Overview of the NeMo Guardrails Library

The NVIDIA NeMo Guardrails library is an open-source Python package for adding programmable guardrails to LLM-based applications. It intercepts inputs and outputs, applies configurable safety checks, and blocks or modifies content based on defined policies.

        %%{init: {'theme': 'neutral', 'themeVariables': { 'background': 'transparent' }}}%%

flowchart TB
  A("Application Code")
  B("NeMo Guardrails Library")
  C("Large Language Model (LLM)")

  A <--> B

subgraph NemoGuard["NemoGuard NIMs"]
  direction TB
  D("NemoGuard Content Safety")
  E("NemoGuard Topic Control")
  F("NemoGuard Jailbreak Detection")
end

  B <--> NemoGuard
  NemoGuard <--> C

  style A fill:#d8d8e8,stroke:#999
  style B fill:#f0f7e6,stroke:#76b900,stroke-width:2px
  style C fill:#d8d8e8,stroke:#999
  style D fill:#f0f7e6,stroke:#76b900
  style E fill:#f0f7e6,stroke:#76b900
  style F fill:#f0f7e6,stroke:#76b900
    

Application code interacting with LLMs through the NeMo Guardrails Library.

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What You Can Do with the NeMo Guardrails Library

The following are the top use cases of the NeMo Guardrails Library that you can apply to protect your LLM applications.

Content Safety - Text

Deploy Nemotron Content Safety to detect harmful content in text inputs and outputs.

Content Safety (Text) with NeMo Content Safety

Content Safety - Multimodal

Add safety checks to images and text using vision models as LLM-as-a-judge.

Multimodal Content Safety with Vision Models as LLM-as-a-judge

Jailbreak Detection

Deploy NemoGuard Jailbreak Detect to block adversarial prompts.

Jailbreak Detection with NeMo Jailbreak Detect

Topic Control

Deploy NemoGuard Topic Control to restrict conversations to allowed topics.

Topic Control with NeMo Topic Control

PII Handling

Identify and mask personally identifiable information (PII) in inputs and outputs using regex patterns, Presidio integration, or custom detection logic.

Knowledge Base / RAG

In RAG scenarios, verify LLM responses against retrieved source documents to detect unsupported claims or hallucinations.

Agentic Workflows

Apply execution rails to LLM agents that perform multi-step reasoning or interact with external systems. Validate agent decisions, restrict allowed actions, and enforce policies before execution proceeds.

Tool Integration

Validate inputs and outputs when the LLM calls external tools or APIs. Execution rails intercept tool calls to check parameters, sanitize inputs, and filter responses before returning results to the LLM.

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Tools

The following are the tools you can use to interact with the NeMo Guardrails Library.

Python SDK

from nemoguardrails import LLMRails, RailsConfig

config = RailsConfig.from_path("./config")
rails = LLMRails(config)

response = rails.generate(
    messages=[{"role": "user", "content": "Hello!"}]
)

The generate method accepts the same message format as the OpenAI Chat Completions API.

CLI Server

nemoguardrails server --config ./config --port 8000

The server exposes an HTTP API compatible with OpenAI’s /v1/chat/completions endpoint.

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Library vs Microservice

This documentation covers the open-source NeMo Guardrails Library. The NeMo Guardrails Microservice is a separate product that packages the same core functionality for Kubernetes deployment.

	Library	Microservice
Distribution	PyPI (pip install)	Container image
Deployment	Self-managed	Kubernetes with Helm
Scaling	Application-level	Managed by orchestrator
Configuration	Same YAML/Colang format	Same YAML/Colang format

Configurations are portable between the library and microservice.