NVIDIA NemoClaw

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NVIDIA NemoClaw is a collection of open blueprints (reference architectures) for building custom autonomous AI agents that packages three moving parts, a model, an agent harness, and a secure runtime, into a single starting point for teams building specialized, domain-specific agents.^[1][2] NVIDIA introduced it on July 8, 2026 alongside a LangChain co-developed "NemoClaw for LangChain Deep Agents" blueprint, and it was a headline item at NVIDIA's SIGGRAPH 2026 keynote on July 20, 2026, where the company showed NemoClaw agents extending into physics simulation and 3D content tools.^[3][4][6] Rather than a single product, NemoClaw is a set of opinionated, ready-to-run stacks that pair an open Nemotron model with an agent framework and NVIDIA's OpenShell runtime, installed with hardened security defaults so an enterprise can stand up a working agent and then tune it for its own workloads.^[1][10]

What is NemoClaw

NVIDIA describes NemoClaw as "open blueprints for building custom autonomous agents, packaging the model, harness and runtime together as a starting point for teams building specialized, domain-specific agents."^[2] Its product page frames the same idea for practitioners: "a collection of open blueprints for building autonomous agents: domain-specialized, always-on AI systems that reason, plan, and act across real-world workflows," running locally on NVIDIA RTX, DGX Spark, and DGX Station hardware.^[1]

The key word is blueprint. NemoClaw is a reference implementation, not a framework you assemble from scratch. Each blueprint answers the three questions that usually stall an AI agent project: which model runs the reasoning, which harness (the loop that plans, calls tools, and manages memory) drives the agent, and which runtime safely executes what the agent decides to do. NVIDIA supplies a default answer for each layer, wired together and installable in effectively one command, then leaves every layer swappable.^[1][10] The blueprints are aimed at "always-on" or long-running agents: systems that persist in the background, check a task list on an interval, and either act or wait for the next cycle, rather than one-shot chatbots that respond only when prompted.^[10]

Background: NVIDIA's agentic-AI push and the LangChain partnership

NemoClaw sits inside NVIDIA's broader 2026 push to make agentic AI run locally on its own hardware. The building blocks predate it: the open Nemotron model family for reasoning, the NVIDIA NeMo Agent Toolkit for connecting models to tools, skills, and data, and the DGX Station and DGX Spark desktop systems that put data-center-class inference on a workstation. NemoClaw is the layer that packages those pieces into a deployable agent instead of a parts bin.^[1][9]

The launch partner was LangChain. On July 8, 2026, the two companies announced the "NemoClaw for LangChain Deep Agents" blueprint, described as a reference architecture "for building open agent systems with benchmark-leading performance and more than 10x lower inference costs."^[3][4][5] LangChain co-founder and CEO Harrison Chase framed the collaboration around tuning the whole system rather than swapping in a bigger model: "The way to build better agents is to keep improving the system around the model. Memory, tool use, evaluation, and model behavior compound when teams can tune them together."^[4] NVIDIA founder and CEO Jensen Huang put it in product terms: "Super agents have arrived. With an open model like NVIDIA Nemotron, a LangChain harness, the NVIDIA OpenShell runtime, and a company's own data, every enterprise can build custom agents."^[4]

Twelve days later, at SIGGRAPH 2026 (July 20, keynote at 3:45 p.m. PT), NVIDIA folded NemoClaw into its graphics-and-simulation story, showing that the same agents could be pushed beyond text and code into 3D simulation and asset workflows through new Omniverse libraries.^[2][6]

Components

Every NemoClaw blueprint is built from the same four-layer stack. Only the harness (and, with it, the tuned model size) changes between blueprints.

