jevcrawl 0.1.0

The web crawler for AI agents that finds the answer, not just pages.

A small decision model picks every link and every passage, so your agent's LLM reads only what answers its question at a few cents per crawl.

Quick start · Benchmarks · Tools · How it works · Configuration

Why jevcrawl

Agents that browse a site have two bad options. They can read everything a crawler fetches, which buries the answer in navigation and boilerplate and burns tokens. Or they can let the LLM pick each link, which is slow and costs dollars per question. jevcrawl does neither:

• 🎯 Finds the answer page more often. 54% of tasks vs 33% when Claude picks the links, and 2x as often as Crawl4AI's goal-directed crawl.
• 💸 About 8x cheaper per task, 10x per correct answer. The crawl decisions cost about a cent.
• ⚡ 2x faster than an LLM-driven crawl. Four pages at a time, one decision request per page.
• 📜 Verbatim, with sources. Every passage is copied from the page with its URL, heading path and character offsets. The decision model only scores, so it cannot invent text.
• 🛡️ Safe against prompt injection by design. Page text is only ever scored, never followed.
• 🔌 Works everywhere MCP does: Claude Code, Claude Desktop, Cursor, or the CLI.

Quick start

You need a Jev API key from console.typesafe.ai and uv.

Claude Code

claude mcp add jevcrawl -e TYPESAFE_API_KEY=<your key> -- uvx jevcrawl

Claude Desktop, Cursor and other MCP clients (mcpServers in the client's config file)

{
  "mcpServers": {
    "jevcrawl": {
      "command": "uvx",
      "args": ["jevcrawl"],
      "env": { "TYPESAFE_API_KEY": "<your key>" }
    }
  }
}

Then ask your agent something like "Use jevcrawl to find which Python version added the walrus operator, starting from docs.python.org". Check the setup with uvx jevcrawl doctor. Agents that load skills can also use skills/jevcrawl/SKILL.md.

Benchmarks

Every system starts on a site's home page with the same 30-page budget and the same question. What each one collects goes to the same answering LLM, and the same judge grades the answer, so the crawler is the only thing that changes.

jevcrawl vs Crawl4AI

Crawl4AI is one of the most popular open-source crawlers for LLMs. Its BestFirstCrawlingStrategy with a keyword scorer is its way to crawl toward a question without an LLM, so we compared the two on whether they fetch the page that holds the answer.

Where the numbers come from

The benchmark code and how to reproduce every number: bench/.

Tools

{"blocks": [{"url": "https://docs.python.org/3/whatsnew/3.8.html",
             "heading_path": "What’s New In Python 3.8 > New Features > Assignment expressions",
             "text": "There is new syntax := that assigns values to variables as part of a larger expression. ...",
             "char_start": 307, "char_end": 580, "relevance": 0.97}],
 "uncertain": [],
 "pages": [{"url": "https://docs.python.org/3/", "title": "3.15.0 Documentation", "lang": "en",
            "text_sha256": "b624ce93..."}],
 "stats": {"pages_fetched": 6, "tokens_raw": 80034, "tokens_returned": 1873, "jev_calls": 11,
           "cache_hits": 0, "jev_input_tokens": 164139, "jev_cost_usd": 0.0069,
           "model": "jev-1.13.0", "degraded": false, "degraded_reason": null,
           "needs_js_urls": [], "stopped_because": "answered", "elapsed_seconds": 8.3}}

The same operations work from the command line (uv tool install jevcrawl or pip install jevcrawl puts jevcrawl on your path):

jevcrawl crawl https://docs.python.org/3/ --goal "What does the walrus operator do?" [--json]
jevcrawl scrape https://example.com/help/returns [--goal "..."] [--json]
jevcrawl map https://example.com --goal "..." [--limit 20]
jevcrawl doctor

How it works

• Jev decides, it does not write. Jev returns a probability per yes/no question, so it cannot produce text. Everything you get back is copied from the page.
• Page text is data, never instructions. It travels only in data fields of a question; every question sentence is a fixed string written by jevcrawl. An injected "ignore previous instructions" can at worst bias a score.
• Links below the priority floor are kept in reserve, not dropped, so a crawl never stops early while it still has page budget.
• Failures degrade, they do not hide. After three failed Jev requests the crawl continues breadth-first, returns every block unscored in uncertain, and sets stats.degraded.
• Repeats are free. Jev's answers are cached locally (~/.jevcrawl/cache), so the same goal on the same pages costs nothing.

Configuration

Everything else (thresholds, links per page, concurrency) has a measured default in jevcrawl/config.py, and Python callers can pass their own Config.

Cost and privacy

Jev charges for input tokens only, $0.042 per million for jev-1.13.0; in the benchmark a crawl cost $0.011 on average. max_decision_usd is a hard limit: a request that could pass it is never sent, and the crawl stops with budget. Every result reports jev_cost_usd, and tokens_raw (what a plain crawl would hand your LLM) against tokens_returned.

Page text is sent to TypeSafe (blocks, link labels and URLs) to be scored. The API key is read from the environment and never written to disk or logs.

Crawling responsibly

jevcrawl does not read robots.txt and does not rate-limit itself beyond fetching four pages at a time. You are responsible for the sites you crawl, their terms of service and the law that applies to you. It never logs in, does not try to get past paywalls or CAPTCHAs, and does not run JavaScript (pages that need it are listed in stats.needs_js_urls). Every call is bounded by max_pages, max_seconds, max_decision_usd and return_tokens.

Contributing

uv sync
uv run ruff check . && uv run ruff format --check . && uv run mypy --strict jevcrawl
uv run pytest                                    # offline: all HTTP is mocked
TYPESAFE_API_KEY=... uv run pytest -m live -s    # one tiny real request, a fraction of a cent

Tests never touch the network or spend money; anything that does is marked live. The README charts are drawn by assets/build.py from the measured numbers. Issues and pull requests are welcome; please report security problems as described in SECURITY.md.

Acknowledgments

• WebWalkerQA by Alibaba's Tongyi Lab, for a benchmark of questions that really need crawling.
• Crawl4AI, the baseline we measure against.
• Model Context Protocol, selectolax and httpx.

License

Apache-2.0