Openclaw is an open-source AI agent that automates tasks, retains context, and integrates with messaging platforms—making it a practical choice for teams that want a local, controllable assistant. Deploying on a VPS is a common, cost-effective way to run Openclaw 24/7 while keeping data under organizational control. This tutorial covers preparation, step-by-step installation on a VPS, and best practices for secure, maintainable automation.
Preparing the VPS and Environment
Choose a VPS provider that offers the resources required for the intended workload. For light usage—message handling, simple summaries—a VPS with 4 CPU cores and 8–16GB RAM is sufficient. For local LLM inference or heavy multi-user workloads, opt for instances with GPU support and 32GB+ RAM. Select a modern Linux distribution (Ubuntu 22.04 is a practical default) and ensure the server has a stable network connection and sufficient disk space for logs and model artifacts.
Harden the server before installing components: create a non-root service account for Openclaw, configure SSH keys for administrative access, and enable a firewall to restrict inbound ports. Only open SSH and the ports necessary for the agent’s API or inbound webhook endpoints. Install Python 3.10+, Node.js (if TypeScript-based skills will be used), and a container runtime like Docker if containerized deployment is preferred. Prepare API tokens for integrations (Telegram, Slack, or OpenAI) and store them in a secrets manager rather than plaintext files.
Plan for persistence and backups: configure periodic snapshots or automated backups for critical directories, and set up log rotation to avoid disk exhaustion. Defining these operational details up front reduces downtime risk when the platform scales from a pilot to a broader production footprint.
Installing Openclaw on the VPS
Begin by cloning the official Openclaw repository or downloading the latest release package. Navigate to the project directory and run the provided installation script, which typically installs Python dependencies, the skill runtime, and supporting utilities. If using a container approach, build and run the provided Docker image to encapsulate dependencies and simplify deployment across environments.
Next, configure the model endpoint. For local inference, install Ollama or another local LLM runtime and register a model compatible with available resources. Edit Openclaw’s configuration to point to the local model endpoint or to a managed hosted model if preferred. Validate connectivity by issuing a simple test prompt through the Openclaw CLI or HTTP API to confirm the model responds and latency is within expectations.
Integrate one messaging channel at a time to minimize troubleshooting complexity. For Telegram, create a bot and obtain its API token, then add the token to Openclaw’s secure configuration and restrict authorized chat IDs. Start with a simple skill—such as a meeting summary or template reply generator—to validate end-to-end behavior: message reception, model inference, skill execution, and response delivery. Monitor logs during initial runs to identify permission or dependency issues promptly.
Designing Skills and Workflows
Skills are the building blocks of Openclaw automations. Design skills with single-responsibility principles: clear inputs, deterministic outputs where possible, and explicit error handling. Keep prompts concise and provide only necessary context; employ retrieval-augmented generation (RAG) to ground outputs in relevant documents stored in a vector index, which reduces hallucination and improves factuality.
Compose skills into reliable workflows: chain data ingestion, contextual retrieval, LLM synthesis, and deterministic actions such as API calls or database updates. Implement retries and fallbacks between steps so a transient failure in one skill does not cascade through the chain. Document each skill’s contract and failure modes; good documentation makes automation maintainable and easier to review for security concerns.
For operational teams, create a curated internal skill registry and a promotion process from staging to production. Require code review, static analysis, and automated tests before approving skills that perform system-level actions. This governance prevents accidental exposure and ensures quality as the automation catalog grows.
Security, Monitoring, and Maintenance
Security is paramount when an agent can access files, APIs, and credentials. Run Openclaw in an isolated environment (container, VM) and adopt least-privilege principles for all service accounts. Use a secrets manager for API keys and rotate credentials routinely. Disable any feature that automatically fetches arbitrary remote content unless it is explicitly validated and allowlisted.
Centralize logs for skill executions, model calls, and outbound connections; forward logs to a monitoring or SIEM system for anomaly detection. Set alerts for unusual patterns—unexpected spikes in model calls, failed authentication attempts, or unfamiliar outbound destinations. Prepare runbooks for incident response that include steps to revoke credentials, isolate the VPS, and roll back recent skill deployments.
Maintenance practices include automating updates for dependencies, performing periodic security scans of installed packages, and testing upgrades in a staging environment. Schedule periodic reviews of enabled skills and their permissions to ensure the system’s attack surface remains minimal as use cases expand.
Conclusion: Start Small, Govern Carefully, Scale Confidently
Deploying Openclaw on a VPS is a practical way to get started with agentic automation: it enables availability, simplifies initial setup, and keeps data under control. By starting with a focused pilot, designing modular, well-tested skills, and enforcing robust security and governance controls, teams can unlock meaningful productivity gains while managing operational risk. With careful monitoring and incremental scaling, Openclaw can transition from a prototype to a reliable production assistant that automates valuable work across the organization.
