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vifreefly/kimuraframework

Write web scrapers in Ruby using a clean, AI-assisted DSL. Kimurai uses AI to figure out where the data lives, then caches the selectors and scrapes with pure Ruby. Get the intelligence of an LLM without the per-request latency or token costs. observed · 2026-08-28

github.com/vifreefly/kimuraframework · homepage · Ruby · MIT (permissive) observed · 2026-08-28

Health v2 · maintenance only

61/100

  • Activity 64
  • Release rhythm 35
  • Longevity 100

Flags: no_releases

How is this computed?

round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.

  • gap_med: n/a
  • age_days: 2932
  • days_rel: n/a
  • days_push: 218
  • n_releases_24m: 0

Full methodology

Adoption not part of the score

1102 stars · 162 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Kimuraframework (Kimurai) is a Ruby web scraping framework with an AI-assisted DSL: an LLM generates XPath selectors from a schema on first run, caches them, and subsequent scrapes run as fast pure-Ruby extraction. It also supports classic manual selectors with headless antidetect Chrome/Firefox, Capybara-style interaction, and parallel crawling.

Use cases

  • scrape structured data from websites without writing selectors
  • crawl paginated search results into JSON
  • extract data from JavaScript-rendered pages with headless Chrome
  • build Ruby spiders with delays and parallel crawling
  • use an LLM once to generate reusable extraction rules
  • scrape sites behind anti-bot protection with an antidetect browser

When to choose

  • you write scrapers in Ruby and want a Scrapy-like framework
  • you want AI-generated selectors without per-request LLM costs
  • you need JS rendering or anti-bot evasion built in
  • you want to mix manual XPath with AI extraction in one codebase

When to avoid

  • you need a Python or Node scraping stack
  • you want fully selector-free LLM extraction on every request
  • your project requires a battle-tested framework with a large community
  • you cannot send page HTML to an external LLM provider

Facets

framework · maturity active

web-scraping parser llm-inference http-client browser-extension web-development crawlers large-language-models ruby scraping-framework ai-assisted-extraction headless-browser antidetect-browser capybara xpath crawler selector-caching vibe-scraping automation data-engineering linux macos docker

3 sources

Member repositories

RepositoryRoleHealth v2
vifreefly/kimuraframeworkmain61

For agents

markdown · JSON · MCP: product_card(name="vifreefly/kimuraframework")

Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem