OpenClaw is a local-first AI agent that runs on your machine, combining memory, file access, tool use, and community skills into a persistent assistant. Running it directly on your system means the agent can access your files, credentials, and network—creating real security risks.
NVIDIA OpenShell solves this problem. It is an open-source sandbox runtime that wraps the agent in kernel-level isolation with declarative YAML policies. OpenShell controls what the agent can read on disk, which network endpoints it can reach, and what privileges it has—without disabling the capabilities that make the agent useful.
By combining OpenClaw with OpenShell on your hardware platform, you get the full power of a local AI agent backed by local model serving, while enforcing explicit controls over filesystem access, network egress, and credential handling.
Notice & Disclaimers
Quick Start Safety Check
Use a clean environment only. Run this playbook on a fresh device or VM with no personal data, confidential information, or sensitive credentials. Think of it like a sandbox—keep it isolated.
By installing this playbook, you're taking responsibility for all third-party components, including reviewing their licenses, terms, and security posture. Read and accept before you install or use.
What You're Getting
The playbook showcases experimental AI agent capabilities. Even with cutting-edge open-source tools like OpenShell in your toolkit, you need to layer in proper security measures for your specific threat model.
Key Risks with AI Agents
Be mindful of these risks with AI agents:
Data leakage – Any materials the agent accesses could be exposed, leaked, or stolen.
Malicious code execution – The agent or its connected tools could expose your system to malicious code or cyber-attacks.
Unintended actions – The agent might modify or delete files, send messages, or access services without explicit approval.
Prompt injection & manipulation – External inputs or connected content could hijack the agent's behavior in unexpected ways.
Security Best Practices
No system is perfect, but these practices help keep your information and systems safe:
Isolate your environment – Run on a clean PC or isolated virtual machine. Only provision the specific data you want the agent to access.
Never use real accounts – Don't connect personal, confidential, or production accounts. Create dedicated test accounts with minimal permissions.
Vet your skills/plugins – Only enable skills from trusted sources that have been vetted by the community.
Lock down access – Ensure your OpenClaw UI or messaging channels aren't accessible over the network without proper authentication.
Restrict network access – Where feasible, limit the agent's internet connectivity.
Clean up after yourself – When you're done, remove OpenClaw and revoke all credentials, API keys, and account access you granted.
What you'll accomplish
You will install the OpenShell CLI (openshell), deploy a gateway on your hardware platform, and launch OpenClaw inside a sandboxed environment using the pre-built OpenClaw community sandbox. The sandbox enforces filesystem, network, and process isolation by default. You will also configure local inference routing so OpenClaw uses a model running on your hardware without needing external API keys.
Popular use cases
Secure agent experimentation: Test OpenClaw skills and integrations without exposing your main filesystem or credentials to the agent.
Private enterprise development: Route all inference to a local model on your hardware. No data leaves the machine unless you explicitly allow it in the policy.
Auditable agent access: Version-control the policy YAML alongside your project. Review exactly what the agent can reach before granting access.
Iterative policy tuning: Monitor denied connections in real time with openshell term, then hot-reload updated policies without recreating the sandbox.
What to know before starting
Required:
Comfort with the Linux terminal and SSH
Basic understanding of Docker (OpenShell runs a k3s cluster inside Docker)
Familiarity with local LLM serving (this playbook uses vLLM)
Use the matrix below to confirm your hardware platform, OS, memory, and whether multi-node applies. The same OpenShell + OpenClaw workflow applies across supported hardware platforms. Model recommendations differ by platform — see the Agent-ready Models tab.
Hardware platform
OS
Memory
Multi-node capable hardware
DGX Spark
DGX OS (Linux)
128 GB Unified Memory
—
DGX Station
DGX OS (Linux)
Large HBM + Grace DRAM
—
NOTE
Only platforms listed in the Supported hardware platforms table above are covered by this playbook.
Prerequisites
Hardware requirements
Supported hardware platform — see Supported hardware platforms matrix above
Sufficient memory for your chosen agent-ready model (see the Agent-ready Models tab)
Software requirements
Linux (DGX OS / Ubuntu 24.04 or compatible)
Docker Desktop or Docker Engine running: docker info
Python 3.12 or later: python3 --version
NVIDIA Container Toolkit configured for Docker
Network access to download packages from PyPI / GitHub, container images, and model weights from Hugging Face
A Hugging Face token when the chosen model requires authentication
Time & risk
Estimated time: 30 MIN (plus model download time, which depends on model size and network speed)
Risk level: Medium
OpenShell sandboxes enforce kernel-level isolation, significantly reducing the risk compared to running OpenClaw directly on the host.
The sandbox default policy denies all outbound traffic not explicitly allowed. Misconfigured policies may block legitimate agent traffic; use openshell logs to diagnose.
Large model downloads may fail on unstable networks.
Rollback: Delete the sandbox with openshell sandbox delete <sandbox-name>, stop the gateway, and remove the vLLM container if you started one. See cleanup in the Instructions tab.
Last Updated: 07/27/2026
Gateway install path updated for the systemd user service; troubleshooting expanded for gateway connectivity and sandbox onboarding
