# NeMo Guardrails

**Category:** Enterprise Security & Guardrails
**Website:** https://github.com/NVIDIA/NeMo-Guardrails
**GitHub:** https://github.com/NVIDIA/NeMo-Guardrails
**Pricing:** Open Source
**Listing status:** verified
**Canonical:** https://aicompliance.ai/tools/nemo-guardrails

**Compliance badges:** OWASP LLM Top 10
**Target frameworks:** NIST AI RMF

## Summary

NVIDIA's open-source toolkit for programmable conversational rails and safety policies inside LLM applications.

## TL;DR

- The most capable open-source option for conversational guardrails — topic control across multi-turn dialogs, not just per-message filtering.
- Colang DSL is a real learning curve; budget engineering time before committing.
- Application-embedded by design: pair with a gateway layer for enforcement consistency across multiple apps.
- Natural fit for LangChain/LangGraph agent stacks and NVIDIA-ecosystem deployments.

## Overview

NeMo Guardrails is what you adopt when you want rails as code. Colang — its dialog DSL — has a learning curve that pays off precisely when your safety requirements are conversational rather than per-message: topic boundaries that hold across turns, flows that route sensitive requests to humans, rails that call external moderation services mid-dialog.

The honest trade-off: each application owns its rail logic. That is power for a single product and drift for a portfolio — which is why enterprises typically pair NeMo inside the app with a gateway or firewall in front of all apps for cross-cutting enforcement.

## Description

NeMo Guardrails is NVIDIA's Apache 2.0 toolkit for adding programmable safety rails to LLM applications. Policies are written in Colang, a domain-specific language that defines topical boundaries, dialog flows, and safety checks across five pipeline stages, with bidirectional screening of both inputs and outputs.

- Colang DSL for declarative rail definitions
- Jailbreak detection and content-safety checks
- Native LangChain, LangGraph, and LlamaIndex integrations
- Custom Python actions for arbitrary validation logic
- Vendor-neutral: works with local and hosted models

## Core features

- Colang DSL for declarative rail definitions
- Jailbreak detection and content-safety checks
- Native LangChain, LangGraph, and LlamaIndex integrations
- Custom Python actions for arbitrary validation logic
- Vendor-neutral: works with local and hosted models

## FAQ

### NeMo Guardrails vs Guardrails AI — they sound identical. What is the actual difference?

Different problems. NeMo governs conversation: what topics are allowed, how dialogs flow, when to refuse or escalate. Guardrails AI validates output: does the response match the schema, is the JSON well-formed, does it pass quality checks. Structured-output apps want Guardrails AI; conversational agents want NeMo; some products legitimately run both.

### Do I need NVIDIA hardware to use it?

No. It is a Python toolkit that works with hosted APIs and local models regardless of hardware. NVIDIA's inference stack integration is an option, not a requirement.

### Can NeMo Guardrails stop prompt injection on its own?

It ships jailbreak and injection checks, but they are heuristic. Rails define policy; detection quality depends on the models behind the checks. Layer it with a dedicated detector (Lakera, LLM Guard scanners) for adversarial robustness rather than treating rails as the whole defense.


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Canonical HTML page: https://aicompliance.ai/tools/nemo-guardrails
Source: AI Compliance Index (https://aicompliance.ai)