What it does
Core capabilities at a glance
- Alerting
- Alertmanager
- Clickhouse
- Golang
- Logs
- Loki
- MCP Server
- Metrics
Deep dive
The full breakdown - performance, comparisons, and setup
nightingale
nightingale is a LLM observability tool - Open-source, self-hosted alerting for metrics, logs, and databases. Includes a built-in AI agent and MCP server.
Overview
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
- 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.
This starts Nightingale, MySQL, Redis, VictoriaMetrics, and Categraf. Open http://localhost:17000 and sign in with 'root' / 'root.2020'.
- 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.
nightingale is open-source, written primarily in Go, with 13,366 GitHub stars under the Apache 2.0 license. The latest release is v9.2.0 (2026-10-11).
Key capabilities
From the project's documentation:
- GitHub Discussions — ask questions and share ideas.
- Slack — join the community.
- Security policy — report vulnerabilities privately and check version support.
How it fits a local-AI stack
nightingale runs on your own hardware, so pair it with a model and a GPU sized to your needs. Use the VRAM calculator to pick a model that fits your card, and see what you can run for hardware guidance. Related LLM observability tools in the directory:
Sources
- Source code & docs: ccfos/nightingale
- Official website: https://n9e.github.io
Stats from GitHub, 2026-10-19.
Frequently asked
Quick answers to common questions
What is nightingale?
nightingale is a observability tool for local AI workloads. Open-source, self-hosted alerting for metrics, logs, and databases. Includes a built-in AI agent and MCP server.
Is nightingale free and open source?
Yes, nightingale has 13,366 GitHub stars and is licensed under Apache 2.0. You can self-host it for free on docker.
What platforms does nightingale support?
nightingale runs on docker.
What hardware do I need for nightingale?
The hardware requirements depend on which models you run. Check our hardware directory for compatible GPUs and systems. nightingale has 13,366 GitHub stars and an active community.
Does nightingale support GPU acceleration?
nightingale's GPU support depends on your specific setup. Check the documentation for details. For the best performance, pair it with an NVIDIA RTX 4090 or 5090.
What are the best alternatives to nightingale?
Popular alternatives include other observability tools in our directory. Browse our full collection at /tool for comparisons, community reviews, and benchmark data to find the right fit for your workflow.
How much does nightingale cost?
nightingale is free-open-source. It is completely free and open source to self-host.
Pairs well with
Complementary tools, models, and hardware