{ "data": { "a": { "slug": "agentgate", "name": "AgentGate", "vendor": "Mohit Singh (mohitdebian)", "vendorUrl": "https://github.com/mohitdebian/AgentGate", "kind": "http-api", "category": "guardrails", "summary": "AgentGate is an open-source, self-hosted gateway that checks an agent's tool calls before they run. A small local classifier scores each call and a YAML policy returns allow, deny or ask, through a REST API or an MCP stdio wrapper.", "url": "https://www.anchorterminal.com/tools/agentgate", "markdownUrl": "https://www.anchorterminal.com/tools/agentgate.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/agentgate.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/agentgate.json", "repo": "https://github.com/mohitdebian/AgentGate", "license": "MIT", "transports": [ "http", "stdio" ], "packages": [], "auth": "none", "authNotes": "No account. The agent endpoints (`/v1/sessions`, `/v1/assess`) are open unless the owner sets `AG_AGENT_KEY`, then need `Authorization: Bearer \u003ckey\u003e` or `x-agent-key`. Dashboard endpoints need `AG_DASHBOARD_KEY`, which falls back to `dev-key-123` when unset and is also accepted as a `?token=` query parameter. The checkpoint and shadow-summary endpoints have no auth check in the source.", "pricing": "free", "pricingNotes": "Free under MIT with nothing to buy. The owner runs four containers (Postgres, the Python decision service, the Fastify proxy and the dashboard), with an NVIDIA GPU recommended for the classifier and a CPU path in the code.", "priceSummary": "Free · OSS", "where": "local", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in the source, the docs or the wiki (checked 2026-10-15). The project has no paid tier.", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 0, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-15" }, "docsUrl": "https://github.com/mohitdebian/AgentGate/wiki/Client-Integration-Guide", "openapi": "https://github.com/mohitdebian/AgentGate/blob/main/docs/openapi.yaml", "capabilities": [ "guard.policy", "guard.injection", "guard.self-host" ], "tags": [ "open-source", "self-hosted", "local", "free", "no-card", "mit", "typescript", "python", "mcp", "docker", "openapi" ], "graded": true, "anchor": { "graded": true, "score": 48.5, "grade": "D", "agentReady": false, "rank": 883, "ranked": true, "rankOf": 1094, "categoryRank": 16, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 53, "maintenance": 28, "payments": 60, "reliability": 44, "schema": 47, "security": 49, "transparency": 56 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-15" }, "negative": 0, "verdict": "An agent or its host can send each tool call to one REST endpoint, or wrap an MCP server, and get allow, deny or ask from a local classifier and a YAML policy, with an audit log and approval queue. The project is three days old, auth is off by default, and no detection accuracy is published.", "bestFor": "A team that wants a self-hosted check between an agent and its shell, database or webhook tools, with a human approval queue.", "strengths": [ "One documented call, `POST /v1/assess`, returns a verdict, reason codes and four head probabilities", "Fail-closed defaults. A model error or timeout falls back to ask, and unmatched tools default to ask", "Decisions are logged in Postgres with redacted arguments, a SHA-256 of the full arguments and approval reviewer notes", "MIT code and MIT model weights on Hugging Face with no gate, run entirely on the owner's machines", "Shadow mode and policy replay let an operator test a policy against past decisions before enforcing it" ], "weaknesses": [ "Agent endpoints need no key unless `AG_AGENT_KEY` is set, the proxy binds 0.0.0.0:8080, and the dashboard key defaults to `dev-key-123`", "Checkpoint revert, which runs git on the proxy's workspace, has no auth check in the source of 15 October 2026", "No tagged release, an empty changelog and version 0.1.0, three days after the first commit", "The MCP wrapper