zenrowsvsfirecrawl

MIT 2 1 18

191.9 thousand (month) Jun 01 2022 1.4.0(2024-11-13 14:49:00 ago)

- - - None

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

ZenRows is the official Python SDK for the ZenRows web scraping API. ZenRows is a scraping API service that handles anti-bot bypass, JavaScript rendering, and proxy rotation, allowing developers to focus on data extraction rather than infrastructure.

Key features of the SDK:

Simple API Send requests through ZenRows' infrastructure with a single function call. The API handles proxy rotation, CAPTCHA solving, and anti-bot bypass automatically.
JavaScript rendering Enable headless browser rendering for JavaScript-heavy pages with a simple parameter.
Anti-bot bypass Automatically bypasses common anti-bot systems including Cloudflare, DataDome, PerimeterX, and others.
Geotargeting Route requests through proxies in specific countries for geo-restricted content.
Auto-parsing Built-in parsers for extracting structured data from common page types (e-commerce, search results, etc.).
Concurrency support SDK supports concurrent requests for efficient large-scale scraping.

ZenRows is a commercial API service (requires API key) that is useful when building and maintaining your own proxy infrastructure and anti-bot bypass logic is not practical. The Python SDK provides a convenient wrapper around the REST API.

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.

Highlights

bypassanti-detectproxy

ai-poweredpopularasync

Example Use

```python from zenrows import ZenRowsClient client = ZenRowsClient("YOUR_API_KEY") # Simple request - anti-bot bypass handled automatically response = client.get("https://example.com") print(response.text) # With JavaScript rendering enabled response = client.get( "https://example.com/dynamic-page", params={"js_render": "true"}, ) print(response.text) # With premium proxy and geotargeting response = client.get( "https://example.com", params={ "premium_proxy": "true", "proxy_country": "us", }, ) # Auto-parsing for structured data extraction response = client.get( "https://example.com/product/123", params={"autoparse": "true"}, ) print(response.json()) # structured product data ```

```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") ```

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