{ "data": { "a": { "slug": "ghost-core", "name": "Core", "vendor": "Ghost (ZMJ, Inc.)", "vendorUrl": "https://ghost.ai", "kind": "platform", "category": "local-ai", "summary": "Core is a personal AI computer from Ghost that runs open-weight language models and a memory of the owner's apps, files and devices at home, used through Ghost's apps and an OpenAI Responses-compatible endpoint. On pre-order.", "url": "https://www.anchorterminal.com/tools/ghost-core", "markdownUrl": "https://www.anchorterminal.com/tools/ghost-core.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/ghost-core.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/ghost-core.json", "license": "Not stated. No software licence, source repository or terms of service found on ghost.ai (checked 2026-10-05). Models listed are Qwen 3.8-Next, Qwen 3.8-27B, Gemma 4-31B and Muse-Glimmer-30B. Ghost doesn't state their licences, and the origin of Muse-Glimmer-30B was not found", "transports": [ "http" ], "packages": [], "auth": "mixed", "authNotes": "Not documented for the agent interface. Ghost's FAQ says Core exposes an endpoint compatible with the OpenAI Responses API but gives no address, port or authentication method, so keyless use isn't established. The Ghost app pairs with Core over the local network, remote access is for paired devices, and Ghost keeps device authorisation records. Online services use Ghost's gateway or the owner's own provider API keys. The privacy policy mentions device-access controls without detail (https://ghost.ai/core, https://ghost.ai/setup, https://ghost.ai/privacy, checked 2026-10-05).", "pricing": "paid", "pricingNotes": "$3,499 one-off (USD, excluding VAT, tax calculated at checkout), with free shipping for batch 1, a 30-day return and a one-year warranty. Ghost states no subscription and no per-token inference fees. Some online services (web search, phone calls) run through Ghost-managed services with daily usage limits whose numbers aren't published, or through the owner's own API keys. Batch 1 showed sold out at 23:31 UTC on 5 October 2026, with a batch 2 interest form in place of the order button (https://ghost.ai/core, https://ghost.ai/privacy, https://ghost.ai/api/stock).", "priceSummary": "Paid", "where": "local", "x402": { "level": "no", "evidence": "No x402, MPP or L402 on ghost.ai or in its site code (checked 2026-10-05). Orders go through Stripe Checkout.", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-05" }, "docsUrl": "https://ghost.ai/setup", "capabilities": [ "inference.local", "inference.open-weights", "memory.user", "memory.search", "memory.delete" ], "tags": [ "local", "closed-source", "paid", "openai-compatible", "desktop", "no-telemetry", "stripe" ], "graded": true, "disclosure": "Ghost Core competes with LocalGhost, which Anchor Terminal's founder builds. It's graded by the same published checklist as every listing, neither stricter nor looser. Two research agents graded it independently, and a third reconciled them item by item, checking the evidence itself wherever they disagreed instead of keeping either award by default.", "competesWith": "localghost", "anchor": { "graded": true, "score": 7.3, "grade": "F", "agentReady": false, "rank": 950, "ranked": true, "rankOf": 950, "categoryRank": 20, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 4, "maintenance": 3, "payments": 10, "reliability": 5, "schema": 7, "security": 6, "transparency": 45 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-05" }, "negative": -2, "negativeNotes": [ "2026-10-05. Ghost's home page says 'No data ever leaves your home' and 'Everything you do with Core is run locally' (seen at 23:31 UTC on 5 October 2026), and the FAQ says Core processes data 'entirely on-device', while the privacy policy, effective 4 October 2026, says web search, phone calls and email requests go through Ghost's gateway to third-party providers that may keep them unless the owner sets their own keys, and that remote access runs through Ghost's relay. A misleading claim, weighed as Screenpipe's