> 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 full documentation content, see https://docs.nvidia.com/nemo/guardrails/llms-full.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/nemo/guardrails/_mcp/server.

# About Configuring Guardrails

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

This section explains how to configure your guardrails system, from defining LLM models and guardrail flows in YAML to implementing advanced features like Colang flows and custom actions.

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## Before You Begin with Configuring Guardrails

Before diving into configuring guardrails, ensure you have the required components ready and understand the overall structure of the guardrails system.

Prepare LLM endpoints, NemoGuard NIMs, and knowledge base documents before configuration.

Get Started

Learn to write config.yml, Colang flows, and custom actions for guardrails.

Concept

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## Core Configuration

Configure the essential components of your guardrails system.

Define models, guardrails, prompts, and tracing settings in the config.yml file.

Reference

Reference for all config.yml options including models, rails, prompts, and advanced settings.

Reference

Reference for pre-built guardrails including content safety, jailbreak detection, and PII handling.

Reference

Learn Colang, the event-driven language for defining guardrails flows and bot behavior.

Concept

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## Advanced Configuration

Optional configurations for extending and optimizing your guardrails system.

Create Python actions to extend guardrails with external APIs and validation logic.

How To

Use config.py to register custom LLM providers, embedding providers, and shared resources at startup.

How To

Additional configuration topics including knowledge base setup and embedding search providers.

Reference

Configure in-memory caching for LLM calls and KV cache reuse to improve performance and reduce latency.

How To

Raise and handle exceptions in guardrails flows to control error behavior and custom responses.

Reference