LLM Regression Monitor
Use this skill when the user wants to monitor LLM behavior over time and get alerted when outputs change unexpectedly. Triggers on requests like "set up LLM regression monitoring",…
Swanand33
@swanand33
Install
$ openclaw skills install @swanand33/llm-regression-monitorLLM Regression Monitor
Overview
Automated behavioral regression monitoring for LLM apps. Captures baseline outputs, detects drift on a schedule, and fires WhatsApp or Slack alerts the moment something regresses.
Workflow Decision Tree
User request
├── "set up monitoring" / first time → Full Setup (steps 1–5)
├── "run the monitor now" → Step 4 only
├── "I changed my prompt/model" → Step 3b (update baseline)
└── "configure alerts" → Step 5
Step 1 — Install
pip install llm-behave[semantic] pyyaml requests python-dotenv
Step 2 — Create test_suite.yaml
Create in the project root. Minimal example:
tests:
- name: support_response
prompt: "A customer says they never received their order. How do you respond?"
provider: openai # openai | anthropic | ollama | custom
model: gpt-4o-mini
assertions:
- type: tone
expected: "empathetic"
drift:
enabled: true
threshold: 0.80
Set the API key for the chosen provider:
export OPENAI_API_KEY=sk-...
export ANTHROPIC_API_KEY=sk-ant-... # if using anthropic
# ollama needs no key
Read references/test-suite-format.md for the full field spec.
Read references/providers.md for env vars and Ollama setup.
Step 3 — Capture Baselines
python scripts/capture_baseline.py
Saves ground-truth outputs to .llm_behave_baselines/. Run once before monitoring begins.
For more reliable baselines on important tests, set baseline_samples: 3 in test_suite.yaml — the script will call the LLM 3 times and save the most representative response, eliminating outliers.
To test real production behavior, add a system_prompt field to your test — it gets sent to the LLM exactly as in your app.
3b — Update after intentional prompt/model change
# Reset one test
python scripts/capture_baseline.py --update-baseline <test-name>
# Reset all
python scripts/capture_baseline.py --force
Step 4 — Run the Monitor
python scripts/run_monitor.py
Writes monitor_report.json. Exits 0 on all-pass, 1 on any failure (CI-compatible).
Step 4b — Track Trends (Predictive Alerts)
Run after each monitor run to log scores and detect gradual drift before it crosses the threshold:
python scripts/trend_tracker.py
Appends each run's scores to monitor_trend.jsonl. If a test's drift score has been declining for the last 5 runs and is within 0.10 of the threshold, it prints a warning. Exit code 2 = trend warning, 0 = all clear.
The daily schedule command (Step 6) already includes it:
python run_monitor.py; python trend_tracker.py; python send_alert.py
Step 5 — Configure Alerts
Set whichever channels you want in .env — scripts load it automatically.
# WhatsApp (requires wacli installed and logged in)
ALERT_WHATSAPP_TO="+1234567890"
# Slack
ALERT_SLACK_WEBHOOK="https://hooks.slack.com/services/..."
# Discord
ALERT_DISCORD_WEBHOOK="https://discord.com/api/webhooks/..."
# Email (Gmail example — use an App Password, not your main password)
ALERT_EMAIL_TO="you@example.com"
ALERT_EMAIL_FROM="sender@gmail.com"
ALERT_EMAIL_PASSWORD="your-app-password"
# Optional — defaults to smtp.gmail.com:587
# ALERT_EMAIL_SMTP="smtp.gmail.com"
# ALERT_EMAIL_PORT="587"
You can set any combination — all configured channels fire on failure. Send via:
python scripts/send_alert.py
Silent on green runs. Logs every alert to monitor_alerts.log regardless.
Step 6 — Schedule with OpenClaw Cron
Confirm the schedule with the user (default: 9am daily), then add:
- Schedule:
0 9 * * * - Command:
python run_monitor.py; python trend_tracker.py; python send_alert.py - Directory: project root (where
test_suite.yamllives)
All three scripts run every day regardless of pass/fail. send_alert.py reads both monitor_report.json (hard failures) and trend_warnings.json (predictive warnings from trend_tracker.py) — it fires alerts only when there is something to report, and stays silent on fully green runs.
Common Errors
| Error | Fix |
|---|---|
llm-behave is not installed | pip install llm-behave[semantic] |
OPENAI_API_KEY is not set | Export key or add to .env |
No baseline found | Run step 3 first |
test_suite.yaml not found | Create it in project root |
| LLM call errors in report | API issue — not a regression |
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