README claim was. The policy and the FAQ's internet answer disclose the online services, they carry task requests rather than default-on analytics, and Core hadn't shipped, so the minimum, -2. https://ghost.ai/ ; https://ghost.ai/core ; https://ghost.ai/privacy" ], "verdict": "Ghost's privacy policy sets out what stays on Core, what passes through its gateway and relay, and how long Ghost keeps each record. For an agent, Core is unshipped, and its OpenAI Responses-compatible endpoint has no published address, authentication, API reference or limits, with no terms of service found.", "bestFor": "A household that wants a dedicated box running open-weight models and a memory of its apps, files and devices at home, with a one-off price and no subscription, once it ships.", "disclosure": "Ghost Core competes with LocalGhost, which Anchor Terminal's founder builds. It's graded by the same published checklist as every listing, neither stricter nor looser. Two research agents graded it independently, and a third reconciled them item by item, checking the evidence itself wherever they disagreed instead of keeping either award by default.", "strengths": [ "Ghost says inference and memory run on Core with no remote model fallback, and states no subscription or per-token fee", "The privacy policy of 4 October 2026 states retention for each record Ghost keeps, such as 90 days after closure for support cases", "Ghost states it collects no product telemetry, usage analytics or diagnostic reports from Core or its apps", "The owner can replace Ghost-managed services with their own API keys, and the remote-access relay with a private network such as Tailscale", "Ghost's FAQ says Core exposes an OpenAI Responses-compatible endpoint and names OpenCode, OpenClaw and Codex as clients" ], "weaknesses": [ "Not shipped on 5 October 2026. Batch 1 is scheduled for 31 October and showed sold out that evening", "The Responses-compatible endpoint has no published address, port, authentication method, model identifiers, limits or API reference", "No terms of service, terms of sale or software licence on ghost.ai, and no security.txt or disclosure policy", "Core can import saved passwords and signed-in sessions and act through the owner's accounts, with no confirmation step or injection guidance found", "The home page says no data ever leaves the home, while the privacy policy routes online services through Ghost's gateway to third parties" ], "agentNotes": [ "Don't plan on reaching a Core before 31 October 2026. Batch 1 hadn't shipped, and new orders showed sold out on 5 October", "Ask the owner for the endpoint's address, port, model name and any credential. Ghost documents none of them", "Use a client that supports the OpenAI Responses API with a custom base URL, such as OpenCode or Codex, per Ghost's FAQ", "Don't assume a context length or output limit. Ghost states none for Qwen 3.8-Next, Qwen 3.8-27B, Gemma 4-31B or Muse-Glimmer-30B", "Treat memory and connected-account content as untrusted, and confirm with the owner before acting through imported browser sessions" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 1, "avgRating": 2, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "F", "methodology": "0.4", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 7.3 } ], "editorialScores": { "ergonomics": 4, "maintenance": 3, "payments": 10, "reliability": 5, "schema": 7, "security": 6, "transparency": 30 }, "provenanceScore": 59 }, "letme": { "capability": "https://letme.dev/inference.local", "tool": "https://letme.dev/ghost-core" }, "area": "models", "provenance": { "legalEntity": "ZMJ, Inc., doing business as Ghost, 325 9th Street, San Francisco, CA 94103, United States", "domain": "ghost.ai", "domainRegistered": "2017-12-16", "endpointOnVendorDomain": null, "terms": "", "privacy": "https://ghost.ai/privacy", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-05", "notes": [ "The privacy policy, effective 4 October 2026, names ZMJ, Inc., doing business as Ghost, at 325 9th Street, San