Beginner-Intermediate

Use Cases & Case Studies

Should You Use NemoClaw?

Find Out If NemoClaw Fits Your Needs

Are you deploying an OpenClaw autonomous agent?

Do you need infrastructure-level security (not just prompt guardrails)?

Are you running on Linux (Ubuntu 22.04+) with 8+ GB RAM?

Use OpenShell directly instead. NemoClaw is specifically built for OpenClaw. For other agents (Claude Code, Cursor, Codex), OpenShell provides the same security primitives with manual configuration.
Standard OpenClaw may suffice. If prompt-level guardrails meet your security requirements, NemoClaw's infrastructure overhead may be unnecessary. Consider NeMo Guardrails for output-level controls.
NemoClaw is a great fit. It provides one-command deployment with infrastructure-level security, privacy routing, and managed inference for your OpenClaw agent. Note: it is still in alpha -- suitable for development and evaluation, not production.
Hardware constraints may be an issue. NemoClaw requires Linux with 8+ GB RAM and 20+ GB disk. Consider a cloud VM or wait for lighter-weight deployment options in future releases.

When to Use NemoClaw

Always-On Enterprise AI Assistants

You need an AI agent running continuously -- answering questions, writing code, automating tasks -- but your security team requires strict control over what the agent can access. NemoClaw provides the isolation guarantees that make always-on agents acceptable in enterprise environments.

Privacy-Sensitive Development Work

Your team works with proprietary code or customer data. You want AI productivity benefits but cannot allow sensitive context to leave your network. The privacy router keeps sensitive inference local while allowing cloud models for non-sensitive tasks.

Sandboxed Code Execution

Your agent generates and runs code. Without sandboxing, a hallucinated destructive command or a malicious package could damage your system. NemoClaw's filesystem and process isolation contain the blast radius to the sandbox directory.

Multi-Provider Inference Management

You use different LLM providers for different tasks. NemoClaw's inference routing centralizes provider management and ensures the right provider handles the right requests based on policy, not agent preference.

When NOT to Use NemoClaw

⚠️
Not for production (yet). NemoClaw is in alpha with breaking changes expected. Also avoid if: you don't use OpenClaw, you need multi-tenant governance, your machines have less than 8 GB RAM, or you only need simple stateless LLM calls.

Real-World Scenarios

Secure Coding Assistant on DGX Spark

A development team deploys NemoClaw on a DGX Spark workstation. Nemotron runs locally for all code review involving proprietary source. Architectural planning routes to a frontier cloud model. The privacy router ensures no proprietary code leaves the network.

Research Lab with Controlled Internet Access

A research lab gives an AI agent access to internal datasets. Network policy allowlists only arXiv and Semantic Scholar. The agent fetches papers and searches literature but cannot access social media or exfiltrate data to arbitrary endpoints.

DevOps Automation with Guardrails

A platform team automates infrastructure tasks: Terraform configs, CI debugging, Kubernetes manifests. Sandbox isolation prevents access to production credentials stored outside the sandbox, and seccomp blocks dangerous system calls.

Messaging-Integrated Team Assistant

A distributed team deploys NemoClaw with Telegram and Slack bridges. Team members interact via existing messaging tools. The bridges run on the host, keeping messaging credentials out of the agent's reach.

📚 References & Resources