# 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. Category scores, facts, verdicts and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/llama-guard-vs-openai-guardrails
- Markdown: https://www.anchorterminal.com/compare/llama-guard-vs-openai-guardrails.md (~2,650 tokens)
- Slim: https://www.anchorterminal.com/compare/llama-guard-vs-openai-guardrails.min.md (~680 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/llama-guard-vs-openai-guardrails.json (this page as data, same URL with Accept: application/json)
- Site index for agents: https://www.anchorterminal.com/llms.txt (full text: https://www.anchorterminal.com/llms-full.txt)
- API: https://www.anchorterminal.com/api/v1/index.json
- Updated: 2026-10-09

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 4: grade D, 49.1/100, rank #713 of 842. Markdown https://www.anchorterminal.com/tools/llama-guard.md · JSON https://www.anchorterminal.com/api/v1/tools/llama-guard.json
- OpenAI Guardrails: grade B, 69.5/100, rank #175 of 842. Markdown https://www.anchorterminal.com/tools/openai-guardrails.md · JSON https://www.anchorterminal.com/api/v1/tools/openai-guardrails.json

## 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

| Category | Weight | Llama Guard 4 | OpenAI Guardrails | Edge |
| --- | --- | --- | --- | --- |
| Reliability | 16% (20 this run) | 38 | 73 | OpenAI Guardrails +35 |
| Performance | 10%, pending | pending | pending | not scored in this run |
| Schema & documentation | 13% (16.2 this run) | 52 | 66 | OpenAI Guardrails +14 |
| Agent ergonomics | 13% (16.2 this run) | 67 | 73 | OpenAI Guardrails +6 |
| Security & auth | 14% (17.5 this run) | 53 | 59 | OpenAI Guardrails +6 |
| Payments & pricing | 10% (12.5 this run) | 45 | 60 | OpenAI Guardrails +15 |
| Task success | 10%, pending | pending | pending | not scored in this run |
| Maintenance & community | 7% (8.8 this run) | 28 | 89 | OpenAI Guardrails +61 |
| Transparency & trust | 7% (8.8 this run) | 55 | 76 | OpenAI Guardrails +21 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **49.1 · D** | **69.5 · B** | |

## Facts side by side

| Fact | Llama Guard 4 | OpenAI Guardrails |
| --- | --- | --- |
| Kind | Model API | Agent framework |
| Vendor | Meta | OpenAI |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | HTTP | HTTP |
| Auth | None | API key |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Llama 4 Community Licence (source-available weights, not an OSI licence), with the Llama 4 acceptable use policy | MIT |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2025-04-29 | 2026-09-10 |
| Terms last updated | 2025-04-05 | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Customer content may train models | not found in the text |  |
| Terms restrict automated access | not found in the text |  |
| Terms restrict benchmarking | not found in the text |  |
| Terms or service can change without notice | not found in the text |  |
| Arbitration or class-action waiver | not found in the text |  |
| Popularity | 4.4k stars | 259 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).


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/llama-guard-vs-openai-guardrails.json, and with the fewest tokens: https://www.anchorterminal.com/compare/llama-guard-vs-openai-guardrails.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "llama-guard", "b": "openai-guardrails"}`. From a terminal: `anchor compare llama-guard openai-guardrails`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/llama-guard.json and https://www.anchorterminal.com/api/v1/tools/openai-guardrails.json

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