treats `allow_with_checkpoint` as an error and does not send the agent key to `/v1/assess`", "No published accuracy or false-positive rate for the classifier, trained in 60 steps per its model card" ], "agentNotes": [ "Create a session with the user's real goal first. The goal-alignment head scores every later call against it", "Pass earlier tool results in `history_override` with `untrusted: true` for web or email content, or the injection head sees only the action", "On `ask`, stop and tell the user the `decision_id`. Polling `/v1/decisions/:id` needs the dashboard key", "Treat `allow_with_checkpoint` as allow and keep the `checkpoint_id` for an undo", "Expect HTTP 404 with `not_found` for an unknown session and 401 when the owner has set an agent key" ], "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": 48.5 } ], "editorialScores": { "ergonomics": 53, "maintenance": 28, "payments": 60, "reliability": 44, "schema": 47, "security": 49, "transparency": 59 }, "provenanceScore": 53 }, "connect": { "install": "git clone https://github.com/mohitdebian/AgentGate.git \u0026\u0026 cd AgentGate \u0026\u0026 docker compose up -d", "http": "curl -s -X POST http://localhost:8080/v1/assess -H \"Content-Type: application/json\" -d '{\"session_id\":\"\u003cid from POST /v1/sessions\u003e\",\"action\":{\"tool\":\"bash_exec\",\"arguments\":{\"command\":\"rm -rf /var/www\"}}}'", "config": { "mcpServers": { "postgres": { "args": [ "/path/to/AgentGate/proxy/dist/src/mcp.js", "npx", "-y", "@modelcontextprotocol/server-postgres", "postgresql://user:pass@localhost:5432/production" ], "command": "node", "env": { "AGENTGATE_URL": "http://localhost:8080" } } } } }, "letme": { "capability": "https://letme.dev/guard.policy", "tool": "https://letme.dev/agentgate" }, "area": "models", "provenance": { "legalEntity": "Mohit Singh (individual)", "domain": "", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://github.com/mohitdebian/AgentGate/blob/main/CHANGELOG.md", "securityTxt": "none", "checked": "2026-10-15", "notes": [ "The licence file reads Copyright (c) 2026 Mohit Singh. The GitHub account mohitdebian links to mohitbuilds.in, a personal developer page.", "No terms or privacy policy. The software runs on the owner's machines, so there is no vendor endpoint or status page to check.", "SECURITY.md routes reports to GitHub private vulnerability reporting or a personal email, with a 48-hour acknowledgement target. No security.txt, since the project has no website.", "CHANGELOG.md holds a single Unreleased line, \"Initial scaffolding for Phase 1\"." ], "score": 53 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/agentgate.json" }, "answer": "Llama Guard 4 and AgentGate score within a point of each other on agent readiness, 49.1 (D) and 48.5 (D). AgentGate leads on reliability and payments \u0026 pricing.", "b": { "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": 867, "ranked": true, "rankOf": 1094, "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", "githubStars": 4430, "securityTxt": { "url": "https://llama.com/.well-known/security.txt", "state": "none", "checkedAt": "2026-10-14T15:47:50.299736607Z" }, "domain": { "domain": "llama.com", "registered": "1994-11-01", "source": "https://rdap.verisign.com/com/v1/domain/llama.com", "checkedAt": "2026-10-11T13:13:11.472883251Z" }, "pages": [ { "url": "https://dev.meta.ai/llama/llama4/license", "kind": "terms", "status": 200, "checkedAt": "2026-10-14T20:56:12.59861224Z", "changedAt": "2026-10-13T20:12:27.34056506Z", "fingerprint": "439f740c9732" } ], "updatedAt": "2026-10-15T00:02:43.029226043Z" } }, "facts": [ { "a": "HTTP API", "b": "Model API", "name": "Kind" }, { "a": "Mohit Singh (mohitdebian)", "b": "Meta", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "HTTP, stdio", "b": "HTTP", "name": "Transports" }, { "a": "None", "b": "None", "name": "Auth" }, { "a": "Free", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "MIT", "b": "Llama 4 