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### Agentic Investigation
AI agents dynamically select from 30+ tools. They run `kubectl`, `aws`, `az`, and `gcloud` in **sandboxed Kubernetes pods**, query logs, check deployments, and correlate data — all autonomously.
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### AI Code Fixes
Aurora doesn't just find root cause — it suggests fixes and can generate pull requests with the remediation.
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### Incident Dashboard
Ingest alerts from PagerDuty, Datadog, Grafana, New Relic, OpsGenie, incident.io and more. Every alert auto-triggers a background investigation.
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### Auto-Generated Postmortems
Detailed reports with timeline, root cause, impact assessment, and remediation steps. Export directly to Confluence, Notion, or SharePoint.
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### Infrastructure Knowledge Graph
Visualize your entire infrastructure as a dependency graph. When an incident occurs, Aurora traces blast radius across services and providers.
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**More capabilities:** Knowledge Base RAG • Multi-Cloud (AWS, Azure, GCP, OVH, Scaleway, Cloudflare) • Any LLM (OpenAI, Anthropic, Gemini, Vertex AI, OpenRouter, Ollama) • Terraform/IaC Analysis • MCP Server (Cursor, Claude Desktop, Windsurf) • Org-level Command Policies • SigmaHQ Guardrails • NeMo Input Rail
---
## Quick Start
Get Aurora running locally in under 5 minutes:
```bash
git clone https://github.com/arvo-ai/aurora.git && cd aurora
make init # Generate secure secrets
nano .env # Add your LLM API key (OpenRouter, OpenAI, etc.)
make prod-prebuilt # Pull prebuilt images and start
```
Open **http://localhost:3000**. The first user to register becomes admin.
> [!TIP]
> Aurora works without any cloud provider accounts. The LLM API key is the only external requirement. Connectors are optional.
**LLMs:** OpenAI • Anthropic • Google Gemini • Vertex AI • AWS Bedrock • OpenRouter • Ollama (air-gapped)
---
## Architecture
```
aurora/
├── server/ # Python API (Flask), Celery workers, LangGraph agents
├── client/ # Next.js frontend
├── deploy/ # Helm chart, Docker Compose, deployment scripts
├── config/ # Default configuration
├── scripts/ # CLI utilities
└── website/ # Documentation (Docusaurus)
```
| Layer | Stack |
|-------|-------|
| AI Orchestration | LangGraph, 30+ tool definitions |
| Backend | Python, Flask, Celery |
| Frontend | Next.js, TypeScript |
| Graph DB | Memgraph |
| Secrets | HashiCorp Vault, AWS Secrets Manager |
| Storage | PostgreSQL, Redis, S3-compatible |
---
## Security
- **Sandboxed execution** — Agent commands run in isolated Kubernetes pods with NetworkPolicy, not on your control plane
- **RBAC** — Three roles (Admin, Editor, Viewer) enforced at API and UI layers via Casbin
- **Closed registration** — First user is admin; all others are invited
- **SigmaHQ guardrails** — 37 threat detection signatures on agent command execution
- **NeMo input rail** — Prompt injection detection on every turn
- **No telemetry** — Zero data sent to Arvo AI. Fully self-hosted.
---
## Data Privacy
Aurora is **100% self-hosted**. Your incident data never leaves your infrastructure.
- All data on your infrastructure (Docker or Kubernetes)
- No telemetry or tracking to Arvo AI
- Secrets encrypted at rest in Vault or AWS Secrets Manager
- LLM calls go directly from your infra to your chosen provider
- Use Ollama for fully air-gapped operation
---
## Community
Click to watch the full demo