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Model: Nemotron 3

The reasoning engine is NVIDIA's open Nemotron 3 family. The flagship Deep Agents blueprint runs on Nemotron 3 Ultra, which NVIDIA describes as "a frontier 550-billion-parameter open model" optimized to run on DGX Station GB300 systems; NVIDIA's technical materials characterize it as a hybrid Mamba-Transformer mixture-of-experts design with roughly 55 billion active parameters, pretrained in the NVFP4 format for efficient long-running reasoning.^[2][11][12] At SIGGRAPH, NVIDIA said Nous Research likewise fine-tuned Nemotron 3 Ultra for its Hermes Agent harness and adopted it for production workloads.^[2] Because the model layer is swappable, blueprints can also run smaller Nemotron variants: NVIDIA's Hermes walkthrough demonstrates the 120B Nemotron 3 Super, and any blueprint can point at a self-hosted model served through NVIDIA NIM or vLLM.^[8] Nemotron 3 has its own detailed article; NemoClaw simply treats it as the default, tuned model.

Harnesses: OpenClaw, Hermes, and Deep Agents

A harness is the control loop around the model: it plans, calls tools, manages memory and state, and decides the next step. NemoClaw ships three, and none of the three was written by NVIDIA, which supplies the packaging, the runtime, and the model instead.^[4][7]

• OpenClaw (default). OpenClaw is the community "always-on" agent that gives the whole family its name. NVIDIA describes it as an independent, MIT-licensed open-source project, created by developer Peter Steinberger and separate from NVIDIA, for self-hosted, persistent assistants that "check their task list, evaluate what needs action, and either act or wait for the next cycle."^[10] At the implementation level NemoClaw is packaged as an OpenClaw plugin plus an onboard setup wizard.^[13]
• Hermes Agent (Nous Research). The Hermes harness contributes a self-evolving skills-and-memory loop: when a user teaches it a preferred output format, "Hermes writes a SKILL.md to the filesystem" with structured scaffolding, and those skills persist across deployments via snapshot and restore, so "the more users work with the agent, the better it gets."^[8]
• LangChain Deep Agents. LangChain's Deep Agents framework adds explicit planning, parallel subagents with isolated context, and a virtual filesystem for persisting knowledge across sessions; it is the harness behind the headline enterprise blueprint and cost claim.^[3][18]

Runtime: OpenShell

NVIDIA OpenShell is the secure-by-design runtime shared by all three harnesses, and it is what makes an autonomous agent safe enough to leave running. NVIDIA positions it as a governance layer that sits "between your agent and your infrastructure," providing programmable system and network isolation, a policy engine that enforces constraints across the filesystem, network, and process layers (declared in a version-controlled policy file), credential brokering so the agent never handles raw tokens, live policy updates, and full audit trails.^[1][9] Its Privacy Router "keeps sensitive context on-device with local open models and routes to frontier models like Claude and GPT only when policy allows," which is the mechanism behind NemoClaw's model-routing pitch of getting "the best of both" local and cloud models under defined controls.^[1][9] OpenShell is documented as part of the NVIDIA Agent Toolkit and is also surfaced on the DGX Station for Windows materials.^[9][14] (This article does not deep-dive OpenShell; see the DGX Station for Windows page.)

Omniverse libraries and MCP

Two extension points connect NemoClaw agents to the outside world. At SIGGRAPH 2026, NVIDIA added Omniverse libraries (ovrtx for RTX camera, lidar, radar, and sensor simulation; ovphysx for GPU-accelerated physics; and CAD-to-SimReady for converting CAD data into simulation-ready assets) that let a NemoClaw agent call physical AI capabilities, not just text tools.^[6] Separately, NemoClaw's documentation exposes a Model Context Protocol server so AI clients such as Claude Code and Cursor can connect to it, and NVIDIA's broader Agent Toolkit and Omniverse work uses MCP to plug agents into creative and engineering applications.^[6][7]

The LangChain Deep Agents blueprint and the cost claim

The flagship blueprint bundles three things: LangChain's Deep Agents Code harness, NVIDIA Nemotron 3 Ultra, and the NVIDIA OpenShell runtime, so a team can tune agents for its own workloads, run them securely, and optimize for quality, cost, and speed.^[3][4] The pitch is not that a bigger model wins, but that "agent performance improves when the model, harness, evals, and runtime are tuned together."^[3]

