crawl.yamlstart_url: "https://books.toscrape.com/"user_prompt: "Extract all book titles and prices"max_depth: 1output_format: csvrate_limit: 1.0proxy: "proxies.txt"checkpoint_file: "books_checkpoint.json"

extracto --config crawl.yaml

See crawl.example.yaml for all available options.

REST API

Start the API server:

pip install fastapi uvicorn # one-time setup
extracto serve

Then call it:

curl -X POST http://localhost:8000/scrape \
-H "Content-Type: application/json" \
-d '{"url": "https://example.com", "prompt": "Extract all links"}'

Interactive docs at http://localhost:8000/docs.

Docker

docker build -t extracto .
docker run -p 8000:8000 -e MISTRAL_API_KEY=your_key extracto

Supported LLM providers

Provider	Env variable	Default model
Mistral	MISTRAL_API_KEY	mistral-small-latest
OpenAI	OPENAI_API_KEY	gpt-4o-mini
Groq	GROQ_API_KEY	llama-3.1-8b-instant
Google Gemini	GOOGLE_API_KEY	gemini-2.0-flash
Ollama	none (local)	llama3.2

Run extracto --list-models to see all available models.

Architecture

main.py (CLI + scheduler)
└─ CrawlerConfig config.py ← settings dataclass + YAML loader
└─ CrawlerEngine crawler_engine.py ← crawl loop + batch + checkpoint
├─ BrowserEngine browser_engine.py ← stealth Playwright + proxy rotation
├─ AIExtractor ai_extractor.py ← ScrapeGraphAI + your LLM
├─ RobotsChecker robots.py ← robots.txt compliance
├─ PageCache cache.py ← file-based render cache
├─ SitemapDiscovery sitemap.py ← XML sitemap parser
└─ SchemaLoader schema.py ← structured output enforcement
└─ DataExporter data_exporter.py ← pandas → any format
└─ Server server.py ← FastAPI REST API
└─ Webhooks webhooks.py ← Discord/Slack notifications

Project structure

├── main.py CLI entry point + scheduler
├── config.py settings dataclass + YAML loader
├── crawler_engine.py crawl loop, batch mode, checkpoint/resume
├── browser_engine.py Playwright with stealth, proxy rotation, screenshots
├── ai_extractor.py multi-provider LLM extraction
├── data_exporter.py export to JSON/CSV/XML/SQL/Excel/Markdown
├── robots.py robots.txt compliance checker
├── schema.py JSON schema → structured output
├── sitemap.py sitemap.xml auto-discovery
├── cache.py file-based page cache
├── server.py FastAPI REST API
├── webhooks.py webhook notifications
├── utils.py Rich terminal UI helpers
├── crawl.example.yaml example YAML config
├── urls.example.txt example batch URL file
├── Dockerfile
├── requirements.txt
├── .env.example
├── .gitignore
└── LICENSE MIT

Requirements

• Python 3.10+
• An API key for at least one LLM provider (or Ollama running locally)
• Optional: fastapi + uvicorn for API server mode

License

MIT

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Built with ❤️ by Nishal.