firecrawlvsferret

None - - -

Apr 01 2024 0.0.0(2025-03-15 00:00:00 ago)

5,964 8 34 Apache-2.0

Oct 28 2020 58.1 thousand (month) v2.0.0-alpha.7(2026-04-07 15:33:51 ago)

Firecrawl is an AI-powered web scraping API that converts web pages into clean Markdown or structured data, optimized for use with large language models (LLMs) and retrieval-augmented generation (RAG) pipelines. It handles JavaScript rendering, anti-bot bypass, and content extraction automatically.

Firecrawl offers multiple modes:

Scrape Convert a single URL into clean Markdown, HTML, or structured data. Handles JavaScript rendering and anti-bot protections automatically.
Crawl Crawl an entire website starting from a URL, with configurable depth, URL patterns, and page limits. Returns all pages as clean Markdown.
Map Quickly discover all URLs on a website without fully scraping each page. Useful for sitemap generation and crawl planning.
Extract Use LLMs to extract specific structured data from pages based on a schema definition.

Key features:

Clean Markdown output ideal for LLM context windows
Automatic JavaScript rendering with headless browsers
Built-in anti-bot bypass for protected websites
Structured extraction with JSON schemas
Batch crawling with webhook notifications
Python and JavaScript SDKs

Firecrawl is a commercial API service (requires API key, has a free tier) backed by Y Combinator. It has become one of the most popular tools for feeding web content into AI applications and is widely used in the LLM/RAG ecosystem.

Note: while the primary service is an API, the core is open source and can be self-hosted.

Ferret is a web scraping system. It aims to simplify data extraction from the web for UI testing, machine learning, analytics and more. ferret allows users to focus on the data. It abstracts away the technical details and complexity of underlying technologies using its own declarative language. It is extremely portable, extensible, and fast.

Features

Declarative language
Support of both static and dynamic web pages
Embeddable
Extensible

Ferret is always implemented in Python through pyfer

Highlights

ai-poweredpopularasync

Example Use

```python from firecrawl import FirecrawlApp app = FirecrawlApp(api_key="YOUR_API_KEY") # Scrape a single page - get clean markdown result = app.scrape_url("https://example.com/blog/article") print(result["markdown"]) # clean markdown content # Extract structured data with a schema result = app.scrape_url( "https://example.com/product/123", params={ "formats": ["extract"], "extract": { "schema": { "type": "object", "properties": { "name": {"type": "string"}, "price": {"type": "number"}, "description": {"type": "string"}, }, } }, }, ) print(result["extract"]) # {"name": "...", "price": 29.99, ...} # Crawl an entire website crawl_result = app.crawl_url( "https://example.com", params={"limit": 100, "scrapeOptions": {"formats": ["markdown"]}}, ) for page in crawl_result["data"]: print(page["metadata"]["title"], page["markdown"][:100]) # Map all URLs on a site map_result = app.map_url("https://example.com") print(f"Found {len(map_result['links'])} URLs") ```

```go // Example scraper for Google in Ferret: LET google = DOCUMENT("https://www.google.com/", { driver: "cdp", userAgent: "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/76.0.3809.87 Safari/537.36" }) HOVER(google, 'input[name="q"]') WAIT(RAND(100)) INPUT(google, 'input[name="q"]', @criteria, 30) WAIT(RAND(100)) CLICK(google, 'input[name="btnK"]') WAITFOR EVENT "navigation" IN google WAIT_ELEMENT(google, "#res") LET results = ELEMENTS(google, X("//*[text() = 'Search Results']/following-sibling::*/*")) FOR el IN results RETURN { title: INNER_TEXT(el, 'h3')?, description: INNER_TEXT(el, X("//em/parent::*")), url: ELEMENT(el, 'a')?.attributes.href } ```

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