Tools #AI agents#Open models Nov 2, 2026 · Originally published Oct 30

NVIDIA introduces NemoClaw open blueprints: OpenClaw, Hermes Agent or LangChain Deep Agents paired with Nemotron models and the OpenShell runtime

NemoClaw is not a new agent harness but a set of recipes that turn an existing harness, open models, observability and a permissions runtime into a full stack.

Primary source NVIDIA Technical Blog · developer.nvidia.com Read the original ↗

// Key points

  • NemoClaw is a set of open blueprints, not a competing harness or runtime; each assembles a full agent stack around OpenClaw, Nous Research's Hermes Agent or LangChain Deep Agents.
  • Each blueprint includes a harness tuned for Nemotron, Nemotron open models, NeMo tools (NeMo Relay for observing and improving agent execution, plus routing for throughput, cost and reliability) and NVIDIA OpenShell, which governs agent access to files, networks, credentials and tools.
  • LangChain tuned Deep Agents for Nemotron 3 Ultra through prompts, tools and middleware without retraining, reaching what NVIDIA calls near-frontier performance at about 10x lower cost per run than leading closed models; the post gives no further benchmark figures.
  • Agents can run in the cloud, in data centers, on DGX Spark and on RTX PCs, and can handle domain-specific subtasks alongside frontier agents such as Claude, Codex or Cursor.

Builder's takeThe "about 10x cheaper" figure is NVIDIA's own claim with no benchmark detail, so I would re-test on my own tasks first. What is worth copying is the layering: use an existing harness, keep the model swappable, and hand file, network and credential permissions to a separate runtime. In a backend like PandaClaws, high-repetition subtasks such as rewriting, classifying and tagging are the first to A/B on open models, while the main flow stays on a frontier model.

// Background · from #AI agents

Full timeline →
  1. Nov 2 NVIDIA builds a fully local voice agent on Nemotron models: a median 1.70 seconds from end of speech to audio, 0.72 seconds for cached replies
  2. Nov 1 AWS lays out an end-to-end Bedrock cost playbook: spend attributed to each IAM principal, over-budget users blocked in seconds to minutes, and more than half of coding-fleet calls on cheaper models
  3. Nov 1 Bedrock AgentCore Runtime adds session endpoints: preview a web app in your own browser while a coding agent edits it in the cloud

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