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Web scraping & search for AI agents: extract, SearXNG/Tavily search, rerank. No API key needed.
About
Web scraping & search for AI agents: extract, SearXNG/Tavily search, rerank. No API key needed.
Security Report
CrawlEyes is a well-architected web scraping and search toolkit with reasonable security practices for its developer-tools category. The codebase demonstrates proper error handling, no hardcoded secrets, and appropriate use of environment variables for configuration. However, there are concerns around unvalidated user input in web scraping operations, missing rate-limiting safeguards, and potential for abuse in extraction features that could be exploited by malicious LLM clients. Supply chain analysis found 6 known vulnerabilities in dependencies (0 critical, 5 high severity). Package verification found 1 issue.
7 files analyzed ยท 14 issues found
Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.
Permissions Required
This plugin requests these system permissions. Most are normal for its category.
What You'll Need
Set these up before or after installing:
Environment variable: SEARXNG_URL
Environment variable: TAVILY_API_KEY
How to Install
Add this to your MCP configuration file:
{
"mcpServers": {
"io-github-waiky-github-crawleyes": {
"env": {
"SEARXNG_URL": "your-searxng-url-here",
"TAVILY_API_KEY": "your-tavily-api-key-here"
},
"args": [
"crawleyes"
],
"command": "uvx"
}
}
}Documentation
View on GitHubFrom the project's GitHub README.
CrawlEyes โ Web Scraping & Search Toolkit for AI Agents
CrawlEyes gives AI agents reliable full-text extraction (web_extract) and robust search (web_search) backends โ 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.

Why CrawlEyes?
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 | |
|---|---|---|
| ๐ Search | API key required, often blocked in CN | โ SearXNG (self-hosted) + Tavily keyless fallback โ zero config, zero key |
| ๐ Extraction | Separate scraper, or Firecrawl SaaS | โ Built-in Crawl4AI full-text extract, ~89% noise removal |
| ๐ง Semantic rerank | Rarely included | โ Local fastembed rerank โ no torch, ~50MB model |
| ๐ MCP server | Often missing | โ
Standard MCP tools (search + extract), any client |
| ๐ China-friendly | Mostly English/GFW-blocked | โ 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.
Features
| Capability | Where | Why it matters |
|---|---|---|
| Full-text extraction | scripts/crawl4ai_cli.py | Headless-browser scraping โ 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 โ measured 24.6kโ2.8k 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 โ no cold-start per URL |
| Keyword-focused extraction | crawl4ai_cli.py --bm25 KEYWORD | Keeps only paragraphs relevant to a keyword (experimental โ 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 โ auto-fallback to Tavily keyless; three-state circuit breaker (3 fails โ 60s cooldown โ 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) โ 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 as standard MCP tools (stdio transport). Works in any MCP client, no Hermes dependency. Extracted content is sanitized against prompt-injection (strips invisible chars + prompt-hijack lines) |
| RAG-ready interfaces | crawleyes/rag.py | One-liners markdown(url) / search_markdown(query) โ clean, sanitized, LLM-ready Markdown for RAG corpora |
| Deep research | crawleyes/deep_research.py | deep_research(topic) โ decomposes topic into sub-questions โ searches โ extracts โ 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 |
Project layout
plugins/searxng-tavily/ Hermes web-search provider plugin (SearXNG โ Tavily keyless fallback)
+ three-state circuit breaker + shared SQLite cache
scripts/
crawl4ai_cli.py Universal scraping CLI (URL โ Markdown), with denoise/retry/session/BM25
crawl_search_standalone.py Standalone search (SearXNG โ Tavily) + optional semantic rerank.
No Hermes dependency โ usable anywhere, powers the MCP server.
mcp_crawl_server.py Standard MCP server exposing search + extract (stdio transport)
single_env_check.py Verify crawl4ai provider registered+available+extracts (one profile)
verify_searxng_tavily.py Verify searxng-tavily provider: normal path + forced fallback
agent_link_check.py Verify full agent tool chain: web_search_tool dispatch + logs
research_log/ Honest test notes (P0 Crawl4AI / P3 Firecrawl / P4 MediaCrawler)
docs/original/ Archived official READMEs (crawl4ai / firecrawl / media-crawler)
Quick start
1. Install Crawl4AI (China-friendly mirrors)
python3 -m venv .venv
# Use Tsinghua PyPI mirror for speed (or any mirror you prefer)
.venv/bin/pip install -i https://pypi.tuna.tsinghua.edu.cn/simple crawl4ai
# Playwright browser kernel โ use npmmirror binary mirror if cdn.playwright.dev is blocked
PLAYWRIGHT_DOWNLOAD_HOST=https://registry.npmmirror.com/-/binary/playwright \
.venv/bin/python -m playwright install chromium
.venv/bin/crawl4ai-setup
2. Scrape a page
.venv/bin/python scripts/crawl4ai_cli.py https://example.com # stdout Markdown
.venv/bin/python scripts/crawl4ai_cli.py https://example.com -o out.md # to file
.venv/bin/python scripts/crawl4ai_cli.py URL --text --max-words 5000 # plain text, truncated
# Denoise nav/ads + retry 3x + reuse session across scrapes
.venv/bin/python scripts/crawl4ai_cli.py URL --noise-filter --retry 3 --session s1
3. Use the search + rerank (standalone, no Hermes)
# Optional: local semantic rerank of results (fastembed + bge-small-zh, auto-downloaded)
.venv/bin/pip install -i https://pypi.tuna.tsinghua.edu.cn/simple fastembed
# SearXNG first, Tavily keyless fallback, then rerank
SEARXNG_URL=https://your-searxng .venv/bin/python -c "
import sys; sys.path.insert(0, 'scripts')
from crawl_search_standalone import CrawlSearch
r = CrawlSearch(rerank=True).search('your query')
print(r['data']['web'])"
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).