The attention-grabbing number is the cost claim. NVIDIA and LangChain say the blueprint delivers "benchmark-leading performance and more than 10x lower inference costs," a figure widely repeated in launch coverage.^[3][4][17] This is a vendor benchmark, and it is worth stating its exact basis rather than repeating the headline. In LangChain's own agent-evaluation suite, Nemotron 3 Ultra driven by Deep Agents reached an aggregate score of 0.86 at an inference cost of $4.48, while the next best-scoring model cost $43.48 for the same evaluation, roughly a 10x cost gap on that specific test.^[3][4] The figure therefore reflects one company-run benchmark on one eval suite, not an independently reproduced result; readers should treat "more than 10x lower inference costs" as a claim measured under conditions NVIDIA and LangChain chose. The blueprint was made available for enterprises to run against their own workloads on day one, which is how the companies invite verification.^[3][4]

The Blender blueprint

At SIGGRAPH, NVIDIA showed what the Omniverse libraries make possible with a sample workflow for Blender, the open-source 3D creation tool. The Blender blueprint is a reference workflow "built in Blender with NVIDIA Omniverse libraries and NVIDIA NemoClaw" that brings agent-ready simulation into a tool artists already use: RTX sensor simulation, physics simulation, and asset validation become callable steps an agent can run while the human keeps creative control.^[6] The goal is to prepare 3D scenes and assets for physical AI and robotics pipelines (for example, generating synthetic data or a validated digital twin) without leaving the modeling application. It is a concrete demonstration that a NemoClaw agent is not limited to text and code: given the right libraries, it can act inside a simulator.^[2][6]

How it fits with the Agent Toolkit and DGX Station

NemoClaw is easy to confuse with the NVIDIA NeMo Agent Toolkit (marketed in 2026 as the "NVIDIA Agent Toolkit"), so the distinction is worth stating precisely from the sources. The Agent Toolkit is the broader framework: NVIDIA describes it as "the full deployment stack: models, tools, evaluation, and runtimes for building, testing, and optimizing long-running agents," and OpenShell is documented as "part of the NVIDIA Agent Toolkit."^[9] NemoClaw is the opinionated blueprint that assembles those parts: its product page states that "each blueprint includes NVIDIA Agent Toolkit components such as NVIDIA OpenShell for runtime policy controls," alongside model routing, skill execution, state, and observability.^[1] In short, the Agent Toolkit is the framework and parts catalog; NemoClaw is a reference build that packages those parts with a harness and a Nemotron model into something you can run today. At SIGGRAPH 2026 the two were announced together under the headline "NVIDIA Agent Toolkit Expands With New Omniverse Libraries," with NemoClaw presented as the agent-building on-ramp and the Omniverse libraries as the new capability the toolkit gained.^[6]

On hardware, NemoClaw targets NVIDIA's local-AI lineup: the DGX Station (a GB300 Grace Blackwell Ultra desktop) as the flagship for running the 550B Nemotron 3 Ultra locally, plus DGX Spark, RTX PRO workstations, and GeForce RTX PCs for smaller footprints.^[1][16] The recurring theme in NVIDIA's messaging is "frontier agents without API rent": keep the model, the data, and the runtime on a machine you own.^[1]

Availability

The NemoClaw for LangChain Deep Agents blueprint was released on July 8, 2026 and described as available immediately for enterprises to evaluate against their own workloads.^[3][4] NemoClaw itself is distributed through NVIDIA's open-source channels: the product page at nvidia.com/en-us/ai/nemoclaw, guided playbooks at build.nvidia.com/station, reference documentation at docs.nvidia.com/nemoclaw, and GitHub repositories.^[1][7][13] The build.nvidia.com "Run NemoClaw on DGX Station" playbook walks through installing Docker, Node.js, Ollama, the OpenShell CLI, and the NemoClaw plugin, then running the onboard wizard to create an OpenShell sandbox with inference routed to a local Nemotron model and a browser-based chat dashboard.^[13] Installation is designed to be a single command that sets up OpenClaw, the OpenShell runtime, and an open Nemotron model with hardened defaults for networking, data access, and security.^[10] NVIDIA also ran a hands-on "Build a Claw" experience showing an autonomous agent built with Nemotron and OpenClaw.^[15]

