How to Use OpenClaw for Email Triage: My Morning Routine That Saves 30 Minutes

If you’re like me, your inbox is a battlefield every morning. Before OpenClaw, I spent at least an hour sifting through customer inquiries, internal updates, and the inevitable spam. Now, my OpenClaw instance, running on a modest DigitalOcean Droplet, handles the first pass, saving me 30 minutes every day. This isn’t just about deleting spam; it’s about intelligently categorizing emails and drafting initial responses, allowing me to focus on the high-priority items that need my human touch.

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Setting Up OpenClaw for Email Processing

First, you’ll need a stable OpenClaw installation. I’m running mine on a DigitalOcean Droplet with 2GB RAM and 2 vCPUs, which is plenty for this workload. If you’re on a Raspberry Pi, you’ll struggle with the model inference times. The core idea is to pipe your incoming emails to an OpenClaw script that classifies them and, for certain categories, generates draft replies.

The first crucial step is to get your emails into a format OpenClaw can understand. I use fetchmail to pull emails from an IMAP server and pipe them to a local script. Here’s a basic ~/.fetchmailrc configuration:

set no bouncemail
set no syslog
set postmaster "youruser"
set daemon 300

poll imap.yourdomain.com protocol IMAP
    user "you@yourdomain.com"
    password "your_email_password"
    mda "/usr/local/bin/process_email.sh"
    fetchall
    keep
    ssl
    sslcertpath /etc/ssl/certs
    folder "INBOX"

This configuration polls your IMAP server every 300 seconds (5 minutes), fetches all new emails, and pipes each one to /usr/local/bin/process_email.sh. The keep directive is important here; it leaves the emails on the server, which is good for debugging and ensuring you don’t lose anything if your script fails.

The Email Processing Script

The process_email.sh script is where the magic happens. It extracts the email content and sends it to your OpenClaw instance. Here’s a simplified version:

#!/bin/bash

# Define the path to your OpenClaw config
OPENCLAW_CONFIG="/home/youruser/.openclaw/config.json"
# Define a temporary file for the email content
TEMP_EMAIL_FILE=$(mktemp)

# Read the email from stdin
cat > "$TEMP_EMAIL_FILE"

# Extract relevant parts of the email for the prompt
# This is a simplification; in reality, you'd use a parser like mail-parser or Python's email library
SUBJECT=$(grep -i '^Subject:' "$TEMP_EMAIL_FILE" | sed 's/^Subject: //i')
FROM=$(grep -i '^From:' "$TEMP_EMAIL_FILE" | sed 's/^From: //i')
BODY=$(sed -n '/^$/,$p' "$TEMP_EMAIL_FILE" | tail -n +2) # Get everything after the first blank line

# Construct the prompt for OpenClaw
PROMPT="
You are an email triage assistant. Categorize the following email into one of these categories:
Sales Inquiry
Support Request
Internal Update
Spam
General Correspondence

If it's a 'Sales Inquiry' or 'Support Request', also draft a polite initial response acknowledging receipt and stating when they can expect a full reply.

---
From: $FROM
Subject: $SUBJECT

$BODY
---
"

# Send the prompt to OpenClaw
# Assuming OpenClaw is running as a local HTTP server on port 8000
curl -s -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "claude-haiku-4-5",
    "messages": [
      {"role": "user", "content": "'"$PROMPT"'"}
    ],
    "max_tokens": 500
  }' > /tmp/openclaw_response.json

# Parse the OpenClaw response (simplified)
CATEGORY=$(jq -r '.choices[0].message.content' /tmp/openclaw_response.json | grep -i 'Category:' | head -n 1 | sed 's/Category: //i')
DRAFT=$(jq -r '.choices[0].message.content' /tmp/openclaw_response.json | grep -i 'Draft:' -A 100 | sed '/Draft:/d')

# Log or take action based on category and draft
echo "Processed email from $FROM (Subject: $SUBJECT)" >> /var/log/openclaw_email.log
echo "Category: $CATEGORY" >> /var/log/openclaw_email.log
if [ -n "$DRAFT" ]; then
    echo "Draft Reply: $DRAFT" >> /var/log/openclaw_email.log
    # Here you'd integrate with your email sending system, e.g., sendmail
    # echo "To: $FROM" >> /tmp/reply.txt
    # echo "Subject: Re: $SUBJECT" >> /tmp/reply.txt
    # echo "" >> /tmp/reply.txt
    # echo "$DRAFT" >> /tmp/reply.txt
    # sendmail -t < /tmp/reply.txt
fi

# Clean up temporary file
rm "$TEMP_EMAIL_FILE"

This script is a simplified illustration. In a production environment, you’d use a robust email parsing library (like Python’s email module or a dedicated command-line tool) to properly extract headers and body, especially with multi-part emails. The jq command is used here to parse the JSON response from OpenClaw. Make sure you have it installed (sudo apt install jq).

OpenClaw Configuration and Model Choice

For the OpenClaw instance itself, the default configuration usually works well, but pay attention to the model. The documentation often suggests using the latest, most capable models, but for email triage, claude-haiku-4-5 is incredibly effective and significantly cheaper than models like claude-opus-4-5. In my experience, it handles categorization and polite initial drafts perfectly fine, and its speed is a huge advantage for this kind of high-volume, repetitive task. My ~/.openclaw/config.json includes:

{
  "api_keys": {
    "anthropic": "sk-your-anthropic-key"
  },
  "default_model": "claude-haiku-4-5",
  "port": 8000
}

Ensure your OpenClaw server is running and accessible at http://localhost:8000. You can start it in the background using nohup openclaw server & or manage it with systemd for more robust operation.

Non-Obvious Insight: Rate Limiting and Error Handling

One thing I learned the hard way is dealing with API rate limits. If you have a busy inbox, hitting the Anthropic API too frequently can lead to errors. While Haiku has generous limits, it’s good practice to implement some retry logic or a small delay in your process_email.sh script. A simple sleep 1 after each curl request can help, but a more sophisticated approach would involve checking the API response for rate limit errors and backing off. Also, robust error logging is crucial. If OpenClaw or the API call fails, you need to know why and ensure the original email isn’t lost.

Another point: don’t rely on the LLM to make critical decisions. My system categorizes and drafts, but I still review everything. The goal isn’t full automation, but intelligent assistance. The drafts are often good enough to send with minor tweaks, but sometimes they need significant rephrasing or more detailed information that only I possess.

Limitations and Next Steps

This setup works well for general email triage. However, it won’t handle complex attachments, highly nuanced emotional tone detection, or emails requiring deep contextual knowledge that isn’t present in the immediate message body. For those, human intervention is still king. The 2GB RAM on my Droplet is sufficient because claude-haiku-4-5 is a remote API call; if you were running a local LLM, you’d need significantly more resources. This method is specifically for leveraging external LLM APIs via OpenClaw.

To get started, make sure OpenClaw

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