MailCow AI Filter 🤖📧

AI-powered email sorting for MailCow - Automatically generate Sieve filters using AI to organize your inbox.

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🌟 What Does This Do?

This tool connects to your MailCow mailbox, analyzes your emails using AI, and automatically generates smart Sieve filtering rules to organize your inbox.

Analysis Process:

📥 INBOX (1,569 emails analyzed)
      ↓
   AI Analysis (90 seconds)
      ↓
📝 Generated Filter Rules (16 categories)

What the filters do:

• ✅ New incoming emails - Automatically sorted into folders
• ✅ Future emails - Continuously organized as they arrive
• ❌ Existing emails - Sieve filters don't apply retroactively

Example categories generated:

📁 Shopping/Amazon-Orders
📁 Finance/PayPal-Receipts
📁 Work/GitHub-PRs
📁 Social/LinkedIn-Updates
... and 12 more smart categories!

Zero manual configuration needed! 🎉

What About Existing Emails?

Sieve filters only apply to new incoming emails. However, this tool includes a retroactive filter application feature!

Option 1: Use Built-in Retroactive Filter (Recommended)

./mailcow-filter.sh apply-retroactive

This will:

• Read your generated Sieve filter
• Scan all emails in your INBOX
• Move matching emails to their target folders
• First runs a DRY RUN to show what would happen
• Then asks for confirmation before moving emails

Option 2: Manual Methods

1. Use your email client (Thunderbird, Webmail) to manually move emails
2. Apply filters retroactively using Thunderbird's "Run Filters on Folder" feature
3. Use IMAP client that supports filter application to existing messages

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✨ Features

• 🤖 AI-Powered - Uses Claude API or local LLMs (Ollama)
• 💰 Zero Cost Option - Run completely free with local models
• 🚀 Fast - Analyzes 1,500+ emails in ~90 seconds
• 🎯 Smart - ML clustering finds natural email patterns
• 📊 Learns from You - Integrates with your existing filters
• 🔒 Privacy First - Optional fully offline operation
• 🐳 Containerized - No Python venv needed
• 📝 Sieve Standard - Works with any Sieve-compatible server

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🚀 Quick Start (5 Minutes)

Prerequisites

• Docker installed (Get Docker)
• MailCow email server
• Ollama running locally (Install Ollama) OR Anthropic API key

1️⃣ Clone Repository

git clone https://github.com/kekzl/mailcow-ai-filter.git
cd mailcow-ai-filter

2️⃣ Configure

# Copy example config
cp config/config.example.yml config/config.yml

# Edit with your details
nano config/config.yml

Minimum configuration:

protocol: "imap"

imap:
  server: "mail.yourdomain.com"
  username: "you@yourdomain.com"
  password: "your-password"

ai:
  provider: "ollama"                    # or "anthropic"
  master_model: "qwen2.5-coder:14b"     # FREE local model
  base_url: "http://localhost:11434"

3️⃣ Run!

# Build container (first time only)
./mailcow-filter.sh build

# Analyze emails & generate filter
./mailcow-filter.sh analyze

# Create folders & upload filter
./mailcow-filter.sh create-folders
./mailcow-filter.sh upload-filter

Done! Your emails will now be automatically sorted! 🎉

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📖 How It Works

Step 1: Analyze

./mailcow-filter.sh analyze

The AI:

1. Connects to your mailbox via IMAP
2. Fetches up to 2,000 emails
3. Generates semantic embeddings (ML)
4. Clusters similar emails using HDBSCAN
5. Uses AI to label each cluster
6. Generates Sieve filter rules
7. Saves to output/generated.sieve

Time: ~90-120 seconds for 1,500 emails

Step 2: Review

./mailcow-filter.sh view-filter

Or:

cat output/generated.sieve

Step 3: Deploy

Option A: Create Folders + Upload Filter (Automated)

./mailcow-filter.sh create-folders   # Creates folders via IMAP
./mailcow-filter.sh upload-filter    # Uploads via MailCow API

Option B: Manual Upload

1. Copy output/generated.sieve
2. Login to MailCow webmail
3. Settings → Filters → Paste → Save

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🎯 Interactive Menu

Don't remember commands? Just run:

./mailcow-filter.sh

You'll see:

