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NVIDIA-NeMo

Guardrails

NVIDIA's programmable guardrails for LLM apps: input, output, dialog and retrieval rails defined in Colang, wrapping any model or LangChain runnable.

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For when policy needs to be programmable, not a blocklist: topic bans, jailbreak checks, tool-use constraints and dialog flows written in Colang, enforced as input/output/dialog/retrieval rails around any LLM — RunnableRails drops it straight into a LangChain pipeline. NOT free at runtime: every rail is extra LLM calls and latency, Colang is its own language to learn, and rails mitigate rather than guarantee — you still red-team the result (that's garak's job).

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README.md

NVIDIA NeMo Guardrails Library

License PyPI PyPI - Python Version Tests/Linux Tests/Windows Tests/macOS Lint Code style: black Documentation arXiv Downloads Downloads

LATEST RELEASE / DEVELOPMENT VERSION: The develop branch tracks the latest top of tree development. The latest released version is 0.23.0.

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📌 The official NeMo Guardrails library documentation is available at docs.nvidia.com/nemo/guardrails.

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NVIDIA NeMo Guardrails library is an open-source toolkit for easily adding programmable guardrails to LLM-based conversational applications. Guardrails (or "rails" for short) are specific ways of controlling the output of a large language model, such as not talking about politics, responding in a particular way to specific user requests, following a predefined dialog path, using a particular language style, extracting structured data, and more.

This paper introduces the NeMo Guardrails library and contains a technical overview of the system and the current evaluation.

Requirements

Python 3.10, 3.11, 3.12 or 3.13.

Installation

To install using pip:

> pip install nemoguardrails

For more detailed instructions, see the Installation Guide.

Overview

The NeMo Guardrails library enables developers building LLM-based applications to add programmable guardrails between the application code and the LLM.

Programmable Guardrails

Key benefits of adding programmable guardrails include:

  • Building Trustworthy, Safe, and Secure LLM-based Applications: you can define rails to guide and safeguard conversations; you can choose to define the behavior of your LLM-based application on specific topics and prevent it from engaging in discussions on unwanted topics.

  • Connecting models, chains, and other services securely: you can connect an LLM to other services (a.k.a. tools) seamlessly and securely.

  • Controllable dialog: you can steer the LLM t

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