nemoclaw✦ Official

nemoclaw-user-deploy-remote

Explains how to run NemoClaw on a remote GPU instance, including the deprecated Brev compatibility path and the preferred installer plus onboard flow. Use when deploying NemoClaw to a remote VM, onboarding a Brev instance, or migrating away from the legacy `nemoclaw deploy` wrapper. Trigger keywords - deploy nemoclaw remote gpu, nemoclaw brev cloud deployment, nemoclaw plugins, openclaw plugins, install openclaw plugin, nemoclaw onboard from dockerfile, nemoclaw brev web ui, nemoclaw getting started, brev quickstart, nvidia nemotron agent, nemoclaw sandbox hardening, container security, docker capabilities, process limits.

nvidia/skillsUpdated Jun 23, 2026

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Install Skill

Run in your terminal

$npx skills install nvidia/skills/nemoclaw-user-deploy-remote

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Installation Guide

How to use nemoclaw-user-deploy-remote on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your machine
  • Node.js 16+ with npm — verify with node --version
  • Active project directory where you want to add nemoclaw-user-deploy-remote
2

Run the install command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills install nvidia/skills/nemoclaw-user-deploy-remote

Fetches nemoclaw-user-deploy-remote from nvidia/skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI shows a list of agents. Use arrow keys and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ────────────────
│ · Cline · Codex · Goose · Windsurf
│ ●Cursor(selected)
│ · Cursor · Aider · Continue
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/nemoclaw-user-deploy-remote

Restart Cursor to activate nemoclaw-user-deploy-remote. Access via /nemoclaw-user-deploy-remote in your agent's command palette.

Security Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.

Documentation

name
"nemoclaw-user-deploy-remote"
description
"Explains how to run NemoClaw on a remote GPU instance, including the deprecated Brev compatibility path and the preferred installer plus onboard flow. Use when deploying NemoClaw to a remote VM, onboarding a Brev instance, or migrating away from the legacy `nemoclaw deploy` wrapper. Trigger keywords - deploy nemoclaw remote gpu, nemoclaw brev cloud deployment, nemoclaw plugins, openclaw plugins, install openclaw plugin, nemoclaw onboard from dockerfile, nemoclaw brev web ui, nemoclaw getting started, brev quickstart, nvidia nemotron agent, nemoclaw sandbox hardening, container security, docker capabilities, process limits."
license
"Apache-2.0"
<!-- SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. --> <!-- SPDX-License-Identifier: Apache-2.0 -->

Deploy NemoClaw to a Remote GPU Instance

Gotchas

  • The nemoclaw deploy command is deprecated.
  • On Brev, set CHAT_UI_URL in the launchable environment configuration so it is available when the installer builds the sandbox image.

Prerequisites

  • The Brev CLI installed and authenticated.
  • A provider credential for the inference backend you want to use during onboarding.
  • HF_TOKEN or HUGGING_FACE_HUB_TOKEN exported when your remote vLLM or Hugging Face workflow needs access to gated models.
  • NemoClaw installed locally if you plan to use the deprecated nemoclaw deploy wrapper. Otherwise, install NemoClaw directly on the remote host after provisioning it.

Run NemoClaw on a remote GPU instance through Brev. The preferred path is to provision the VM, run the standard NemoClaw installer on that host, and then run nemoclaw onboard.

Quick Start

If your Brev instance is already up and has already been onboarded with a sandbox, start with the standard sandbox chat flow:

$ nemoclaw my-assistant connect
$ openclaw tui

This gets you into the sandbox shell first and opens the OpenClaw chat UI right away. If the VM is fresh, run the standard installer on that host and then run nemoclaw onboard before trying nemoclaw my-assistant connect.

If you are connecting from your local machine and still need to provision the remote VM, you can still use nemoclaw deploy <instance-name> as the legacy compatibility path described below.

Deploy the Instance

Warning:

The nemoclaw deploy command is deprecated. Prefer provisioning the remote host separately, then running the standard NemoClaw installer and nemoclaw onboard on that host.

Create a Brev instance and run the legacy compatibility flow:

$ nemoclaw deploy <instance-name>

Replace <instance-name> with a name for your remote instance, for example my-gpu-box. The sandbox created on the remote VM uses NEMOCLAW_SANDBOX_NAME, or my-assistant when the variable is unset. Sandbox names must be lowercase, start with a letter, contain only letters, numbers, and internal hyphens, and end with a letter or number. The deploy wrapper validates the sandbox name before it provisions the Brev instance, opens SSH, or starts the remote installer.