Community Licence (source-available weights, not an OSI licence), with the Llama 4 acceptable use policy", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "none", "b": "2025-04-29", "name": "Last release" }, { "a": "no document linked", "b": "2025-04-05", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "", "b": "not found in the text", "name": "Customer content may train models" }, { "a": "", "b": "not found in the text", "name": "Terms restrict automated access" }, { "a": "", "b": "not found in the text", "name": "Terms restrict benchmarking" }, { "a": "", "b": "not found in the text", "name": "Terms or service can change without notice" }, { "a": "", "b": "not found in the text", "name": "Arbitration or class-action waiver" }, { "a": "0 stars", "b": "4.4k stars", "name": "Popularity" } ], "faq": [ { "answer": "Llama Guard 4 and AgentGate score within a point of each other on agent readiness, 49.1 (D) and 48.5 (D). AgentGate leads on reliability and payments \u0026 pricing.", "question": "Which is better for AI agents, AgentGate or Llama Guard 4?" }, { "answer": "Neither needs a key.", "question": "Do AgentGate and Llama Guard 4 need an API key?" }, { "answer": "AgentGate runs on your own machine, with no hosted endpoint listed. No hosted endpoint is listed for Llama Guard 4.", "question": "Can an agent call AgentGate and Llama Guard 4 without installing anything?" }, { "answer": "AgentGate is open source (MIT). No open-source release is listed for Llama Guard 4.", "question": "Are AgentGate and Llama Guard 4 open source?" } ], "goodFor": [ { "aheadOn": [ "Reliability, 44 against 38", "Payments \u0026 pricing, 60 against 45" ], "also": [ "Runs on your own machine", "Free to start without a card", "Open source" ], "goodFor": "A team that wants a self-hosted check between an agent and its shell, database or webhook tools, with a human approval queue.", "slug": "agentgate", "watchFor": "Agent endpoints need no key unless `AG_AGENT_KEY` is set, the proxy binds 0.0.0.0:8080, and the dashboard key defaults to `dev-key-123`" }, { "aheadOn": [ "Schema \u0026 documentation, 52 against 47", "Agent ergonomics, 67 against 53" ], "also": null, "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" } ], "job": { "capability": "guard.policy", "name": "Policy enforcement" }, "others": [ { "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/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-guardrails.json", "title": "Llama Guard 4 vs OpenAI Guardrails", "url": "https://www.anchorterminal.com/compare/llama-guard-vs-openai-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/agentgate-vs-amazon-bedrock-guardrails.json", "title": "AgentGate vs Amazon Bedrock Guardrails", "url": "https://www.anchorterminal.com/compare/agentgate-vs-amazon-bedrock-guardrails" }, { "json": "https://www.anchorterminal.com/compare/agentgate-vs-azure-ai-content-safety.json", "title": "AgentGate vs Azure AI Content Safety (Prompt Shields)", "url": "https://www.anchorterminal.com/compare/agentgate-vs-azure-ai-content-safety" }, { "json": "https://www.anchorterminal.com/compare/agentgate-vs-cisco-ai-defense-inspection.json", "title": "AgentGate vs Cisco AI Defense Inspection API", "url": "https://www.anchorterminal.com/compare/agentgate-vs-cisco-ai-defense-inspection" }, { "json": "https://www.anchorterminal.com/compare/agentgate-vs-google-model-armor.json", "title": "AgentGate vs Google Cloud Model Armor", "url": "https://www.anchorterminal.com/compare/agentgate-vs-google-model-armor" }, { "json": "https://www.anchorterminal.com/compare/agentgate-vs-granite-guardian.json", "title": "AgentGate vs Granite Guardian", "url": "https://www.anchorterminal.com/compare/agentgate-vs-granite-guardian" }, { "json": "https://www.anchorterminal.com/compare/agentgate-vs-guardrails-ai.json", "title": "AgentGate vs Guardrails AI", "url": "https://www.anchorterminal.com/compare/agentgate-vs-guardrails-ai" }, { "json": "https://www.anchorterminal.com/compare/agentgate-vs-lakera-guard.json", "title": "AgentGate