You know that feeling when a user reports "the bot didn't respond" and you have absolutely no idea what happened? No logs, no traces, no timestamps. Just vibes and guesswork.
Proper log management is the difference between a 5-minute fix and a 5-hour investigation. If you're running an OpenClaw Lark bot on Tencent Cloud Lighthouse, here's how to set up logging that actually helps you debug.
Out of the box, OpenClaw on Lighthouse runs as a systemd service, which means your logs are already captured by journald. That's a decent starting point:
# View the last 100 lines of bot logs
journalctl -u clawdbot -n 100 --no-pager
# Follow logs in real time (like tail -f)
journalctl -u clawdbot -f
But here's the problem: journald logs are unstructured, they rotate based on system defaults (often too aggressively), and they're hard to search when you need to correlate a specific Lark user's message with a model API call.
Let's redirect OpenClaw output to dedicated log files with proper structure:
# Create log directory
sudo mkdir -p /var/log/clawdbot
sudo chown clawdbot:clawdbot /var/log/clawdbot
# Update the systemd service to log to files
sudo systemctl edit clawdbot
Add this override:
[Service]
StandardOutput=append:/var/log/clawdbot/output.log
StandardError=append:/var/log/clawdbot/error.log
Reload and restart:
sudo systemctl daemon-reload
sudo systemctl restart clawdbot
Now you have separate stdout and stderr streams — model responses in one file, errors and stack traces in another.
A chatty Lark bot can generate gigabytes of logs in a week. Set up logrotate to keep things manageable:
cat > /etc/logrotate.d/clawdbot <<'EOF'
/var/log/clawdbot/*.log {
daily
rotate 30
compress
delaycompress
missingok
notifempty
copytruncate
dateext
dateformat -%Y%m%d
}
EOF
This gives you 30 days of compressed logs, rotated daily with date-stamped filenames. The copytruncate directive is critical — it avoids the need to restart the daemon after rotation.
Test it:
sudo logrotate -d /etc/logrotate.d/clawdbot
When debugging Lark webhook issues, you don't want to wade through every log line. Use grep and jq to isolate what matters:
# Find all incoming Lark events in the last hour
journalctl -u clawdbot --since "1 hour ago" --no-pager | grep -i "event_type"
# If logs are JSON-formatted, extract specific fields
cat /var/log/clawdbot/output.log | \
jq -r 'select(.source == "lark") | "\(.timestamp) [\(.event_type)] \(.user_id): \(.message)"'
For error triage, this one-liner surfaces the most common failure patterns:
grep -c "ERROR" /var/log/clawdbot/error.log
grep "ERROR" /var/log/clawdbot/error.log | sort | uniq -c | sort -rn | head -10
If you're running multiple bots or want long-term log retention, ship logs to a centralized store. A lightweight approach using rsyslog:
# /etc/rsyslog.d/50-clawdbot.conf
module(load="imfile")
input(type="imfile"
File="/var/log/clawdbot/output.log"
Tag="clawdbot"
Severity="info"
Facility="local0")
This feeds your bot logs into the syslog pipeline, where you can forward them to any log aggregation service.
All of this assumes you have a properly configured OpenClaw environment. If you're still running your Lark bot on a local machine or a bare VM, you're making log management harder than it needs to be.
The fastest path to a production-ready setup:
Don't just collect logs — react to them. A simple cron job that watches for error spikes:
#!/bin/bash
# /opt/clawdbot/log-alert.sh
ERROR_COUNT=$(grep -c "ERROR" /var/log/clawdbot/error.log)
if [ "$ERROR_COUNT" -gt 50 ]; then
curl -X POST "https://open.larksuite.com/open-apis/bot/v2/hook/YOUR_WEBHOOK" \
-H "Content-Type: application/json" \
-d "{\"msg_type\":\"text\",\"content\":{\"text\":\"[LOG ALERT] $ERROR_COUNT errors in clawdbot error.log. Check immediately.\"}}"
fi
Schedule it every 15 minutes via cron, and you'll catch issues before your users do.
Log management isn't glamorous, but it's the backbone of operational confidence. With structured files, proper rotation, and basic alerting, you'll spend less time guessing and more time building.
Start with the right foundation:
Your future self — the one debugging at midnight — will thank you.