zebu-server-monitor 1.0.0.2

Plug-and-play monitoring agent for FastAPI/Flask apps and standalone hosts.

Server Monitor

A plug-and-play monitoring agent for FastAPI and Flask apps — and for plain hosts, workers, and scripts. It collects system, service, process, and Docker metrics, instruments inbound requests and outbound HTTP calls, and pushes everything to a monitoring backend.

• Zero-boilerplate integration — one monitor.init(app) call auto-detects the framework.
• Host agent — a server-monitor CLI that auto-detects running services/processes.
• Lazy framework deps — the core installs without FastAPI/Flask; add them only when needed.

PyPI distribution name: zebu-server-monitor · import name: server_monitor.

Installation

# core (host agent + standalone worker monitoring)
pip install zebu-server-monitor

# with FastAPI integration
pip install "zebu-server-monitor[fastapi]"

# with Flask integration
pip install "zebu-server-monitor[flask]"

# everything (FastAPI + Flask + requests instrumentation)
pip install "zebu-server-monitor[all]"

Requires Python 3.8+.

Quickstart

FastAPI

from fastapi import FastAPI
from server_monitor import monitor

app = FastAPI()

MONITOR_CONFIG = {
    "server": {"name": "web-01", "environment": "production"},
    "backend": {"url": "https://your-backend", "api_key": "${MONITORING_API_KEY}"},
}

monitor.init(app, MONITOR_CONFIG)

Flask

from flask import Flask
from server_monitor import monitor

app = Flask(__name__)
monitor.init(app, MONITOR_CONFIG)   # same config dict as above

monitor.init() auto-detects the framework, registers /health and /metrics, instruments requests, patches outbound HTTP clients (httpx, requests, urllib), and starts a background pusher.

Standalone worker / script (no web framework)

from server_monitor import monitor

sm = monitor.create_standalone(MONITOR_CONFIG)
sm.start()    # background thread pushes metrics on an interval
# ... your work ...
sm.stop()

Host agent (CLI)

Installing the package adds a command that monitors the current machine and pushes host metrics (CPU/RAM/disk/network/load/uptime, OS info, Docker containers). It auto-detects which known services and processes are actually running:

MONITOR_API_KEY=... server-monitor

Naming: the three names are different on purpose — install with pip install zebu-server-monitor, import with import server_monitor, and run with server-monitor (or the alias zebu-server-monitor — both are installed and do the same thing). There is no zebu_server_monitor import.

Configuration precedence is CLI flags > environment variables > defaults:

Flag	Env var	Default
--server-name	MONITOR_SERVER_NAME	machine hostname
--backend-url	MONITOR_BACKEND_URL	https://<url backend>>
--api-key	MONITOR_API_KEY	(required)
--environment	MONITOR_ENVIRONMENT	production
--push-interval	MONITOR_PUSH_INTERVAL	15

server-monitor --server-name edge-1 --backend-url https://your-backend --api-key "$KEY"

Configuring & editing the agent

The CLI has no config file — you configure it three ways, where each later source overrides the earlier one:

1. Built-in defaults (in code).
2. Environment variables (MONITOR_*, table above).
3. CLI flags (--server-name, etc.) — highest priority.

One-off run — just pass flags or inline env vars:

MONITOR_API_KEY=your-key server-monitor --server-name edge-1

Persistent (recommended on a server) — keep settings in an env file and run the agent as a service so it survives reboots and restarts on failure.

On Linux with systemd, create /etc/server-monitor.env:

MONITOR_SERVER_NAME=my-ubuntu-server
MONITOR_BACKEND_URL=https://your-backend
MONITOR_API_KEY=your-key
MONITOR_ENVIRONMENT=production
MONITOR_PUSH_INTERVAL=15

Then a unit at /etc/systemd/system/server-monitor.service. ExecStart must be the absolute path to the installed command — find it with command -v server-monitor, as it depends on how you installed (pip install --user → ~/.local/bin/server-monitor; system/sudo install → /usr/local/bin/server-monitor; venv → <venv>/bin/server-monitor):

[Unit]
Description=Server Monitor Agent
After=network-online.target
Wants=network-online.target

