> For clean Markdown of any page, append .md to the page URL. > For a complete documentation index, see https://docs.nvidia.com/nemoclaw/llms.txt. > For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/nemoclaw/_mcp/server. # Overview of NVIDIA NemoClaw > NemoClaw is an open-source reference stack for running sandboxed AI agents more safely inside OpenShell. 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](https://github.com/NVIDIA/OpenShell). NemoClaw adds the blueprint, 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](../inference/inference-options). | | Declarative network policy | YAML defines egress rules. OpenShell blocks unknown hosts and surfaces them to the operator for approval. | | Single CLI | The 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](how-it-works) to understand how NemoClaw works. * Read [Ecosystem](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](../get-started/quickstart) to install NemoClaw and run your first Deep Agents sandbox with `nemo-deepagents`. * Read [AI Agent Docs](../resources/agent-skills) to let your AI coding assistant fetch NemoClaw Markdown docs. * Explore [NemoClaw Community](https://github.com/NVIDIA/nemoclaw-community) for community-driven blueprint examples, showcases, and integrations. * Read [Inference Options](../inference/inference-options) to check the inference providers that NemoClaw supports and how inference routing works.