--- title: Build Specialized Agents on a Stack You Control with... description: NVIDIA NemoClaw is a set of open blueprints for assembling a full agent stack around an existing harness rather than a new harness itself. It supports three... canonical: https://daily.dev/posts/build-specialized-agents-on-a-stack-you-control-with-nvidia-nemoclaw-2auklvdir twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Build Specialized Agents on a Stack You Control with NVIDIA NemoClaw | daily.dev og:description: NVIDIA NemoClaw is a set of open blueprints for assembling a full agent stack around an existing harness rather than a new harness itself. It supports three... og:url: https://daily.dev/posts/build-specialized-agents-on-a-stack-you-control-with-nvidia-nemoclaw-2auklvdir og:image: https://api.daily.dev/og/posts/2AUklvdiR.png og:image:alt: Build Specialized Agents on a Stack You Control with NVIDIA NemoClaw og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Build Specialized Agents on a Stack You Control with NVIDIA NemoClaw **[NVIDIA Developer](https://daily.dev/sources/nvidiadev)** · 7 min read · 1 upvotes · 0 comments ## Summary NVIDIA NemoClaw is a set of open blueprints for assembling a full agent stack around an existing harness rather than a new harness itself. It supports three harnesses: OpenClaw, Hermes Agent, and LangChain Deep Agents, pairing them with Nemotron open models, NeMo Relay for observability, and the OpenShell runtime for governing file, network, credential, and tool access. LangChain's harness tuning for Nemotron 3 Ultra reportedly reached near-frontier performance at about 10x lower cost per run than leading closed models. NemoClaw agents can run in the cloud, in data centers, on NVIDIA DGX Spark, or on RTX PCs, and can work alongside Claude, Codex, or Cursor agents by handling delegated domain-specific subtasks. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### What is NVIDIA NemoClaw and how does it relate to agent harnesses like LangChain Deep Agents? NemoClaw is a collection of open blueprints, not a harness or runtime itself, that assembles a full agent stack on top of a harness you choose, such as OpenClaw, Hermes Agent, or LangChain Deep Agents. Each blueprint combines a tuned harness, NVIDIA Nemotron open models, NeMo Relay optimization and observability tools, and the OpenShell secure runtime. _Teams assembling a production agent stack can track NemoClaw and similar blueprint releases on daily.dev._ ### How does NVIDIA OpenShell secure an AI agent's access to files and credentials? OpenShell is the secure runtime included in every NemoClaw reference that enforces policy around what an agent harness and its tools can access. It governs file system, process, and network access, and keeps credentials for managed inference and Model Context Protocol connections outside the sandbox, keeping the agent bound by policy even if its memory or retrieved context pushes it toward an unintended action. _Developers hardening agent deployments can follow runtime security practices like this on daily.dev._ ### How much cheaper is Nemotron 3 Ultra than leading closed models when used with a tuned LangChain Deep Agents harness? Nemotron 3 Ultra reaches near-frontier performance at approximately 10 times lower cost per run compared with leading closed models, after LangChain tuned its Deep Agents harness for it by adjusting prompts, tools, and middleware without retraining the model itself. The tuned model profile is available through LangChain on GitHub. _Anyone comparing open versus closed model costs for agent workloads can follow benchmarks like this on daily.dev._ ## Similar posts on daily.dev - [LangChain and NVIDIA Launch NemoClaw Deep Agents Blueprint](https://daily.dev/posts/langchain-and-nvidia-launch-nemoclaw-deep-agents-blueprint-ursnabiai) · LangChain · 2 upvotes · 0 comments - [Build a Secure, Always-On Local AI Agent with OpenClaw and NVIDIA NemoClaw](https://daily.dev/posts/build-a-secure-always-on-local-ai-agent-with-openclaw-and-nvidia-nemoclaw-64lushnzq) · NVIDIA Developer · 1 upvotes · 0 comments - [Nemotron Labs: What OpenClaw Agents Mean for Every Organization](https://daily.dev/posts/nemotron-labs-what-openclaw-agents-mean-for-every-organization-r4grxfecs) · NVIDIA · 1 upvotes · 0 comments --- Tags: [#ai-agents](https://daily.dev/tags/ai-agents), [#langchain](https://daily.dev/tags/langchain) [View this post on daily.dev](https://daily.dev/posts/build-specialized-agents-on-a-stack-you-control-with-nvidia-nemoclaw-2auklvdir) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"Build Specialized Agents on a Stack You Control with NVIDIA NemoClaw","url":"https://daily.dev/posts/build-specialized-agents-on-a-stack-you-control-with-nvidia-nemoclaw-2auklvdir","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/build-specialized-agents-on-a-stack-you-control-with-nvidia-nemoclaw-2auklvdir"},"datePublished":"2026-10-30T16:00:50.838Z","dateModified":"2026-10-30T16:01:21.062Z","description":"NVIDIA NemoClaw is a set of open blueprints for assembling a full agent stack around an existing harness rather than a new harness itself. 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