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Quickstart
Get started with Opslane in minutes

Setup Opslane

Follow these steps to set up Opslane and start improving your on-call experience.

Prerequisites

Before you begin, make sure you have:

  • Docker installed on your system
  • A Slack workspace
  • A Datadog account
  • OpenAI API key (for AI-powered features)
  • Ngrok account (for external URL setup)

Installation

Open your terminal and run the following command to clone the Opslane repository: 
```bash
git clone https://github.com/opslane/opslane.git
cd opslane
```
Opslane requires an external URL accessible from the internet for features like Slack integration and Datadog webhooks. We'll use Ngrok to create this URL. 
1. Sign up for a free Ngrok account at [https://ngrok.com](https://ngrok.com) if you haven't already.
2. Install Ngrok following their [installation guide](https://ngrok.com/download).
3. Authenticate Ngrok with your account token:
```bash
ngrok authtoken YOUR_NGROK_AUTH_TOKEN
```
4. Start your ngrok tunnel

The ngrok tunnel should point to port 8001 (where the API server will run):

```bash
ngrok http --subdomain=xxx --host-header=rewrite 8001 --region us
```
1. Copy the example environment file: 
```bash
cp .env.example .env
```

2. Leave the dummy values for now. We will come back and update them.
Build and run the Opslane Docker container: 
```bash
docker-compose up --build
```

This command will start Opslane. Before we can get the alert classification to work,
we need to get the Slack and Datadog integrations to work.

Configure Integrations

For detailed instructions on setting up the Opslane Slack bot, including adding it to your channels and configuring permissions, please refer to our [Slack Bot Setup Guide](/slack-bot-setup). For detailed instructions on setting up the Datadog integration, please refer to our [Datadog Setup Guide](/datadog-setup).

Start Using Opslane

Before using Opslane, e want to index the slack channels that have the alert history as well as annotate 25 alerts to generate a model.

To do these run the following commands:

  1. docker ps to find out the id of the app container.

  2. docker exec -it <container_id> /bin/bash

  3. Index Slack content: python -m app.scripts.seed_data

  4. Classify Datadog alerts: python -m app.ml.training.train_alert_classifier --num-alerts 20

  5. Opslane is ready to use. Any future datadog alerts will be annotated!