katanavsferret

MIT 18 6 16,499

Nov 07 2022 v1.5.0(2026-03-10 14:52:47 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)

Katana is a next-generation web crawling and spidering framework written in Go by ProjectDiscovery. It is designed for fast, comprehensive endpoint and asset discovery and is widely used in the security research and bug bounty communities.

Katana offers multiple crawling modes:

Standard mode Fast HTTP-based crawling without a browser. Parses HTML, JavaScript files, and other resources to discover endpoints and links.
Headless mode Uses a headless Chrome browser for crawling JavaScript-rendered pages and single-page applications (SPAs).
Passive mode Discovers URLs from external sources (Wayback Machine, CommonCrawl, etc.) without actively visiting the target.

Key features include:

Scope control Configurable crawl scope with regex patterns for including/excluding URLs, domains, and file extensions.
JavaScript parsing Extracts endpoints from JavaScript files, inline scripts, and AJAX requests even in standard (non-headless) mode.
Customizable output Filter and format output with field selection, JSON output, and custom templates.
Rate limiting Built-in rate limiting and concurrency control to avoid overwhelming targets.
Proxy support HTTP and SOCKS5 proxy support with rotation.
Form filling Can detect and auto-fill forms to discover endpoints behind form submissions.

While Katana was designed for security research and reconnaissance, its fast crawling capabilities and JavaScript parsing make it equally useful for web scraping discovery and sitemap generation.

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

fastpopularlarge-scale

Example Use

```go package main import ( "context" "math" "github.com/projectdiscovery/katana/pkg/engine/standard" "github.com/projectdiscovery/katana/pkg/output" "github.com/projectdiscovery/katana/pkg/types" ) func main() { // Configure crawl options options := &types.Options{ MaxDepth: 3, FieldScope: "rdn", // restrict to root domain BodyReadSize: math.MaxInt, Timeout: 10, Concurrency: 10, Parallelism: 10, Delay: 0, RateLimit: 150, Strategy: "depth-first", OnResult: func(result output.Result) { // Process each discovered URL println(result.Request.URL) }, } // Create and run the crawler crawlerOptions, _ := types.NewCrawlerOptions(options) defer crawlerOptions.Close() crawler, _ := standard.New(crawlerOptions) defer crawler.Close() // Start crawling _ = crawler.Crawl("https://example.com") } ```

```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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