Significance

NemoClaw matters less as a new model than as a stance on how agents should be built and run. Three ideas stand out. First, it argues that agent quality comes from tuning the model, harness, evaluation, and runtime together, and it ships that whole system as a blueprint rather than leaving teams to integrate four projects themselves.^[3][4] Second, it is a local-first, open-stack answer to metered cloud APIs: an open Nemotron model plus OpenShell plus the enterprise's own data, running on a workstation the company owns, with the "more than 10x lower inference costs" claim as the economic argument.^[1][4] Third, by treating the harness as a swappable slot filled by community and partner projects (OpenClaw, Hermes, Deep Agents) rather than an NVIDIA-only framework, NVIDIA positions itself as the infrastructure layer (model, runtime, hardware, packaging) under whichever agent framework a team prefers.^[7][10] The SIGGRAPH extension into Omniverse and Blender then stretches the same agents from text and code toward simulation and physical AI, which is where NVIDIA wants the next wave of agents to operate.^[2][6]

Limitations and caveats

• New and fast-moving. NemoClaw and the Deep Agents blueprint were announced in July 2026. Interfaces, defaults, and even the set of supported harnesses are likely to shift; treat exact command lines and version-specific details as provisional.^[3][7]
• Vendor benchmarks. "Benchmark-leading performance" and "more than 10x lower inference costs" are claims from NVIDIA and LangChain, measured on LangChain's own eval suite (0.86 aggregate at $4.48 versus $43.48 for the next model).^[3][4] They had not been independently reproduced at launch. Real-world cost and quality depend on the workload, the model size chosen, and how much tuning a team does.
• Not a turnkey product for every model. The headline economics rest on running a large open model (Nemotron 3 Ultra, 550B) locally, which in practice means DGX Station GB300-class hardware; smaller machines run smaller Nemotron variants with different performance.^[2][8]
• Overlapping names. NemoClaw, OpenClaw, OpenShell, the NeMo Agent Toolkit, and Nemotron are distinct layers that are easy to conflate. OpenClaw in particular is a third-party open-source project, not an NVIDIA harness, despite the shared "Claw" branding.^[7][10]
• Scope of MCP support. Public documentation confirms an MCP server for connecting AI clients (Claude Code, Cursor) to NemoClaw; broader MCP client and tool integration is part of NVIDIA's wider Agent Toolkit and Omniverse story rather than fully detailed on the NemoClaw page itself.^[6][7]

ELI5

Imagine you want a robot assistant that runs by itself all day, checking things and doing tasks without you typing every command. To build one you normally need three hard-to-fit pieces: a brain (the AI model), a control loop that decides what to do next and remembers things (the harness), and a safety cage so it cannot touch files or send data it should not (the runtime). NemoClaw is a set of ready-made kits from NVIDIA that snap those three pieces together for you. It brings a strong open brain (a Nemotron model), lets you pick one of three control loops (OpenClaw, Hermes, or LangChain's Deep Agents), and locks it inside a safety cage called OpenShell, all set up with one command on an NVIDIA computer you own. NVIDIA says its kit is cheaper to run than paying a cloud service by the message, and at SIGGRAPH it showed the same assistant learning to work inside 3D and physics tools like Blender.

See also

• NVIDIA NeMo Agent Toolkit
• Nemotron 3
• NVIDIA DGX Station
• Deep Agents
• LangChain
• Nous Research
• Model Context Protocol
• NVIDIA Omniverse

