{ "data": { "a": { "slug": "llama-guard", "name": "Llama Guard 4", "vendor": "Meta", "vendorUrl": "https://dev.meta.ai/llama", "kind": "model", "category": "guardrails", "summary": "Llama Guard 4 is Meta's 12-billion-parameter open-weight safety classifier for text and images. It labels a prompt or a model response safe or unsafe against 14 hazard categories, and the owner runs it on a GPU.", "url": "https://www.anchorterminal.com/tools/llama-guard", "markdownUrl": "https://www.anchorterminal.com/tools/llama-guard.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/llama-guard.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/llama-guard.json", "repo": "https://github.com/meta-llama/PurpleLlama", "license": "Llama 4 Community Licence (source-available weights, not an OSI licence), with the Llama 4 acceptable use policy", "transports": [ "http" ], "packages": [], "auth": "none", "authNotes": "Running the model needs no account or key. Getting the weights does. The Hugging Face repository is gated with manual review by Meta and asks for a legal name, date of birth and organisation, and downloads then use a Hugging Face access token. Meta's own download form emails a signed link after the licence is accepted. A vLLM or SGLang server has whatever authentication the owner adds.", "pricing": "free", "pricingNotes": "Free to download and run under the Llama 4 Community Licence, with the owner's GPU as the cost. Meta sells no hosted version that we found. Third parties do, with DeepInfra at $0.18 per 1M tokens and the same price listed on OpenRouter (checked 2026-10-08).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "No x402, MPP or L402. Llama Guard 4 is a model the owner runs, with no payment route (checked 2026-10-08).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 4423, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-08" }, "docsUrl": "https://dev.meta.ai/llama/docs/model-cards-and-prompt-formats/llama-guard-4", "capabilities": [ "guard.moderation", "guard.policy", "guard.self-host" ], "tags": [ "model", "open-weights", "self-hosted", "local", "free", "gated", "multimodal", "python", "openai-compatible" ], "lastRelease": "2025-04-29", "graded": true, "anchor": { "graded": true, "score": 49.1, "grade": "D", "agentReady": false, "rank": 713, "ranked": true, "rankOf": 842, "categoryRank": 15, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 67, "maintenance": 28, "payments": 45, "reliability": 38, "schema": 52, "security": 53, "transparency": 55 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-08" }, "negative": 0, "verdict": "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.", "bestFor": "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.", "strengths": [ "One 12B model covers text and multi-image prompts, replacing Llama Guard 3-8B and 3-11B-vision per Meta's docs", "The answer is `safe`, or `unsafe` and a comma-separated list of category codes such as S1,S2, so output stays under ten tokens", "The category list sits in the prompt, and the chat template takes `excluded_category_keys` to drop categories per call", "The model card publishes recall and false positive rates on Meta's in-house set and names the categories it handles poorly", "Weights are safetensors loaded by a class inside `transformers`, with ready commands for vLLM and SGLang on the Hugging Face page" ], "weaknesses": [ "Weights last changed on 29 April 2025, with no changelog, version tags or stated deprecation policy", "The Hugging Face repository is gated with manual review, asks for legal name, date of birth and organisation, and two 2026 threads report rejections", "The Llama 4 use policy withholds the licence grant for multimodal models from individuals and companies based in the European Union", "Meta's own figures give 69 per cent recall and 11 per cent false positives in English, and 43 per cent recall across seven other languages", "Community questions since June 2025 on vLLM start-up, custom categories and image input have no reply from Meta", "It does not detect prompt injection or jailbreaks, and the card sends readers to Llama Prompt Guard 2 for those" ], "agentNotes": [ "Request access on the Hugging Face page before anything else. Approval is manual, and the form cannot be edited after