Show HN: Nightwatch, The open-source, read-only AI SRE

The open-source, read-only AI SRE. ninoxAI turns alert storms into incidents, investigates root cause over your live systems, and proposes human-approved fixes — without ever touching production. Quickstart · AI SRE · Demo lab · Docs · Discord Your monitoring tells you something broke. It pages you at 3am with fifty alerts for one outage and leaves the hard part to you: ninoxAI is a thin, local-first, monitoring-agnostic AI SRE layer that answers that question. It sits above Checkmk, Prometheus, Icinga2, Zabbix, webhooks, Docker, Kubernetes, AWS, Grafana, GitHub, Git and plain VMs, and: ninoxAI observes, reasons, and recommends — it never executes anything. No commands run, no alerts acked, no thresholds changed, no write-back to production. Every fix is a copyable artifact a human approves. Gated, governed remediation is on the roadmap; unconditional auto-execute is not. Try it in 60 seconds — no LLM, no API keys, fully offline: cp .env.example .env # set NINOXAI_SECRET_KEY (one-liner is in the file) docker compose up --build # → http://127.0.0.1:8765 # No live monitoring? Watch it triage synthetic alert noise: docker compose exec ninoxai ninoxai generate-mocks docker compose exec ninoxai ninoxai import data/mock_alerts.json docker compose exec ninoxai ninoxai reprocess # → /recommendations now shows reasoned threshold + flapping fixes Local Python install (for development) python -m venv .venv && source .venv/bin/activate # Windows: .venv\Scripts\Activate.ps1 pip install -e ".[dev,embeddings]" python -m ninoxai generate-mocks python -m ninoxai import data/mock_alerts.json python -m ninoxai reprocess python -m ninoxai serve # → http://127.0.0.1:8765 Then light up the AI SRE: point ninoxAI at a tool-calling LLM (Anthropic / OpenAI / Mistral / a local Ollama) and connect your systems — either directly or via a ninox runner (below). The full end-to-end scenario — real monitoring tools and live investigator capabilities (Docker/Kubernetes/host/AWS/Grafana/GitHub) against a genuinely failing workload — lives in lab/. ingest → normalize → cluster → score noise → recommend → dashboard ↓ agentic, read-only root-cause investigator Stage What happens ingest Read-only adapters pull non-OK alerts from each source + JSON/CSV import. normalize Maps every source onto one schema + message fingerprint. cluster Groups by host / service / severity / time-window. Semantic embeddings optional. noise Frequency, ack-rate, ticket-rate, short-recovery, flapping → one 0–1 score. recommend Rule-based tuning recommendations with rationale + evidence. investigate A tool-calling LLM gathers live evidence → root-cause hypothesis + classified fixes. Cross-tool correlation: the same fault fires in every tool. The Incidents view groups clusters that share (host, severity, time-window) into one incident — "confirmed by N tools" — read-only, no merge. ninoxAI's standout capability. A tool-calling LLM drives a typed allowlist of read-only capabilities (a ReAct loop on native function-calling — reason → act → observe), builds a root-cause hypothesis from live evidence, and proposes classified fixes a human approves. Run it live-streaming in the agent console (/agent) or from the CLI. → Investigator internals The agent can investigate systems it can't reach directly. A ninox is a thin, outbound-only runner that lives inside one environment (cluster, VPC, on-prem segment), holds that environment's credentials locally, and dials home to the brain — no inbound firewall hole. It advertises a read-only capability surface the brain calls as if local. ┌────────────────────┐ ┌────────────────────┐ │ ninoxAI brain │ ◀── outbound only ─── │ ninox runner │ │ dashboard · API │ (the ninox dials │ inside k8s/Docker/ │ │ incidents · RCA │ home; no inbound │ AWS/on-prem/VM │ │ AI SRE investigator│ firewall hole) │ credentials stay │ └────────────────────┘ ◀── read-only evidence │ local │ └────────────────────┘ Capabilities self-select by environment — one binary, the right tools for the box it lands on. Connected ninoxes show up in the Parliament of Owls (/parliament). → Deployment & on-prem All adapters are read-only — no ack, no downtime, no write-back. Configured in the UI (/connections), credentials Fernet-encrypted. Want to teach the AI SRE to read your stack (Jira, Sentry, Postgres…)? Point it at any MCP server, write a Python capability plugin, or expose tools via the runner protocol — every external tool runs through the same safety shell (namespaced, injection-scanned, classification-coerced). → Extending capabilities Default is template — fully offline: no LLM, no network, no API keys, no tracking. It works out of the box for summaries/recommendations but deliberately can't drive the agent (that needs tool-calling). Pick a remote per role — a cheap model for high-volume summaries, a strong one for the rare investigation: Redaction + secret-scrubbing run before every remote call — hostnames, IPs, UUIDs, emails, paths become deterministi

Show HN: Nightwatch, The open-source, read-only AI SRE

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