References

1. NVIDIA, "NVIDIA NemoClaw" (product page), nvidia.com/en-us/ai/nemoclaw/ (accessed July 2026). https://www.nvidia.com/en-us/ai/nemoclaw/ ↩
2. NVIDIA, "At SIGGRAPH, NVIDIA Advances Graphics and Simulation With Agentic and Physical AI," NVIDIA Blog, July 20, 2026. https://blogs.nvidia.com/blog/siggraph-news-2026/ ↩
3. LangChain, "LangChain and NVIDIA Launch the NemoClaw Deep Agents Blueprint," LangChain Blog, July 8, 2026. https://www.langchain.com/blog/langchain-and-nvidia-launch-the-nemoclaw-deep-agents-blueprint ↩
4. LangChain / NVIDIA (via PR Newswire), "LangChain and NVIDIA Launch NemoClaw Deep Agents Blueprint for Enterprise Agents," July 8, 2026. https://www.prnewswire.com/news-releases/langchain-and-nvidia-launch-nemoclaw-deep-agents-blueprint-for-enterprise-agents-302820446.html ↩
5. AIwire (HPCwire), "LangChain and NVIDIA Launch NemoClaw Deep Agents Blueprint for Enterprise Agents," July 8, 2026. https://www.hpcwire.com/aiwire/2026/07/08/langchain-and-nvidia-launch-nemoclaw-deep-agents-blueprint-for-enterprise-agents/ ↩
6. NVIDIA Newsroom, "NVIDIA Agent Toolkit Expands With New Omniverse Libraries, Putting AI Agents to Work Building Simulation-Ready Worlds," July 20, 2026. https://nvidianews.nvidia.com/news/nvidia-agent-toolkit-expands-with-new-omniverse-libraries-putting-ai-agents-to-work-building-simulation-ready-worlds ↩
7. NVIDIA, "NemoClaw Quickstart with OpenClaw," NVIDIA NemoClaw User Guide (docs), accessed July 2026. https://docs.nvidia.com/nemoclaw/user-guide/openclaw/get-started/quickstart ↩
8. NVIDIA, "Deploy Self-Evolving Agents for Faster, More Secure Research with a Hermes Agent and NVIDIA NemoClaw," NVIDIA Technical Blog, 2026. https://developer.nvidia.com/blog/deploy-self-evolving-agents-for-faster-more-secure-research-with-a-hermes-agent-and-nvidia-nemoclaw/ ↩
9. NVIDIA, "Run Autonomous, Self-Evolving Agents More Safely with NVIDIA OpenShell," NVIDIA Technical Blog, 2026. https://developer.nvidia.com/blog/run-autonomous-self-evolving-agents-more-safely-with-nvidia-openshell/ ↩
10. NVIDIA, "Nemotron Labs: What OpenClaw Agents Mean for Every Organization," NVIDIA Blog, April 30, 2026. https://blogs.nvidia.com/blog/what-openclaw-agents-mean-for-every-organization/ ↩
11. NVIDIA, "NVIDIA Nemotron 3 Ultra Powers Faster, More Efficient Reasoning for Long-Running Agents," NVIDIA Technical Blog, 2026. https://developer.nvidia.com/blog/nvidia-nemotron-3-ultra-powers-faster-more-efficient-reasoning-for-long-running-agents/ ↩
12. NVIDIA Research, "NVIDIA Nemotron 3 Ultra," Nemotron Labs, accessed July 2026. https://research.nvidia.com/labs/nemotron/Nemotron-3-Ultra/ ↩
13. NVIDIA, "Run NemoClaw on DGX Station" (playbook), build.nvidia.com/station, accessed July 2026. https://build.nvidia.com/station/nemoclaw/overview ↩
14. NVIDIA, "Build a More Secure, Always-On Local AI Agent with OpenClaw and NVIDIA NemoClaw," NVIDIA Technical Blog, 2026. https://developer.nvidia.com/blog/build-a-secure-always-on-local-ai-agent-with-nvidia-nemoclaw-and-openclaw/ ↩
15. NVIDIA, "Build an Autonomous AI Agent with Nemotron and OpenClaw at GTC," nvidia.com/en-us/ai/build-a-claw/ (accessed July 2026). https://www.nvidia.com/en-us/ai/build-a-claw/ ↩
16. Wccftech, "NVIDIA Brings Local AI Agents To Its Most Powerful Workstation PC, The DGX Station, With the NVIDIA Agent Toolkit and Omniverse," July 2026. https://wccftech.com/nvidia-brings-local-ai-agents-to-dgx-station-pc-with-agent-toolkit/ ↩
17. TechTimes, "Open Agent Stack Cuts Enterprise AI Cost 10x: NemoClaw Blueprint Ships," July 9, 2026. https://www.techtimes.com/articles/320036/20260709/open-agent-stack-cuts-enterprise-ai-cost-10x-nemoclaw-blueprint-ships.htm ↩
18. LangChain, "Deep Agents," langchain.com/deep-agents (accessed July 2026). https://www.langchain.com/deep-agents ↩