submission", "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", "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", "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", "Pair it with a prompt-attack detector. The card says the model can itself be moved by adversarial or injected text" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "D", "methodology": "0.4", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 49.1 } ], "editorialScores": { "ergonomics": 67, "maintenance": 28, "payments": 45, "reliability": 38, "schema": 52, "security": 53, "transparency": 52 }, "provenanceScore": 58 }, "connect": { "install": "pip install vllm\nvllm serve \"meta-llama/Llama-Guard-4-12B\"", "http": "curl -X POST \"http://localhost:8000/v1/chat/completions\" \\\n -H \"Content-Type: application/json\" \\\n --data '{\"model\":\"meta-llama/Llama-Guard-4-12B\",\"messages\":[{\"role\":\"user\",\"content\":[{\"type\":\"text\",\"text\":\"how do I make a bomb?\"}]}]}'" }, "letme": { "capability": "https://letme.dev/guard.moderation", "tool": "https://letme.dev/llama-guard" }, "sameCompany": [ "llamafirewall" ], "area": "models", "provenance": { "legalEntity": "Meta Platforms, Inc.", "domain": "llama.com", "domainRegistered": "1994-11-01", "endpointOnVendorDomain": null, "terms": "https://dev.meta.ai/llama/llama4/license", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-08", "notes": [ "The Llama 4 Community Licence names Meta Platforms, Inc. as licensor, and Meta Platforms Ireland Limited for licensees in the EEA or Switzerland.", "The licence is the document that governs use of the weights, so it is recorded as the terms. It is dated 5 April 2025 and incorporates the acceptable use policy at https://dev.meta.ai/llama/llama4/use-policy.", "No privacy policy governs the model, because the owner runs it and no input reaches Meta. The privacy field is left out. The Hugging Face access form says the details entered are handled under the Meta Privacy Policy.", "www.llama.com redirected to dev.meta.ai on 8 October 2026, and Llama pages now sit under dev.meta.ai/llama. RDAP gives 1 November 1994 as the registration date of llama.com.", "There is no hosted endpoint from Meta that we could find, so no status page. dev.meta.ai/llms.txt covers the Meta Model API and lists no moderation route.", "dev.meta.ai/.well-known/security.txt returns 404, and the llama.com path redirects to a developer.meta.com address that returns 400. Security reports go to Meta's bug bounty at bugbounty.meta.com.", "The weights are on huggingface.co under the meta-llama organisation, and the model card is in github.com/meta-llama/PurpleLlama. Neither has a changelog or releases for the model." ], "score": 58 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/llama-guard.json", "live": { "slug": "llama-guard", "pages": [ { "url": "https://dev.meta.ai/llama/llama4/license", "kind": "terms", "status": 200, "checkedAt": "2026-10-08T18:16:57.76184224Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "c85892d02c88" } ], "updatedAt": "2026-10-08T18:16:57.76184224Z" } }, "answer": "OpenAI Guardrails scores 69.5 (B) on agent readiness against Llama Guard 4's 49.1 (D), and leads in every scored category.", "b": { "slug": "openai-guardrails", "name": "OpenAI Guardrails", "vendor": "OpenAI", "vendorUrl": "https://openai.com", "kind": "framework", "category": "guardrails", "summary": "OpenAI's open-source Python library that wraps the OpenAI client and runs configured checks on inputs, outputs and tool calls, including moderation, jailbreak, prompt injection, personal data, URL and off-topic checks. It is labelled a preview.", "url": "https://www.anchorterminal.com/tools/openai-guardrails", "markdownUrl": "https://www.anchorterminal.com/tools/openai-guardrails.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/openai-guardrails.