vs Lakera Guard (Check Point AI Guardrails)", "url": "https://www.anchorterminal.com/compare/agentgate-vs-lakera-guard" }, { "json": "https://www.anchorterminal.com/compare/agentgate-vs-llamafirewall.json", "title": "AgentGate vs LlamaFirewall", "url": "https://www.anchorterminal.com/compare/agentgate-vs-llamafirewall" }, { "json": "https://www.anchorterminal.com/compare/agentgate-vs-mistral-moderation.json", "title": "AgentGate vs Mistral Moderation API", "url": "https://www.anchorterminal.com/compare/agentgate-vs-mistral-moderation" }, { "json": "https://www.anchorterminal.com/compare/agentgate-vs-nemo-guardrails.json", "title": "AgentGate vs NVIDIA NeMo Guardrails", "url": "https://www.anchorterminal.com/compare/agentgate-vs-nemo-guardrails" }, { "json": "https://www.anchorterminal.com/compare/agentgate-vs-openai-guardrails.json", "title": "AgentGate vs OpenAI Guardrails", "url": "https://www.anchorterminal.com/compare/agentgate-vs-openai-guardrails" }, { "json": "https://www.anchorterminal.com/compare/agentgate-vs-prisma-airs.json", "title": "AgentGate vs Prisma AIRS AI Runtime Security API", "url": "https://www.anchorterminal.com/compare/agentgate-vs-prisma-airs" }, { "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" }, { "json": "https://www.anchorterminal.com/compare/agentgate-vs-microsoft-presidio.json", "title": "AgentGate vs Presidio", "url": "https://www.anchorterminal.com/compare/agentgate-vs-microsoft-presidio" }, { "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" } ], "scores": [ { "agentgate": 44, "by": 6, "edge": "agentgate", "key": "reliability", "llama-guard": 38, "name": "Reliability", "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "agentgate": 47, "by": 5, "edge": "llama-guard", "key": "schema", "llama-guard": 52, "name": "Schema \u0026 documentation", "weight": 13 }, { "agentgate": 53, "by": 14, "edge": "llama-guard", "key": "ergonomics", "llama-guard": 67, "name": "Agent ergonomics", "weight": 13 }, { "agentgate": 49, "by": 4, "edge": "llama-guard", "key": "security", "llama-guard": 53, "name": "Security \u0026 auth", "weight": 14 }, { "agentgate": 60, "by": 15, "edge": "agentgate", "key": "payments", "llama-guard": 45, "name": "Payments \u0026 pricing", "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "agentgate": 28, "by": 0, "edge": "", "key": "maintenance", "llama-guard": 28, "name": "Maintenance \u0026 community", "weight": 7 }, { "agentgate": 56, "by": 1, "edge": "agentgate", "key": "transparency", "llama-guard": 55, "name": "Transparency \u0026 trust", "weight": 7 } ], "summary": "Llama Guard 4 and AgentGate score within a point of each other on agent readiness, 49.1 (D) and 48.5 (D). AgentGate leads on reliability and payments \u0026 pricing. Both do policy enforcement.", "verdicts": { "agentgate": "An agent or its host can send each tool call to one REST endpoint, or wrap an MCP server, and get allow, deny or ask from a local classifier and a YAML policy, with an audit log and approval queue. The project is three days old, auth is off by default, and no detection accuracy is published.", "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." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/agentgate-vs-llama-guard", "json": "https://www.anchorterminal.com/compare/agentgate-vs-llama-guard.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/agentgate-vs-llama-guard.md", "slim": "https://www.anchorterminal.com/compare/agentgate-vs-llama-guard.min.md" }, "markdown": "Llama Guard 4 and AgentGate score within a point of each other on agent readiness, 49.1 (D) and 48.5 (D). AgentGate leads on reliability and payments \u0026 pricing. Both do policy enforcement.