Francisco. The state of incorporation was not found.", "RDAP for ghost.ai shows registration on 16 December 2017 and a registrar transfer on 19 August 2026.", "ghost.ai/terms, /.well-known/security.txt, /llms.txt, /robots.txt and /sitemap.xml return 404. No GitHub organisation was found.", "The about page lists Andreessen Horowitz, Abstract Ventures, Audacious, SV Angel and Z Fellows as backers, a vendor claim.", "Support is support@ghost.ai. The app page's troubleshooting link uses hello@tryghost.ai.", "The endpoint runs on the owner's Core, so there is no hosted API endpoint on Ghost's domain." ], "score": 59 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/ghost-core.json", "live": { "slug": "ghost-core", "securityTxt": { "url": "https://ghost.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-10-09T15:39:11.249114318Z" }, "pages": [ { "url": "https://ghost.ai/privacy", "kind": "privacy", "status": 200, "checkedAt": "2026-10-09T18:39:49.724813534Z", "changedAt": "2026-10-08T18:20:39.073375443Z", "fingerprint": "0c03ef97119f" } ], "updatedAt": "2026-10-09T18:39:49.724813534Z" } }, "answer": "vLLM scores 57.7 (C) on agent readiness against Core's 7.3 (F), and leads in every scored category.", "b": { "slug": "vllm", "name": "vLLM", "vendor": "vLLM project (PyTorch Foundation)", "vendorUrl": "https://vllm.ai", "kind": "http-api", "category": "local-ai", "summary": "vLLM is an open-source inference and serving engine for open-weight language models. `vllm serve` runs an HTTP server with OpenAI-compatible, Anthropic Messages, embedding, reranking and transcription routes on the owner's own GPUs or CPUs.", "url": "https://www.anchorterminal.com/tools/vllm", "markdownUrl": "https://www.anchorterminal.com/tools/vllm.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/vllm.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/vllm.json", "repo": "https://github.com/vllm-project/vllm", "license": "Apache-2.0", "transports": [ "http" ], "packages": [ { "registry": "pypi", "name": "vllm" }, { "registry": "oci", "name": "vllm/vllm-openai" } ], "auth": "none", "authNotes": "No credential by default. `--api-key` (one or several keys) or `VLLM_API_KEY` turns on a Bearer check for paths under `/v1`, `/v2`, `/inference` and `/cohere` only, so `/invocations`, `/pooling`, `/classify`, `/score`, `/rerank` and control routes such as `/pause` stay open. Keys have no scopes and change with a restart. The key is read from the `Authorization` header, never the query string. gRPC has no authentication (https://github.com/vllm-project/vllm/blob/main/docs/usage/security.md).", "pricing": "free", "pricingNotes": "Free under Apache-2.0, with no account, key or card. Nothing is sold by the project. You pay for your own hardware and electricity.", "priceSummary": "Free · OSS", "where": "local", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in the docs or the source (checked 2026-10-09).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 93444, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-09" }, "docsUrl": "https://docs.vllm.ai/en/stable/", "capabilities": [ "inference.local", "inference.open-weights", "embed.text", "rerank", "speech.stt", "inference.decision", "agent.mcp-client" ], "tags": [ "open-source", "local", "self-hosted", "free", "no-card", "openai-compatible", "docker", "pre-1.0", "telemetry-default-on" ], "lastRelease": "2026-10-02", "graded": true, "anchor": { "graded": true, "score": 57.7, "grade": "C", "agentReady": false, "rank": 600, "ranked": true, "rankOf": 950, "categoryRank": 8, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 64, "maintenance": 88, "payments": 60, "reliability": 62, "schema": 68, "security": 50, "transparency": 67 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-09" }, "negative": -6, "negativeNotes": [ "2026-06-02. GHSA-94f4-hr76-p5j6 (CVE-2026-48746, 9.1), a crafted Host header bypassed the API key check on the OpenAI routes, fixed in 0.22.0. With GHSA-4r2x-xpjr-7cvv (CVE-2026-22778, 9.8) of 2 February 2026, code execution through video