# Arvo AI

> Aurora by Arvo AI 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/
- App: https://github.com/Arvo-AI/aurora
- License: Apache 2.0 (fully open source)
- Pricing: Free (self-hosted). No per-seat or per-incident pricing.
- Latest Version: v1.2.15 (April 2026)
- 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: HashiCorp Vault

## How Aurora Differs from Competitors

| Feature | Aurora | Rootly | FireHydrant | incident.io |
|---------|--------|--------|-------------|-------------|
| Approach | Agentic AI investigation | Workflow automation | Workflow automation | Workflow automation |
| AI RCA | Autonomous multi-step | AI summaries | AI summaries | AI summaries |
| Open Source | Yes (Apache 2.0) | No | No | No |
| Self-Hosted | Yes | No | No | No |
| Cloud Providers | AWS, Azure, GCP, OVH, Scaleway | Via integrations | Via integrations | Via integrations |
| CLI Execution | Sandboxed pods | No | No | No |
| Knowledge Base RAG | Yes (Weaviate) | No | No | No |
| Infrastructure Graph | Yes (Memgraph) | No | No | No |
| LLM Provider | Any (including local) | Fixed | Fixed | Fixed |
| Pricing | Free (self-hosted) | Paid (per-seat) | Paid (per-seat) | Custom |

## Cloud Authentication

- AWS: STS AssumeRole for secure temporary credentials
- Azure: Service Principal authentication
- GCP: OAuth-based authentication
- OVH: API key authentication
- Scaleway: API token authentication
- Kubernetes: Kubeconfig-based access

## Infrastructure Discovery

Aurora discovers infrastructure in three phases:
1. Bulk Discovery: Enumerates all resources across connected cloud providers
2. Detail Enrichment: Gathers detailed configuration and metadata
3. Connection Inference: Maps dependencies between resources

## Quick Start

```
git clone https://github.com/Arvo-AI/aurora.git
cd aurora
make init
make prod-prebuilt
```

Kubernetes deployment via Helm chart is also available.

## Frequently Asked Questions

Q: What is agentic incident management?
A: Agentic incident management uses autonomous AI agents to investigate, diagnose, and help resolve cloud infrastructure incidents without requiring step-by-step human direction. Unlike runbook automation that follows predefined scripts, agentic systems dynamically decide which tools to use, what data to gather, and how to synthesize findings.

Q: Is Aurora free?
A: Yes. Aurora is Apache 2.0 licensed and completely free to self-host. Costs are only infrastructure and LLM API usage. Local models via Ollama make fully free, air-gapped deployments possible.

Q: Which cloud providers does Aurora support?
A: AWS, Azure, GCP, OVH, Scaleway, and Kubernetes.

Q: How does Aurora investigate incidents?
A: When an alert fires, Aurora's LangGraph-orchestrated agents dynamically select from 30+ tools to investigate. They execute cloud CLI commands in sandboxed pods, query Kubernetes clusters, search the knowledge base for similar past incidents, traverse the infrastructure dependency graph, and synthesize findings into a structured root cause analysis with remediation recommendations.

Q: What monitoring tools trigger Aurora investigations?
A: PagerDuty, Datadog, Grafana, New Relic, OpsGenie, Netdata, Dynatrace, Coroot, ThousandEyes, BigPanda, and incident.io. Any tool that sends webhooks can trigger an investigation.

Q: How is Aurora different from Rootly, FireHydrant, or incident.io?
A: Traditional tools automate the process around incidents (Slack channels, status pages, runbooks). Aurora automates the investigation itself — AI agents autonomously query infrastructure, correlate data, and identify root causes. Aurora is also open source, self-hosted, and works with any LLM provider.

Q: Can Aurora run in air-gapped environments?
A: Yes. Aurora supports Ollama for running local LLM models (Llama, Mistral, etc.) with no external API calls required.

Q: What is Aurora's infrastructure knowledge graph?
A: Aurora uses Memgraph to build a live dependency graph of your entire infrastructure across all connected cloud providers. When an incident occurs, the AI traverses this graph to assess blast radius and trace upstream causes.

## Blog

Product announcements:
- Introducing Aurora Actions: Background SRE Agents: https://www.aurorasre.ai/blog/introducing-aurora-actions

