Setup

The easy way — npm (installs the crawlie CLI and the crawlie-mcp server):

npm i -g @spronta/crawlie

The macOS app — grab the signed .dmg from Releases.

From source — needs Rust (engine/CLI/MCP) and, for the desktop app, pnpm + Node:

git clone https://github.com/spronta/crawlie
cd crawlie
cargo build --release
# → target/release/crawlie  and  target/release/crawlie-mcp

# or install onto your PATH:
cargo install --path crates/crawlie-cli      # installs `crawlie`
cargo install --path crates/crawlie-mcp      # installs `crawlie-mcp`

How it ships: the CLI + MCP come only through npm — the right native binary installs automatically as a platform package (nothing to download or unblock). The desktop app is the only direct download: a Spronta-signed, notarized .dmg on Releases.

How to use (CLI)

# Crawl a whole site (respects robots.txt, seeds from sitemap.xml)
crawlie crawl https://example.com --format pretty

# Audit a single page, or a specific set of pages
crawlie audit https://example.com/pricing
crawlie audit https://example.com/a https://example.com/b

# Save a shareable, self-contained HTML report
crawlie crawl https://example.com --format html -o report.html

# Clean JSON on stdout (perfect for piping / scripting / agents)
crawlie crawl https://example.com --format json -o report.json

# Learn why any finding matters and how to fix it
crawlie explain geo-not-answerable

Output formats: pretty (terminal), json (machine-readable, the default), csv (issues), html (shareable file).

Common flags:

Every crawl returns two scores: a Health score (technical SEO) and a GEO score (AI-search readiness).

Use with agents (MCP)

crawlie ships a Model Context Protocol server so an LLM agent can run a full audit and act on it — no human in the loop. This is the part most SEO tools don't have.

Connect it

After npm i -g @spronta/crawlie, crawlie-mcp is on your PATH. For Claude Desktop, edit claude_desktop_config.json:

For Claude Code:

claude mcp add crawlie crawlie-mcp

(If you built from source instead, use the absolute path to target/release/crawlie-mcp.)

(Any MCP-compatible client works — Cursor, Cline, your own agent. It speaks JSON-RPC over stdio.)

Tools exposed

Example agent prompts

"Crawl spronta.com, then give me the top 5 fixes that would most improve my GEO score, with the exact change for each."

"Audit these three landing pages and tell me which is least ready to be cited by AI search, and why."

"Run a crawl with --fail-on error semantics — are there any broken links or 5xx pages blocking launch?"

The agent calls crawl_site, reads the structured issues, and uses explain_issue to turn findings into a prioritized, actionable plan.

Use cases

• Pre-launch QA — catch broken links, redirects, 4xx/5xx, and missing metadata before you ship.
• GEO optimization — make pages citable by AI search: structured data, semantic HTML, answer-ready content, authorship/E-E-A-T.
• Agent workflows — let a marketing/SEO agent audit a site and propose fixes autonomously via MCP.
• CI/CD gating — crawlie crawl … --fail-on error in a pipeline to block regressions.
• Client reporting — generate a polished, shareable HTML report in one command.
• Auditing AI-generated sites — verify that the site your agent just built is actually built for search.

Why I built this

I'm Sean Ryan. I've spent 6+ years at Pendo.io as a Lead Marketing Engineer and lead engineer, and on the side I'm building Spronta — AI for marketers.

With AI, it's faster than ever to ship a marketing site — but most of what gets generated is slop that was never built to be found. And the tools meant to catch that fall short: most SEO auditors cost money, don't play nicely with your agents, or tell you what's wrong without telling you how to actually rank for SEO and GEO (Generative Engine Optimization — being cited by AI search like ChatGPT, Perplexity, and Google AI Overviews).

crawlie fixes that. It's free, it's local-first, it's agent-native, and every issue it finds comes with why it matters and how to fix it.

If this is useful to you, connect with me on LinkedIn → — I share what I'm learning building AI for marketers and SEO/GEO tooling, and I'd love to hear how you're using crawlie.

Desktop app

A beautiful Tauri + React app (Geist design, light/dark, seamless window chrome):

cd apps/desktop
pnpm install
pnpm tauri dev          # live native crawls
pnpm dev                # preview the UI in a browser (demo data, no backend)

Whole-site / single-page / URL-list modes, live progress, Health & GEO score rings, issues with built-in why-it-matters guidance, a sortable pages table, a per-page drawer (GEO signals, headers, schema, hreflang…), auto-saved report history, and one-click shareable HTML export.

First run, generate the icon set: cd src-tauri/icons && python3 generate.py && cd .. && pnpm tauri icon icons/source.png

What it checks

46 rules and counting.

Technical SEO — broken links · 4xx/5xx · redirects & chains · titles & meta descriptions (missing / duplicate / length) · H1s · canonicals · noindex / nofollow / X-Robots-Tag · robots.txt blocking · images missing alt · thin & duplicate content · orphan & deep pages

Performance & security — slow responses · large pages · missing compression · HTTPS · mixed content · HSTS

Mobile, international & social — viewport · lang · hreflang · Open Graph · Twitter cards · structured data

GEO — Generative Engine Optimization — structured data, semantic HTML, answer-readiness, authorship/E-E-A-T, dated content, question-style headings, and extractable blocks, rolled into a per-page GEO score.

Every finding links to plain-English guidance: why it matters, how to fix it, and what happens if you ignore it.

How it compares

Architecture

crates/
  crawlie-core    # the engine — crawl, audit, score, knowledge base, reports
  crawlie-cli     # `crawlie` — JSON / pretty / CSV / HTML output
  crawlie-mcp     # `crawlie-mcp` — Model Context Protocol server (stdio)
apps/
  desktop         # Tauri v2 + React (Geist) desktop app

crawlie-core has zero host dependencies — the same audited engine drops straight into a cloud worker (it already targets wasm32). One engine, every surface, identical results.

Roadmap

• Cloud workers (shared Rust core) for scheduled/remote crawls
• JavaScript rendering for SPA-heavy sites
• Crawl-to-crawl comparison & regression alerts
• Internal-link graph visualization

License & author

MIT © Sean Ryan / Spronta.

Built by Sean Ryan — Lead Marketing Engineer at Pendo.io, building AI for marketers at Spronta on the side. Connect on LinkedIn →

If crawlie saves you time, a ⭐ on the repo and a hello on LinkedIn mean a lot.