Writing load test scenarios by hand is tedious in a specific way. You read through an API's endpoints. Guess at realistic payloads. Decide on request patterns. Write scripts for all of it before you've run a single test.
I built API Stress Lab to remove that first step. Upload an OpenAPI spec, and AI generates the test scenarios for you.
What the AI Actually Does
The tool takes an OpenAPI 3.x spec. It can be JSON or YAML. From that spec, it generates test scenarios based on the endpoints and schemas it finds. I want to be precise about this, because it's easy to overstate what the tool actually does.
The AI isn't simulating traffic or running the test. It's turning a spec into a plausible test plan: realistic payloads and sensible request sequences. A person doesn't have to hand-write a script for every endpoint before testing anything.
The actual load, meaning the requests hitting the target API, runs through k6 underneath. That's a mature, purpose-built load-testing engine. AI replaces the scripting step, not the engine.
Beyond Clean Load: Chaos Testing
Running a system under clean, steadily increasing load tells you one thing. It doesn't tell you how the system behaves when things go wrong. In production, that's most of what matters.
The tool supports chaos testing on top of standard load profiles. Injecting latency. Simulating failures. Forcing degraded conditions that are hard to reproduce on purpose.
Seeing how a system degrades under partial failure is a different signal than seeing how fast it responds when everything is healthy. Often a more useful one.
Turning Results Into Something Actionable
Raw load test output is a lot of numbers. The dashboard shows latency percentiles (p50, p95, p99) and RPS curves through Recharts. It pairs that with AI-generated analysis that flags likely bottlenecks and suggests fixes, so nobody has to eyeball a chart and guess what's constraining the system.
Architecture
The frontend is Next.js 14 with Tailwind. The backend is FastAPI. A load test can run for a while and shouldn't block the request that started it.
Test execution happens asynchronously through Celery workers backed by a Redis queue. Postgres holds test configuration and results. Test artifacts live in MinIO or Cloudflare R2 depending on deployment.
Security Was Not Optional Here
This tool takes a user-supplied API target and credentials, then generates real traffic against it. That makes it a very different security surface than a typical CRUD app. I treated it that way from the start, not as an afterthought.
SSRF protection blocks requests to private IP ranges and cloud metadata endpoints. A load tester that will hit any URL you give it is a textbook SSRF vector if internal targets aren't blocked.
Credentials are encrypted with Fernet (AES-128), not stored in plaintext. JWT authentication enforces per-user data isolation. One user's test configurations and stored credentials are never visible to another user of the same instance.
What Building This Taught Me
AI-assisted scenario generation genuinely cuts the time to a first useful test. That part of the pitch holds up.
But the value of a tool like this still rests on the unglamorous parts. A real load-testing engine doing the actual work. Async job handling that doesn't block on long-running tests. And security built for one assumption: this will eventually be pointed at real infrastructure with real credentials, not just a demo environment.
The AI layer is the part people notice first. It's not the part that makes the tool trustworthy to run against something that matters.
Learn More
The full project is open source: API Stress Lab on GitHub.