nemo-guardrails

NVIDIA's runtime safety framework for LLM applications. Features jailbreak detection, input/output validation, fact-checking, hallucination detection, PII filtering, toxicity detection. Uses Colang 2.0 DSL for programmable rails. Production-ready, runs on T4 GPU.

by Orchestra-Research 12k GitHub stars MIT Updated Jun 16, 2026
nemo-guardrails skill by Orchestra-Research
nemo-guardrails — Backend skill by Orchestra-Research

Overview

nemo-guardrails is an open-source backend skill for Claude Code and compatible agents, published by Orchestra-Research. Its author describes it as: “NVIDIA's runtime safety framework for LLM applications. Features jailbreak detection, input/output validation, fact-checking, hallucination detection, PII filtering, toxicity detection. Uses Colang 2.0 DSL for program…”. The project has 12k stars on GitHub and is available under the MIT license. Add it to your setup with `/plugin marketplace add Orchestra-Research/AI-Research-SKILLs`.

What nemo-guardrails does

NeMo Guardrails adds programmable safety rails to LLM applications at runtime.

What’s inside

The SKILL.md for nemo-guardrails is organised into these sections:

  • Quick start
  • Common workflows
  • Workflow 1: Jailbreak detection
  • Workflow 2: Self-check input/output
  • Workflow 3: Fact-checking with retrieval
  • Workflow 4: PII detection with Presidio
  • Workflow 5: LlamaGuard integration
  • When to use vs alternatives
  • Common issues
  • Advanced topics
  • Hardware requirements
  • Resources

Installation

Add nemo-guardrails to your agent with:

/plugin marketplace add Orchestra-Research/AI-Research-SKILLs

Review a skill’s source before installing it — skills can instruct your agent to run commands. This command comes from the skill’s public repository, which is the source of truth for setup steps. New to skills? Read how to install Claude Code skills.

Supported agents

  • Claude Code — ships a claude code plugin marketplace
  • Codex — declared in the repository topics

License

nemo-guardrails is published under the MIT license.

Strengths

  • Clear MIT license — safe to read and adapt within its terms
  • Ships in Orchestra-Research/AI-Research-SKILLs, an established project with 11,807 GitHub stars
  • Works with Claude Code, Codex

Author

Orchestra-Research 41 skills in the catalog · GitHub profile ↗
View skills

Topics

Need a skill like this, tailored to your team?Build a custom skill →

Frequently asked questions

What does nemo-guardrails do?
NVIDIA's runtime safety framework for LLM applications. Features jailbreak detection, input/output validation, fact-checking, hallucination detection, PII filtering, toxicity detection. Uses Colang 2.0 DSL for programmable rails. Production-ready, runs on T4 GPU.
How do I install nemo-guardrails?
Run /plugin marketplace add Orchestra-Research/AI-Research-SKILLs in your agent, then reload your skills. Review the source at https://github.com/Orchestra-Research/AI-Research-SKILLs before installing.
Is nemo-guardrails free to use?
Yes. nemo-guardrails is free and open source under the MIT license, so you can read, run, and adapt it within that license's terms.
Which agents does nemo-guardrails work with?
nemo-guardrails lists Claude Code and Codex — named in its SKILL.md, its plugin marketplace or its repository topics. Skills use the portable SKILL.md format, so other agents that read that format may load it too.
Where does nemo-guardrails come from?
nemo-guardrails ships inside Orchestra-Research/AI-Research-SKILLs, a repository that contains 41 catalogued skills in total. The repository's 11,807 GitHub stars apply to that whole collection, not to this skill on its own.

Meta's 7-8B specialized moderation model for LLM input/output filtering. 6 safety categories - violence/hate, sexual content, weapons, substances, self-harm, criminal planning. 94-95% accuracy. Deploy with vLLM, HuggingFace, Sagemaker. Integrates with NeMo Guardrails.

Claude CodeCodex
12k GitHub stars MIT Updated Jun 16, 2026 Free

Fast tokenizers optimized for research and production. Rust-based implementation tokenizes 1GB in <20 seconds. Supports BPE, WordPiece, and Unigram algorithms. Train custom vocabularies, track alignments, handle padding/truncation. Integrates seamlessly with transformers. Use when you need high-performance tokenization or custom tokenizer training.

Claude CodeCodex
12k GitHub stars MIT Updated Jun 16, 2026 Free

Simplest distributed training API. 4 lines to add distributed support to any PyTorch script. Unified API for DeepSpeed/FSDP/Megatron/DDP. Automatic device placement, mixed precision (FP16/BF16/FP8). Interactive config, single launch command. HuggingFace ecosystem standard.

Claude CodeCodex
12k GitHub stars MIT Updated Jun 16, 2026 Free

High-level PyTorch framework with Trainer class, automatic distributed training (DDP/FSDP/DeepSpeed), callbacks system, and minimal boilerplate. Scales from laptop to supercomputer with same code. Use when you want clean training loops with built-in best practices.

Claude CodeCodex
12k GitHub stars MIT Updated Jun 16, 2026 Free

Autonomous research review loop using any OpenAI-compatible LLM API. Configure via llm-chat MCP server or environment variables. Trigger with "auto review loop llm" or "llm review".

Claude CodeCodex
15k GitHub stars MIT Updated Aug 18, 2026 Free