\n\n- AgentGate: grade D, 48.5/100, rank #883 of 1094. Markdown https://www.anchorterminal.com/tools/agentgate.md · JSON https://www.anchorterminal.com/api/v1/tools/agentgate.json\n- Llama Guard 4: grade D, 49.1/100, rank #867 of 1094. Markdown https://www.anchorterminal.com/tools/llama-guard.md · JSON https://www.anchorterminal.com/api/v1/tools/llama-guard.json\n- Best guardrails and safety filters for AI agents: https://www.anchorterminal.com/best/guardrails/index.md\n- All 132 guardrails comparisons: https://www.anchorterminal.com/compare/guardrails/index.md\n\n## Which one, for what\n\n### AgentGate (D)\n\nGood for: A team that wants a self-hosted check between an agent and its shell, database or webhook tools, with a human approval queue.\n\nAhead on:\n- Reliability, 44 against 38\n- Payments \u0026 pricing, 60 against 45\n\nAlso in its favour:\n- Runs on your own machine\n- Free to start without a card\n- Open source\n\nWatch for: Agent endpoints need no key unless `AG_AGENT_KEY` is set, the proxy binds 0.0.0.0:8080, and the dashboard key defaults to `dev-key-123`\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\nAhead on:\n- Schema \u0026 documentation, 52 against 47\n- Agent ergonomics, 67 against 53\n\nWatch for: Weights last changed on 29 April 2025, with no changelog, version tags or stated deprecation policy\n\n\n## Score by category\n\n| Category | Weight | AgentGate | Llama Guard 4 | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 44 | 38 | AgentGate +6 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 47 | 52 | Llama Guard 4 +5 |\n| Agent ergonomics | 13% (16.2 this run) | 53 | 67 | Llama Guard 4 +14 |\n| Security \u0026 auth | 14% (17.5 this run) | 49 | 53 | Llama Guard 4 +4 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 60 | 45 | AgentGate +15 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 28 | 28 | even |\n| Transparency \u0026 trust | 7% (8.8 this run) | 56 | 55 | AgentGate +1 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **48.5 · D** | **49.1 · D** | |\n\n## Facts side by side\n\n| Fact | AgentGate | Llama Guard 4 |\n| --- | --- | --- |\n| Kind | HTTP API | Model API |\n| Vendor | Mohit Singh (mohitdebian) | Meta |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | HTTP, stdio | HTTP |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | MIT | Llama 4 Community Licence (source-available weights, not an OSI licence), with the Llama 4 acceptable use policy |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | none | 2025-04-29 |\n| Terms last updated | no document linked | 2025-04-05 |\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 | 0 stars | 4.4k stars |\n\n## Verdicts\n\n**AgentGate.** An agent or its host can send each tool call to one REST endpoint, or wrap an MCP server, and get allow, deny or ask from a local classifier and a YAML policy, with an audit log and approval queue. The project is three days old, auth is off by default, and no detection accuracy is published.\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## Before you call either\n\n### AgentGate\n\n1. Create a session with the user's real goal first. The goal-alignment head scores every later call against it\n2. Pass earlier tool results in `history_override` with `untrusted: true` for web or email content, or the injection head sees only the action\n3. On `ask`, stop and tell the user the `decision_id`. Polling `/v1/decisions/:id` needs the dashboard key\n4. Treat `allow_with_checkpoint` as allow and keep the `checkpoint_id` for an undo\n5. Expect HTTP 404 with `not_found` for an unknown session and 401 when the owner has set an agent key\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## Questions\n\n### Which is better for AI agents, AgentGate or Llama Guard 4?\n\nLlama Guard 4 and AgentGate score within a point of each other on agent readiness, 49.1 (D) and 48.5 (D). AgentGate leads on reliability and payments \u0026 pricing.\n\n### Do AgentGate and Llama Guard 4 need an API key?\n\nNeither needs a key.\n\n### Can an agent call AgentGate and Llama Guard 4 without installing anything?\n\nAgentGate runs on your own machine, with no hosted endpoint listed. No hosted endpoint is listed for Llama Guard 4.