reqly

Self-hosted API monitoring for FastAPI, Flask, Django, Starlette and Litestar — two lines of code. Latency percentiles, error rates and release tracking for every route, shipped to your own Reqly collector — which turns them into deploy-aware hourly alerts and weekly AI anomaly reports.

Install

pip install reqly

Usage

FastAPI

import reqly
from fastapi import FastAPI

app = FastAPI()
reqly.instrument(
    app,
    service_name="checkout-api",
    collector_url="https://reqly.example.com",
    api_key="your-ingest-key",
)
# Every route is now tracked: latency, errors, status codes, release

Flask

import reqly
from flask import Flask

app = Flask(__name__)
reqly.instrument(app, service_name="checkout-api")  # settings from REQLY_* env vars

Starlette / Litestar — same call:

reqly.instrument(app, service_name="checkout-api")

Django (also Django REST Framework and Django Ninja) — Django has no app object, so add the middleware first in MIDDLEWARE:

# settings.py
MIDDLEWARE = [
    "reqly.integrations.django.ReqlyMiddleware",
    # ...
]
REQLY = {"service_name": "checkout-api", "api_key": "your-ingest-key"}  # optional

instrument() detects the framework by itself — no decorators, no middleware to wire up. Routes are recorded as templates in one style across frameworks: Django's users/<int:pk>/ and DRF's ^users/(?P<pk>[^/.]+)/$ both become /users/{pk}/. The release you're running is picked up automatically from your CI or host (GITHUB_SHA, RENDER_GIT_COMMIT, VERCEL_GIT_COMMIT_SHA, …), so deploys show up in Reqly with no extra code.

What you get

From the SDK, per request: method, route template (/orders/{id}, never the raw path), status code, duration, error type, host, release, environment and request/response body size.

In the Reqly dashboard and collector:

• p50 / p95 / p99 latency per route and per service — real percentiles from mergeable sketches, not the max of per-route numbers
• Error rates, status codes and top routes over 1h / 6h / 24h / 7d
• Deploy markers and per-release health — each release's error rate and p95
• Hourly alerts to Slack, Discord or a webhook when a route breaks from its usual weekday-hour pattern, with root-cause hints
• API surface vs your OpenAPI spec — undocumented endpoints that get traffic, documented ones nobody calls, and deprecated ones still in use (push_openapi=True)
• Weekly AI report — statistics find the anomalies, Groq (gpt-oss-120b) writes the summary; plain-text fallback without an API key

An alert from the demo data looks like this:

🔴 Anomaly — flask-demo /orders (Friday 15:00-16:00 UTC, z=5.37)
• error rate 30.0% vs 2.2% usual · p95 6588ms vs 1576ms usual
• running release v2 — vs v1: errors 2.6% → 33.1%, p95 2072ms → 4501ms
• 100% of errors came from host pod-3, which served 23% of requests

Not on Python? Node, Java, Go and .NET apps can report to the same collector through OpenTelemetry — no Reqly SDK needed. See the OpenTelemetry guide.

Configuration

Every option can be passed to instrument() or set as an environment variable. Resolution order: argument → environment variable → default.

With sample_rate below 1.0, request counts in the dashboard are the sampled volume; latency percentiles and error rates stay unbiased.

Design guarantees

Fail-open — any internal SDK error is caught and logged once; instrumentation disables itself rather than raise into your app. A slow or unreachable collector never blocks request threads — shipping happens on a background thread with strict HTTP timeouts.

Bounded cardinality — routes are recorded as the framework's matched template (/users/{id}), never the raw path (/users/123). Unmatched paths (404s, scanners) collapse into a single __unmatched__ bucket.

Bounded memory — events wait in a fixed-size in-memory queue; under backpressure the oldest events are dropped and counted instead of growing without limit.

Safe retries — batches are retried with exponential backoff on 408, 429 and any 5xx (for example a collector restart behind a proxy); other 4xx responses are dropped immediately. Every event carries a unique event_id the collector deduplicates on, so a retry never double-counts.

Pre-fork servers — under gunicorn --preload (or uWSGI without lazy-apps) each forked worker restarts its own flush thread and HTTP connection pool, so workers' events are shipped instead of silently queuing forever.

Compatibility

Self-hosting the collector

The SDK sends data to a Reqly collector you run. The full stack — collector, TimescaleDB and dashboard — starts with Docker Compose:

git clone https://github.com/tanisheesh/reqly.git
cd reqly
docker compose up -d

Setup, configuration and AWS deployment: docs/SETUP.md · infra/DEPLOY.md · ingest API spec

Live demo

Reqly monitors EventFlow, a Flask event management app, in production:

• Demo app → eventflow-g2h5.onrender.com
• Metrics dashboard → reqly-eventflow-dashboard.onrender.com

Log in as Administrator (admin@eventhub.com / Admin@123) → click Metrics in the nav.

Changelog

See CHANGELOG.md.

License

GPL-3.0-or-later — see LICENSE.