> 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.

## Get Started

Copy the starter prompt and paste it into your AI coding agent to help it install the NVIDIA NeMo Guardrails library, choose the right docs, or follow contributor guidance.

Browse the following introductory guides to learn more about the NVIDIA NeMo Guardrails library.

Overview of the NVIDIA NeMo Guardrails library, including its capabilities, architecture, and supported LLMs.

Concept

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

Get Started

Connect Cursor, Claude Code, Codex, or another AI agent to the documentation MCP server or starter prompt.

Get Started

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

Tutorial

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.

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

Concept

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

Get Started

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

How To

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

How To

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

How To

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

How To

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

How To