docs: nemoclaw integration (#14962)

---------

Co-authored-by: ParthSareen <parth.sareen@ollama.com>
This commit is contained in:
Bruce MacDonald
2026-03-20 15:27:37 -07:00
committed by GitHub
parent 6df6d097d9
commit 22c2bdbd8a
2 changed files with 73 additions and 0 deletions

View File

@@ -160,6 +160,12 @@
"group": "More information",
"pages": [
"/cli",
{
"group": "Assistant Sandboxing",
"pages": [
"/integrations/nemoclaw"
]
},
"/modelfile",
"/context-length",
"/linux",

View File

@@ -0,0 +1,67 @@
---
title: NemoClaw
---
NemoClaw is NVIDIA's open source security stack for [OpenClaw](/integrations/openclaw). It wraps OpenClaw with the NVIDIA OpenShell runtime to provide kernel-level sandboxing, network policy controls, and audit trails for AI agents.
## Quick start
Pull a model:
```bash
ollama pull nemotron-3-nano:30b
```
Run the installer:
```bash
curl -fsSL https://www.nvidia.com/nemoclaw.sh | \
NEMOCLAW_NON_INTERACTIVE=1 \
NEMOCLAW_PROVIDER=ollama \
NEMOCLAW_MODEL=nemotron-3-nano:30b \
bash
```
Connect to your sandbox:
```bash
nemoclaw my-assistant connect
```
Open the TUI:
```bash
openclaw tui
```
<Note>Ollama support in NemoClaw is still experimental.</Note>
## Platform support
| Platform | Runtime | Status |
|----------|---------|--------|
| Linux (Ubuntu 22.04+) | Docker | Primary |
| macOS (Apple Silicon) | Colima or Docker Desktop | Supported |
| Windows | WSL2 with Docker Desktop | Supported |
CMD and PowerShell are not supported on Windows — WSL2 is required.
<Note>Ollama must be installed and running before the installer runs. When running inside WSL2 or a container, ensure Ollama is reachable from the sandbox (e.g. `OLLAMA_HOST=0.0.0.0`).</Note>
## System requirements
- CPU: 4 vCPU minimum
- RAM: 8 GB minimum (16 GB recommended)
- Disk: 20 GB free (40 GB recommended for local models)
- Node.js 20+ and npm 10+
- Container runtime (Docker preferred)
## Recommended models
- `nemotron-3-super:cloud` — Strong reasoning and coding
- `qwen3.5:cloud` — 397B; reasoning and code generation
- `nemotron-3-nano:30b` — Recommended local model; fits in 24 GB VRAM
- `qwen3.5:27b` — Fast local reasoning (~18 GB VRAM)
- `glm-4.7-flash` — Reasoning and code generation (~25 GB VRAM)
More models at [ollama.com/search](https://ollama.com/search).