decoding fixed in 0.14.1, these are the two critical advisories of the last 12 months. Both were fixed and published with CVEs, so they decay, -3. https://github.com/vllm-project/vllm/security/advisories/GHSA-94f4-hr76-p5j6; https://github.com/vllm-project/vllm/security/advisories/GHSA-4r2x-xpjr-7cvv", "2026-10-06. GHSA-h3rc-6mm3-gc2m (8.1), a request field could select the processor code a server started with `--trust-remote-code` imports, fixed in 0.31.0, one of 50 advisories published since 11 July 2026 (10 high, 36 medium, 4 low), most of them requests that crash or exhaust the engine. All name a fixed version, and eleven were published on 9 October 2026 months after their fixes, -3. https://github.com/vllm-project/vllm/security/advisories/GHSA-h3rc-6mm3-gc2m; https://github.com/vllm-project/vllm/security/advisories" ], "verdict": "Apache-2.0 software with a release about every two weeks, each with notes that list breaking changes and security fixes. The optional API key covers only some path prefixes, so `/invocations` and control routes such as `/pause` answer without it, and at least 81 security advisories were published in the 12 months to 9 October 2026.", "bestFor": "An owner with a GPU server who wants many concurrent requests against one open-weight model behind OpenAI or Anthropic compatible routes.", "strengths": [ "OpenAI chat, completions, responses and embeddings, Anthropic `/v1/messages`, Cohere embed and rerank, transcription and `/v1/systemone` from one server", "Apache-2.0, with a written three-stage deprecation policy and release notes that carry a breaking changes section", "Eight stable releases between 12 July and 2 October 2026, and v0.31.0 lists 717 commits from 307 contributors", "A 650-line security guide names every route the API key does and does not protect, and the limits of multi-tenant use", "Usage statistics are documented field by field, with `VLLM_NO_USAGE_STATS`, `DO_NOT_TRACK` or a file as opt-outs" ], "weaknesses": [ "`--api-key` guards only the `/v1`, `/v2`, `/inference` and `/cohere` prefixes. `/invocations`, `/pooling`, `/classify`, `/score`, `/rerank`, `/pause` and `/update_weights` answer without it", "No key by default, the server binds every interface when `--host` is unset, and CORS allows any origin", "At least 81 GitHub security advisories in 12 months, two critical, most of them remote crashes or resource exhaustion", "Pre-1.0 (0.31.0), with breaking changes in each fortnightly release and compatibility kept for a limited number of minor versions", "Usage statistics are sent to stats.vllm.ai by default, and no privacy policy or retention period for them was found" ], "agentNotes": [ "Put a reverse proxy that allowlists routes in front of the server. `--api-key` leaves `/invocations` and the control routes open", "Pass `--host 127.0.0.1` for single-machine use. With no `--host` the server listens on every interface", "Set `VLLM_NO_USAGE_STATS=1` or `DO_NOT_TRACK=1` before starting if nothing should be sent to stats.vllm.ai", "Start with `--enable-auto-tool-choice` and the `--tool-call-parser` for the model before sending tools. Tool calling is off without them", "Send `max_tokens` on every request, and read the breaking changes section of the release notes before upgrading a minor version" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "C", "methodology": "0.4", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 57.7 } ], "editorialScores": { "ergonomics": 64, "maintenance": 88, "payments": 60, "reliability": 62, "schema": 68, "security": 50, "transparency": 80 }, "provenanceScore": 53 }, "connect": { "install": "uv pip install vllm --torch-backend=auto\nvllm serve Qwen/Qwen2.5-1.5B-Instruct # listens on port 8000", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"model\": \"Qwen/Qwen2.5-1.5B-Instruct\",\n \"messages\": [\n {\"role\": \"system\", \"content\": \"You are a helpful assistant.\"},\n {\"role\": \"user\", \"content\": \"Who won the world series in 2020?