4. Run as an MCP server (any client)
# Any MCP client can connect via stdio:
.venv/bin/python scripts/mcp_crawl_server.py
# Exposes tools: search(query, limit) + extract(url, max_words)
For Hermes specifically, add to config.yaml:
mcp_servers:
crawl:
command: "/path/to/crawl/.venv/bin/python"
args: ["/path/to/crawl/scripts/mcp_crawl_server.py"]
timeout: 90
connect_timeout: 60
5. Install the search plugin (Hermes)
Copy plugins/searxng-tavily/ into a Hermes plugins dir, then:
hermes plugins enable web/searxng-tavily
hermes config set web.search_backend searxng-tavily
Set SEARXNG_URL in your Hermes profile .env to point at your SearXNG instance. If unset or unreachable, the provider automatically falls back to the Tavily keyless API (no API key required).
Note: the plugin only takes effect for newly started agent sessions.
6. Verify
# Requires the Hermes source tree + its venv
venv/bin/python scripts/verify_searxng_tavily.py $HERMES_HOME
venv/bin/python scripts/agent_link_check.py $HERMES_HOME
Design notes
- Layered composition: no single tool covers everything. Crawl4AI handles extraction; SearXNG + Tavily cover search; each layer has a tested fallback.
- Tavily keyless works with zero configuration and no account โ a cheap resilience net for the whole search path.
- Circuit breaker is SearXNG-only: a Tavily fallback success does not reset the breaker (otherwise it would never trip).
record_success()is only called when SearXNG itself succeeds. - Shared SQLite cache lives in the real user home (via
pwd.getpwuid, not$HOMEโ which Hermes profiles override), so all profiles share one cache. WAL + 5s timeout + try/except degrade-to-no-cache under concurrency. - Semantic rerank is cheap: fastembed (ONNX) avoids the ~2GB torch dependency; model loads in ~0.6s once cached, embeddings in ~50ms.
- MCP server is standalone: it does not import Hermes internals, so it runs on any Python 3.12 env and serves any MCP client.
- Honest limitations are documented in
research_log/(e.g. CAPTCHA walls on some sites are deliberately recorded as not-solvable rather than being worked around).
Credits & inspiration
This project builds on a set of excellent open-source tools. All code here is an independent implementation (no copied code), but the ideas and interfaces are drawn from the following projects โ full credit to their authors:
| Feature in this repo | Inspired by | License |
|---|---|---|
| Extraction engine (Crawl4AI wrapper) | Crawl4AI โ direct dependency | Apache-2.0 |
| Content denoising (P1) | Readability, GeneralNewsExtractor (idea) | Apache-2.0 / MIT |
| Semantic reranking (P2) | Vane, Perplexica (idea) | MIT / MIT |
| Retry with backoff (P3) | Crawlee (idea) | Apache-2.0 |
| Browser session reuse (P4) | camoufox (idea) | MIT |
| MCP server (P5) | playwright-mcp, exa-mcp-server (idea) | Apache-2.0 / MIT |
| Search orchestration / fallback | SearXNG โ self-hosted (official Docker image, no source modification), accessed via HTTP API only ยท Tavily keyless | AGPL-3.0 (server software, not linked/embedded) / proprietary API |
Design independence: the implementations here are written from scratch โ we studied the above projects' approaches (denoising thresholds, rerank pipelines, backoff strategies, MCP tool patterns) but did not copy their source code. Dependencies are declared in
requirements.txt. If you believe any attribution is missing or incorrect, please open an issue.
Compliance
Respect each target site's robots.txt and terms of service. This toolkit is for legitimate research and personal use. It deliberately does not include proxy pools, fingerprint rotation, or CAPTCHA-solving (anti-scraping evasion) โ those are out of scope.
License
MIT โ see LICENSE.
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