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/openai-guardrails.json", "repo": "https://github.com/openai/openai-guardrails-python", "license": "MIT", "transports": [ "http" ], "packages": [ { "registry": "pypi", "name": "openai-guardrails" }, { "registry": "npm", "name": "@openai/guardrails" } ], "auth": "api-key", "authNotes": "No credential of its own. The wrapped client takes an OpenAI API key from `OPENAI_API_KEY` or the constructor, an Azure OpenAI key through `GuardrailsAzureOpenAI`, or the key of any OpenAI-compatible endpoint set with `base_url`, such as a local Ollama server (https://openai.github.io/openai-guardrails-python/quickstart/).", "pricing": "free", "pricingNotes": "Free under the MIT licence, with no hosted or paid edition and no account of its own. The README states that Guardrails calls paid OpenAI APIs. Each LLM-based check is one extra model call billed at OpenAI's rates, the moderation check is documented as no cost, and the keyword, URL, secret key and personal data checks run locally (https://github.com/openai/openai-guardrails-python).", "priceSummary": "Free · OSS", "where": "library", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in the README or docs (checked 2026-10-08).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 259, "npmWeekly": 18502, "pypiWeekly": 104530, "asOf": "2026-10-08" }, "docsUrl": "https://openai.github.io/openai-guardrails-python/", "capabilities": [ "guard.injection", "guard.pii", "guard.moderation", "guard.policy", "guard.self-host" ], "tags": [ "official", "framework", "open-source", "self-hosted", "local", "python", "typescript", "free", "preview", "openai-compatible" ], "lastRelease": "2026-09-10", "graded": true, "anchor": { "graded": true, "score": 69.5, "grade": "B", "agentReady": false, "rank": 175, "ranked": true, "rankOf": 842, "categoryRank": 4, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 73, "maintenance": 89, "payments": 60, "reliability": 73, "schema": 66, "security": 59, "transparency": 76 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-08" }, "negative": 0, "verdict": "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.", "bestFor": "Teams already on the OpenAI client or Agents SDK that want several checks from one config file with little code.", "strengths": [ "MIT licence, source on GitHub, and three PyPI releases in the 90 days to 8 October 2026 (0.3.0, 0.3.2, 0.3.3)", "Twelve built-in checks set in one versioned JSON file across pre-flight, input and output stages", "`GuardrailAgent` runs the prompt injection check before and after every tool call in the OpenAI Agents SDK", "CI runs ruff, mypy, pyright and tests on Python 3.11 to 3.14, with CodeQL, Dependabot and SHA-pinned actions", "The jailbreak page publishes ROC AUC, precision, recall and latency per model on a 4,000-conversation sample" ], "weaknesses": [ "By default a check that fails to run returns `tripwire_triggered=False`, so the request continues. Strict mode is opt-in", "The README titles the package a preview, the version is 0.3.3, and no release was published between 15 December 2025 and 21 July 2026", "With `stream=True` the output checks run alongside the stream, and the docs say violating content may appear briefly", "The docs' own table gives the default jailbreak model, `gpt-4.1-mini`, a recall of 0.000 at a 1 per cent false positive rate", "LLM-based checks add a billed model call each. The docs list a median of 1,538 ms for `gpt-4.1-mini` on the jailbreak check", "Pull requests from non-collaborators are not accepted, and CHANGELOG.md starts at 0.3.3" ], "agentNotes": [ "Pass `raise_guardrail_errors=True` to the client. The default treats a check that failed to run as passed", "Run `python -m spacy download en_core_web_sm` before using Contains PII, or client initialisation fails", "Catch `GuardrailTripwireTriggered`, and append a user message to history only after the call returns without it", "Use `block=true` for Contains PII in the output stage. Masking works only in the pre-flight stage", "Keep `stream=False` where output must be checked before it is shown, and budget one extra model call per LLM-based check" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "B", "methodology": "0.4", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 69.5 } ], "editorialScores": { "ergonomics": 73, "maintenance": 89, "payments": 60, "reliability": 73, "schema": 66, "security": 59, "transparency": 65 }, "provenanceScore": 87 }, "connect": { "install": "pip install openai-guardrails\npython -m spacy download en_core_web_sm # only for Contains PII" }, "letme": { "capability": "https://letme.dev/guard.injection", "tool": "https://letme.dev/openai-guardrails" }, "sameCompany": [ "openai-api", "openai-embeddings", "openai-moderation", "openai-image-api", "openai-sora", "openai-agents-sdk", "openai-decisions-api", "openai-codex" ], "area": "models", "provenance": { "legalEntity": "OpenAI (as named in the LICENSE copyright line and the PyPI author field)", "domain": "openai.com", "domainRegistered": "2007-01-19", "domainNote": "A library, not a service. The code is on github.com under the openai organisation, the docs on openai.github.io and the configuration wizard on guardrails.openai.com.", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://github.com/openai/openai-guardrails-python/blob/main/CHANGELOG.md", "securityTxt": "valid", "checked": "2026-10-08", "notes": [ "The library is governed by the MIT licence in the repository. The model and moderation calls it makes are OpenAI API calls on the user's own key, under OpenAI's API terms.", "https://openai.com/policies/services-agreement/ and https://openai.com/policies/privacy-policy/ answered HTTP 403 to us on 8 October 2026, so the terms and privacy fields are left out as unread.", "https://openai.com/.well-known/security.txt is PGP-signed and lists a Bugcrowd contact, a disclosure address and a policy link. It has no Expires line. The copy at cdn.openai.com/security.txt that SECURITY.md links carries an Expires date of 17 January 2024.", "No status page applies to the library. The OpenAI API it calls has one at https://status.openai.com.", "RDAP gives 19 January 2007 as the registration date of openai.com. The repository was created on 9 April 2025 and the first PyPI release is dated 6 October 2025." ], "score": 87 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/openai-guardrails.json" }, "facts": [ { "a": "Model API", "b": "Agent framework", "name": "Kind" }, { "a": "Meta", "b": "OpenAI", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "HTTP", "b": "HTTP", "name": "Transports" }, { "a": "None", "b": "API key", "name": "Auth" }, { "a": "Free", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Llama 4 Community Licence (source-available weights, not an OSI licence), with the Llama 4 acceptable use policy", "b": "MIT", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2025-04-29", "b": "2026-09-10", "name": "Last release" }, { "a": "2025-04-05", "b": "no document linked", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "not found in the text", "b": "", "name": "Customer content may train models" }, { "a": "not found in the text", "b": "", "name": "Terms restrict automated access" }, { "a": "not found in the text", "b": "", "name": "Terms restrict benchmarking" }, { "a": "not found in the text", "b": "", "name": "Terms or service can change without notice" }, { "a": "not found in the text", "b": "", "name": "Arbitration or class-action waiver" }, { "a": "4.4k stars", "b": "259 stars, 19k npm/wk, 105k PyPI/wk", "name": "Popularity" } ], "faq": [ { "answer": "OpenAI Guardrails scores 69.5 (B) on agent readiness against Llama Guard 4's 49.1 (D), and leads in every scored category.", "question": "Which is better for AI agents, Llama Guard 4 or OpenAI Guardrails?" }, { "answer": "No hosted endpoint is listed for Llama Guard 4. No hosted endpoint is listed for OpenAI Guardrails.", "question": "Can an agent call Llama Guard 4 and OpenAI Guardrails without installing anything?" }, { "answer": "No open-source release is listed for Llama Guard 4. OpenAI Guardrails is open source (MIT).", "question": "Are Llama Guard 4 and OpenAI Guardrails open source?" } ], "goodFor": [ { "aheadOn": null, "also": [ "No key needed to call it" ], "goodFor": "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.", "slug": "llama-guard", "watchFor": "Weights last changed on 29 April 2025, with no changelog, version tags or stated deprecation policy" }, { "aheadOn": [ "Reliability, 73 against 38", "Schema \u0026 documentation, 66 against 52", "Agent ergonomics, 73 against 67", "Security \u0026 auth, 59 against 53", "Payments \u0026 pricing, 60 against 45", "Maintenance \u0026 community, 89 against 28", "Transparency \u0026 trust, 76 against 55" ], "also": [ "Open source" ], "goodFor": "Teams already on the OpenAI client or Agents SDK that want several checks from one config file with little