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AppRisingofficial publisher

NeMo Guardrails

App : A product you use directly

NVIDIA's programmable guardrails to control dialogues, topics, and tool calls.

The most complete solution for conversational guardrails in enterprise settings; demanding to configure.

Overall score
8.5 /10
Security: nothing to flag
Utility
7.5
Reliability
10.0
Security
No caveat on record
Ease
Technical · Technical

Security reflects where data goes, requested permissions and incidents on record at the verification date. It is not a guarantee of zero risk: check the deployment mode and the connections you enable.

What it does, concretely

Conversational applications built on language models can produce off-topic, inaccurate, or potentially harmful responses. NeMo Guardrails addresses this by letting developers insert a programmable control layer between their application code and the language model.

In practice, the tool relies on a dedicated configuration language called Colang, with a Python-like syntax. Developers define rules in text files: which topics are allowed or forbidden, which dialogue paths to follow, and how to react to specific user inputs. These rules apply at five distinct checkpoints: user input, dialogue, retrieved chunks (RAG), tool calls, and model output.

NeMo Guardrails is aimed at technical teams building conversational assistants, LLM endpoints, or enterprise RAG pipelines who need verifiable behavioural guarantees. It is compatible with several language models, including GPT-3.5, GPT-4, LLaMA-2, Falcon, and Mosaic, as well as with LangChain.

It is not suited for use without Python development skills: configuration requires writing code and Colang files, managing a Python 3.10 to 3.13 environment, and understanding one's LLM application architecture. Non-technical users will find no graphical interface for setting up rules.

What it offers

  • ·Input rails: filter, reject, or transform the user message before any further processing.
  • ·Dialog rails: steer the conversational path through Colang-defined flows, with predefined responses or conditional model invocation.
  • ·Retrieval rails (RAG): filter or modify retrieved chunks before they are passed to the model.
  • ·Execution rails: control the inputs and outputs of custom actions (tools) called by the model.
  • ·Output rails: fact-checking, hallucination detection, and content moderation of generated responses before they reach the user.
  • ·Built-in guardrails library: jailbreak and prompt injection detection, sensitive data masking, NVIDIA safety models, and third-party integrations.
  • ·HTTP server exposing an API compatible with the OpenAI Chat Completions format, deployable via CLI or Docker.
  • ·Compatibility with multiple language models and optional integration with LangChain.

In practice

Price
Free
Commercial use
Commercial use: yes
Available on
cli, docker
Licence
Apache-2.0

Security and data

Rating
Security: nothing to flag
Your data
Data stays local
Maintenance
Active
Last release
16 September 2026

Links and repository checked on 25 October 2026 · added on 1 April 2023

How to install NeMo Guardrails

1. Verify that Python 3.10, 3.11, 3.12, or 3.13 is installed on your machine. 2. Install the library via pip: pip install nemoguardrails. 3. Create a configuration folder containing at minimum a config.yml file specifying the LLM model to use. 4. Add your Colang files (.co) to define dialogue and filtering rules. 5. Test your configuration locally with nemoguardrails chat before any deployment. 6. For server deployment, run nemoguardrails server --config PATH/TO/CONFIGS or use the Dockerfile provided in the repository; never commit API keys inside versioned configuration files.
pip install nemoguardrails

In the same category

Whole category Frame (methods, guardrails) →