NemoClaw
Run always-on agents with greater control, privacy, and flexibility. Installed with a single command. Built by NVIDIA.
Try NemoClaw →
Run always-on agents with greater control, privacy, and flexibility. Installed with a single command. Built by NVIDIA.
Try NemoClaw →
NemoClaw is NVIDIA's answer to the messy reality of deploying persistent AI agents: too many services, too many integration headaches, too little control over where your data lives. If your team needs always-on AI assistants running on your own infrastructure with real privacy guarantees, this is one of the most streamlined paths to get there. It is currently in early preview and free, which makes it low-risk to evaluate, but you should expect rough edges and limited enterprise environment support until it matures.
Current pricing as of May 2026. NemoClaw is open-source.
Open-source. Self-hosted on your infrastructure.
No paid tiers announced yet. Infrastructure costs (GPU compute, storage) are your own.
What NemoClaw actually brings to the table.
No stitching together a dozen microservices. A single CLI command bootstraps the entire agent orchestration stack on your hardware. This is the headline feature, and it genuinely reduces the time from "I want an agent" to "I have an agent running" from days to minutes.
Safety guardrails are baked into the orchestration layer, not bolted on as an afterthought. This includes execution sandboxing and output filtering. For teams in regulated industries, this is a meaningful differentiator over rolling your own agent stack.
Everything runs on your own infrastructure. No data leaves your network. No API calls to third-party cloud endpoints unless you explicitly configure them. This is the core value proposition for enterprises that cannot send sensitive data to external LLM providers.
Agents can chain together multi-step workflows, not just answer one-off questions. Think: monitor a data source, trigger analysis, generate a report, and notify a team. The orchestration layer handles state management and retry logic.
Supports multiple execution modes and model backends. You are not locked into a single NVIDIA model; the architecture is designed to be swappable. The OpenShell runtime provides the sandboxed execution environment for agent actions.
First-class support for NVIDIA's Nemotron family of models, which are optimized for NVIDIA hardware. If you are already running NVIDIA GPUs, this tight coupling translates to better inference performance and lower latency compared to generic model serving.
Agents are designed to run continuously, not just respond to one-shot prompts. The stack handles process management, health checks, and recovery. This is the "always-on" part of the pitch, and it is what separates NemoClaw from simpler agent frameworks.
Full source code available on GitHub. You can audit the security model, contribute fixes, and fork it for your own needs. For teams that need to vet every dependency before deploying to production, this is non-negotiable.
NemoClaw is not for everyone. Here is where it makes the most sense.
If you work in healthcare, finance, defense, or any sector where sending data to external APIs is a non-starter, NemoClaw gives you a production-grade agent stack that stays entirely on-premises. This is the primary audience.
Teams already managing NVIDIA GPU infrastructure who want to add persistent AI assistants without adopting yet another SaaS platform. The one-command setup and self-hosted model mean it fits into existing infrastructure workflows.
If you are building agents that need to run 24/7, handle multi-step tasks, and recover from failures gracefully, NemoClaw provides the orchestration plumbing so you can focus on agent logic rather than infrastructure.
The early preview status and open-source nature make it ideal for teams evaluating agent frameworks. You can dig into the internals, benchmark against alternatives, and contribute upstream. Zero cost to experiment.
What you should know before committing.
This is explicitly labeled "Early Preview." Expect breaking changes, incomplete documentation, and gaps in edge case handling. Do not deploy this to production-critical systems without thorough testing and a fallback plan. NVIDIA is iterating fast, but stability guarantees are not there yet.
While the architecture is technically flexible, the tight integration with Nemotron models and the OpenShell runtime means you will get the best experience on NVIDIA GPUs. If your infrastructure runs on AMD or custom silicon, this may not be the right fit. The "vendor-neutral" story is not fully realized.
NVIDIA acknowledges that not all enterprise environments are supported. If you are running complex Kubernetes setups, air-gapped networks, or non-standard Linux distributions, you may hit compatibility walls. The documentation does not yet cover these scenarios comprehensively.
Right now, the listed integrations are limited to NVIDIA's own OpenShell runtime and Nemotron models. There is no mention of connectors for popular tools like Slack, databases, or third-party APIs out of the box. You will likely need to build custom integrations for anything beyond the core NVIDIA stack.
As a new open-source project, the community around NemoClaw is still forming. Stack Overflow answers, third-party tutorials, and battle-tested deployment guides are sparse. If you hit a problem, you are largely relying on GitHub issues and NVIDIA's developer forums.
NemoClaw is free, open-source, and designed for teams that take data privacy seriously.