AI Defense Stack

Comparison: Guardrails

LLM Guard vs NeMo Guardrails: status and alternatives

LLM Guard's README says the project is archived and no longer maintained, so teams that screen LLM traffic with it will get no fixes or new scanners from the project. NeMo Guardrails is a free, open-source Python toolkit whose repository is not archived. This page compares what each documents and what a move from one to the other involves.

  • Last reviewed

Executive summary

What it is
LLM Guard and NeMo Guardrails are free, open-source Python toolkits that screen prompts and responses. LLM Guard is archived and unmaintained; NeMo Guardrails is not archived and builds rails around a whole application instead of offering a scanner list.
The risk
An archived screening library gets no fixes for new jailbreaks, bypasses or vulnerable dependencies, so checks that worked when you adopted them weaken over time while still appearing to run in production.
What to buy or do
If you run LLM Guard, plan a migration or fork it and own the maintenance. Moving to NeMo Guardrails means redesigning scanners as input, output and execution rails or Python actions, then checking detection again on your own traffic.

Which should you choose?

Our analysis, built from the facts in the table below.

Choose LLM Guard if

Our analysis
  • You already run LLM Guard and need time to plan a move; the code stays readable on GitHub.
  • You want a ready-made list of input and output scanners for prompts and responses, and you accept that nobody maintains them.
  • You are willing to fork the MIT-licensed code and maintain it yourself.

See LLM Guard.

Choose NeMo Guardrails if

Our analysis
  • You want an open-source toolkit whose repository is not archived and whose README points to current development.
  • You want rails around a whole application: input, retrieval, dialog, execution and output.
  • You want to write dialog rules in Colang and custom checks as Python actions, configured in YAML.

See NeMo Guardrails.

Side by side

Every cell links to the page that states it, with the date we checked it. We compile from public pages and repositories and do not run either product. Coverage Score is computed within one category from sourced facts (how it works).