The legacy compatibility flow performs the following steps on the VM:

  1. Installs Docker and the NVIDIA Container Toolkit if a GPU is present.
  2. Installs the OpenShell CLI.
  3. Runs nemoclaw onboard (the setup wizard) to create the gateway, register providers, and launch the sandbox.
  4. Starts optional host auxiliary services (for example the cloudflared tunnel) when cloudflared is available. Channel messaging is configured during onboarding and runs through OpenShell-managed processes, not through nemoclaw tunnel start.

By default, the compatibility wrapper asks Brev to provision on gcp. Override this with NEMOCLAW_BREV_PROVIDER if you need a different Brev cloud provider. If you export HF_TOKEN or HUGGING_FACE_HUB_TOKEN, the wrapper forwards those values to the VM so remote setup can pull gated Hugging Face model repositories.

Connect to the Remote Sandbox

After deployment finishes, the deploy command opens an interactive shell inside the remote sandbox. To reconnect after closing the session, run the command again:

$ nemoclaw deploy <instance-name>

Monitor the Remote Sandbox

SSH to the instance and run the OpenShell TUI to monitor activity and approve network requests:

$ ssh <instance-name> 'cd ~/nemoclaw && set -a && . .env && set +a && openshell term'

Verify Inference

Run a test agent prompt inside the remote sandbox:

$ openclaw agent --agent main -m "Hello from the remote sandbox" --session-id test

Remote Dashboard Access

The NemoClaw dashboard validates the browser origin against an allowlist baked into the sandbox image at build time. By default the allowlist only contains http://127.0.0.1:18789. When accessing the dashboard from a remote browser (for example through a Brev public URL or an SSH port-forward), set CHAT_UI_URL to the origin the browser will use before running setup:

$ export CHAT_UI_URL="https://openclaw0-<id>.brevlab.com"
$ nemoclaw deploy <instance-name>

For SSH port-forwarding, the origin is typically http://127.0.0.1:18789 (the default), so no extra configuration is needed.

Warning:

On Brev, set CHAT_UI_URL in the launchable environment configuration so it is available when the installer builds the sandbox image. If CHAT_UI_URL is not set on a headless host, the compatibility wrapper prints a warning.

NEMOCLAW_DISABLE_DEVICE_AUTH is also evaluated at image build time. When CHAT_UI_URL points at a non-loopback origin, NemoClaw disables OpenClaw device pairing in the generated sandbox configuration because browser-only remote users cannot complete terminal-based pairing. Any device that can reach the configured dashboard origin can connect without pairing, so avoid exposing that origin on internet-reachable or shared-network deployments.

First-Run Readiness Budget

On a remote GPU host, the first nemoclaw onboard typically does the slowest work of the lifecycle: the sandbox image is built locally and uploaded into the OpenShell gateway, which can stream hundreds of MiB over the VM's link before the readiness wait even starts. The post-create readiness wait defaults to 180 seconds (NEMOCLAW_SANDBOX_READY_TIMEOUT), which is sized for warm-cache, workstation-class onboarding and can be exceeded on:

  • DGX Station first runs with large quantised models (70B+ parameter footprints, NVFP4 weights).
  • Cloud VMs where the local image-build cache is cold and the upload runs over the public network.
  • Hosts onboarding the Brave Web Search preset on the first run (the egress policy stack adds boot work).

Raise the budget before re-running onboard:

$ export NEMOCLAW_SANDBOX_READY_TIMEOUT=600
$ nemoclaw onboard

If onboard ends with Sandbox '<name>' was created but did not become ready within 180s, onboard deletes the partially-created sandbox first, so the next attempt with the raised budget starts from a clean state. For the inference-probe budget that runs earlier in onboarding, see NEMOCLAW_LOCAL_INFERENCE_TIMEOUT (use the nemoclaw-user-configure-inference skill).

Proxy Configuration

NemoClaw routes sandbox traffic through a gateway proxy that defaults to 10.200.0.1:3128. If your network requires a different proxy, set NEMOCLAW_PROXY_HOST and NEMOCLAW_PROXY_PORT before onboarding:

$ export NEMOCLAW_PROXY_HOST=proxy.example.com
$ export NEMOCLAW_PROXY_PORT=8080
$ nemoclaw onboard

These values are baked into the sandbox image at build time. They are also forwarded into the runtime container during sandbox creation, so /tmp/nemoclaw-proxy-env.sh uses the same host and port that the image build used. Only alphanumeric characters, dots, hyphens, and colons are accepted for the host. The port must be numeric (0-65535). Changing the proxy after onboarding requires re-running nemoclaw onboard.

