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.

Stars
22.7K
+29 today
Forks
3.1K
Watchers
112
Open issues
623
Open PRs
137
Contributors
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Commits
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Branches
1.4K

TypeScriptApache-2.0Created Mar 15, 2026Last push today+114 stars this week+291 this month

Quick answers

What is NemoClaw?
A high-performance toolkit for fine-tuning and deploying large language models on NVIDIA GPUs using NeMo.
What does NemoClaw do?
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.
Who is NemoClaw for?
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.
How do I get started with NemoClaw?
pip install nemo_toolkit[all] && git clone https://github.com/NVIDIA/NemoClaw.git
How popular is NemoClaw on GitHub?
NVIDIA/NemoClaw has 22,650 stars and 3,129 forks on GitHub, and gained 114 stars in the last 7 days.
What license does NemoClaw use?
NVIDIA/NemoClaw is released under the Apache-2.0 license.

Star history

since Jul 28, 2026
010K20KJul 2026Aug 2026Sep 2026Oct 2026
22.7K stars as of Oct 4, 2026. Measured daily since Jul 28, 2026; GitHub no longer exposes earlier star timestamps.

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Signals and awards

derived from tracked data
  • Widely adopted

    22,650 stars

  • Very active

    5,944 commits in 52 weeks

  • Community-driven

    ~186 contributors

  • Well documented

    High community health score

  • Permissive license

    Apache-2.0

  • Continuous integration

    Automated checks passing

  • 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 supported AI agents more safely inside NVIDIA OpenShell sandboxes. It provides guided onboarding, managed inference, network policy, managed integrations, OpenShell-backed state persistence, and lifecycle operations through the NemoClaw CLI and its agent-specific aliases.

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 Installer in Your Terminal

Review Prerequisites before installing. On a supported DGX or Windows Subsystem for Linux (WSL) host, press Enter at the Run express install with these settings? [Y/n]: prompt to use the recommended preset settings for that platform. Express install mode installs OpenClaw by default. If you accept, refer to NemoClaw Quickstart with OpenClaw. Enter n if you want to choose Hermes or LangChain Deep Agents Code, a sandbox name, an inference provider, and a model interactively. 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 the host CLI, agent integration layer, 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 agent integration 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
Examples Community Examples · Contribute an example
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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Recent activity

commits and pull requests

Recent open issues

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Discussions

all 185

Releases and announcements

1 total
  1. Native Podman E2E toolchains — 2026-10-01ci-native-podman-e2e-2026-10-01Oct 1, 2026pre-release16 downloads

    Pinned CI toolchains for NemoClaw's native Podman E2E jobs. These assets preserve existing reviewed binaries so E2E startup no longer depends on expiring GitHub Actions artifacts. Recovered without modification from trusted main E2E run 36537895087, generate-matrix job 109331743180, at commit 2272c5c15dd873c79436120ac0bfd21f70ae962e. That job staged the original toolchains through action 8d7409d66a0e664829f9ddab177aa8460974291f before candidate checkout, verifying source run 33211526093 and its artifact identities. The original source artifacts have since expired. | Architecture | Retained artifact ID | Exact ZIP SHA-256 | |---|---|---| | AMD64 | 11019217721 | 2a07674b09da33f24772936e5fc3e8d7e80dbbe096cc70e9fa32babc9177a006 | | ARM64 | 11019212869 | dba39bc41923592bef67980e4d179c8b6d8442a6157e67621e53abf969072e3a | Each archive contains its source-revision manifest and SHA256SUMS. Both archive digests and every listed file checksum were verified before staging these assets. Components remain Podman 6.1.0, Netavark 2.1.0, Aardvark DNS 2.1.0, and pasta 2026_07_28.f8df3f1. No binaries were rebuilt or executed during recovery.

