NVIDIA/NemoClawPublic

Run agents like Hermes, LangChain Deep Agents, and OpenClaw more securely inside NVIDIA OpenShell with managed inference

AI summary: A high-performance toolkit for fine-tuning and deploying large language models on NVIDIA GPUs using NeMo.

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Open issues
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Open PRs
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TypeScriptApache-2.0Created Mar 15, 2026Last push today+77 stars this week+119 this month

Star history

since Mar 15, 2026
010K20KMar 2026May 2026Jun 2026Aug 2026
22.1K stars as of Aug 7, 2026, tracked back to Mar 15, 2026. Historical curve reconstructed from public GitHub event archives, calibrated to the current total.

Signals and awards

derived from tracked data
  • Widely adopted

    22,079 stars

  • Actively maintained

    Pushed within 48 hours

  • Community-driven

    ~159 contributors

  • Well documented

    High community health score

  • Permissive license

    Apache-2.0

  • Repeat trending

    4 trending appearances

What NemoClaw does

NemoClaw is an extension of the NVIDIA NeMo framework designed specifically for accelerating the fine-tuning and deployment of large language models (LLMs). It provides optimized recipes and scripts for adapting foundation models to specific tasks using techniques like LoRA, P-Tuning, and full parameter fine-tuning. By leveraging NVIDIA's TensorRT-LLM and Megatron-LM backends, NemoClaw maximizes GPU utilization and memory efficiency during training. It simplifies the transition from training to inference, offering pre-configured export pipelines that compile tuned models into highly optimized engines for low-latency serving. The tool acts as a bridge between raw model weights and production-ready deployments on NVIDIA hardware.

NemoClaw is for machine learning engineers, AI researchers, and enterprise deployment teams working extensively with NVIDIA hardware. It requires deep knowledge of LLM architectures, distributed training concepts, and the NVIDIA software stack.

  • Optimized fine-tuning: Built-in support for parameter-efficient techniques like LoRA and QLoRA.
  • TensorRT-LLM integration: Seamless export of trained models to highly optimized inference engines.
  • Megatron-LM backend: Distributed training capabilities for scaling across multiple GPUs and nodes.
  • Pre-configured recipes: Ready-to-use scripts for tuning popular open-weights models (Llama, Mistral, etc.).
  • Memory efficiency: Implements advanced memory management techniques to train larger models on limited hardware.
  • Production ready: Generates artifacts ready for deployment with NVIDIA Triton Inference Server.

Where teams use it

Domain-specific LLM adaptation

Enterprise teams fine-tune open-source foundation models on internal proprietary data for specialized tasks.

High-performance inference deployment

ML engineers compile trained models into TensorRT engines to achieve minimal latency and maximum throughput in production.

Resource-constrained training

Researchers use QLoRA recipes to fine-tune massive models on single nodes or smaller GPU clusters.

Scaling model training

Infrastructure teams leverage the Megatron-LM backend to distribute training workloads across large-scale supercomputers.

Getting started: pip install nemo_toolkit[all] && git clone https://github.com/NVIDIA/NemoClaw.git

README

main branch

NVIDIA NemoClaw: Reference Stack for Sandboxed AI Agents in OpenShell

License Security Policy Discord

NVIDIA NemoClaw is an open source reference stack for running always-on AI agents more safely inside NVIDIA OpenShell sandboxes. It provides guided onboarding, a hardened blueprint, routed inference, network policy, and lifecycle management through a single CLI.

Supported agents:

For capabilities, architecture, security controls, and the full feature list, see the NemoClaw documentation.

Get Started

Start with Your Coding Agent

Use the starter prompt when you want Cursor, Claude Code, Codex, Copilot, or another local coding agent to install NemoClaw with you.

Copy the NemoClaw starter prompt.

The prompt tells your agent to use NemoClaw docs and skills, ask one question at a time, run commands only with your approval, and keep secrets out of chat.

Install Using the Interactive Installer in Your Terminal

Review Prerequisites before installing. For Hermes, set NEMOCLAW_AGENT=hermes before running the installer, or use the nemohermes alias after install. When connecting to a Hermes sandbox from a light terminal, NemoClaw may install a managed nemoclaw-light Hermes skin for readable assistant text; it removes that managed skin state again when the terminal no longer needs it and preserves any user-selected Hermes skin.