code.", "slug": "openai-guardrails", "watchFor": "By default a check that fails to run returns `tripwire_triggered=False`, so the request continues. Strict mode is opt-in" } ], "job": { "capability": "guard.moderation", "name": "Guard moderation" }, "others": [ { "json": "https://www.anchorterminal.com/compare/amazon-bedrock-guardrails-vs-openai-guardrails.json", "title": "Amazon Bedrock Guardrails vs OpenAI Guardrails", "url": "https://www.anchorterminal.com/compare/amazon-bedrock-guardrails-vs-openai-guardrails" }, { "json": "https://www.anchorterminal.com/compare/azure-ai-content-safety-vs-openai-guardrails.json", "title": "Azure AI Content Safety (Prompt Shields) vs OpenAI Guardrails", "url": "https://www.anchorterminal.com/compare/azure-ai-content-safety-vs-openai-guardrails" }, { "json": "https://www.anchorterminal.com/compare/cisco-ai-defense-inspection-vs-openai-guardrails.json", "title": "Cisco AI Defense Inspection API vs OpenAI Guardrails", "url": "https://www.anchorterminal.com/compare/cisco-ai-defense-inspection-vs-openai-guardrails" }, { "json": "https://www.anchorterminal.com/compare/google-model-armor-vs-openai-guardrails.json", "title": "Google Cloud Model Armor vs OpenAI Guardrails", "url": "https://www.anchorterminal.com/compare/google-model-armor-vs-openai-guardrails" }, { "json": "https://www.anchorterminal.com/compare/guardrails-ai-vs-openai-guardrails.json", "title": "Guardrails AI vs OpenAI Guardrails", "url": "https://www.anchorterminal.com/compare/guardrails-ai-vs-openai-guardrails" }, { "json": "https://www.anchorterminal.com/compare/lakera-guard-vs-openai-guardrails.json", "title": "Lakera Guard (Check Point AI Guardrails) vs OpenAI Guardrails", "url": "https://www.anchorterminal.com/compare/lakera-guard-vs-openai-guardrails" }, { "json": "https://www.anchorterminal.com/compare/llamafirewall-vs-openai-guardrails.json", "title": "LlamaFirewall vs OpenAI Guardrails", "url": "https://www.anchorterminal.com/compare/llamafirewall-vs-openai-guardrails" }, { "json": "https://www.anchorterminal.com/compare/nemo-guardrails-vs-openai-guardrails.json", "title": "NVIDIA NeMo Guardrails vs OpenAI Guardrails", "url": "https://www.anchorterminal.com/compare/nemo-guardrails-vs-openai-guardrails" }, { "json": "https://www.anchorterminal.com/compare/openai-guardrails-vs-prisma-airs.json", "title": "OpenAI Guardrails vs Prisma AIRS AI Runtime Security API", "url": "https://www.anchorterminal.com/compare/openai-guardrails-vs-prisma-airs" }, { "json": "https://www.anchorterminal.com/compare/amazon-bedrock-guardrails-vs-llama-guard.json", "title": "Amazon Bedrock Guardrails vs Llama Guard 4", "url": "https://www.anchorterminal.com/compare/amazon-bedrock-guardrails-vs-llama-guard" }, { "json": "https://www.anchorterminal.com/compare/azure-ai-content-safety-vs-llama-guard.json", "title": "Azure AI Content Safety (Prompt Shields) vs Llama Guard 4", "url": "https://www.anchorterminal.com/compare/azure-ai-content-safety-vs-llama-guard" }, { "json": "https://www.anchorterminal.com/compare/cisco-ai-defense-inspection-vs-llama-guard.json", "title": "Cisco AI Defense Inspection API vs Llama Guard 4", "url": "https://www.anchorterminal.com/compare/cisco-ai-defense-inspection-vs-llama-guard" }, { "json": "https://www.anchorterminal.com/compare/google-model-armor-vs-llama-guard.json", "title": "Google Cloud Model Armor vs Llama Guard 4", "url": "https://www.anchorterminal.com/compare/google-model-armor-vs-llama-guard" }, { "json": "https://www.anchorterminal.com/compare/granite-guardian-vs-llama-guard.json", "title": "Granite Guardian vs Llama Guard 4", "url": "https://www.anchorterminal.com/compare/granite-guardian-vs-llama-guard" }, { "json": "https://www.anchorterminal.com/compare/granite-guardian-vs-openai-guardrails.json", "title": "Granite Guardian vs OpenAI Guardrails", "url": "https://www.anchorterminal.com/compare/granite-guardian-vs-openai-guardrails" }, { "json": "https://www.anchorterminal.com/compare/guardrails-ai-vs-llama-guard.json", "title": "Guardrails AI vs Llama Guard 4", "url": "https://www.anchorterminal.com/compare/guardrails-ai-vs-llama-guard" }, { "json": "https://www.anchorterminal.com/compare/lakera-guard-vs-llama-guard.json", "title": "Lakera Guard (Check Point AI Guardrails) vs Llama