LLM Guard compared with NeMo Guardrails: each row shows the fact for both tools with its source
Attribute LLM Guard NeMo Guardrails
Vendor Palo Alto Networks (Protect AI) Source, checked October 21, 2026: paloaltonetworks.com (opens in a new tab) NVIDIA Source, checked October 21, 2026: github.com (opens in a new tab)
Status Archived Source, checked October 21, 2026: github.com (opens in a new tab) Active Source, checked October 21, 2026: api.github.com (opens in a new tab)
Ownership Palo Alto Networks announced on July 22, 2025 that it completed its acquisition of Protect AI. Source, checked October 21, 2026: paloaltonetworks.com (opens in a new tab) No ownership change documented in the sources we checked
Open source and license Open source, MIT Source, checked October 21, 2026: api.github.com (opens in a new tab) Open source, Apache-2.0 Source, checked October 21, 2026: github.com (opens in a new tab)
GitHub stars 3,217 GitHub stars as of Oct 21, 2026; last push Jul 8, 2026 archived Source, checked October 21, 2026: api.github.com (opens in a new tab) 7,341 GitHub stars as of Oct 21, 2026; last push Oct 22, 2026 Source, checked October 21, 2026: api.github.com (opens in a new tab)
Latest release Not documented in the sources we checked September 16, 2026 Source, checked October 21, 2026: api.github.com (opens in a new tab)
Deployment Library, Self-hosted, API Source, checked October 21, 2026: github.com (opens in a new tab) Library, Self-hosted, API Source, checked October 21, 2026: docs.nvidia.com (opens in a new tab)
Pricing model Free Source, checked October 21, 2026: github.com (opens in a new tab) Free Source, checked October 21, 2026: github.com (opens in a new tab)
OWASP LLM risks Our analysis
  • LLM01 Prompt Injection
  • LLM02 Sensitive Information Disclosure
  • LLM05 Improper Output Handling
  • LLM10 Unbounded Consumption
  • LLM01 Prompt Injection
  • LLM02 Sensitive Information Disclosure
  • LLM05 Improper Output Handling
  • LLM06 Excessive Agency
Documented capabilities
  • Scans prompts for prompt injection with an input scanner
  • Anonymizes personal data and detects secrets in prompts, and restores values in output with a Deanonymize scanner
  • Flags toxic and biased text in prompts and responses
  • Blocks banned topics with a Ban Topics scanner
  • Checks model output for malicious URLs
  • Validates model output with JSON and regex scanners
  • Caps prompt length with a Token Limit input scanner
Source, checked October 21, 2026: protectai.github.io (opens in a new tab)
  • Detects jailbreak and adversarial prompts using self-check, heuristic patterns, NemoGuard or third-party services
  • Checks inputs and outputs for harmful content
  • Keeps conversations within defined subject boundaries
  • Detects and masks personal data in inputs, outputs and retrieved content
  • Validates tool inputs and outputs around tool calls (execution rails)
  • Checks model responses before they reach the user (output rails)
  • Controls conversation flow with dialog rules written in the Colang modeling language
Source, checked October 21, 2026: docs.nvidia.com (opens in a new tab)
Integrations Not documented in the sources we checked 4 listed
  • OpenAI models
  • LangChain (opt-in integration)
  • LangGraph (agent workflow integration)
  • Presidio-based sensitive data detection
Source, checked October 21, 2026: github.com (opens in a new tab) Source, checked October 21, 2026: docs.nvidia.com (opens in a new tab) Source, checked October 21, 2026: docs.nvidia.com (opens in a new tab)
Documentation
  • Documentation site
  • Quickstart
Source, checked October 21, 2026: protectai.github.io (opens in a new tab) Source, checked October 21, 2026: protectai.github.io (opens in a new tab)
  • Documentation site
  • Quickstart
Source, checked October 21, 2026: docs.nvidia.com (opens in a new tab) Source, checked October 21, 2026: docs.nvidia.com (opens in a new tab)
Status in the sources we read README warning headed THIS PROJECT HAS BEEN ARCHIVED: no longer under active development or maintained, models on Hugging Face included Source, checked October 21, 2026: github.com (opens in a new tab) No archived or deprecated notice on the README, which says the develop branch tracks current top-of-tree development Source, checked October 21, 2026: github.com (opens in a new tab)
What it screens Input scanners such as Anonymize, Prompt Injection, Secrets, Toxicity and Token Limit, and output scanners such as Malicious URLs, Bias, JSON and Relevance Source, checked October 21, 2026: protectai.github.io (opens in a new tab) Input, retrieval, dialog, execution and output rails, which run at different stages of an LLM interaction Source, checked October 21, 2026: docs.nvidia.com (opens in a new tab)
How you install and run it pip install llm-guard, with an API deployment option linked from the README Source, checked October 21, 2026: github.com (opens in a new tab) pip install nemoguardrails on Python 3.10 to 3.13, then the Guardrails API server or Docker Source, checked October 21, 2026: docs.nvidia.com (opens in a new tab)
How you write your own checks Regex, Ban Substrings, Ban Topics and Ban Competitors scanners, among the listed scanners Source, checked October 21, 2026: protectai.github.io (opens in a new tab) YAML configuration, Colang for flows and guardrail logic, and Python custom actions Source, checked October 21, 2026: docs.nvidia.com (opens in a new tab)
Coverage Score 66 out of 100 Computed from sourced facts. What this measures 76 out of 100 Computed from sourced facts. What this measures
Where each is stronger Our analysis
  • Documents a long list of ready-made scanners, including secrets, gibberish, invisible text, language, relevance, malicious URLs and token limits.
  • Documents anonymizing personal data in prompts and restoring it in output with a Deanonymize scanner; we did not find a restore step in the NeMo Guardrails sources we checked.
  • Is not archived, while the LLM Guard README says the project is no longer under active development or maintained.
  • Documents execution rails that check tool inputs and outputs, and LangGraph support for multi-agent safety through the LangChain opt-in.
  • Documents more ways to run: a library, a Guardrails API server and a Docker option.

Verdict

Our analysis

Our analysis

LLM Guard's own README says the project is archived and no longer maintained, which makes maintenance risk the deciding factor. If you run it today, plan a move, or fork it and own the maintenance yourself. NeMo Guardrails is also free and open source, and its repository is not archived. It has a different shape, with rails around a whole application instead of a list of scanners, so a move means redesigning checks as rails or Python actions instead of swapping one scanner for another. We compare documented features only and did not measure detection quality, so test any replacement on your own traffic.

LLM Guard vs NeMo Guardrails: FAQ

Is LLM Guard still maintained?

No. The README warns that the project is archived and that it and its associated models on Hugging Face are no longer under active development or maintained, and the GitHub repository is archived.

Can I keep using LLM Guard?

It is MIT licensed, so you can keep using or fork the code. Because the README says it is no longer maintained, fixes and new scanners will not come from the project, so weigh that before you rely on it.

What can replace LLM Guard?

NeMo Guardrails is an open-source option that is not archived, with rails for input, retrieval, dialog, execution and output. It is built differently from LLM Guard, so plan to redesign your checks. Our guardrails category lists other options, including hosted services.

Does NeMo Guardrails have scanners like LLM Guard?

Its documentation lists rails and key features rather than a scanner list: content safety, jailbreak protection, topic control, personal data detection and checks on tool calls. You configure them in YAML, Colang and Python custom actions.

Key facts you can cite

Keep exploring

Sources & last reviewed

Last reviewed . We compile from the vendors' own public pages and repositories and we do not run the products. Source links open in a new tab. Found a mistake? Report a correction.

Drafted with AI assistance; facts checked against primary sources on October 21, 2026.

LLM Guard (5 sources)
NVIDIA NeMo Guardrails (7 sources)
Comparison rows (8 sources)