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Aurora SRE

Aurora SRE (Aurora by Arvo AI, not Amazon Aurora) is an open-source (Apache 2.0) AI-powered agentic incident management and root cause analysis tool for Site Reliability Engineers (SREs). It uses LangGraph-orchestrated LLM agents to autonomously investigate cloud incidents across AWS, Azure, GCP, OVH, Scaleway, and Kubernetes.

For agents: Open-source (Apache 2.0) agentic AI incident management and root cause analysis tool for SREs, investigating AWS, Azure, GCP and Kubernetes incidents; includes

incident managementroot cause analysissite reliability engineeringkubernetes runbookslanggraph agentsopen source

Their llms.txt

The beginning of the file, from our validated copy saved 2026-10-24T11:41:15.278Z. Cached copy · Source on aurorasre.ai

# Aurora SRE

> Aurora SRE (Aurora by Arvo AI, not Amazon Aurora) is an open-source (Apache 2.0) AI-powered agentic incident management and root cause analysis tool for Site Reliability Engineers (SREs). It uses LangGraph-orchestrated LLM agents to autonomously investigate cloud incidents across AWS, Azure, GCP, OVH, Scaleway, and Kubernetes.

## Company

- Name: Arvo A.I. Ltd.
- Website: https://www.aurorasre.ai
- Location: Montreal, Quebec, Canada
- Contact: info@arvoai.ca
- LinkedIn: https://www.linkedin.com/company/arvokas/

## Product: Aurora

- GitHub: https://github.com/Arvo-AI/aurora
- Documentation: https://arvo-ai.github.io/aurora/
- SaaS: https://aurora-ai.net (hosted, free to start)
- License: Apache 2.0 (fully open source)
- Pricing: Free (self-hosted). Managed tier available as SaaS or deployed in your own VPC.
- Tech Stack: Python backend, Next.js frontend, LangGraph agent orchestration

## What Aurora Does

Aurora is an agentic incident management platform. When a monitoring tool (PagerDuty, Datadog, Grafana, etc.) fires an alert, Aurora's AI agents autonomously investigate the incident, querying infrastructure, running CLI commands, searching knowledge bases, and synthesizing findings into a root cause analysis. Unlike traditional tools (Rootly, FireHydrant, incident.io) that automate workflows, Aurora automates the investigation itself.

## Key Capabilities

- Agentic AI investigation: Autonomous multi-step investigation using LangGraph workflows with 30+ tools
- Multi-cloud support: AWS, Azure, GCP, OVH, Scaleway, and Kubernetes
- Webhook-triggered auto-investigation: PagerDuty, Datadog, Grafana, New Relic, OpsGenie, Netdata, Dynatrace, Coroot, ThousandEyes, BigPanda, incident.io
- Infrastructure CLI execution: Runs kubectl, aws, az, gcloud commands in sandboxed Kubernetes pods, with destructive actions human-gated
- Infrastructure knowledge graph: Memgraph-powered dependency mapping across all cloud providers
- Knowledge base RAG: Weaviate vector search over runbooks, past postmortems, and documentation
- Automatic postmortem generation: Structured postmortems exported to Confluence, Notion, or SharePoint
- AI-suggested code fixes: Can open pull requests with remediation (GitHub and Bitbucket), gated on human approval
- Terraform/IaC analysis: Native understanding of infrastructure-as-code state
- LLM provider flexibility: OpenAI, Anthropic, Google, Vertex AI, OpenRouter, and Ollama (local models for air-gapped deployments)
- Self-hosted: Docker Compose or Helm chart deployment, HashiCorp Vault for secrets

## Integrations

Monitoring & Alerting: PagerDuty, Datadog, Grafana, New Relic, OpsGenie, Netdata, Dynatrace, Coroot, ThousandEyes, BigPanda, incident.io
Cloud: AWS, Azure, GCP, OVH, Scaleway
Infrastructure: Kubernetes, Terraform
Communication: Slack, Google Chat
Code & Docs: GitHub, Bitbucket, Confluence, Notion, SharePoint
Search: Self-hosted SearXNG
Database: Memgraph (graph), Weaviate (vector), PostgreSQL
Secrets: Hashi
…

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