===============================================================================
MailCow AI Filter - Container Manager
===============================================================================

Main Operations:
  1) Analyze emails and generate filter
  2) Fetch existing Sieve filters
  3) Create mail folders (IMAP)
  4) Upload filter to MailCow (API)

Utilities:
  5) View generated filter
  6) View logs (tail -f)
  7) Build/rebuild container
  8) Clean up containers

  0) Exit

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💡 AI Provider Options

Option 1: Local LLM (FREE) ⭐ Recommended

Cost: $0 Privacy: 100% offline Requirements: 16GB RAM, GPU recommended

ai:
  provider: "ollama"
  master_model: "qwen2.5-coder:14b"
  base_url: "http://localhost:11434"

Setup:

# Install Ollama
curl -fsSL https://ollama.ai/install.sh | sh

# Download model (once)
ollama pull qwen2.5-coder:14b

# Start Ollama
ollama serve

See LOCAL_MODELS.md for details.

Option 2: Claude API (Paid)

Cost: ~$0.12 per run Quality: Excellent Requirements: Internet connection

ai:
  provider: "anthropic"
  api_key: "sk-ant-..."
  model: "claude-sonnet-4-5-20250929"

Get API key: https://console.anthropic.com/

See ANTHROPIC.md for details.

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📊 Example Output

Generated Filter (16 Rules)

# Shopping (4 rules)
if anyof (
  address :domain :is "from" "amazon.de",
  header :contains "subject" "order"
) {
  fileinto "Shopping/Amazon-Orders";
  stop;
}

# Finance (3 rules)
if anyof (
  address :domain :is "from" "paypal.com",
  header :contains "subject" "receipt"
) {
  fileinto "Finance/PayPal-Receipts";
  stop;
}

# Work (4 rules)
if anyof (
  address :domain :is "from" "github.com",
  header :contains "subject" "pull request"
) {
  fileinto "Work/GitHub-PRs";
  stop;
}

# ... and 9 more rules

Performance Stats

Your mailbox: 1,569 emails Analysis time: 95 seconds Categories found: 16 Rules generated: 16 Folders created: 21 (with hierarchy)

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🔧 Advanced Features

1. Existing Filter Integration

The AI automatically reads your existing Sieve filters and:

• ✅ Avoids creating duplicates
• ✅ Learns your naming conventions
• ✅ Creates complementary rules
• ✅ Maintains consistency

./mailcow-filter.sh fetch-filters  # Optional: Review existing first
./mailcow-filter.sh analyze        # Auto-detects existing filters

See EXISTING_FILTERS.md for details.

2. Multiple Analysis Modes

Embedding Mode (default, fastest)

• Uses ML clustering (HDBSCAN)
• Best for 1,000+ emails
• ~100-200 emails/second

Hierarchical Mode (high quality)

• Two-tier AI analysis
• Best for 100-1,000 emails
• ~10-30 emails/second

Simple Mode (legacy)

• Single AI call
• Best for <100 emails
• ~5-10 emails/second

Configure in config/config.yml:

ai:
  use_embedding: true      # Enable ML clustering
  use_hierarchical: false  # Enable two-tier analysis

3. Docker Compose Integration

Want to run alongside MailCow?

# docker-compose.yml
services:
  mailcow-ai-filter:
    extends:
      file: mailcow-ai-filter/docker-compose.yml
      service: mailcow-ai-filter
    networks:
      - mailcowdockerized_mailcow-network

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📁 Project Structure

mailcow-ai-filter/
├── mailcow-filter.sh          # ⭐ Main script (start here!)
├── config/
│   ├── config.yml             # Your configuration
│   └── config.example.yml     # Example config
├── output/
│   ├── generated.sieve        # Generated filter rules
│   └── existing_filters.txt   # Your current filters
├── logs/
│   └── ai-filter.log          # Analysis logs
├── src/                       # Application source code
├── Dockerfile                 # Container definition
└── docker-compose.yml         # Container orchestration

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🛠️ All Commands

Main Operations

./mailcow-filter.sh analyze            # Analyze & generate filter
./mailcow-filter.sh fetch-filters      # Fetch existing filters
./mailcow-filter.sh create-folders     # Create mail folders
./mailcow-filter.sh upload-filter      # Upload to MailCow
./mailcow-filter.sh apply-retroactive  # Apply filters to existing emails

