OpenClaw DingTalk Robot Log Analysis

Collecting logs is easy. Analyzing them is where the value lives. If you're sitting on gigabytes of OpenClaw DingTalk bot logs and only ever grep for "ERROR", you're leaving insights on the table.

Let's turn your raw logs into actionable intelligence.

What Your Logs Are Telling You

Every log line from your OpenClaw DingTalk bot contains a story. The trick is knowing which stories matter:

Usage patterns: When are users most active? Which groups generate the most traffic?
Performance trends: Is response time getting worse? Are timeouts increasing?
Error patterns: Are errors random or correlated with specific events?
Token economics: Which conversations are expensive? Where can you optimize?

Setting Up Structured Logging

Before you can analyze, you need structured data. Configure JSON logging on your Tencent Cloud Lighthouse instance:

# /opt/clawdbot/config/dingtalk.yaml
logging:
  format: json
  level: info
  output: /var/log/clawdbot/output.log
  fields:
    - timestamp
    - level
    - event_type
    - user_id
    - group_id
    - model
    - tokens_input
    - tokens_output
    - response_time_ms
    - skill_used
    - status

This gives you log lines like:

{"timestamp":"2026-03-06T10:23:45Z","level":"info","event_type":"msg_processed","user_id":"dingtalk_user_123","group_id":"group_eng_456","model":"claude-sonnet","tokens_input":150,"tokens_output":320,"response_time_ms":1847,"skill_used":"code-review","status":"success"}

Analysis Script: Daily Report

Build a comprehensive daily analysis:

#!/bin/bash
# /opt/clawdbot/daily-analysis.sh
LOG="/var/log/clawdbot/output.log"
TODAY=$(date +%Y-%m-%d)

echo "========================================="
echo "  DingTalk Bot Daily Analysis: $TODAY"
echo "========================================="

echo ""
echo "--- Traffic ---"
echo "Total messages: $(grep "$TODAY" "$LOG" | grep -c 'msg_processed')"
echo "Unique users: $(grep "$TODAY" "$LOG" | jq -r '.user_id' 2>/dev/null | sort -u | wc -l)"
echo "Active groups: $(grep "$TODAY" "$LOG" | jq -r '.group_id' 2>/dev/null | sort -u | wc -l)"

echo ""
echo "--- Performance ---"
echo "Avg response time: $(grep "$TODAY" "$LOG" | jq -r '.response_time_ms' 2>/dev/null | awk '{sum+=$1;n++} END{print sum/n "ms"}')"
echo "P95 response time: $(grep "$TODAY" "$LOG" | jq -r '.response_time_ms' 2>/dev/null | sort -n | awk 'NR==int(NR*0.95){print $1 "ms"}')"
echo "Timeouts: $(grep "$TODAY" "$LOG" | grep -c 'timeout')"

echo ""
echo "--- Token Usage ---"
TOTAL_IN=$(grep "$TODAY" "$LOG" | jq -r '.tokens_input' 2>/dev/null | awk '{sum+=$1} END{print sum}')
TOTAL_OUT=$(grep "$TODAY" "$LOG" | jq -r '.tokens_output' 2>/dev/null | awk '{sum+=$1} END{print sum}')
echo "Input tokens: $TOTAL_IN"
echo "Output tokens: $TOTAL_OUT"
echo "Total tokens: $((TOTAL_IN + TOTAL_OUT))"

echo ""
echo "--- Top Skills ---"
grep "$TODAY" "$LOG" | jq -r '.skill_used' 2>/dev/null | sort | uniq -c | sort -rn | head -5

echo ""
echo "--- Errors ---"
echo "Total errors: $(grep "$TODAY" "$LOG" | grep -c '"level":"error"')"
echo "Error types:"
grep "$TODAY" "$LOG" | jq -r 'select(.level=="error") | .error_type' 2>/dev/null | sort | uniq -c | sort -rn | head -5

Hourly Traffic Heatmap

Understand when your bot is busiest:

#!/bin/bash
# /opt/clawdbot/hourly-heatmap.sh
echo "Hour | Messages | ████████████████"
for hour in $(seq -w 0 23); do
  COUNT=$(grep "$(date +%Y-%m-%d)T${hour}:" /var/log/clawdbot/output.log | grep -c 'msg_processed')
  BAR=$(printf '%*s' $((COUNT / 5)) '' | tr ' ' '█')
  printf "%s   | %4d     | %s\n" "$hour" "$COUNT" "$BAR"
done

This reveals patterns like: "Most usage happens between 10-11 AM and 2-3 PM" — which helps you plan maintenance windows and capacity.

Token Cost Analysis

Find your most expensive conversations:

#!/bin/bash
# /opt/clawdbot/token-analysis.sh
echo "=== Token Cost Analysis ==="
echo ""

echo "Top 10 most expensive users (by total tokens):"
grep "$(date +%Y-%m-%d)" /var/log/clawdbot/output.log | \
  jq -r '[.user_id, (.tokens_input + .tokens_output)] | @tsv' 2>/dev/null | \
  awk '{a[$1]+=$2} END{for(k in a) print a[k], k}' | sort -rn | head -10

echo ""
echo "Top 5 most expensive skills:"
grep "$(date +%Y-%m-%d)" /var/log/clawdbot/output.log | \
  jq -r '[.skill_used, (.tokens_input + .tokens_output)] | @tsv' 2>/dev/null | \
  awk '{a[$1]+=$2} END{for(k in a) print a[k], k}' | sort -rn | head -5

Getting Your Analysis Infrastructure

All of this requires a stable server with persistent logs and enough compute to run analysis scripts. Tencent Cloud Lighthouse provides exactly that.

Visit the Tencent Cloud Lighthouse OpenClaw page to get your instance.
Select the "OpenClaw (Clawdbot)" application template under "AI Agents".
Deploy by clicking "Buy Now" — structured logging and analysis tools from day one.

Automated Anomaly Detection

Don't just analyze — alert on anomalies:

#!/bin/bash
# /opt/clawdbot/anomaly-check.sh
AVG_RESPONSE=$(grep "$(date +%Y-%m-%d)" /var/log/clawdbot/output.log | \
  jq -r '.response_time_ms' 2>/dev/null | awk '{sum+=$1;n++} END{print sum/n}')

if (( $(echo "$AVG_RESPONSE > 5000" | bc -l) )); then
  curl -X POST "https://oapi.dingtalk.com/robot/send?access_token=YOUR_TOKEN" \
    -H "Content-Type: application/json" \
    -d "{\"msgtype\":\"text\",\"text\":{\"content\":\"[ALERT] Avg response time is ${AVG_RESPONSE}ms (threshold: 5000ms)\"}}"
fi

From Data to Decisions

Log analysis isn't a one-time activity — it's a feedback loop. Analyze weekly, identify trends, make changes, and measure the impact.

Visit https://www.tencentcloud.com/act/pro/intl-openclaw to deploy your analysis-ready bot.
Select the "OpenClaw (Clawdbot)" template under "AI Agents".
Deploy and start making data-driven decisions about your DingTalk bot.

What you measure, you can improve.