GPU Configuration

The deploy script uses the NEMOCLAW_GPU environment variable to select the GPU type. The default value is a2-highgpu-1g:nvidia-tesla-a100:1. Set this variable before running nemoclaw deploy to use a different GPU configuration:

$ export NEMOCLAW_GPU="a2-highgpu-1g:nvidia-tesla-a100:2"
$ nemoclaw deploy <instance-name>

References

  • Load references/install-openclaw-plugins.md when users ask how to install, build, or configure OpenClaw plugins under NemoClaw. Explains the difference between OpenClaw plugins and agent skills, and shows the current Dockerfile-based workflow for baking a plugin into a NemoClaw sandbox.
  • Load references/brev-web-ui.md when a user wants to try NemoClaw without installing the CLI, or asks how to get started on Brev. Guides users through deploying NemoClaw with the Brev web UI.
  • Load references/sandbox-hardening.md when reviewing sandbox image security controls, auditing capability drops, or looking up the runtime resource limits. Includes the sandbox container image hardening reference, covering Docker capabilities and process limits.

Related Skills

  • nemoclaw-user-manage-sandboxes — Set Up Messaging Channels (use the nemoclaw-user-manage-sandboxes skill) to connect Telegram, Discord, or Slack through OpenShell-managed channel messaging
  • nemoclaw-user-monitor-sandbox — Monitor Sandbox Activity (use the nemoclaw-user-monitor-sandbox skill) for sandbox monitoring tools
  • nemoclaw-user-reference — Commands (use the nemoclaw-user-reference skill) for the full deploy command reference

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Use Cases

Task Automation & Efficiency

Automate repetitive workflows and reduce manual effort

Example

Generate reports, summarize documents, draft communications

Save 3-5 hours per week on routine tasks

Knowledge Enhancement

Learn new skills, understand complex topics, get expert guidance

Example

Explain concepts, provide examples, suggest learning resources

Accelerate learning and skill development by 2x

Quality Improvement

Enhance output quality through reviews, suggestions, and refinements

Example

Review drafts, suggest improvements, catch errors

Improve work quality by 30-40% with less effort

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client with skill support
  • Clear understanding of task or problem to solve
  • Willingness to iterate and refine outputs

Time Estimate

15-45 minutes depending on use case complexity

Steps

  1. 1Install skill using provided installation command
  2. 2Test with simple use case relevant to your work
  3. 3Evaluate output quality and relevance
  4. 4Iterate on prompts to improve results
  5. 5Integrate into regular workflow if valuable

Common Pitfalls

  • Expecting perfect results without iteration
  • Not providing enough context in prompts
  • Using skill for tasks outside its intended scope
  • Accepting outputs without review and validation

Best Practices

✓ Do

  • +Start with clear, specific prompts
  • +Provide relevant context and constraints
  • +Review and refine all outputs before using
  • +Iterate to improve output quality
  • +Document successful prompt patterns

✗ Don't

  • Don't use without understanding skill limitations
  • Don't skip validation of outputs
  • Don't share sensitive information in prompts
  • Don't expect skill to replace human judgment

💡 Pro Tips

  • Be specific about desired format and style
  • Ask for multiple options to choose from
  • Request explanations to understand reasoning
  • Combine AI efficiency with human expertise

When to Use This

✓ Use when

Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.

✗ Avoid when

Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.

Learning Path

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Related Skills

Reviews

4.834 reviews
  • K
    Kwame TorresDec 28, 2024

    nemoclaw-user-deploy-remote fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • S
    Shikha MishraDec 16, 2024

    We added nemoclaw-user-deploy-remote from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • M
    Min AndersonDec 12, 2024

    I recommend nemoclaw-user-deploy-remote for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • R
    Ren ShahDec 4, 2024

    Keeps context tight: nemoclaw-user-deploy-remote is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • A
    Ama MartinezNov 19, 2024

    nemoclaw-user-deploy-remote is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • R
    Rahul SantraNov 7, 2024

    Useful defaults in nemoclaw-user-deploy-remote — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • P
    Pratham WareOct 26, 2024

    Registry listing for nemoclaw-user-deploy-remote matched our evaluation — installs cleanly and behaves as described in the markdown.

  • R
    Ren BrownOct 10, 2024

    Solid pick for teams standardizing on skills: nemoclaw-user-deploy-remote is focused, and the summary matches what you get after install.

  • O
    OshnikdeepSep 5, 2024

    Solid pick for teams standardizing on skills: nemoclaw-user-deploy-remote is focused, and the summary matches what you get after install.

  • R
    Ren NdlovuSep 5, 2024

    Useful defaults in nemoclaw-user-deploy-remote — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

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