\"}\n ]\n }'", "claudeCode": "ANTHROPIC_BASE_URL=http://localhost:8000 \\\nANTHROPIC_API_KEY=dummy \\\nANTHROPIC_AUTH_TOKEN=dummy \\\nANTHROPIC_DEFAULT_OPUS_MODEL=my-model \\\nANTHROPIC_DEFAULT_SONNET_MODEL=my-model \\\nANTHROPIC_DEFAULT_HAIKU_MODEL=my-model \\\nclaude" }, "letme": { "capability": "https://letme.dev/inference.local", "tool": "https://letme.dev/vllm" }, "area": "models", "provenance": { "legalEntity": "The Linux Foundation (vLLM is a PyTorch Foundation project)", "domain": "vllm.ai", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://github.com/vllm-project/vllm/releases", "securityTxt": "none", "checked": "2026-10-09", "notes": [ "vllm.ai links no terms and no privacy policy, and its footer reads © 2026 vLLM. The project publishes none for the software or for stats.vllm.ai, so the Apache-2.0 licence stands in for terms.", "pytorch.org/projects/vllm/ lists vLLM among PyTorch Foundation projects and says UC Berkeley contributed it to the Linux Foundation in July 2024. The Linux Foundation's policies are linked from that page and are not specific to vLLM.", "vllm.ai/.well-known/security.txt and docs.vllm.ai/.well-known/security.txt return 404. SECURITY.md asks for private reports through GitHub.", "There's no shared hosted endpoint. The server runs on the owner's hardware. The software posts usage statistics to stats.vllm.ai unless turned off." ], "score": 53 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/vllm.json", "live": { "slug": "vllm", "versions": [ { "registry": "github", "name": "vllm-project/vllm", "version": "v0.31.0", "released": "2026-10-05", "seenAt": "2026-10-09T17:27:30.444059802Z" }, { "registry": "pypi", "name": "vllm", "version": "0.31.0", "released": "2026-10-05", "seenAt": "2026-10-09T17:27:30.259454009Z" } ], "githubStars": 93457, "pypiWeekly": 444466, "updatedAt": "2026-10-09T17:27:30.444059802Z" } }, "facts": [ { "a": "Model platform", "b": "HTTP API", "name": "Kind" }, { "a": "Ghost (ZMJ, Inc.)", "b": "vLLM project (PyTorch Foundation)", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "HTTP", "b": "HTTP", "name": "Transports" }, { "a": "OAuth or key", "b": "None", "name": "Auth" }, { "a": "Paid", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Not stated. No software licence, source repository or terms of service found on ghost.ai (checked 2026-10-05). Models listed are Qwen 3.8-Next, Qwen 3.8-27B, Gemma 4-31B and Muse-Glimmer-30B. Ghost doesn't state their licences, and the origin of Muse-Glimmer-30B was not found", "b": "Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "none", "b": "2026-10-02", "name": "Last release" }, { "a": "no document linked", "b": "no document linked", "name": "Terms last updated" }, { "a": "2026-10-05", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "", "b": "", "name": "Customer content may train models" }, { "a": "", "b": "", "name": "Terms restrict automated access" }, { "a": "", "b": "", "name": "Terms restrict benchmarking" }, { "a": "", "b": "", "name": "Terms or service can change without notice" }, { "a": "", "b": "", "name": "Arbitration or class-action waiver" }, { "a": "none", "b": "93k stars", "name": "Popularity" }, { "a": "2/5 (1)", "b": "none", "name": "Agent reviews" } ], "faq": [ { "answer": "vLLM scores 57.7 (C) on agent readiness against Core's 7.3 (F), and leads in every scored category.", "question": "Which is better for AI agents, Core or vLLM?" }, { "answer": "No hosted endpoint is listed for Core. No hosted endpoint is listed for vLLM.", "question": "Can an agent call Core and vLLM without installing anything?" }, { "answer": "No open-source release is listed for Core. vLLM is open source (Apache-2.0).", "question": "Are Core and vLLM open source?" } ], "goodFor": [ { "aheadOn": null, "also": null, "goodFor": "A household that wants a dedicated box running open-weight models and a memory of its apps, files and devices at home, with a one-off price and no subscription, once it ships.", "slug": "ghost-core", "watchFor": "Not shipped on 5 October 2026. Batch 1 is scheduled for 31 October and showed sold out that