Agent Guide
OpenClaw (default) Quickstart with OpenClaw
Hermes Quickstart with Hermes
LangChain Deep Agents Code Quickstart with LangChain Deep Agents Code

Documentation

Refer to the following pages on the official documentation website for more information on NemoClaw.

Page Description
Overview What NemoClaw does and how it fits together.
Architecture Overview High-level overview of Plugin, blueprint, sandbox lifecycle, and protection layers.
Ecosystem How OpenClaw, OpenShell, and NemoClaw form a stack and when to use NemoClaw versus OpenShell alone.
Architecture Details Detailed description of Plugin structure, blueprint lifecycle, sandbox environment, and host-side state.
Prerequisites Hardware, software, and supported platforms, with any platform-specific pre-setup.
Choose an Inference Provider Supported providers, validation, and routed inference configuration.
Network Policies Baseline rules, operator approval flow, and egress control.
Customize Network Policy Static and dynamic policy changes, presets.
Security Best Practices Controls reference, risk framework, and posture profiles for sandbox security.
Sandbox Hardening Container security measures, capability drops, process limits.
CLI Commands Full NemoClaw CLI command reference.
Troubleshooting Common issues and resolution steps.

Community

Join the NemoClaw community to ask questions, share feedback, and report issues. NemoClaw is an alpha project, so maintainers review issues, discussions, and pull requests on a best effort basis without guaranteed response timelines.

Need Channel
Setup or usage questions GitHub Discussions or Discord
Reproducible bugs GitHub Issues
Feature proposals Start with GitHub Discussions, then open an issue when the scope is clear
Current priorities Current Priorities
Contribution help CONTRIBUTING.md
Security vulnerabilities Use the private channels in SECURITY.md; do not open public issues

Contributing

We welcome contributions. See CONTRIBUTING.md for development setup, coding standards, and the PR process.

Prepare a source checkout without creating a runtime sandbox:

./scripts/dev-setup.sh

Or ask a compatible coding agent to use the repository's contributor-onboarding skill:

Set up this machine as a NemoClaw contributor and prepare it for a first PR.

The contributor path is separate from the end-user installer above. The default and --repair modes change only repository-local dependencies, builds, and hooks. Use ./scripts/dev-setup.sh --expose-cli only when you explicitly want a host-visible development CLI. Use ./scripts/dev-setup.sh --with-runtime only when your change needs sandbox validation; that approved flow also opts into CLI exposure.

Security

NVIDIA takes security seriously. If you discover a vulnerability in NemoClaw, DO NOT open a public issue. Use one of the private reporting channels described in SECURITY.md:

For security bulletins and PSIRT policies, visit the NVIDIA Product Security portal.

Current Priorities

NemoClaw's current priorities are maintained here as a public orientation point for contributors and community members. This list is not a delivery commitment, support promise, or fixed roadmap; priorities can change as maintainers respond to security, quality, platform readiness, and community feedback.

  • Improve install and onboarding reliability across tested platforms.
  • Strengthen sandbox hardening, credential handling, and network-policy defaults.
  • Validate local and routed inference behavior for supported provider paths.
  • Keep documentation, troubleshooting guidance, and agent skills aligned with supported workflows.

For specific scoped work, use GitHub Issues and start broader proposals in GitHub Discussions. Security vulnerabilities must use the private reporting channels in SECURITY.md, not public issues.

Notice and Disclaimer

This software automatically retrieves, accesses or interacts with external materials. Those retrieved materials are not distributed with this software and are governed solely by separate terms, conditions and licenses. You are solely responsible for finding, reviewing and complying with all applicable terms, conditions, and licenses, and for verifying the security, integrity and suitability of any retrieved materials for your specific use case. This software is provided "AS IS", without warranty of any kind. The author makes no representations or warranties regarding any retrieved materials, and assumes no liability for any losses, damages, liabilities or legal consequences from your use or inability to use this software or any retrieved materials. Use this software and the retrieved materials at your own risk.

License

Apache 2.0. See LICENSE.

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