Overview of NVIDIA NemoClaw

NVIDIA NemoClaw is an open-source reference stack for running always-on AI agents more safely inside OpenShell containers. NemoClaw provides onboarding, lifecycle management, and agent operations for supported runtimes in OpenShell sandboxes. It adds policy-based privacy and security controls for agent behavior and data handling. These controls help agents run in clouds, on-premises environments, RTX PCs, and DGX Spark.

NemoClaw pairs hosted inference providers or local model endpoints with a hardened sandbox, routed inference, and declarative egress policy. This keeps deployments repeatable and easier to constrain. The sandbox runtime comes from NVIDIA OpenShell. NemoClaw adds the blueprint, nemoclaw CLI, onboarding, and related tooling as the reference way to run supported agents there.

Capability	Description
Sandbox supported agents	Creates an OpenShell sandbox pre-configured for your selected agent, with filesystem and network policies applied from the first boot.
Route inference	Configures OpenShell inference routing so agent traffic goes to the provider and model you chose during onboarding (NVIDIA Endpoints, OpenAI, Anthropic, Gemini, compatible endpoints, local Ollama, and others). The agent uses `inference.local` inside the sandbox; credentials stay on the host.
Manage the lifecycle	Handles blueprint versioning, digest verification, and sandbox setup.

Key Features

NemoClaw provides these product capabilities.

Feature	Description
Guided onboarding	Validates credentials, selects providers, and creates a working sandbox in one command.
AI-agent docs	Publishes Markdown docs and a small routing skill so AI coding assistants can guide setup, inference configuration, policy management, monitoring, deployment, security review, and troubleshooting.
Hardened blueprint	A Dockerfile with capability drops, least-privilege network rules, and declarative policy.
State management	Safe migration of agent state across machines with credential stripping and integrity verification.
Messaging channels	OpenShell-managed processes connect Telegram, Discord, Slack, and similar platforms to supported messaging agents. NemoClaw configures channels during onboarding where the selected agent supports them; OpenShell supplies the native constructs, credential flow, and runtime supervision.
Routed inference	Provider-routed model calls through the OpenShell gateway, transparent to the agent. Supports NVIDIA Endpoints, OpenAI, Anthropic, Google Gemini, compatible endpoints, local Ollama, local vLLM, and the Model Router.
Layered protection	Network, filesystem, process, and inference controls that can be hot-reloaded or locked at creation.

Benefits of Using NemoClaw

Autonomous AI agents can make arbitrary network requests, access the host filesystem, and call any inference endpoint. Without controls, this creates security, cost, and compliance risks that grow as agents run unattended.

NemoClaw provides these benefits to mitigate those risks.

Benefit	Description
Sandboxed execution	Every agent runs inside an OpenShell sandbox with Landlock, seccomp, and network namespace isolation. The sandbox grants no access by default.
Routed inference	The OpenShell gateway routes model traffic to your selected provider, transparent to the agent. You can switch providers or models. Refer to Inference Options.
Declarative network policy	YAML defines egress rules. OpenShell blocks unknown hosts and surfaces them to the operator for approval.
Single CLI	The `nemoclaw` command orchestrates the full stack: gateway, sandbox, inference provider, and network policy.
Blueprint lifecycle	Versioned blueprints handle sandbox creation, digest verification, and reproducible setup.

Use Cases

Use NemoClaw for these use cases.

Use Case	Description
Always-on assistant	Run a sandboxed agent with controlled network access and operator-approved egress.
Terminal coding harness	Run `dcode` inside an OpenShell sandbox with host-owned inference credentials and a managed terminal workflow.
Sandboxed testing	Test agent behavior in a locked-down environment before granting broader permissions.
Remote GPU deployment	Deploy a sandboxed agent to a remote GPU instance for persistent operation.

Next Steps

Use these topics to learn more about NemoClaw and how to install and use it.

Read Architecture Overview to understand how NemoClaw works.
Read Ecosystem to understand how Deep Agents, OpenShell, and NemoClaw relate in the wider stack, and when to use NemoClaw versus OpenShell.
Follow Quickstart with Deep Agents to install NemoClaw and run your first Deep Agents sandbox with nemo-deepagents.
Read AI Agent Docs to let your AI coding assistant fetch NemoClaw Markdown docs.
Explore NemoClaw Community for community-driven blueprint examples, showcases, and integrations.
Read Inference Options to check the inference providers that NemoClaw supports and how inference routing works.