[Service]
Type=simple
User=ubuntu
EnvironmentFile=/etc/server-monitor.env
# Replace with the output of `command -v server-monitor`:
ExecStart=/home/ubuntu/.local/bin/server-monitor
Restart=always
RestartSec=10

[Install]
WantedBy=multi-user.target

sudo systemctl daemon-reload
sudo systemctl enable --now server-monitor
# after editing /etc/server-monitor.env later:
sudo systemctl restart server-monitor
sudo journalctl -u server-monitor -f      # live logs

Troubleshooting status=203/EXEC ("No such file or directory") — ExecStart points at a path that doesn't exist. The installed command is server-monitor (or zebu-server-monitor), not the import name. Run command -v server-monitor and paste that exact path into ExecStart. If it prints nothing, the package isn't installed for that user — pip install --user zebu-server-monitor first. Note that the binary must be readable by the unit's User= (a --user install under /home/ubuntu/.local/bin is fine when User=ubuntu).

On Windows, set the variables persistently, then open a new shell and run it:

setx MONITOR_API_KEY "your-key"
setx MONITOR_BACKEND_URL "https://your-backend"
setx MONITOR_SERVER_NAME "win-host-01"
# open a NEW terminal so the vars take effect, then:
server-monitor

The MONITOR_* variables above configure only the standalone host agent. When you embed the agent in a FastAPI/Flask app instead, configuration comes from the MONITOR_CONFIG dict passed to monitor.init() — see below.

Configuration reference

MONITOR_CONFIG is a plain dict validated by server_monitor.AgentConfig. Every key is optional — anything you omit falls back to the default below. The dict has six top-level sections:

MONITOR_CONFIG = {
    "server":        {...},   # how this host/app identifies itself
    "backend":       {...},   # where to push metrics
    "collectors":    {...},   # what to collect (system/services/process/app/docker/logs)
    "endpoints":     {...},   # health/metrics route names (web integration only)
    "thresholds":    {...},   # health-check limits
    "error_logging": {...},   # unhandled-exception capture (web integration only)
}

server — identity

Key	Type	Default	Description
name	str	machine hostname	Unique name shown on the dashboard.
environment	str	"production"	Logical environment: production / staging / development.
group	str	""	Grouping bucket on the dashboard (e.g. web, worker, db).
tags	list[str]	[]	Free-form labels for filtering (e.g. ["api", "frontend"]).
host	str	"127.0.0.1"	Advertised host/IP of this server.
port	int	8080	Advertised port of the app.
url	str / None	None	Public URL of the service, if any.
deployment	dict	{}	Optional release metadata, e.g. {"version": "1.4.2", "commit": "ab12cd", "build": "42"}.

backend — where metrics are pushed

Key	Type	Default	Description
url	str	""	Base URL of the monitoring backend. Metrics POST to {url}/api/v1/ingest.
api_key	str	""	Ingestion key, sent as the X-API-Key header.
push_interval	int	30	Seconds between pushes.
timeout	int	10	HTTP timeout (seconds) for each push.
enabled	bool	True	Set False to collect locally without pushing (useful for testing).

collectors — what gets gathered

Each collector has its own enabled flag and most have a polling interval (seconds).

collectors.system

Key	Type	Default	Description
enabled	bool	True	Collect CPU / RAM / disk / network / load / uptime.
interval	int	10	Polling interval.

collectors.services

Key	Type	Default	Description
enabled	bool	True	Collect service health.
interval	int	30	Polling interval.
monitor	list[str]	[]	Built-in service names to watch (see list below).
custom_services	list[dict]	[]	Ad-hoc service checks (see below).

Built-in service names you can drop into monitor (each knows its systemd unit / process name / default port): nginx, apache, httpd, postgres, mysql, mariadb, redis, mongodb, docker, gunicorn, uvicorn, celery, supervisord, rabbitmq, elasticsearch.

