What is AI-powered incident management?

AI-powered incident management uses intelligent agents to automate incident response tasks like alert triage, runbook execution, root cause analysis, and team notifications. Instead of relying solely on on-call engineers, AI agents can handle initial diagnosis and remediation steps 24/7, reducing MTTR and on-call burnout.

Is Akmatori open source?

Yes, Akmatori is an Apache 2.0 open-source project. You can inspect the code, run it on your own infrastructure using Docker or Kubernetes, and keep complete control over your data and incident workflows.

What LLM providers does Akmatori support?

Akmatori supports multiple LLM providers including OpenAI (GPT-4), Anthropic (Claude), Google (Gemini), and OpenRouter. You can also use on-premise models like Mistral, GLM, Kimi, or Minimax for data sovereignty requirements.

How does Akmatori integrate with PagerDuty?

Akmatori integrates with PagerDuty via webhooks and the PagerDuty API. When an incident is triggered, Akmatori's AI agents can automatically acknowledge alerts, gather context from your observability stack, execute diagnostic runbooks, and post updates to incident channels.

Can Akmatori reduce on-call burnout?

Yes. Akmatori handles routine incidents autonomously, filters alert noise, and only escalates to human engineers when necessary. This significantly reduces the number of pages during off-hours and allows SRE teams to focus on high-impact work instead of repetitive troubleshooting.

What observability tools does Akmatori work with?

Akmatori ingests alerts from Prometheus Alertmanager, Grafana, Datadog, Zabbix, and PagerDuty, and can monitor Slack channels. AI agents reach your infrastructure through real tools such as SSH and Kubernetes to diagnose issues.

Is Akmatori fully open source?

Yes. Akmatori is an Apache 2.0 open-source project. You can inspect the code, run it yourself, adapt it to your environment, and contribute improvements through GitHub.

How long does it take to set up?

Most teams are up and running in under 15 minutes. Deploy with a single Docker Compose command, connect your alerting tools, and the AI agent starts learning your environment immediately.

What should we connect first for a proof of concept?

Start with one noisy alert source, one production service, and the runbook or dashboard your responders already trust. Akmatori can then show a focused before-and-after: evidence collection, likely root cause, recommended remediation, and the approval step before anything changes.

Will the AI make changes to my production systems?

Only if you allow it. Akmatori supports multiple autonomy levels: from read-only diagnostics to fully automated remediation. You control exactly what actions the agent can take, with approval workflows for sensitive operations.

What LLM providers do you support?

Akmatori works with OpenAI, Anthropic, Google, OpenRouter, and any custom OpenAI-compatible endpoint. You can also run fully on-premise with local models via Ollama.

How does Akmatori handle sensitive data?

Self-hosted deployments keep all data within your infrastructure. No telemetry, no external calls unless you configure an external LLM. For maximum security, run with local models and your data never leaves your network.

Can I integrate with my existing tools?

Yes. Akmatori integrates with Prometheus, Grafana, PagerDuty, Slack, Kubernetes, and more.

Akmatori | Open-Source AI Incident Response Platform for SRE Teams

Self-Hosted AI Incident Response for SRE, Platform, and DevOps Teams

Akmatori gives SRE and platform teams an AI incident responder that triages alerts, gathers evidence from your stack, and drafts safe next actions in minutes. Run it in your own infrastructure, connect the tools you already use, and keep humans in control with approval gates for production changes.

Open-source Apache 2.0Deploy with Docker Compose or HelmWorks with your existing alerts and runbooks

Apache 2.0 and self-hosted. Clone it, run it on your own alerts, and keep every incident, runbook, and model inside your infrastructure.

Get Started in 60 Seconds

Deploy Akmatori locally, install it on Kubernetes, or trigger your first AI investigation from the CLI. No vendor lock-in, full data control.

# Clone and start Akmatori in under a minute
git clone https://github.com/akmatori/akmatori.git
cd akmatori
docker compose up -d

# Open http://localhost:8080 to access the UI

Quick start guide

Best for local evaluation and fast team demos

Boot the full stack on localhost with Docker Compose.
Open the UI immediately and connect your first alert source.
Validate incident workflows before touching production.

Prerequisites

Docker EngineDocker ComposeGit access

You are ready when

The web UI loads on localhost.
Core services show healthy in Docker Compose.
You can create a test incident from the dashboard.

Verify the stack is healthy

docker compose ps

You should see the Akmatori services in a running or healthy state before opening the UI.

Read Docker quick start View source on GitHub

# Trigger an AI-powered incident investigation curl -X POST https://akmatori.example.com/api/incidents \ -H "Authorization: Bearer $AKMATORI_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "task": "Investigate sustained CPU saturation on prod-web-01 and summarize likely root cause", "context": { "host": "prod-web-01", "severity": "critical", "source": "prometheus", "metric": "cpu_usage", "value": 98.5 } }' # Response: Akmatori starts investigating immediately # { # "id": "inc_abc123", # "status": "investigating", # "agent": "sre-agent-01" # }

AI incident response your SREs can operate

Akmatori turns production alerts into governed response workflows: it gathers logs, metrics, traces, recent deploy context, and runbook knowledge, then proposes or executes the next safe action with human approval gates. Teams can self-host it, connect existing observability and on-call tools, and prove value on real incidents before expanding automation.

What Akmatori Does

The capabilities that ship in the open-source platform today

Multi-Source Alert Ingestion

Ingest webhooks from Alertmanager, PagerDuty, Grafana, Datadog, and Zabbix, plus Slack channel monitoring — no rip-and-replace of your tooling.

Agentic Investigation

Every incident runs a full agent session that gathers logs, metrics, and context, with subagents for runbook lookup and cross-incident memory recall.

Real Tools, Not Just Chat

Agents reach your infrastructure through SSH, Kubernetes, scripts, and API clients — reusable tools you scope per skill or cron job.

Approval-Gated Remediation

Risky actions become proposals you approve in the web UI or Slack before anything executes. You decide how much autonomy each agent gets.

Bring Any LLM

OpenAI, Anthropic, Google, OpenRouter, NVIDIA NIM, MiniMax, or any OpenAI-compatible on-prem endpoint (GLM, Kimi, Mistral, LLaMA). Swap providers anytime — no lock-in.

Scheduled Investigations

Cron jobs run recurring agent investigations with their own prompt and tool allowlist, posting results straight to a Slack channel.

Self-Hosted by Design

Deploy with Docker Compose or Helm. Your incident data never leaves your infrastructure — run fully on-prem with local models for air-gapped setups.

Web Dashboard + Slack

Manage incidents, skills, tools, and settings from a modern, mobile-responsive UI, and drive the same workflows from Slack.