Guard 4", "url": "https://www.anchorterminal.com/compare/lakera-guard-vs-llama-guard" }, { "json": "https://www.anchorterminal.com/compare/llama-guard-vs-mistral-moderation.json", "title": "Llama Guard 4 vs Mistral Moderation API", "url": "https://www.anchorterminal.com/compare/llama-guard-vs-mistral-moderation" }, { "json": "https://www.anchorterminal.com/compare/llama-guard-vs-nemo-guardrails.json", "title": "Llama Guard 4 vs NVIDIA NeMo Guardrails", "url": "https://www.anchorterminal.com/compare/llama-guard-vs-nemo-guardrails" }, { "json": "https://www.anchorterminal.com/compare/llama-guard-vs-openai-moderation.json", "title": "Llama Guard 4 vs OpenAI Moderation API", "url": "https://www.anchorterminal.com/compare/llama-guard-vs-openai-moderation" }, { "json": "https://www.anchorterminal.com/compare/llama-guard-vs-prisma-airs.json", "title": "Llama Guard 4 vs Prisma AIRS AI Runtime Security API", "url": "https://www.anchorterminal.com/compare/llama-guard-vs-prisma-airs" }, { "json": "https://www.anchorterminal.com/compare/mistral-moderation-vs-openai-guardrails.json", "title": "Mistral Moderation API vs OpenAI Guardrails", "url": "https://www.anchorterminal.com/compare/mistral-moderation-vs-openai-guardrails" }, { "json": "https://www.anchorterminal.com/compare/openai-guardrails-vs-openai-moderation.json", "title": "OpenAI Guardrails vs OpenAI Moderation API", "url": "https://www.anchorterminal.com/compare/openai-guardrails-vs-openai-moderation" }, { "json": "https://www.anchorterminal.com/compare/microsoft-presidio-vs-openai-guardrails.json", "title": "Presidio vs OpenAI Guardrails", "url": "https://www.anchorterminal.com/compare/microsoft-presidio-vs-openai-guardrails" }, { "json": "https://www.anchorterminal.com/compare/llama-guard-vs-microsoft-presidio.json", "title": "Llama Guard 4 vs Presidio", "url": "https://www.anchorterminal.com/compare/llama-guard-vs-microsoft-presidio" }, { "json": "https://www.anchorterminal.com/compare/llama-guard-vs-llamafirewall.json", "title": "Llama Guard 4 vs LlamaFirewall", "url": "https://www.anchorterminal.com/compare/llama-guard-vs-llamafirewall" } ], "scores": [ { "by": 35, "edge": "openai-guardrails", "key": "reliability", "llama-guard": 38, "name": "Reliability", "openai-guardrails": 73, "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 14, "edge": "openai-guardrails", "key": "schema", "llama-guard": 52, "name": "Schema \u0026 documentation", "openai-guardrails": 66, "weight": 13 }, { "by": 6, "edge": "openai-guardrails", "key": "ergonomics", "llama-guard": 67, "name": "Agent ergonomics", "openai-guardrails": 73, "weight": 13 }, { "by": 6, "edge": "openai-guardrails", "key": "security", "llama-guard": 53, "name": "Security \u0026 auth", "openai-guardrails": 59, "weight": 14 }, { "by": 15, "edge": "openai-guardrails", "key": "payments", "llama-guard": 45, "name": "Payments \u0026 pricing", "openai-guardrails": 60, "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 61, "edge": "openai-guardrails", "key": "maintenance", "llama-guard": 28, "name": "Maintenance \u0026 community", "openai-guardrails": 89, "weight": 7 }, { "by": 21, "edge": "openai-guardrails", "key": "transparency", "llama-guard": 55, "name": "Transparency \u0026 trust", "openai-guardrails": 76, "weight": 7 } ], "summary": "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.", "verdicts": { "llama-guard": "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." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/llama-guard-vs-openai-guardrails", "json": "https://www.anchorterminal.com/compare/llama-guard-vs-openai-guardrails.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/llama-guard-vs-openai-guardrails.md", "slim": "https://www.anchorterminal.com/compare/llama-guard-vs-openai-guardrails.min.md" }, "markdown": "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.\n\n- 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\n- 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\n\n## Which one, for what\n\n### Llama Guard 4 (D)\n\nGood 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.\n\nAlso in its favour:\n- No key needed to call it\n\nWatch for: Weights last changed on 29 April 2025, with no changelog, version tags or stated deprecation policy\n\n### OpenAI Guardrails (B)\n\nGood for: Teams already on the OpenAI client or Agents SDK that want several checks from one config file with little code.