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Head to head · Guard moderation · October 2026 research run

Llama Guard 4 vs OpenAI Guardrails

OpenAI Guardrails scores 69.5 (B) on agent readiness against Llama Guard 4's 49.1 (D), and leads in every scored category. Both do guard moderation.

Llama Guard 4Meta · Model API D49.1/100#712 of 841 OpenAI GuardrailsOpenAI · Agent framework B69.5/100#175 of 841

Which one, for what

Llama Guard 4 D

Good for A team outside the EU with a GPU that wants content moderation of text and images on its own hardware, against a fixed 14-category policy it can edit in the prompt.

Also in its favour

  • No key needed to call it

Watch for

Weights last changed on 29 April 2025, with no changelog, version tags or stated deprecation policy

OpenAI Guardrails B

Good for Teams already on the OpenAI client or Agents SDK that want several checks from one config file with little code.

Ahead on

  • Reliability, 73 against 38
  • Schema & documentation, 66 against 52
  • Agent ergonomics, 73 against 67
  • Security & auth, 59 against 53
  • Payments & pricing, 60 against 45
  • Maintenance & community, 89 against 28
  • Transparency & trust, 76 against 55

Also in its favour

  • Open source

Watch for

By default a check that fails to run returns tripwire_triggered=False, so the request continues. Strict mode is opt-in

Score by category

CategoryWeight this runLlama Guard 4OpenAI GuardrailsEdge
Reliability16%203873OpenAI Guardrails +35
Performance10%pendingpendingpendingnot scored in this run
Schema & documentation13%16.25266OpenAI Guardrails +14
Agent ergonomics13%16.26773OpenAI Guardrails +6
Security & auth14%17.55359OpenAI Guardrails +6
Payments & pricing10%12.54560OpenAI Guardrails +15
Task success10%pendingpendingpendingnot scored in this run
Maintenance & community7%8.82889OpenAI Guardrails +61
Transparency & trust7%8.85576OpenAI Guardrails +21
Negative events≤1500
Total49.1 · D69.5 · B

Facts side by side

FactLlama Guard 4OpenAI Guardrails
KindModel APIAgent framework
VendorMetaOpenAI
Hosted endpointno (local only)no (local only)
TransportsHTTPHTTP
AuthNoneAPI key
PricingFreeFree
x402nono
LicenceLlama 4 Community Licence (source-available weights, not an OSI licence), with the Llama 4 acceptable use policyMIT
Read-only variant documentednono
llms.txtnono
Last release2025-04-292026-09-10
Terms last updated2025-04-05no document linked
Privacy policy last updatedno document linkedno document linked
Customer content may train modelsnot found in the text
Terms restrict automated accessnot found in the text
Terms restrict benchmarkingnot found in the text
Terms or service can change without noticenot found in the text
Arbitration or class-action waivernot found in the text
Popularity4.4k stars259 stars, 19k npm/wk, 105k PyPI/wk

Verdicts

Llama Guard 4

A single self-hosted model classifies text and multi-image prompts against 14 MLCommons-aligned hazard categories and answers in a few tokens. The weights have not changed since 29 April 2025, download access needs Meta's manual approval, and the licence withholds the grant from individuals and companies based in the European Union.

OpenAI Guardrails

MIT-licensed wrapper that adds twelve configurable checks to OpenAI client calls from one JSON file, with tool-level injection checks for the Agents SDK. The README labels it a preview at version 0.3.3, and by default a check that fails to run is reported as passed unless raise_guardrail_errors=True is set.

Before you call either

Llama Guard 4

  1. Request access on the Hugging Face page before anything else. Approval is manual, and the form cannot be edited after submission
  2. Send only the user turn to check an input, and the user turn plus the model's answer to check an output. The template picks the role from the message count
  3. Parse the first line for safe or unsafe and the second for category codes. Set max_new_tokens to about 10 and turn sampling off
  4. Do not send an image with no text. Meta says the model is not an image-only classifier, and S14 is skipped when an image is present
  5. Pair it with a prompt-attack detector. The card says the model can itself be moved by adversarial or injected text

OpenAI Guardrails

  1. Pass raise_guardrail_errors=True to the client. The default treats a check that failed to run as passed
  2. Run python -m spacy download en_core_web_sm before using Contains PII, or client initialisation fails
  3. Catch GuardrailTripwireTriggered, and append a user message to history only after the call returns without it
  4. Use block=true for Contains PII in the output stage. Masking works only in the pre-flight stage
  5. Keep stream=False where output must be checked before it is shown, and budget one extra model call per LLM-based check

Questions

Which is better for AI agents, Llama Guard 4 or OpenAI Guardrails?

OpenAI Guardrails scores 69.5 (B) on agent readiness against Llama Guard 4's 49.1 (D), and leads in every scored category.

Can an agent call Llama Guard 4 and OpenAI Guardrails without installing anything?

No hosted endpoint is listed for Llama Guard 4. No hosted endpoint is listed for OpenAI Guardrails.

Are Llama Guard 4 and OpenAI Guardrails open source?

No open-source release is listed for Llama Guard 4. OpenAI Guardrails is open source (MIT).

Other comparisons with Llama Guard 4 or OpenAI Guardrails

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