Custom services — for anything not built in, add entries to custom_services:

"services": {
    "enabled": True,
    "monitor": ["nginx", "redis"],
    "custom_services": [
        {"name": "my-api",   "check": "port",      "port": 9000},
        {"name": "worker",   "check": "process",   "process_name": "my_worker"},
        {"name": "pgbouncer","check": "systemctl", "service": "pgbouncer"},
    ],
}

Custom field	Type	Default	Description
name	str	— (required)	Label shown on the dashboard.
check	str	"systemctl"	Check method: systemctl, process, or port.
service	str	""	systemd unit name (when check="systemctl").
process_name	str	""	Process name to search for (when check="process").
port	int / None	None	TCP port to probe (when check="port").

collectors.process

Key	Type	Default	Description
enabled	bool	True	Track per-process CPU/RAM for named processes.
interval	int	30	Polling interval.
track_processes	list[str]	[]	Process names to track, e.g. ["python", "gunicorn", "celery"].

collectors.app — application worker tracking

Key	Type	Default	Description
enabled	bool	True	Track the app's own worker processes.
interval	int	10	Polling interval.
track_workers	bool	True	Include child/worker processes (e.g. Gunicorn/Uvicorn workers).

collectors.docker

Key	Type	Default	Description
enabled	bool	True	Collect container stats via the docker CLI. Auto-disables if docker isn't installed.
interval	int	30	Polling interval.

collectors.logs — application log shipping

Key	Type	Default	Description
enabled	bool	False	Attach a log handler and ship recent lines with each push.
max_lines	int	100	Number of recent log lines to include per push.

endpoints — route names (web integration only)

Key	Type	Default	Description
health	str	"/health"	Health-check route registered on your app.
metrics	str	"/metrics"	Prometheus metrics route.
monitor_health	str	"/__monitor/health"	Internal agent health route.
prometheus_enabled	bool	True	Expose the Prometheus exporter at metrics.

thresholds — health limits

A host is flagged unhealthy when any metric exceeds its threshold (percent).

Key	Type	Default	Description
cpu_percent	float	90.0	CPU usage ceiling.
memory_percent	float	85.0	RAM usage ceiling.
disk_percent	float	80.0	Disk usage ceiling.

error_logging — exception capture (web integration only)

Key	Type	Default	Description
enabled	bool	True	Capture unhandled exceptions with stack trace + request context.
capture_request_body	bool	False	Include the request body (off by default for privacy).
max_body_size	int	1024	Max bytes of request body to capture when enabled.
sanitize_headers	list[str]	["authorization", "cookie", "x-api-key", "x-auth-token"]	Headers redacted before logging.

Environment-variable substitution

Any string value in MONITOR_CONFIG may reference environment variables — handy for keeping secrets out of source:

• ${VAR} → value of VAR, or empty string if unset.
• ${VAR:default} → value of VAR, or default if unset.

"backend": {
    "url": "${MONITOR_BACKEND_URL:https://localhost:8701}",
    "api_key": "${MONITORING_API_KEY}",
}

Worker processes (Gunicorn/Uvicorn)

When you run multiple workers per host, set MONITORING_DISABLE_SYSTEM_METRICS=true in the worker environment so only one process reports system/service/process metrics — this avoids duplicate host data. Request and error metrics are still collected per worker.

Full example

MONITOR_CONFIG = {
    "server": {
        "name": "web-01",
        "environment": "production",
        "group": "web",
        "tags": ["api"],
        "deployment": {"version": "1.4.2", "commit": "ab12cd"},
    },
    "backend": {
        "url": "${MONITOR_BACKEND_URL:https://your-backend}",
        "api_key": "${MONITORING_API_KEY}",
        "push_interval": 30,
        "timeout": 10,
        "enabled": True,
    },
    "collectors": {
        "system":   {"enabled": True, "interval": 10},
        "services": {
            "enabled": True,
            "monitor": ["nginx", "redis"],
            "custom_services": [
                {"name": "my-api", "check": "port", "port": 9000},
            ],
        },
        "process":  {"enabled": True, "track_processes": ["python", "gunicorn"]},
        "app":      {"enabled": True, "track_workers": True},
        "docker":   {"enabled": True},   # auto-disables if the docker CLI is absent
        "logs":     {"enabled": False, "max_lines": 100},
    },
    "endpoints": {"health": "/health", "metrics": "/metrics", "prometheus_enabled": True},
    "thresholds": {"cpu_percent": 90, "memory_percent": 85, "disk_percent": 80},
    "error_logging": {
        "enabled": True,
        "capture_request_body": False,
        "max_body_size": 1024,
        "sanitize_headers": ["authorization", "cookie", "x-api-key"],
    },
}

License

MIT — see the bundled LICENSE file.