> ## Documentation Index
> Fetch the complete documentation index at: https://docs.incidentfox.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Quick Start

> Get IncidentFox investigating incidents in minutes

## Overview

This guide walks you through setting up IncidentFox for your team. By the end, you'll have an AI SRE agent ready to investigate incidents from Slack.

<Info>
  IncidentFox is deployed as a managed service. Contact your account team for access credentials and your team token.
</Info>

## Step 1: Get Your Credentials

After onboarding, you'll receive:

* **Team Token** - Used to authenticate API requests
* **Web UI Access** - Dashboard to configure agents and view investigation history
* **Slack App** - Bot to install in your workspace

<Tip>
  Your team token follows the format `tokid.toksecret`. Keep it secure and never commit to version control.
</Tip>

## Step 2: Install the Slack Bot

The Slack bot is the primary interface for triggering investigations.

<Steps>
  <Step title="Install the App">
    Your IncidentFox admin will provide an installation link. Click it and authorize the app for your Slack workspace.
  </Step>

  <Step title="Invite to Channels">
    Invite `@incidentfox` to channels where you want to trigger investigations:

    ```
    /invite @incidentfox
    ```
  </Step>

  <Step title="Test the Connection">
    Send a test message:

    ```
    @incidentfox hello
    ```

    The bot should respond confirming it's connected.
  </Step>
</Steps>

## Step 3: Configure Data Sources

Connect IncidentFox to your observability stack to enable investigations.

### Via Web UI

1. Log in to your IncidentFox dashboard
2. Navigate to **Team Console** > **Integrations**
3. Click **Add Integration** and select your data source
4. Enter the required credentials

### Common Data Sources

| Platform  | Required Credentials                   |
| --------- | -------------------------------------- |
| Coralogix | API Key, Domain                        |
| Snowflake | Account, Username, Password, Warehouse |
| AWS       | Access Key, Secret Key, Region         |
| Datadog   | API Key, Application Key               |
| Grafana   | URL, API Key                           |
| GitHub    | Personal Access Token                  |

<Note>
  See the [Data Sources](/data-sources/overview) section for detailed setup instructions for each platform.
</Note>

## Step 4: Enable Tools

IncidentFox comes with 300+ built-in tools. Enable the ones relevant to your stack.

In the Web UI:

1. Go to **Team Console** > **Tools**
2. Toggle on the tools you need
3. Configure any tool-specific settings

Example configuration:

```json theme={null}
{
  "tools": {
    "kubernetes": {
      "enabled": true,
      "kubeconfig_path": "~/.kube/config"
    },
    "aws": {
      "enabled": true,
      "region": "us-west-2"
    },
    "coralogix": {
      "enabled": true,
      "api_key": "vault://secrets/coralogix"
    }
  }
}
```

## Step 5: Start Investigating

You're ready to go! Try these commands in Slack:

### Basic Investigation

```
@incidentfox investigate high latency in the payments service
```

### Check Pod Status

```
@incidentfox check the status of cart pods in production
```

### Analyze CI Failure

```
@incidentfox why did the build fail on PR #123?
```

### Query Logs

```
@incidentfox search for errors in the checkout service logs from the last hour
```

## What Happens During an Investigation

When you trigger an investigation, IncidentFox:

1. **Acknowledges** - Reacts to your message to confirm it's working
2. **Plans** - The Planner agent creates an investigation strategy
3. **Gathers Data** - Pulls logs, metrics, and events from your data sources
4. **Analyzes** - Correlates data to identify root cause
5. **Reports** - Posts findings back to the Slack thread

<Frame>
  <img src="https://mintlify.s3.us-west-1.amazonaws.com/incidentfox/images/investigation-flow.png" alt="Investigation Flow" />
</Frame>

## Example Investigation Output

```json theme={null}
{
  "summary": "Payment service experiencing elevated latency due to database connection pool exhaustion",
  "root_cause": {
    "description": "RDS connection pool at maximum capacity (100/100 connections)",
    "confidence": 92,
    "evidence": [
      "CloudWatch RDS connections metric at 100%",
      "Application logs show 'connection timeout' errors",
      "Spike correlates with deployment at 14:32 UTC"
    ]
  },
  "timeline": [
    "14:32 - New deployment rolled out",
    "14:35 - Connection count started increasing",
    "14:42 - Connection pool exhausted",
    "14:43 - Latency alerts fired"
  ],
  "affected_systems": ["payment-service", "checkout-service", "RDS primary"],
  "recommendations": [
    "Increase RDS max_connections parameter",
    "Review connection pool settings in application config",
    "Consider rolling back deployment if issue persists"
  ]
}
```

## Next Steps

<CardGroup cols={2}>
  <Card title="Configure Agents" icon="robot" href="/configuration/agents">
    Customize agent behavior with system prompts
  </Card>

  <Card title="Add Data Sources" icon="database" href="/data-sources/overview">
    Connect all your observability tools
  </Card>

  <Card title="Set Up GitHub" icon="github" href="/integrations/github">
    Enable CI/CD auto-fix capabilities
  </Card>

  <Card title="View Tools Catalog" icon="wrench" href="/tools/overview">
    See all 300+ available tools
  </Card>
</CardGroup>