How does it impact application latency?

While it adds a small overhead (typically 100-500ms), many checks like pattern matching are sub-1ms, and the framework is optimized for high-performance deployment on NVIDIA GPUs.

Does this work with any LLM?

Yes, NeMo Guardrails is model-agnostic and can be used as a proxy or wrapper around models from providers like Anthropic, OpenAI, or self-hosted models.

Can I use this for PII masking?

Yes, it integrates directly with tools like Microsoft Presidio to automatically detect and mask sensitive information such as SSNs and emails before they are processed.

What is NeMo Guardrails?

NeMo Guardrails is an open-source framework by NVIDIA that allows developers to add programmable safety, security, and content rails to Large Language Model applications.

Colang is a domain-specific language (DSL) designed specifically for defining the dialog flows, state transitions, and safety rules used within NeMo Guardrails.

NeMo Guardrails Safety

Name: NeMo Guardrails Safety
Author: zechenzhangAGI

byzechenzhangAGI

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384

Data Science & ML

Implements programmable safety rails and validation for LLM applications to prevent jailbreaks, hallucinations, and PII leaks.

About

NeMo Guardrails provides a comprehensive framework for adding programmable safety boundaries to LLM-powered applications using NVIDIA's runtime safety framework. By utilizing the Colang 2.0 DSL, developers can define specific flows for detecting jailbreak attempts, filtering toxic content, verifying factual accuracy, and scrubbing sensitive PII before it reaches or leaves the model. This skill is essential for productionizing AI agents where security, alignment, and reliability are critical requirements, offering high-performance filtering optimized for production environments.

Key Features

Programmable safety rules using Colang 2.0 DSL
Automated fact-checking and hallucination detection
384 GitHub stars
Real-time jailbreak and prompt injection detection
Built-in PII filtering and sensitive data masking
Seamless integration with LlamaGuard and Microsoft Presidio

Use Cases

Ensuring compliance by filtering PII and toxic content in real-time
Securing customer-facing AI chatbots against malicious prompt injection
Preventing LLM hallucinations in data-sensitive enterprise applications

About

Key Features

Programmable safety rules using Colang 2.0 DSL
Automated fact-checking and hallucination detection
384 GitHub stars
Real-time jailbreak and prompt injection detection
Built-in PII filtering and sensitive data masking
Seamless integration with LlamaGuard and Microsoft Presidio

Use Cases

Ensuring compliance by filtering PII and toxic content in real-time
Securing customer-facing AI chatbots against malicious prompt injection
Preventing LLM hallucinations in data-sensitive enterprise applications