nightingale

Open-source alerting for your existing stack

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Nightingale brings alert rules, event processing, and notification routing into one place. Connect Prometheus, VictoriaMetrics, Elasticsearch, Loki, ClickHouse, and other data sources you already run, then deliver alerts to Slack, PagerDuty, and your team's existing tools.

Self-hosted · Apache-2.0 · Built-in AI agent · MCP server

Why Nightingale?

• Give teams ownership of their alerts. Organize rules into business groups, assign team permissions, and manage rules and notification settings through the UI and API.
• Bring scattered alerting into one place. Evaluate rules against metrics, logs, and SQL data sources while keeping your existing collectors, storage, and Grafana dashboards.
• Understand what happened to an alert. Inspect rule evaluation records, historical events, and notification results to trace a query through to delivery.

Quick start

With Git and Docker Compose installed, start the included evaluation stack:

git clone https://github.com/ccfos/nightingale.git
cd nightingale/docker/compose-bridge
docker compose up -d

This starts Nightingale, MySQL, Redis, VictoriaMetrics, and Categraf. Open http://localhost:17000 and sign in with root / root.2020.

To get your first alert:

1. Connect a data source. Add your existing Prometheus-compatible endpoint, or use http://victoriametrics:8428 for the VictoriaMetrics instance in this Compose stack.
2. Create an alert rule. Choose a business group and data source, then enter a PromQL expression. For an always-firing test, use vector(1) > 0 and set the duration to 0.
3. Configure delivery. Set up a notification channel, create a notification rule, and attach it to the alert rule. Check that the test notification arrives, then disable the test alert.

The Compose stack uses example credentials and exposes service ports for local evaluation. Change credentials and review network access before deploying on a shared or public host. See deployment options for other ways to run Nightingale.

How it fits into your stack

Nightingale queries your data sources, evaluates alert conditions, processes events, and routes notifications. Your existing data stays in its current stores.

Keep Grafana for visualization and your on-call platform for scheduling, escalation, and incident response. Nightingale also includes dashboards for teams that want to explore data alongside their alerts.

Starting without a monitoring stack? The standalone configuration includes an optional embedded TSDB for small, single-instance deployments. Categraf is an optional collector for hosts, middleware, databases, and network devices.

Key capabilities

Capability	What you can do
Alerting across data sources	Use PromQL, log queries, or SQL to define conditions. Apply a rule to multiple instances of the same data source type.
Team ownership	Group rules and dashboards by business group, assign team permissions, and connect OIDC, OAuth2, LDAP, or CAS for sign-in.
Event processing and routing	Mute notifications, subscribe to alerts, enrich or rewrite labels, and route events through conditional pipelines.
Alert execution records	Review evaluation queries and results, active and historical events, and notification delivery records.
Reusable integrations	Start with bundled collector configurations, alert rules, and dashboards. Review queries and thresholds for your environment before enabling them.
Distributed evaluation	Distribute rules across alerting engines and use n9e-edge to evaluate alerts close to remote data sources.

Notification integrations include Slack, PagerDuty, Discord, Email, Telegram, Mattermost, Jira, and Jira Service Management, with HTTP webhooks and scripts for custom destinations. See the native notification channel reference for configuration details.

For automated responses, connect ibex to run predefined remediation scripts when an alert fires.

Built-in AI agent

Nightingale includes an AI agent you can use directly in the web UI. The agent runs inside Nightingale, calls tools to inspect your monitoring data and configuration, and works through tasks over multiple steps.

• Investigate alerts. Query related metrics and logs, inspect affected hosts, and explain findings using the data it retrieves.
• Troubleshoot the alerting process. Inspect rules, evaluation logs, mute settings, event processing, and notification results to find why an alert did not fire or reach its destination.
• Create and update configuration. Build alert rules and dashboards, generate PromQL and SQL, and configure notification rules, mutes, and subscriptions through conversation. The built-in tools for updating existing alert rules and dashboards present proposed changes for confirmation.
• Extend it with Skills. Bundled Skills cover common monitoring workflows. Add your team's procedures, reference material, and scripts as custom Skills, including Skills imported from Git.

For example:

Why did this alert fire? Check the affected host's metrics and logs around the trigger time.

Create a memory-usage alert for the production hosts and notify our team in Slack.

Configure a model endpoint to get started. The agent supports OpenAI-compatible APIs, Claude, Gemini, and compatible self-hosted models. You choose the model service and credentials; core alerting works independently of the agent.