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When work happens

weekday and hour
SunMonTueWedThuFriSat036912151821Sun 0:00 — 17 commitsSun 1:00 — 26 commitsSun 2:00 — 14 commitsSun 3:00 — 6 commitsSun 4:00 — 6 commitsSun 5:00 — 8 commitsSun 6:00 — 6 commitsSun 7:00 — 4 commitsSun 8:00 — 15 commitsSun 9:00 — 26 commitsSun 10:00 — 24 commitsSun 11:00 — 17 commitsSun 12:00 — 31 commitsSun 13:00 — 34 commitsSun 14:00 — 28 commitsSun 15:00 — 22 commitsSun 16:00 — 23 commitsSun 17:00 — 27 commitsSun 18:00 — 19 commitsSun 19:00 — 42 commitsSun 20:00 — 20 commitsSun 21:00 — 31 commitsSun 22:00 — 17 commitsSun 23:00 — 17 commitsMon 0:00 — 24 commitsMon 1:00 — 6 commitsMon 2:00 — 6 commitsMon 3:00 — 11 commitsMon 4:00 — 6 commitsMon 5:00 — 5 commitsMon 6:00 — 7 commitsMon 7:00 — 17 commitsMon 8:00 — 30 commitsMon 9:00 — 26 commitsMon 10:00 — 35 commitsMon 11:00 — 46 commitsMon 12:00 — 49 commitsMon 13:00 — 55 commitsMon 14:00 — 54 commitsMon 15:00 — 54 commitsMon 16:00 — 42 commitsMon 17:00 — 87 commitsMon 18:00 — 52 commitsMon 19:00 — 44 commitsMon 20:00 — 37 commitsMon 21:00 — 43 commitsMon 22:00 — 45 commitsMon 23:00 — 38 commitsTue 0:00 — 43 commitsTue 1:00 — 39 commitsTue 2:00 — 30 commitsTue 3:00 — 31 commitsTue 4:00 — 27 commitsTue 5:00 — 21 commitsTue 6:00 — 14 commitsTue 7:00 — 16 commitsTue 8:00 — 38 commitsTue 9:00 — 22 commitsTue 10:00 — 52 commitsTue 11:00 — 60 commitsTue 12:00 — 70 commitsTue 13:00 — 83 commitsTue 14:00 — 67 commitsTue 15:00 — 57 commitsTue 16:00 — 48 commitsTue 17:00 — 52 commitsTue 18:00 — 37 commitsTue 19:00 — 51 commitsTue 20:00 — 44 commitsTue 21:00 — 49 commitsTue 22:00 — 44 commitsTue 23:00 — 37 commitsWed 0:00 — 47 commitsWed 1:00 — 34 commitsWed 2:00 — 33 commitsWed 3:00 — 25 commitsWed 4:00 — 19 commitsWed 5:00 — 16 commitsWed 6:00 — 22 commitsWed 7:00 — 24 commitsWed 8:00 — 34 commitsWed 9:00 — 37 commitsWed 10:00 — 38 commitsWed 11:00 — 44 commitsWed 12:00 — 56 commitsWed 13:00 — 64 commitsWed 14:00 — 73 commitsWed 15:00 — 73 commitsWed 16:00 — 53 commitsWed 17:00 — 61 commitsWed 18:00 — 37 commitsWed 19:00 — 60 commitsWed 20:00 — 49 commitsWed 21:00 — 46 commitsWed 22:00 — 44 commitsWed 23:00 — 37 commitsThu 0:00 — 45 commitsThu 1:00 — 47 commitsThu 2:00 — 31 commitsThu 3:00 — 28 commitsThu 4:00 — 22 commitsThu 5:00 — 26 commitsThu 6:00 — 15 commitsThu 7:00 — 22 commitsThu 8:00 — 31 commitsThu 9:00 — 37 commitsThu 10:00 — 44 commitsThu 11:00 — 62 commitsThu 12:00 — 61 commitsThu 13:00 — 55 commitsThu 14:00 — 77 commitsThu 15:00 — 52 commitsThu 16:00 — 57 commitsThu 17:00 — 72 commitsThu 18:00 — 45 commitsThu 19:00 — 28 commitsThu 20:00 — 50 commitsThu 21:00 — 34 commitsThu 22:00 — 40 commitsThu 23:00 — 40 commitsFri 0:00 — 41 commitsFri 1:00 — 38 commitsFri 2:00 — 27 commitsFri 3:00 — 14 commitsFri 4:00 — 22 commitsFri 5:00 — 25 commitsFri 6:00 — 24 commitsFri 7:00 — 26 commitsFri 8:00 — 28 commitsFri 9:00 — 28 commitsFri 10:00 — 54 commitsFri 11:00 — 62 commitsFri 12:00 — 52 commitsFri 13:00 — 58 commitsFri 14:00 — 66 commitsFri 15:00 — 63 commitsFri 16:00 — 43 commitsFri 17:00 — 40 commitsFri 18:00 — 48 commitsFri 19:00 — 37 commitsFri 20:00 — 24 commitsFri 21:00 — 38 commitsFri 22:00 — 45 commitsFri 23:00 — 36 commitsSat 0:00 — 43 commitsSat 1:00 — 32 commitsSat 2:00 — 21 commitsSat 3:00 — 15 commitsSat 4:00 — 14 commitsSat 5:00 — 21 commitsSat 6:00 — 13 commitsSat 7:00 — 23 commitsSat 8:00 — 19 commitsSat 9:00 — 28 commitsSat 10:00 — 34 commitsSat 11:00 — 30 commitsSat 12:00 — 40 commitsSat 13:00 — 29 commitsSat 14:00 — 20 commitsSat 15:00 — 18 commitsSat 16:00 — 36 commitsSat 17:00 — 17 commitsSat 18:00 — 26 commitsSat 19:00 — 34 commitsSat 20:00 — 36 commitsSat 21:00 — 27 commitsSat 22:00 — 26 commitsSat 23:00 — 36 commits
Commit volume by weekday and hour (UTC). Larger dots mean more commits.
DateListRankStars gained
Mar 20, 2026daily#4+368
Mar 19, 2026daily#1+613
Mar 18, 2026daily#3+483
Mar 17, 2026daily#2+532
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