apiforgepy

API observability & intelligence for FastAPI/Starlette — local-first, privacy-first.

Track latency, error rates, and behavioral trends of your APIs. Everything stays on your machine.

→ Full documentation

Install

pip install apiforgepy

Quick start

from fastapi import FastAPI
from apiforgepy import ApiForgeMiddleware

app = FastAPI()

app.add_middleware(ApiForgeMiddleware)

@app.get("/users/{user_id}")
def get_user(user_id: int):
    return {"id": user_id}

# Dashboard auto-starts at http://localhost:4242

Dashboard

Open http://localhost:4242 after starting your app. No configuration needed — the dashboard server starts automatically in the background.

• Health Score (0–100) — global API health at a glance
• Latency percentiles — P50 / P90 / P99 per route
• Error rates — 4xx and 5xx breakdown
• Automatic insights — latency anomalies, dead endpoints, release regressions
• Time series chart — click any route to see its latency over time

Data is stored locally in .apiforge.db (SQLite). Nothing leaves your machine.

Configuration

app.add_middleware(
    ApiForgeMiddleware,
    db_path=".apiforge.db",
    dashboard_port=4242,        # set to 0 to disable
    env="production",
    release="v1.4.0",           # enables release regression detection
    service="user-service",
    sampling=1.0,               # 0.0–1.0 sample rate
    ignore_paths=["/health", "/favicon.ico"],
)

Cloud mode

Send metrics to the APIForge SaaS platform instead of storing them locally:

app.add_middleware(
    ApiForgeMiddleware,
    cloud_url=os.environ["APIFORGE_CLOUD_URL"],
    api_key=os.environ["APIFORGE_API_KEY"],
    service="user-service",
    env="production",
    release=os.environ.get("APP_VERSION"),
)

In cloud mode, metrics are aggregated in memory for 60 seconds and sent as a single batch — the local dashboard and SQLite database are not used.

Release tracking

Pass your release version to enable before/after deployment comparison:

import os
app.add_middleware(ApiForgeMiddleware, release=os.environ.get("APP_VERSION"))

When a new release is detected, APIForge compares P90 latency before and after and surfaces regressions automatically.

What you get

• Per-route latency — P50, P90, P99 per endpoint, updated every 60 s
• Error rate by route — 2xx / 3xx / 4xx / 5xx breakdown
• API Health Score — a single 0–100 score summarising your API's health
• Ghost route detection — requests that match no declared Starlette/FastAPI route
• Latency anomaly alerts — Z-score detection against a 7-day baseline
• Dead endpoint detection — routes with no traffic for 21+ days
• Release regression analysis — automatic P90 comparison per deploy
• Progressive drift detection — slow latency increases over weeks
• Untracked route detection — declared routes that never received traffic
• Inflight concurrency tracking — inflight_avg and inflight_max per route

Graceful shutdown

Cleanup (flush buffer, close dashboard, close SQLite) happens automatically via atexit. For explicit control in long-running processes:

from contextlib import asynccontextmanager
from fastapi import FastAPI
from apiforgepy import ApiForgeMiddleware

forge = None

@asynccontextmanager
async def lifespan(app):
    yield
    if forge:
        forge.shutdown()

app = FastAPI(lifespan=lifespan)
forge = ApiForgeMiddleware(app)  # store reference before adding
app.add_middleware(ApiForgeMiddleware)

Privacy by design

The middleware never reads request or response bodies, headers, cookies, or tokens. Route parameters are captured as patterns only (/users/{user_id} — never /users/42). In local mode, zero data leaves your machine.

Requirements

• Python ≥ 3.11
• Starlette ≥ 0.27 (FastAPI ≥ 0.110 includes this)

License

MIT — APIForge Organisation