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docs: nemoclaw integration (#14962)
--------- Co-authored-by: ParthSareen <parth.sareen@ollama.com>
This commit is contained in:
@@ -160,6 +160,12 @@
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"group": "More information",
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"group": "More information",
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"pages": [
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"pages": [
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"/cli",
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"/cli",
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{
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"group": "Assistant Sandboxing",
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"pages": [
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"/integrations/nemoclaw"
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]
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},
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"/modelfile",
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"/modelfile",
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"/context-length",
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"/context-length",
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"/linux",
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"/linux",
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67
docs/integrations/nemoclaw.mdx
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67
docs/integrations/nemoclaw.mdx
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---
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title: NemoClaw
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---
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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.
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## Quick start
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Pull a model:
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```bash
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ollama pull nemotron-3-nano:30b
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```
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Run the installer:
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```bash
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curl -fsSL https://www.nvidia.com/nemoclaw.sh | \
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NEMOCLAW_NON_INTERACTIVE=1 \
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NEMOCLAW_PROVIDER=ollama \
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NEMOCLAW_MODEL=nemotron-3-nano:30b \
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bash
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```
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Connect to your sandbox:
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```bash
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nemoclaw my-assistant connect
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```
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Open the TUI:
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```bash
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openclaw tui
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```
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<Note>Ollama support in NemoClaw is still experimental.</Note>
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## Platform support
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| Platform | Runtime | Status |
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|----------|---------|--------|
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| Linux (Ubuntu 22.04+) | Docker | Primary |
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| macOS (Apple Silicon) | Colima or Docker Desktop | Supported |
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| Windows | WSL2 with Docker Desktop | Supported |
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CMD and PowerShell are not supported on Windows — WSL2 is required.
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<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>
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## System requirements
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- CPU: 4 vCPU minimum
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- RAM: 8 GB minimum (16 GB recommended)
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- Disk: 20 GB free (40 GB recommended for local models)
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- Node.js 20+ and npm 10+
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- Container runtime (Docker preferred)
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## Recommended models
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- `nemotron-3-super:cloud` — Strong reasoning and coding
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- `qwen3.5:cloud` — 397B; reasoning and code generation
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- `nemotron-3-nano:30b` — Recommended local model; fits in 24 GB VRAM
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- `qwen3.5:27b` — Fast local reasoning (~18 GB VRAM)
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- `glm-4.7-flash` — Reasoning and code generation (~25 GB VRAM)
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More models at [ollama.com/search](https://ollama.com/search).
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