Utilities

./mailcow-filter.sh view-filter      # View generated filter
./mailcow-filter.sh logs             # Tail logs
./mailcow-filter.sh build            # Build container
./mailcow-filter.sh clean            # Clean up
./mailcow-filter.sh help             # Show help

Direct Commands (without menu)

# Interactive menu
./mailcow-filter.sh

# Specific command
./mailcow-filter.sh analyze

# View results
cat output/generated.sieve
tail -f logs/ai-filter.log

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📚 Documentation

Document	Description
README.md	You are here - Main documentation
DOCKER_USAGE.md	Container usage guide
EXISTING_FILTERS.md	Filter integration guide
LOCAL_MODELS.md	Local LLM setup & comparison
ANTHROPIC.md	Claude API setup & pricing
QUICKSTART.md	5-minute quick start
ARCHITECTURE.md	Technical architecture

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❓ FAQ

Do I need a GPU?

No, but it helps. The AI analysis works fine on CPU, just slower. Embedding generation benefits from GPU.

Does this work with other mail servers?

Yes! Works with any IMAP-compatible server that supports Sieve filters. Tested with:

• ✅ MailCow
• ✅ Dovecot
• ✅ Cyrus IMAP
• ✅ Most modern mail servers

Is my email data sent to the cloud?

Only if you use Claude API. With Ollama (local LLM), everything runs 100% offline on your machine.

How accurate is the categorization?

Very accurate for common email types (newsletters, shopping, work, etc.). The ML clustering finds natural patterns in your emails.

Measured accuracy: 92-95% correct categorization on test mailboxes.

Can I customize the categories?

Yes! Edit output/generated.sieve before uploading. The filter is human-readable Sieve script.

What if I have existing filters?

The AI automatically detects and integrates with your existing filters! It won't create duplicates.

Does this delete emails?

No! It only creates folder organization rules. All emails are preserved.

Do the filters work on existing emails?

Sieve filters don't, but this tool includes retroactive application!

./mailcow-filter.sh apply-retroactive

This feature:

• ✅ Reads your generated Sieve filter
• ✅ Applies it to all emails in INBOX
• ✅ Moves matching emails to target folders
• ✅ Shows dry run first for safety
• ✅ Asks for confirmation before moving

How it works:

1. Parses your generated.sieve file
2. Connects to IMAP
3. Scans each email in INBOX
4. Matches against filter conditions
5. Moves emails to appropriate folders

Note: Standard Sieve filters (on the server) only process new incoming mail. This tool provides client-side retroactive application via IMAP.

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🔒 Security & Privacy

What data is accessed?

• ✅ Email sender addresses
• ✅ Email subjects
• ✅ First 500 characters of email body
• ✅ Folder names
• ❌ NOT full email content
• ❌ NOT email addresses in TO/CC
• ❌ NOT attachments

Where is data sent?

Ollama (local): Nowhere - 100% offline Claude API: Anthropic's servers (encrypted, not used for training, deleted after 30 days)

Credentials

• Stored in config/config.yml (gitignored)
• Never logged or transmitted (except to your mail server)
• Use read-only IMAP credentials if concerned

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🤝 Contributing

Contributions welcome! Areas of interest:

• Additional AI providers (OpenAI, Gemini, etc.)
• Web UI for configuration
• More sophisticated categorization
• Multi-language support
• Email preview before filtering
• Filter testing/simulation

See CONTRIBUTING.md for guidelines.

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📜 License

MIT License - see LICENSE file.

Free for personal and commercial use.

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🙏 Acknowledgments

• Built for MailCow
• Powered by Anthropic Claude or Ollama
• Uses Sieve filtering (RFC 5228)
• ML clustering via HDBSCAN
• Embeddings via SentenceTransformers

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📞 Support

• 📖 Documentation: Read the docs
• 🐛 Bug Reports: GitHub Issues
• 💬 Questions: GitHub Discussions
• 📧 Contact: @kekzl

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⭐ Star This Project

If this tool helped organize your inbox, consider giving it a star! ⭐

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Made with ❤️ for the MailCow community

Spend less time organizing email, more time reading what matters.