Load Test Results Analyzer
On load-test completion: analyze results (p99 latency, throughput, error rate, CPU/mem), LLM verdict (pass/fail), and posts to #performance or escalates to #incidents.
Overview
**What it does**: Webhook (load test complete: k6, JMeter, Locust, custom) → parse results (p99 latency, throughput req/s, error %, resource usage) → LLM compares to targets (p99 <500ms?, throughput ≥1000req/s?, error% <0.1%?) → IF passed → Slack to #performance (green); IF failed → escalate to #incidents (red) with regression summary. **Setup (5 steps)**: 1. Instrument load test: add webhook POST on completion (k6, JMeter, custom script) 2. Set performance targets: latency, throughput, error % thresholds 3. Configure Slack channels: #performance (pass), #incidents (fail) 4. Define baseline: historical p99, throughput for comparison 5. Test: run test; verify results appear + comparison made **Apps/nodes**: Webhook, code node (result parsing), IF condition, OpenAI (analysis), Slack (multi-channel). **Credentials required**: OpenAI account, Slack workspace. **Difficulty**: Medium | **Setup time**: 10 minutes. **Business outcome**: Performance regressions caught pre-deploy → user experience ↑, release velocity ↑ (confident perf gate), infrastructure costs ↓ (right-sizing visible).
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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