Deploying Openclaw on a VPS lets users run a powerful AI assistant that’s accessible, private, and scalable. This guide walks through the practical steps to provision a VPS, install and configure Openclaw, and adopt workflows that deliver measurable productivity gains. Emphasis is placed on secure defaults, efficient model integration, and operational best practices for teams.
Provisioning a VPS and Preparing the Environment
Choosing the right VPS tier is the first decision: for light testing, a small instance with 4 CPU cores and 8GB RAM may suffice, while production deployments that host local LLMs often require 16GB+ RAM and GPU support. Providers that offer on-demand GPUs or specialized ML instances make it easier to scale model performance as automations move from pilot to production.
Once a VPS is provisioned, secure the server before installing Openclaw. Update the OS, create a dedicated non-root service account, and configure SSH keys for access. Enable a basic firewall and only open necessary ports—typically SSH and the ports used by the agent’s API and model runtime. Consider enabling automatic security updates to reduce exposure to known vulnerabilities.
Next, install core dependencies: Python 3.10+, Node.js for TypeScript-based skills if required, and a container runtime if choosing containerized deployment. Use a package manager to keep installs reproducible and document the environment in an infra-as-code template or shell script to simplify future reprovisioning and audits.
Installing Openclaw and Integrating LLMs
With the environment ready, clone the official Openclaw repository and run the provided installation script to bootstrap dependencies. This script typically installs Python packages, skill tooling, and default configuration files. Configure the agent to use the chosen model runtime—Ollama or another local LLM host—by setting the model endpoint and selecting an appropriate model size for the VPS resources.
For responsiveness and cost-efficiency, start with a midsize local model for interactive workflows and offload heavier inference to dedicated GPU instances or cloud-hosted models when necessary. Configure retrieval-augmented generation (RAG) by adding a vector store for documents or notes, enabling the agent to ground responses in local content. Validate the end-to-end stack with a simple skill—summarizing a document or drafting a reply—to confirm the agent, model, and integrations communicate correctly.
Integrations matter: set up connectors for messaging platforms, webhooks, and storage. When connecting to services like Telegram or Slack, create scoped service accounts and store tokens in a secrets manager rather than plaintext files. Implement health checks and a basic monitoring stack to track model latency, skill error rates, and resource usage so issues surface before they impact users.
Best Workflows, Security, and Operational Practices
Start with workflows that deliver clear ROI and low risk: meeting summaries, inbox triage, and draft generation are common examples. Pilot automations with a small user group, measure time savings and accuracy, and iterate on prompt design and skill logic. Use metrics to prioritize additional automations and to justify operational investment in scaling resources or adding GPUs.
Security and governance are essential when an agent can access files and systems. Run Openclaw in a container or isolated VM to contain potential issues, and enforce least-privilege access for each skill. Maintain a curated skill registry: require code reviews, static analysis, and a staging promotion process before enabling skills in production. Disable arbitrary remote content fetching and use allowlists for trusted endpoints to reduce supply-chain risk.
Operationally, implement logging and observability from day one. Centralize logs for skill executions, model calls, and outbound requests, and feed them into a monitoring or SIEM system. Configure alerts for anomalous behavior such as unexpected credential use or sudden spikes in model traffic. Establish an incident response playbook that includes revoking tokens, isolating the VPS, and rolling back recent skill deployments if suspicious activity is detected.
Finally, plan for maintainability: version skills in a code repository, automate tests for skill inputs/outputs, and schedule periodic reviews of permissions and dependencies. Document runbooks for routine maintenance and scaling decisions, and ensure that backups and automated updates are part of the operational checklist. These processes make scaling from a pilot to a broader deployment predictable and safe.
In conclusion, deploying Openclaw on a VPS provides a flexible, private platform for AI automation when combined with thoughtful provisioning, secure installation, and disciplined operational practices. Starting with low-risk, high-impact workflows lets teams demonstrate value quickly, while governance, isolation, and observability protect systems as automations expand. With these foundations, Openclaw can become a reliable, productive AI assistant for individuals and teams alike.
