What Is OpenClaw? The Complete Guide to Self-Hosted AI Assistants

Introduction

OpenClaw is an open-source platform that lets you run your own AI chatbot on your own server. Instead of depending on external services like ChatGPT Teams or Microsoft Copilot, you install OpenClaw on a VPS or local machine and connect it to the LLM model of your choice — from OpenAI GPT-4o to Anthropic Claude and Google Gemini.

The platform is designed for businesses and technical teams that want control over their data, costs, and integrations. With built-in connectors for Telegram, Discord, and webchat, you can have a fully working AI assistant up and running within an hour that your customers or employees can use immediately.

How OpenClaw Works

OpenClaw runs as a Docker container on any Linux server. After installation, you configure which LLM model to use through the web dashboard by entering your own API keys. The platform routes user messages to the selected model, manages conversation history, and sends responses back through the chosen channel — whether that is a Telegram bot, a Discord server, or an embedded webchat widget.

Under the hood, OpenClaw uses an event-driven architecture with Node.js and a PostgreSQL database for storing conversations and user settings. The Docker setup means you can run the platform on a Europese cloud CX22 for less than ten euros per month, including sufficient resources for hundreds of daily conversations.

Each chatbot instance is configurable with its own system prompt, temperature setting, and maximum token length. This allows you to run multiple bots for different purposes — a customer service bot with a formal tone and an internal knowledge base bot with a more technical approach — all from the same installation.

Supported Models and Integrations

OpenClaw supports all major LLM providers through their APIs. You can work with OpenAI models like GPT-4o and GPT-4o-mini, Anthropic Claude 3.5 Sonnet and Claude 3 Haiku, Google Gemini 1.5 Pro and Flash, and local models through Ollama. Switching between models is a matter of changing a dropdown in the dashboard — no reconfiguration needed.

On the channel side, OpenClaw offers native integrations with the Telegram Bot API, Discord via bot tokens, and a JavaScript widget for webchat that you can embed in any website. Each integration is configured separately, so you can run a GPT-4o bot on Telegram and a Claude bot on Discord, each with its own persona and instruction set.

Privacy and Data Control

The most important advantage of OpenClaw over cloud services is data control. All conversations are stored in your own PostgreSQL database, on your server, in your data center. No data is shared with third parties, except for the API calls to the LLM model itself — and even those can be eliminated by using local models through Ollama.

For European businesses subject to GDPR, this means you have full control over data storage and deletion. You can configure conversations to be automatically deleted after a set period, and you have direct access to the database for data export or deletion requests.

Getting Started with OpenClaw

Installing OpenClaw requires basic knowledge of Linux and Docker. You need a server with at least 2 GB of RAM and 20 GB of storage — a Europese cloud CX22 or similar VPS is sufficient. The installation process is documented in the official guide at openclawinstalleren.nl and takes an average of twenty to thirty minutes.

After installation, the dashboard is accessible through a secured HTTPS connection. There you configure your first bot by entering an API key, writing a system prompt, and connecting a channel. The platform includes a test interface in the dashboard itself, allowing you to test your bot before going live.

Conclusion

OpenClaw provides a realistic and affordable way to deploy AI assistants without becoming dependent on large cloud platforms. Due to its open-source nature and straightforward installation, it is suitable for both individual developers and SMBs that want to manage their own AI solution.

In upcoming articles on this blog, we will dive deeper into specific integrations, model choices, and best practices for running OpenClaw in production.