> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify.com/Arvo-AI/aurora/llms.txt
> Use this file to discover all available pages before exploring further.

# Introduction

> Aurora is an automated root cause analysis investigation tool that uses AI agents to help Site Reliability Engineers resolve incidents faster.

# Welcome to Aurora

Aurora is an automated root cause analysis investigation tool that uses AI agents to help Site Reliability Engineers resolve incidents. Built with a powerful combination of AI orchestration and cloud integrations, Aurora accelerates incident response by automatically investigating issues across your infrastructure.

<Note>
  Aurora works **without any cloud provider accounts**! The only external requirement is an LLM API key. Connectors for cloud providers and third-party services are optional and can be enabled later.
</Note>

## Key Features

<CardGroup cols={2}>
  <Card title="AI-Powered Root Cause Analysis" icon="magnifying-glass">
    Automated investigation using LangGraph agents that analyze logs, metrics, and infrastructure state to identify root causes.
  </Card>

  <Card title="Multi-Cloud Support" icon="cloud">
    Built-in connectors for AWS, GCP, Azure, and more. Optional integrations allow Aurora to investigate across your entire infrastructure.
  </Card>

  <Card title="Self-Hosted & Secure" icon="lock">
    Run entirely on your infrastructure with HashiCorp Vault for secrets management. No external dependencies except your chosen LLM provider.
  </Card>

  <Card title="Real-Time Investigation" icon="bolt">
    WebSocket-based chatbot interface provides live updates as agents investigate incidents and gather information.
  </Card>
</CardGroup>

## Architecture Overview

Aurora consists of several key components that work together:

* **Python Backend**: Flask REST API and WebSocket chatbot powered by LangGraph agents
* **Next.js Frontend**: Modern React interface for incident management and investigation
* **PostgreSQL**: Primary database for incident data and configurations
* **Weaviate**: Vector database for semantic search and knowledge retrieval
* **Redis**: Message queue for Celery background tasks
* **HashiCorp Vault**: Secure secrets storage for API keys and credentials
* **SeaweedFS**: S3-compatible object storage (Apache 2.0 licensed)
* **Memgraph**: Graph database for infrastructure topology

<Info>
  All services run in Docker containers for easy deployment. See the [Quickstart](/quickstart) guide to get up and running in minutes.
</Info>

## Repository Structure

```
aurora/
├── server/          # Python API, chatbot, Celery workers
├── client/          # Next.js frontend
├── config/          # Configuration files for services
├── scripts/         # Setup and initialization scripts
├── deploy/          # Kubernetes Helm charts
└── docker-compose.yaml
```

## Get Started

<CardGroup cols={2}>
  <Card title="Quickstart" icon="rocket" href="/quickstart">
    Get Aurora running locally in under 5 minutes with prebuilt images.
  </Card>

  <Card title="Installation" icon="download" href="/installation">
    Detailed installation instructions for development and production deployments.
  </Card>

  <Card title="Configuration" icon="gear" href="/configuration">
    Learn how to configure LLM providers, cloud connectors, and integrations.
  </Card>

  <Card title="API Reference" icon="code" href="/reference/api-overview">
    Complete API documentation for building custom integrations.
  </Card>
</CardGroup>

## What's Next?

After getting Aurora running, you can:

1. **Add Cloud Connectors**: Connect AWS, GCP, Azure, or other providers to enable infrastructure investigation
2. **Configure Integrations**: Set up Slack, PagerDuty, GitHub, and other third-party services
3. **Customize Agents**: Configure agent behavior and recursion limits for your use case
4. **Deploy to Production**: Use Docker Compose or Kubernetes for production deployments

<Warning>
  Aurora is designed for SRE teams and requires familiarity with cloud infrastructure concepts. Ensure you understand the security implications of granting Aurora access to your cloud accounts.
</Warning>

## Open Source

Aurora is licensed under the Apache License 2.0. We welcome contributions from the community!

* **GitHub**: [arvo-ai/aurora](https://github.com/arvo-ai/aurora)
* **Documentation**: [arvo-ai.github.io/aurora](https://arvo-ai.github.io/aurora/)
* **Issues**: Report bugs and request features on GitHub

***

Ready to get started? Head to the [Quickstart](/quickstart) guide to deploy Aurora in minutes.