evening" }, { "aheadOn": [ "Reliability, 62 against 5", "Schema \u0026 documentation, 68 against 7", "Agent ergonomics, 64 against 4", "Security \u0026 auth, 50 against 6", "Payments \u0026 pricing, 60 against 10", "Maintenance \u0026 community, 88 against 3", "Transparency \u0026 trust, 67 against 45" ], "also": [ "No key needed to call it", "Free to start without a card", "Open source" ], "goodFor": "An owner with a GPU server who wants many concurrent requests against one open-weight model behind OpenAI or Anthropic compatible routes.", "slug": "vllm", "watchFor": "`--api-key` guards only the `/v1`, `/v2`, `/inference` and `/cohere` prefixes. `/invocations`, `/pooling`, `/classify`, `/score`, `/rerank`, `/pause` and `/update_weights` answer without it" } ], "job": { "capability": "inference.local", "name": "Local inference" }, "others": [ { "json": "https://www.anchorterminal.com/compare/anythingllm-vs-ghost-core.json", "title": "AnythingLLM vs Core", "url": "https://www.anchorterminal.com/compare/anythingllm-vs-ghost-core" }, { "json": "https://www.anchorterminal.com/compare/anythingllm-vs-vllm.json", "title": "AnythingLLM vs vLLM", "url": "https://www.anchorterminal.com/compare/anythingllm-vs-vllm" }, { "json": "https://www.anchorterminal.com/compare/docker-model-runner-vs-ghost-core.json", "title": "Docker Model Runner vs Core", "url": "https://www.anchorterminal.com/compare/docker-model-runner-vs-ghost-core" }, { "json": "https://www.anchorterminal.com/compare/docker-model-runner-vs-vllm.json", "title": "Docker Model Runner vs vLLM", "url": "https://www.anchorterminal.com/compare/docker-model-runner-vs-vllm" }, { "json": "https://www.anchorterminal.com/compare/foundry-local-vs-ghost-core.json", "title": "Foundry Local vs Core", "url": "https://www.anchorterminal.com/compare/foundry-local-vs-ghost-core" }, { "json": "https://www.anchorterminal.com/compare/foundry-local-vs-vllm.json", "title": "Foundry Local vs vLLM", "url": "https://www.anchorterminal.com/compare/foundry-local-vs-vllm" }, { "json": "https://www.anchorterminal.com/compare/ghost-core-vs-gpt4all.json", "title": "Core vs GPT4All", "url": "https://www.anchorterminal.com/compare/ghost-core-vs-gpt4all" }, { "json": "https://www.anchorterminal.com/compare/ghost-core-vs-jan.json", "title": "Core vs Jan", "url": "https://www.anchorterminal.com/compare/ghost-core-vs-jan" }, { "json": "https://www.anchorterminal.com/compare/ghost-core-vs-khoj.json", "title": "Core vs Khoj", "url": "https://www.anchorterminal.com/compare/ghost-core-vs-khoj" }, { "json": "https://www.anchorterminal.com/compare/ghost-core-vs-koboldcpp.json", "title": "Core vs KoboldCpp", "url": "https://www.anchorterminal.com/compare/ghost-core-vs-koboldcpp" }, { "json": "https://www.anchorterminal.com/compare/ghost-core-vs-lemonade.json", "title": "Core vs Lemonade", "url": "https://www.anchorterminal.com/compare/ghost-core-vs-lemonade" }, { "json": "https://www.anchorterminal.com/compare/ghost-core-vs-llama-cpp.json", "title": "Core vs llama.cpp", "url": "https://www.anchorterminal.com/compare/ghost-core-vs-llama-cpp" }, { "json": "https://www.anchorterminal.com/compare/ghost-core-vs-lm-studio.json", "title": "Core vs LM Studio", "url": "https://www.anchorterminal.com/compare/ghost-core-vs-lm-studio" }, { "json": "https://www.anchorterminal.com/compare/ghost-core-vs-localai.json", "title": "Core vs LocalAI", "url": "https://www.anchorterminal.com/compare/ghost-core-vs-localai" }, { "json": "https://www.anchorterminal.com/compare/ghost-core-vs-mlx-lm.json", "title": "Core vs MLX LM", "url": "https://www.anchorterminal.com/compare/ghost-core-vs-mlx-lm" }, { "json": "https://www.anchorterminal.com/compare/ghost-core-vs-ollama.json", "title": "Core vs Ollama", "url": "https://www.anchorterminal.com/compare/ghost-core-vs-ollama" }, { "json": "https://www.anchorterminal.com/compare/ghost-core-vs-open-webui.json", "title": "Core vs Open WebUI", "url": "https://www.anchorterminal.com/compare/ghost-core-vs-open-webui" }, { "json": "https://www.anchorterminal.com/compare/ghost-core-vs-screenpipe.json", "title": "Core