Onboarding Wizard

Interactive setup guide for first-time configuration

Quick Start

The onboarding wizard runs automatically after installation, or you can run it anytime with jarvis onboard

What the Wizard Does

The onboarding wizard guides you through:

  1. AI Provider Selection - Choose between local (Ollama) or cloud providers
  2. Model Setup - Download and configure your AI model
  3. Gateway Configuration - Set port, authentication, and network settings
  4. Channel Setup - Connect messaging apps (optional)
  5. Workspace Configuration - Set your working directory

Running the Wizard

Interactive mode (recommended):

jarvis onboard

With daemon install:

jarvis onboard --install-daemon

Step-by-Step Guide

Step 1: AI Provider Selection

Choose your AI provider:

  • Ollama (Local) - Recommended for privacy, runs offline, zero cost
  • OpenAI - GPT-4, GPT-3.5-turbo
  • Anthropic - Claude models
  • OpenRouter - Access multiple models
  • Custom - Any OpenAI-compatible API

Step 2: Model Setup

For Ollama:

  • Wizard checks if Ollama is installed
  • Installs Ollama if missing
  • Shows available models to download
  • Downloads your selected model (e.g., llama3.2)

For Cloud Providers:

  • Enter your API key
  • Select default model
  • Test connection

Step 3: Gateway Configuration

Configure the gateway service:

  • Port - Default: 18789
  • Authentication - Token or password
  • Network - Localhost, LAN, or Tailscale

Step 4: Channel Setup (Optional)

Connect messaging channels:

  • Telegram - Enter bot token
  • Discord - Enter bot token
  • WhatsApp - QR code pairing
  • Skip to add channels later

Step 5: Workspace

Set your workspace directory (default: ~/.jarvis/workspace)

After Onboarding

Once complete, the wizard will:

  • Start the gateway service
  • Show you the web dashboard URL
  • Display first commands to try

Re-running Onboarding

You can re-run onboarding anytime to:

  • Add new AI providers
  • Change configuration
  • Add more channels
  • Reset settings
jarvis onboard
Jarvis

© 2026 Nilkanth Desai. MIT licensed.