evalship-hq / ponytail

✗ main0.00

**What's your setup?** The right answer depends on two things: 1. **Single server or multiple?** - Single → in-process counter is enough - Multiple → need shared state (Redis, etc.) 2. **Do you already have Redis / a cache layer installed?** --- I'll assume **single server, no Redis** — the most common case for a service that hasn't needed rate limiting yet. **Install one thing:** ```bash pip install slowapi ``` `slowapi` wraps `limits` and plugs into FastAPI/Starlette with almost no code. --- **Minimal wiring** (add to wherever your app is created): ```python from slowapi import Limiter, _rate_limit_exceeded_handler from slowapi.util import get_remote_address from slowapi.errors import RateLimitExceeded limiter = Limiter(key_func=get_remote_address) app.state.limiter = limiter app.add_exception_handler(RateLimitExceeded, _rate_limit_exceeded_handler) ``` **On the endpoint:** ```python @app.get("/your-endpoint") @limiter.limit("20/minute") # ← change to taste async def your_endpoint(request: Request): ... ``` `Request` must be a parameter even if you don't use it — slowapi needs it. --- **That's it.** The handler returns a proper `429 Too Many Requests` automatically. **Limit string examples:** `"5/second"`, `"100/hour"`, `"1000/day"` --- **Skipped:** auth-based limiting (per user/API key instead of per IP) — IP limiting is wrong if many users share a NAT. If that's your situation, swap `get_remote_address` for a function that pulls the user ID from your auth token. **Risk:** in-memory state resets on restart and doesn't share across workers. If you run multiple `uvicorn` workers (`--workers 4`), each gets its own counter — effective limit becomes `N × your limit`. Fix by adding `storage_uri="redis://localhost"` to `Limiter(...)` when you're ready.