> 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.

# Use the NeMo Guardrails Python APIs

> Run guardrailed inference using the NeMo Guardrails Python API.

This section covers how to use the NeMo Guardrails library Python API to run guardrailed inference and integrate the guardrails into your application.

#### [Overview](/run-guardrailed-inference/using-python-apis/overview)

RailsConfig and LLMRails core classes for generating guarded responses.

Concept

#### [Core Classes](/run-guardrailed-inference/using-python-apis/core-classes)

RailsConfig and LLMRails class reference for loading and running guardrails.

Reference

#### [Generation Options](/run-guardrailed-inference/using-python-apis/generation-options)

Configure logging, LLM parameters, and rail selection for generation.

Reference

#### [Streaming](/run-guardrailed-inference/using-python-apis/streaming)

Stream LLM responses in real-time with the stream\_async method.

Tutorial

#### [Check Messages](/run-guardrailed-inference/using-python-apis/check-messages)

Validate messages against input and output rails using check\_async and check methods.

Reference

#### [Event-Based API](/run-guardrailed-inference/using-python-apis/event-based-api)

Use generate\_events for low-level control over guardrails execution.

Reference