Install AI assistant on server

You can install an AI assistant on your server using open-source frameworks like OpenClaw or Vezlo, or enterprise solutions such as JetBrains AI or Azure DevOps MCP Server, depending on your requirements.

Choosing the Right AI Assistant

• OpenClaw: An open-source AI assistant framework for Linux servers that allows full control over your data and integration with models like GPT-4 and Claude. Ideal for self-hosted deployments with custom workflows .
• Vezlo AI Assistant Server: A production-ready Node.js/TypeScript API server with Docker deployment, database migrations, and interactive setup wizard for easy configuration .
• JetBrains AI Assistant: A plugin for JetBrains IDEs that can run locally with organizational subscriptions, suitable for developers who want AI assistance integrated into their IDE .
• Azure DevOps MCP Server: Provides secure, local access to Azure DevOps data for AI assistants, enabling project-specific insights without sending data to the cloud .

General Installation Steps

1. Prepare Your Server
   • Ensure your server meets the system requirements (e.g., Node.js 20+ for Azure MCP Server, Node.js 22+ for OpenClaw).
   • Install necessary dependencies such as Docker, database engines (PostgreSQL, Supabase), and any required libraries.
2. Download and Configure
   • For OpenClaw, you can run the onboarding script: openclaw onboard --install-daemon for a quick setup .
   • For Vezlo, clone the repository or install locally, then run the interactive setup wizard to configure environment variables, database connections, and API keys .
   • For JetBrains AI, install the plugin via the AI Chat tool window, JetBrains AI widget, or Marketplace, then verify your subscription .
   • For Azure MCP Server, follow the environment-specific instructions to connect your AI assistant in agent mode to your Azure DevOps organization .
3. Database and API Configuration
   • Set up your database tables using automated scripts or manual SQL commands.
   • Configure API keys for AI models (OpenAI, Claude, etc.) and any optional features like rate limiting or CORS.
4. Start the Server
   • Use the provided CLI or Docker commands to start the server.
   • Validate the setup by checking logs and ensuring the AI assistant responds to queries.
5. Optional Enhancements
   • Integrate messaging platforms like WhatsApp or Telegram (OpenClaw).
   • Add custom skills or workflows tailored to your business needs.
   • Enable enterprise-grade security and monitoring.

Tips for a Successful Deployment

• Always backup your configuration and database before major updates.
• Test the AI assistant in a staging environment before production deployment.
• Monitor resource usage, especially for large language models, as they can be resource-intensive.
• Consider subscription tiers or licensing for enterprise solutions to unlock advanced features. By following these steps, you can deploy a fully functional AI assistant on your server, whether for personal use, development, or enterprise integration .