Log Spike Detector
Every 10 minutes: measure log volume, detect spikes (>2x baseline), LLM analyzes top error/warning types and likely source service — alert to #log-alerts.
Overview
**What it does**: Schedule (every 10 min) → query log platform (Datadog, Splunk, ELK, Loki) log volume/min → code detects anomaly: current > 2x baseline? → IF spike → fetch top 5 error types + services → LLM diagnoses (which service generating noise, error type, severity, cause: deploy, config, integration) → Slack to #log-alerts. **Setup (4 steps)**: 1. Connect log platform: Datadog, Splunk, ELK, Loki (API endpoint + token) 2. Set baseline: measure normal log rate (automatic rolling 7-day avg) 3. Configure spike threshold: multiplier (default 2x = 100% increase) 4. Set Slack channel: #log-alerts **Apps/nodes**: Schedule, HTTP request (log API), code node (anomaly math), OpenAI (diagnosis), Slack. **Credentials required**: Log platform API key, OpenAI account, Slack workspace. **Difficulty**: Easy | **Setup time**: 6 minutes. **Business outcome**: Catches chatty deployments, error loops, spam instantly → log storage costs ↓, real errors not buried, noise-to-signal ↑ clarity.
What's included
Quickstart
- 01
Download the JSON
With an active Library Pass, download the workflow file right from this page or your dashboard.
- 02
Import into n8n
Choose Import from File and select the downloaded JSON. The full agent graph appears, ready to configure.
- 03
Plug in credentials
Each integration node prompts for credentials on first run. The setup guide lists every credential the agent expects.
- 04
Activate and test
Run once with sample input, confirm the expected output, then flip the activate toggle.
Frequently Asked Questions
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