\n\nAhead on:\n- Reliability, 73 against 38\n- Schema \u0026 documentation, 66 against 52\n- Agent ergonomics, 73 against 67\n- Security \u0026 auth, 59 against 53\n- Payments \u0026 pricing, 60 against 45\n- Maintenance \u0026 community, 89 against 28\n- Transparency \u0026 trust, 76 against 55\n\nAlso in its favour:\n- Open source\n\nWatch for: By default a check that fails to run returns `tripwire_triggered=False`, so the request continues. Strict mode is opt-in\n\n\n## Score by category\n\n| Category | Weight | Llama Guard 4 | OpenAI Guardrails | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 38 | 73 | OpenAI Guardrails +35 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 52 | 66 | OpenAI Guardrails +14 |\n| Agent ergonomics | 13% (16.2 this run) | 67 | 73 | OpenAI Guardrails +6 |\n| Security \u0026 auth | 14% (17.5 this run) | 53 | 59 | OpenAI Guardrails +6 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 45 | 60 | OpenAI Guardrails +15 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 28 | 89 | OpenAI Guardrails +61 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 55 | 76 | OpenAI Guardrails +21 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **49.1 · D** | **69.5 · B** | |\n\n## Facts side by side\n\n| Fact | Llama Guard 4 | OpenAI Guardrails |\n| --- | --- | --- |\n| Kind | Model API | Agent framework |\n| Vendor | Meta | OpenAI |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | HTTP | HTTP |\n| Auth | None | API key |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | Llama 4 Community Licence (source-available weights, not an OSI licence), with the Llama 4 acceptable use policy | MIT |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2025-04-29 | 2026-09-10 |\n| Terms last updated | 2025-04-05 | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models | not found in the text | |\n| Terms restrict automated access | not found in the text | |\n| Terms restrict benchmarking | not found in the text | |\n| Terms or service can change without notice | not found in the text | |\n| Arbitration or class-action waiver | not found in the text | |\n| Popularity | 4.4k stars | 259 stars, 19k npm/wk, 105k PyPI/wk |\n\n## Verdicts\n\n**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.\n\n**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.\n\n## Before you call either\n\n### Llama Guard 4\n\n1. Request access on the Hugging Face page before anything else. Approval is manual, and the form cannot be edited after submission\n2. 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\n3. 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\n4. 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\n5. Pair it with a prompt-attack detector. The card says the model can itself be moved by adversarial or injected text\n\n### OpenAI Guardrails\n\n1. Pass `raise_guardrail_errors=True` to the client. The default treats a check that failed to run as passed\n2. Run `python -m spacy download en_core_web_sm` before using Contains PII, or client initialisation fails\n3. Catch `GuardrailTripwireTriggered`, and append a user message to history only after the call returns without it\n4. Use `block=true` for Contains PII in the output stage. Masking works only in the pre-flight stage\n5. Keep `stream=False` where output must be checked before it is shown, and budget one extra model call per LLM-based check\n\n## Questions\n\n### Which is better for AI agents, Llama Guard 4 or OpenAI Guardrails?\n\nOpenAI Guardrails scores 69.5 (B) on agent readiness against Llama Guard 4's 49.1 (D), and leads in every scored category.\n\n### Can an agent call Llama Guard 4 and OpenAI Guardrails without installing anything?\n\nNo hosted endpoint is listed for Llama Guard 4. No hosted endpoint is listed for OpenAI Guardrails.\n\n### Are Llama Guard 4 and OpenAI Guardrails open source?\n\nNo open-source release is listed for Llama Guard 4. OpenAI Guardrails is open source (MIT).