\n\n### Are AgentGate and Llama Guard 4 open source?\n\nAgentGate is open source (MIT). No open-source release is listed for Llama Guard 4.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/agentgate-vs-llama-guard.json, and with the fewest tokens: https://www.anchorterminal.com/compare/agentgate-vs-llama-guard.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"agentgate\", \"b\": \"llama-guard\"}`. From a terminal: `anchor compare agentgate llama-guard`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/agentgate.json and https://www.anchorterminal.com/api/v1/tools/llama-guard.json\n\n## Other comparisons with AgentGate or Llama Guard 4\n\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- [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 Guardrails](https://www.anchorterminal.com/compare/llama-guard-vs-openai-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- [AgentGate vs Amazon Bedrock Guardrails](https://www.anchorterminal.com/compare/agentgate-vs-amazon-bedrock-guardrails.md)\n- [AgentGate vs Azure AI Content Safety (Prompt Shields)](https://www.anchorterminal.com/compare/agentgate-vs-azure-ai-content-safety.md)\n- [AgentGate vs Cisco AI Defense Inspection API](https://www.anchorterminal.com/compare/agentgate-vs-cisco-ai-defense-inspection.md)\n- [AgentGate vs Google Cloud Model Armor](https://www.anchorterminal.com/compare/agentgate-vs-google-model-armor.md)\n- [AgentGate vs Granite Guardian](https://www.anchorterminal.com/compare/agentgate-vs-granite-guardian.md)\n- [AgentGate vs Guardrails AI](https://www.anchorterminal.com/compare/agentgate-vs-guardrails-ai.md)\n- [AgentGate vs Lakera Guard (Check Point AI Guardrails)](https://www.anchorterminal.com/compare/agentgate-vs-lakera-guard.md)\n- [AgentGate vs LlamaFirewall](https://www.anchorterminal.com/compare/agentgate-vs-llamafirewall.md)\n- [AgentGate vs Mistral Moderation API](https://www.anchorterminal.com/compare/agentgate-vs-mistral-moderation.md)\n- [AgentGate vs NVIDIA NeMo Guardrails](https://www.anchorterminal.com/compare/agentgate-vs-nemo-guardrails.md)\n- [AgentGate vs OpenAI Guardrails](https://www.anchorterminal.com/compare/agentgate-vs-openai-guardrails.md)\n- [AgentGate vs Prisma AIRS AI Runtime Security API](https://www.anchorterminal.com/compare/agentgate-vs-prisma-airs.md)\n- [Llama Guard 4 vs LlamaFirewall](https://www.anchorterminal.com/compare/llama-guard-vs-llamafirewall.md)\n- [AgentGate vs Presidio](https://www.anchorterminal.com/compare/agentgate-vs-microsoft-presidio.md)\n- [Llama Guard 4 vs Presidio](https://www.anchorterminal.com/compare/llama-guard-vs-microsoft-presidio.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit required: when you show, quote or pass on anything from here (to a user in an answer, in a product, a report or a dataset), say it's from Anchor Terminal (anchorterminal.com) and link the page or API address you took it from. Reviews also credit their author.", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-10-15", "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", "terms": "https://www.anchorterminal.com/terms/#data" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "AgentGate vs Llama Guard 4", "url": "" } ], "description": "Llama Guard 4 and AgentGate score within a point of each other for policy enforcement, 49.1 and 48.5 out of 100. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "AgentGate D 48.5", "Llama Guard 4 D 49.1", "scores" ], "h1": "AgentGate vs Llama Guard 4", "image": "https://www.anchorterminal.com/assets/og/compare-agentgate-vs-llama-guard.png", "path": "/compare/agentgate-vs-llama-guard", "published": "2026-10-01", "section": "tools", "title": "AgentGate vs Llama Guard 4 for AI agents in 2026: scores and prices", "toc": null, "updated": "2026-10-15", "url": "https://www.anchorterminal.com/compare/agentgate-vs-llama-guard" }, "tokens": { "markdown": 2650, "slim": 630 }, "version": 1 }