Web scraping & search for AI agents: extract, SearXNG/Tavily search, rerank. No API key needed.
Copy the AI prompt to install this server into Claude Code, Cursor, or another agent β or use 1-click editor setup below.
π‘ Paste the JSON block into your client's configuration file under mcpServers, then restart the application.
CrawlEyes gives AI agents reliable full-text extraction (web_extract) and robust search (web_search) backends β the "eyes" that let agents see and read the web. Built and tested against Hermes Agent.
Also ships as a standard MCP server, so any MCP client (Claude Desktop, Cursor, other agents) can reuse the same search + extraction capabilities.

Most agent toolkits cover one slice of the pipeline. CrawlEyes is the rare all-in-one that you can actually run behind the Great Firewall without external accounts.
| Typical agent toolkit | CrawlEyes | |
|---|---|---|
| π Search | API key required, often blocked in CN | β SearXNG (self-hosted) + Tavily keyless fallback β zero config, zero key |
| π Extraction | Separate scraper, or Firecrawl SaaS | β Built-in Crawl4AI full-text extract, ~89% noise removal |
| π§ Semantic rerank | Rarely included | β Local fastembed rerank β no torch, ~50MB model |
| π MCP server | Often missing | β
Standard MCP tools (search + extract + deep_research + sitemap), any client |
| π China-friendly | Mostly English/GFW-blocked | β Tested on a real mainland China server (baidu + yandex) |
Zero API keys. Zero external accounts. One command. CrawlEyes is the only toolkit in this space that combines search + extraction + semantic reranking + MCP in a single, China-friendly, self-hosted package.
| Capability | Where | Why it matters |
|---|---|---|
| Full-text extraction | scripts/crawl4ai_cli.py | Headless-browser scraping β clean Markdown; handles ~80% of JS/dynamic/UA-blocked pages |
| Content denoising (P1) | crawl4ai_cli.py --noise-filter | Prunes nav/ads/comments via Crawl4AI's PruningContentFilter β measured 24.6kβ2.8k chars (~89% noise removed) on a typical article |
| Retry with backoff (P3) | crawl4ai_cli.py --retry N | Exponential backoff (1s/2s/4s) on transient failures |
| Browser session reuse (P4) | crawl4ai_cli.py --session NAME | Reuses the browser context across scrapes in one process β no cold-start per URL |
| Keyword-focused extraction | crawl4ai_cli.py --bm25 KEYWORD | Keeps only paragraphs relevant to a keyword (experimental β BM25 is English-centric; works best on English docs) |
| Search (primary) | SearXNG (self-hosted meta-search) | Privacy-friendly search aggregator |
| Search (fallback) | Tavily keyless API | Zero-config, no-key fallback when SearXNG is down/empty |
| Search orchestration | plugins/searxng-tavily/ | Hermes plugin provider: SearXNG first β auto-fallback to Tavily keyless; three-state circuit breaker (3 fails β 60s cooldown β half-open) + shared SQLite cache (TTL 3600s) |
| Semantic reranking (P2) | scripts/crawl_search_standalone.py | Local embedding rerank of search results with fastembed + BAAI/bge-small-zh-v1.5 (512-dim, no torch dependency, ~50MB, cached) β puts relevant results first. Measured: crawler-relevant items 0.817/0.732 float to top, irrelevant 0.302/0.139 sink |
| MCP server (P5) | scripts/mcp_crawl_server.py | Exposes search + extract + deep_research + sitemap as standard MCP tools (stdio default, or streamable-http for remote clients). Works in any MCP client, no Hermes dependency. Extracted content is sanitized against prompt-injection (strips invisible chars + prompt-hijack lines). Unified rate limiting + exponential backoff guard every tool (sliding window, per-tool cost) so concurrent agent calls can't hammer downstream services |
| Sitemap discovery (P1) | crawleyes/sitemap.py | sitemap(origin) β parses sitemap.xml (plain / gzip / index-recursion) with robots.txt fallback, returns a deduped URL map. Zero-key way to discover a site's URL surface for whole-site fetch or deep-research seeding |
| Multi-format extract (P0) | extract(..., format=) | markdown (default) / fit (denoised) / raw (unfiltered) / markdown_with_citations β pick the level of cleanup you need |
| RAG-ready interfaces | crawleyes/rag.py | One-liners markdown(url) / search_markdown(query) β clean, sanitized, LLM-ready Markdown for RAG corpora |
| Deep research | crawleyes/deep_research.py | deep_research(topic) β decomposes topic into sub-questions β searches β extracts β synthesizes a cited Markdown report. Optional LLM (any OpenAI-compatible endpoint); degrades to evidence-aggregate mode without one |
| Verification | scripts/ | Clean subprocess scripts to verify each backend end-to-end per Hermes profile |
China-network note: the embedding model downloads from HuggingFace, which is blocked on mainland networks. Set
HF_ENDPOINT=https://hf-mirror.comandHF_HUB_DISABLE_XET=1(hf-mirror doesn't support the xet protocol and returns 401 without this).
For Hermes specifically, add to config.yaml:
/scrape endpointAlready using Firecrawl's Python SDK? Point it at CrawlEyes and keep your code:
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