NVIDIA announced NemoClaw at GTC on March 16, 2026. It’s built on OpenClaw and NVIDIA’s OpenShell runtime. I built CageClaw. Here’s an honest comparison.
The Short Version
They solve the same problem differently. CageClaw gives you visibility and control over what your AI agent is doing. NemoClaw deploys isolated agent environments at enterprise scale.
NVIDIA entering this space validates the problem. AI agents running with unrestricted access to your filesystem, credentials, and network is a real security risk. Both projects exist because of that.
At a Glance
| CageClaw | NemoClaw | |
|---|---|---|
| By | Digital Signet | NVIDIA |
| Licence | Apache 2.0 | Open source |
| Isolation | Docker containers (all caps dropped, non-root, read-only fs) | Linux kernel: Landlock + seccomp + network namespaces |
| Network control | Rust proxy with domain allowlisting and real-time traffic logging | Declarative policy with operator approval |
| Desktop app | Yes (Tauri v2, setup wizard, live dashboard) | No (CLI + text UI) |
| Platform | Windows, macOS and Linux via Docker | Linux only (kernel 5.13+) |
| Inference | Agnostic | NVIDIA cloud, local NIM, or vLLM |
Where CageClaw Wins
User experience. CageClaw is a desktop app with a GUI. Setup wizard, live traffic dashboard, one-click domain approval via toast notifications, network activity view with filters. NemoClaw is CLI-only. For anyone who isn’t a Linux sysadmin, CageClaw is more accessible.
Traffic visibility. CageClaw logs every request to SQLite: method, URL, host, status, bytes, timestamp, allowed or blocked. You can see exactly what an agent is doing in real time and review it historically. NemoClaw surfaces new destinations for approval but doesn’t provide the same ongoing visibility.
Cross-platform. CageClaw runs anywhere Docker runs. NemoClaw requires Linux kernel 5.13+ because Landlock and seccomp are kernel features. No Windows, no macOS.
Vendor agnostic. CageClaw doesn’t care what LLM you use. NemoClaw is tied to NVIDIA’s ecosystem: Nemotron models, NIM inference, OpenShell runtime.
Credential protection. CageClaw has a hardcoded deny list that blocks mounting sensitive paths: .ssh, .aws, .azure, .env, .docker/config.json, browser profiles, password managers, GPG keys. Tuned to the developer workstation threat model.
Where NemoClaw Wins
Deeper isolation. Landlock + seccomp + network namespaces is genuinely stronger than Docker containers. Container escapes are a known attack vector. NemoClaw’s kernel-level security requires bypassing multiple independent mechanisms.
NVIDIA’s brand. “Runs on NVIDIA’s security infrastructure” is an enterprise sales argument. CTOs sign off on NVIDIA. The GTC keynote, CNBC, Wired, and The Register coverage gives instant credibility.
Enterprise deployment. Blueprints (versioned configs for sandbox creation, policy, and inference) are designed for deploying agents at scale. One command spins up a fully isolated environment per client.
Inference routing. Built-in support for NVIDIA cloud, local NIM, or local vLLM. Enterprises that can’t send data to external APIs get local inference out of the box.
Policy-as-code. Security defined in declarative YAML. Auditable, version-controllable, reviewable by security teams.
Different Approaches
| CageClaw | NemoClaw | |
|---|---|---|
| User | Individual developer | Enterprise or agency |
| Threat model | Agent on my machine steals credentials | Production agents need per-client isolation |
| Deployment | My laptop | Linux servers, NVIDIA hardware |
| Value prop | See and control what my agent does | Deploy secure environments per client at scale |
What This Means
NVIDIA building NemoClaw means the industry recognises that AI agent security is a real problem, not a niche concern. That’s good for everyone working in this space.
CageClaw gives you visibility and control without needing Linux servers or the NVIDIA stack. NemoClaw gives you the isolation and brand credibility that procurement teams want to see.
They can coexist. Different approaches to the same problem at different stages.
CageClaw is open source under Apache 2.0. You can find it on GitHub.