> This page is for version v0.22.0.
> For other versions, use one of these documentation indexes:
> - Latest (default): https://docs.nvidia.com/nemo/guardrails/latest/llms.txt
> - v0.24.1: https://docs.nvidia.com/nemo/guardrails/v0.24.1/llms.txt
> - v0.24.0: https://docs.nvidia.com/nemo/guardrails/v0.24.0/llms.txt
> - v0.23.0: https://docs.nvidia.com/nemo/guardrails/v0.23.0/llms.txt
> - v0.22.0: https://docs.nvidia.com/nemo/guardrails/v0.22.0/llms.txt

> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://docs.nvidia.com/nemo/guardrails/llms.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/nemo/guardrails/_mcp/server.

# NVIDIA NeMo Guardrails Library

> Add programmable guardrails to LLM applications with an open-source Python library.

The 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.

## About the NeMo Guardrails Library

Learn about the library and its capabilities in the following sections.

#### [Overview](/nemo/guardrails/v0.22.0/about-nemo-guardrails-library/overview)

Add programmable guardrails to LLM applications with this open-source Python library.

Concept

#### [Guardrail Types](/nemo/guardrails/v0.22.0/about-nemo-guardrails-library/rail-types)

Apply input, retrieval, dialog, execution, and output rails to protect LLM applications.

Concept

#### [How It Works](/nemo/guardrails/v0.22.0/about-nemo-guardrails-library/how-it-works)

High level explanation of how Guardrails works.

Concept

#### [Supported LLMs](/nemo/guardrails/v0.22.0/about-nemo-guardrails-library/supported-llms)

Connect to NVIDIA NIM, OpenAI, Azure, Anthropic, HuggingFace, and LangChain providers.

Reference

## Get Started

Use the following guides to get started with the NeMo Guardrails library and browse the catalog of pre-built guardrails.

#### [Installation](/nemo/guardrails/v0.22.0/get-started/installation-guide)

Install NeMo Guardrails with pip, configure your environment, and verify the installation.

Get Started

#### [Tutorials](/nemo/guardrails/v0.22.0/get-started/tutorials)

Follow hands-on tutorials to deploy Nemotron Content Safety, Nemotron Topic Control, and Nemotron Jailbreak Detect

Tutorial

#### [Guardrail Catalog](/nemo/guardrails/v0.22.0/configure-guardrails/guardrail-catalog)

Reference for pre-built guardrails including content safety, jailbreak detection, topic control, PII handling, agentic security, and third party APIs.

Reference

## Next Steps

Once you've completed the get-started tutorials, explore the following areas to deepen your understanding.

#### [About Configuring Guardrails](/nemo/guardrails/v0.22.0/configure-guardrails/configure-rails)

Configure YAML files, Colang flows, custom actions, and other components to control LLM behavior.

Concept

#### [About Running Guardrailed Inference](/nemo/guardrails/v0.22.0/run-guardrailed-inference/run-rails)

Run guardrailed inference using the Python API or Guardrails API server.

Get Started

#### [Evaluate](/nemo/guardrails/v0.22.0/evaluation/evaluate-guardrails)

Measure accuracy and performance of dialog, fact-checking, moderation, and hallucination rails.

How To

#### [Logging](/nemo/guardrails/v0.22.0/observability/logging)

Debug guardrails with verbose mode, explain method, and generation log options.

How To

#### [Observability](/nemo/guardrails/v0.22.0/observability/observability)

Use OpenTelemetry to trace requests, forward Python logs, and emit metrics for end-to-end visibility into guardrails.

How To

#### [Deployment Options](/nemo/guardrails/v0.22.0/more-deployment-options/deployment)

Deploy guardrails using the local API server, Docker containers, or production microservices.

How To

#### [LangChain Frameworks](/nemo/guardrails/v0.22.0/integration-with-third-party-libraries/langchain)

Integrate NeMo Guardrails with LangChain chains, runnables, and LangGraph workflows.

How To