find-emails（查找电子邮件）

Find Emails

CLI for crawling websites locally via crawl4ai and extracting contact emails from pages likely to contain them (contact, about, support, team, etc.).

Setup

1. Install dependencies: pip install crawl4ai
2. Run the script:

python scripts/find_emails.py https://example.com

Quick Start

t

# Crawl a site
python scripts/find_emails.py https://example.com

# Multiple URLs
python scripts/find_emails.py https://example.com https://other.com

# JSON output
python scripts/find_emails.py https://example.com -j

# Save to file
python scripts/find_emails.py https://example.com -o emails.txt

Script

find_emails.py — Crawl and Extract Emails

python scripts/find_emails.py <url> [url ...]
python scripts/find_emails.py https://example.com
python scripts/find_emails.py https://example.com -j -o results.json
python scripts/find_emails.py --from-file page.md

Arguments:

Output format (human-readable):

Emails are grouped by domain. Clear structure for multi-URL runs:

Found 3 unique email(s) across 2 domain(s)

## example.com

  • contact@example.com
    Found on: /contact, /about
  • support@example.com
    Found on: /support

## other.com

  • info@other.com
    Found on: /contact-us

Output format (JSON):

LLM-friendly structure with summary and per-domain breakdown:

{
  "summary": {
    "domains_crawled": 2,
    "total_unique_emails": 3
  },
  "emails_by_domain": {
    "example.com": {
      "emails": {
        "contact@example.com": ["/contact", "/about"],
        "support@example.com": ["/support"]
      },
      "count": 2
    },
    "other.com": {
      "emails": {
        "info@other.com": ["/contact-us"]
      },
      "count": 1
    }
  }
}

Configuration

Edit scripts/url_patterns.json to customize which URLs the crawler follows. Only links matching these glob-style patterns are included:

{
  "url_patterns": [
    "*contact*",
    "*support*",
    "*about*",
    "*team*",
    "*email*",
    "*reach*",
    "*staff*",
    "*inquiry*",
    "*enquir*",
    "*get-in-touch*",
    "*contact-us*",
    "*about-us*"
  ]
}

If the file is missing or invalid, default patterns are used.

Workflow

1. Crawl a site:

   python scripts/find_emails.py https://example.com -o emails.json

1. Extract from local file (e.g., cached markdown):

   python scripts/find_emails.py --from-file crawled.md -j

1. Customize URL filters by editing scripts/url_patterns.json.

Dependencies

pip install crawl4ai
playwright install

Requires a browser (Playwright) for local crawling.

Batch Processing

# Crawl multiple sites – results grouped by domain for clear attribution
python scripts/find_emails.py https://site1.com https://site2.com -j -o combined.json

# Extract from multiple local files
for f in crawled/*.md; do
  echo "=== $f ==="
  python scripts/find_emails.py --from-file "$f" -q
done

Multiple URLs are fully supported; output clearly associates each email with its source domain. Domains are normalized (e.g. www.techbullion.com and techbullion.com merge into one) so duplicate sites are not listed separately.

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