\n\n\n## For agents\n\n- 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\n- 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`\n- 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\n\n## Other comparisons with Llama Guard 4 or OpenAI Guardrails\n\n- [Amazon Bedrock Guardrails vs OpenAI Guardrails](https://www.anchorterminal.com/compare/amazon-bedrock-guardrails-vs-openai-guardrails.md)\n- [Azure AI Content Safety (Prompt Shields) vs OpenAI Guardrails](https://www.anchorterminal.com/compare/azure-ai-content-safety-vs-openai-guardrails.md)\n- [Cisco AI Defense Inspection API vs OpenAI Guardrails](https://www.anchorterminal.com/compare/cisco-ai-defense-inspection-vs-openai-guardrails.md)\n- [Google Cloud Model Armor vs OpenAI Guardrails](https://www.anchorterminal.com/compare/google-model-armor-vs-openai-guardrails.md)\n- [Guardrails AI vs OpenAI Guardrails](https://www.anchorterminal.com/compare/guardrails-ai-vs-openai-guardrails.md)\n- [Lakera Guard (Check Point AI Guardrails) vs OpenAI Guardrails](https://www.anchorterminal.com/compare/lakera-guard-vs-openai-guardrails.md)\n- [LlamaFirewall vs OpenAI Guardrails](https://www.anchorterminal.com/compare/llamafirewall-vs-openai-guardrails.md)\n- [NVIDIA NeMo Guardrails vs OpenAI Guardrails](https://www.anchorterminal.com/compare/nemo-guardrails-vs-openai-guardrails.md)\n- [OpenAI Guardrails vs Prisma AIRS AI Runtime Security API](https://www.anchorterminal.com/compare/openai-guardrails-vs-prisma-airs.md)\n- [Amazon Bedrock Guardrails vs Llama Guard 4](https://www.anchorterminal.com/compare/amazon-bedrock-guardrails-vs-llama-guard.md)\n- [Azure AI Content Safety (Prompt Shields) vs Llama Guard 4](https://www.anchorterminal.com/compare/azure-ai-content-safety-vs-llama-guard.md)\n- [Cisco AI Defense Inspection API vs Llama Guard 4](https://www.anchorterminal.com/compare/cisco-ai-defense-inspection-vs-llama-guard.md)\n- [Google Cloud Model Armor vs Llama Guard 4](https://www.anchorterminal.com/compare/google-model-armor-vs-llama-guard.md)\n- [Granite Guardian vs Llama Guard 4](https://www.anchorterminal.com/compare/granite-guardian-vs-llama-guard.md)\n- [Granite Guardian vs OpenAI Guardrails](https://www.anchorterminal.com/compare/granite-guardian-vs-openai-guardrails.md)\n- [Guardrails AI vs Llama Guard 4](https://www.anchorterminal.com/compare/guardrails-ai-vs-llama-guard.md)\n- [Lakera Guard (Check Point AI Guardrails) vs Llama Guard 4](https://www.anchorterminal.com/compare/lakera-guard-vs-llama-guard.md)\n- [Llama Guard 4 vs Mistral Moderation API](https://www.anchorterminal.com/compare/llama-guard-vs-mistral-moderation.md)\n- [Llama Guard 4 vs NVIDIA NeMo Guardrails](https://www.anchorterminal.com/compare/llama-guard-vs-nemo-guardrails.md)\n- [Llama Guard 4 vs OpenAI Moderation API](https://www.anchorterminal.com/compare/llama-guard-vs-openai-moderation.md)\n- [Llama Guard 4 vs Prisma AIRS AI Runtime Security API](https://www.anchorterminal.com/compare/llama-guard-vs-prisma-airs.md)\n- [Mistral Moderation API vs OpenAI Guardrails](https://www.anchorterminal.com/compare/mistral-moderation-vs-openai-guardrails.md)\n- [OpenAI Guardrails vs OpenAI Moderation API](https://www.anchorterminal.com/compare/openai-guardrails-vs-openai-moderation.md)\n- [Presidio vs OpenAI Guardrails](https://www.anchorterminal.com/compare/microsoft-presidio-vs-openai-guardrails.md)\n- [Llama Guard 4 vs Presidio](https://www.anchorterminal.com/compare/llama-guard-vs-microsoft-presidio.md)\n- [Llama Guard 4 vs LlamaFirewall](https://www.anchorterminal.com/compare/llama-guard-vs-llamafirewall.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-10-09", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.4", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "Llama Guard 4 vs OpenAI Guardrails", "url": "" } ], "description": "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.", "facts": [ "Llama Guard 4 D 49.1", "OpenAI Guardrails B 69.5", "scores" ], "h1": "Llama Guard 4 vs OpenAI Guardrails", "image": "https://www.anchorterminal.com/assets/og/compare-llama-guard-vs-openai-guardrails.png", "path": "/compare/llama-guard-vs-openai-guardrails", "published": "2026-10-01", "section": "tools", "title": "Llama Guard 4 vs OpenAI Guardrails for AI agents, D 49.1 vs B 69.5", "toc": null, "updated": "2026-10-09", "url": "https://www.anchorterminal.com/compare/llama-guard-vs-openai-guardrails" }, "tokens": { "markdown": 2650, "slim": 680 }, "version": 1 }