I built a load tester with an AI diagnosis layer—because no existing tool does both

Load testing and LLM observability are two separate categories of tools. Nobody has combined them. So I built something that does. It's called QueryScope . The problem k6, JMeter, and Locust are great tools. They fire requests, measure latency, and produce a report. But the report just tells you what happened. P99 spiked. Error rate went up. It doesn't tell you why . LangSmith and Langfuse are also great. But they monitor AI apps passively. They don't run load tests. If you want to benchmark an endpoint AND ask "why did tail latency get worse after my last deploy?", you're stitching together multiple tools manually. You are still the workflow engine. And that was the part that bothered me. What QueryScope does Users can point QueryScope at any REST or LLM endpoint. They can configure requests and concurrency and get real p50/p95/p99 (percentiles), throughput, and error rate in a live dashboard. Now that's just the load testing layer. Here's the interesting part : Every completed run ge