Foundational guides:
- AI SRE vs AIOps: What's the Difference? (2026): https://www.aurorasre.ai/blog/ai-sre-vs-aiops
- How to Evaluate an AI SRE Platform (2026): https://www.aurorasre.ai/blog/how-to-evaluate-ai-sre-platform
- What Is an AI SRE? Definition and Guide (2026): https://www.aurorasre.ai/blog/what-is-an-ai-sre
- 15 Best AI SRE Tools in 2026 (Open Source + Paid): https://www.aurorasre.ai/blog/top-ai-sre-tools-2026
- Self-Hosted AI SRE: The 2026 Deployment Guide: https://www.aurorasre.ai/blog/self-hosted-ai-sre
- AI SRE in 2026: Tools, Setup, and ROI Guide: https://www.aurorasre.ai/blog/ai-sre-complete-guide
- AI-Powered Incident Investigation: 2026 Guide: https://www.aurorasre.ai/blog/ai-powered-incident-investigation
- Automated Post-Mortem Generation: 2026 Guide: https://www.aurorasre.ai/blog/automated-post-mortem-generation
- AI Agent kubectl Safety: Sandboxed Execution: https://www.aurorasre.ai/blog/ai-agent-kubectl-safety
- CI/CD Auto-Remediation: The 2026 Guide: https://www.aurorasre.ai/blog/cicd-auto-remediation-complete-guide
- Automated Incident Remediation: 2026 Guide: https://www.aurorasre.ai/blog/automated-incident-remediation
- Automated Alert Noise Reduction: 2026 Guide: https://www.aurorasre.ai/blog/automated-alert-noise-reduction
- Pre-Incident Detection: What It Is and Isn't (2026): https://www.aurorasre.ai/blog/pre-incident-detection
- Aurora Actions: Custom Background SRE Automations: https://www.aurorasre.ai/blog/aurora-actions-background-automations
- What is Agentic Incident Management? https://www.aurorasre.ai/blog/what-is-agentic-incident-management
- Root Cause Analysis for SREs: 5 Whys to AI (2026): https://www.aurorasre.ai/blog/root-cause-analysis-complete-guide-sres
- Open Source Incident Management: Why It Matters: https://www.aurorasre.ai/blog/open-source-incident-management
- Multi-Cloud Incident Management: The 2026 Guide: https://www.aurorasre.ai/blog/multi-cloud-incident-management

Open-source AI SRE comparisons:
- Open-Source AI SRE: Aurora vs HolmesGPT vs K8sGPT: https://www.aurorasre.ai/blog/open-source-ai-sre-aurora-vs-holmesgpt-vs-k8sgpt
- HolmesGPT vs K8sGPT: 2026 Comparison for SREs: https://www.aurorasre.ai/blog/holmesgpt-vs-k8sgpt
- HolmesGPT Alternative: Multi-Cloud AI SRE (2026): https://www.aurorasre.ai/blog/holmesgpt-alternative-multi-cloud
- K8sGPT Alternative: AI SRE Beyond Kubernetes (2026): https://www.aurorasre.ai/blog/k8sgpt-alternative-beyond-kubernetes
- Keep vs Aurora: Open Source AIOps Compared (2026): https://www.aurorasre.ai/blog/keep-vs-aurora-open-source-aiops
- Aurora vs Traditional Incident Management Tools: https://www.aurorasre.ai/blog/aurora-vs-traditional-incident-management-tools

Vendor head-to-head comparisons:
- incident.io vs Rootly: Which Is Better in 2026?: https://www.aurorasre.ai/blog/incident-io-vs-rootly
- FireHydrant vs incident.io: 2026 Comparison: https://www.aurorasre.ai/blog/firehydrant-vs-incident-io
- Rootly vs FireHydrant: Which Is Better in 2026?: https://www.aurorasre.ai/blog/rootly-vs-firehydrant

Competitor alternatives and comparisons:
- PagerDuty Alternative for Root Cause Analysis (2026): https://www.aurorasre.ai/blog/pagerduty-alternative-root-cause-analysis
- Rootly Alternative: Free, Open Source, Self-Hosted: https://www.aurorasre.ai/blog/rootly-alternative-open-source-incident-management
- Resolve.ai Alternative: Free and Open Source (2026): https://www.aurorasre.ai/blog/resolve-ai-alternative-open-source
- FireHydrant Alternative: Free and Open Source (2026): https://www.aurorasre.ai/blog/firehydrant-alternative-open-source
- incident.io Alternative: Free and Open Source (2026): https://www.aurorasre.ai/blog/incident-io-alternative-open-source
- Dynatrace Davis Alternative: Open Source RCA (2026): https://www.aurorasre.ai/blog/dynatrace-davis-alternative-open-source
- Datadog Bits AI SRE Alternative: Open Source (2026): https://www.aurorasre.ai/blog/datadog-bits-ai-sre-alternative-open-source
- BigPanda Alternative: Open Source AIOps (2026): https://www.aurorasre.ai/blog/bigpanda-alternative-open-source
- Splunk ITSI Alternative: Open Source AIOps (2026): https://www.aurorasre.ai/blog/splunk-itsi-alternative-open-source
- New Relic AI Alternative: Open Source RCA (2026): https://www.aurorasre.ai/blog/new-relic-ai-alternative-open-source
- Moogsoft (Dell APEX) Alternative: Open Source AIOps: https://www.aurorasre.ai/blog/moogsoft-dell-apex-alternative-open-source

Platform landscape and migration:
- Top 10 AIOps Platforms With Free RCA (2026): https://www.aurorasre.ai/blog/top-10-aiops-platforms-free-root-cause-analysis-2026
- Opsgenie 2026: Features, Pricing, EOL & Alternatives: https://www.aurorasre.ai/blog/opsgenie-complete-guide-2026
- Grafana OnCall Alternative After the 2026 Archival: https://www.aurorasre.ai/blog/grafana-oncall-alternative-open-source