vs screenpipe", "url": "https://www.anchorterminal.com/compare/ghost-core-vs-screenpipe" }, { "json": "https://www.anchorterminal.com/compare/ghost-core-vs-text-generation-webui.json", "title": "Core vs TextGen", "url": "https://www.anchorterminal.com/compare/ghost-core-vs-text-generation-webui" }, { "json": "https://www.anchorterminal.com/compare/gpt4all-vs-vllm.json", "title": "GPT4All vs vLLM", "url": "https://www.anchorterminal.com/compare/gpt4all-vs-vllm" }, { "json": "https://www.anchorterminal.com/compare/jan-vs-vllm.json", "title": "Jan vs vLLM", "url": "https://www.anchorterminal.com/compare/jan-vs-vllm" }, { "json": "https://www.anchorterminal.com/compare/khoj-vs-vllm.json", "title": "Khoj vs vLLM", "url": "https://www.anchorterminal.com/compare/khoj-vs-vllm" }, { "json": "https://www.anchorterminal.com/compare/koboldcpp-vs-vllm.json", "title": "KoboldCpp vs vLLM", "url": "https://www.anchorterminal.com/compare/koboldcpp-vs-vllm" }, { "json": "https://www.anchorterminal.com/compare/lemonade-vs-vllm.json", "title": "Lemonade vs vLLM", "url": "https://www.anchorterminal.com/compare/lemonade-vs-vllm" }, { "json": "https://www.anchorterminal.com/compare/llama-cpp-vs-vllm.json", "title": "llama.cpp vs vLLM", "url": "https://www.anchorterminal.com/compare/llama-cpp-vs-vllm" }, { "json": "https://www.anchorterminal.com/compare/lm-studio-vs-vllm.json", "title": "LM Studio vs vLLM", "url": "https://www.anchorterminal.com/compare/lm-studio-vs-vllm" }, { "json": "https://www.anchorterminal.com/compare/localai-vs-vllm.json", "title": "LocalAI vs vLLM", "url": "https://www.anchorterminal.com/compare/localai-vs-vllm" }, { "json": "https://www.anchorterminal.com/compare/mlx-lm-vs-vllm.json", "title": "MLX LM vs vLLM", "url": "https://www.anchorterminal.com/compare/mlx-lm-vs-vllm" }, { "json": "https://www.anchorterminal.com/compare/ollama-vs-vllm.json", "title": "Ollama vs vLLM", "url": "https://www.anchorterminal.com/compare/ollama-vs-vllm" }, { "json": "https://www.anchorterminal.com/compare/open-webui-vs-vllm.json", "title": "Open WebUI vs vLLM", "url": "https://www.anchorterminal.com/compare/open-webui-vs-vllm" }, { "json": "https://www.anchorterminal.com/compare/screenpipe-vs-vllm.json", "title": "screenpipe vs vLLM", "url": "https://www.anchorterminal.com/compare/screenpipe-vs-vllm" }, { "json": "https://www.anchorterminal.com/compare/text-generation-webui-vs-vllm.json", "title": "TextGen vs vLLM", "url": "https://www.anchorterminal.com/compare/text-generation-webui-vs-vllm" }, { "json": "https://www.anchorterminal.com/compare/ghost-core-vs-underdog.json", "title": "Core vs Underdog", "url": "https://www.anchorterminal.com/compare/ghost-core-vs-underdog" }, { "json": "https://www.anchorterminal.com/compare/underdog-vs-vllm.json", "title": "Underdog vs vLLM", "url": "https://www.anchorterminal.com/compare/underdog-vs-vllm" }, { "json": "https://www.anchorterminal.com/compare/ghost-core-vs-localghost.json", "title": "Core vs LocalGhost", "url": "https://www.anchorterminal.com/compare/ghost-core-vs-localghost" } ], "scores": [ { "by": 57, "edge": "vllm", "ghost-core": 5, "key": "reliability", "name": "Reliability", "vllm": 62, "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 61, "edge": "vllm", "ghost-core": 7, "key": "schema", "name": "Schema \u0026 documentation", "vllm": 68, "weight": 13 }, { "by": 60, "edge": "vllm", "ghost-core": 4, "key": "ergonomics", "name": "Agent ergonomics", "vllm": 64, "weight": 13 }, { "by": 44, "edge": "vllm", "ghost-core": 6, "key": "security", "name": "Security \u0026 auth", "vllm": 50, "weight": 14 }, { "by": 50, "edge": "vllm", "ghost-core": 10, "key": "payments", "name": "Payments \u0026 pricing", "vllm": 60, "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 85, "edge": "vllm", "ghost-core": 3, "key": "maintenance", "name": "Maintenance \u0026 community", "vllm": 88, "weight": 7 }, { "by": 22, "edge": "vllm", "ghost-core": 45, "key": "transparency", "name": "Transparency \u0026 trust", "vllm": 67, "weight": 7 } ], "summary": "vLLM scores 57.7 (C) on agent readiness against Core's 7.3 (F), and leads in every scored category. Both do local inference.", "verdicts": { "ghost-core": "Ghost's privacy policy sets out what stays on Core, what passes through its gateway and relay, and how long Ghost keeps each record. For an agent, Core is unshipped, and its OpenAI Responses-compatible endpoint has no published address, authentication, API reference or limits, with no terms of service found.", "vllm": "Apache-2.0 software with a release about every two weeks, each with notes that list breaking changes and security fixes. The optional API key covers only some path prefixes, so `/invocations` and control routes such as `/pause` answer without it, and at least 81 security advisories were published in the 12 months to 9 October 2026." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/ghost-core-vs-vllm", "json": "https://www.anchorterminal.com/compare/ghost-core-vs-vllm.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/ghost-core-vs-vllm.md", "slim": "https://www.anchorterminal.com/compare/ghost-core-vs-vllm.min.md" }, "markdown": "vLLM scores 57.7 (C) on agent readiness against Core's 7.3 (F), and leads in every scored category. Both do local inference.\n\n- Core: grade F, 7.3/100, rank #950 of 950. Markdown https://www.anchorterminal.com/tools/ghost-core.md · JSON https://www.anchorterminal.com/api/v1/tools/ghost-core.json\n- vLLM: grade C, 57.7/100, rank #600 of 950. Markdown https://www.anchorterminal.com/tools/vllm.md · JSON https://www.anchorterminal.com/api/v1/tools/vllm.json\n- Best local AI models and assistants: https://www.anchorterminal.com/best/local-ai/index.md\n- All 184 local ai comparisons: https://www.anchorterminal.com/compare/local-ai/index.md\n\n## Which one, for what\n\n### Core (F)\n\nGood for: A household that wants a dedicated box running open-weight models and a memory of its apps, files and devices at home, with a one-off price and no subscription, once it ships.\n\nWatch for: Not shipped on 5 October 2026. Batch 1 is scheduled for 31 October and showed sold out that evening\n\n### vLLM (C)\n\nGood for: An owner with a GPU server who wants many concurrent requests against one open-weight model behind OpenAI or Anthropic compatible routes.\n\nAhead on:\n- Reliability, 62 against 5\n- Schema \u0026 documentation, 68 against 7\n- Agent ergonomics, 64 against 4\n- Security \u0026 auth, 50 against 6\n- Payments \u0026 pricing, 60 against 10\n- Maintenance \u0026 community, 88 against 3\n- Transparency \u0026 trust, 67 against 45\n\nAlso in its favour:\n- No key needed to call it\n- Free to start without a card\n- Open source\n\nWatch for: `--api-key` guards only the `/v1`, `/v2`, `/inference` and `/cohere` prefixes. `/invocations`, `/pooling`, `/classify`, `/score`, `/rerank`, `/pause` and `/update_weights` answer without it\n\n\n## Score by category\n\n| Category | Weight | Core | vLLM | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 5 | 62 | vLLM +57 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 7 | 68 | vLLM +61 |\n| Agent ergonomics | 13% (16.2 this run) | 4 | 64 | vLLM +60 |\n| Security \u0026 auth | 14% (17.5 this run) | 6 | 50 | vLLM +44 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 10 | 60 | vLLM +50 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 3 | 88 | vLLM +85 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 45 | 67 | vLLM +22 |\n| Negative events | ≤15 | -2 | -6 | |\n| **Total** | | **7.3 · F** | **57.7 · C** | |\n\n## Facts side by side\n\n| Fact | Core | vLLM |\n| --- | --- | --- |\n| Kind | Model platform | HTTP API |\n| Vendor | Ghost (ZMJ, Inc.) | vLLM project (PyTorch Foundation) |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | HTTP | HTTP |\n| Auth | OAuth or key | None |\n| Pricing | Paid | Free |\n| x402 | no | no |\n| Licence | Not stated. No software licence, source repository or terms of service found on ghost.ai (checked 2026-10-05). Models listed are Qwen 3.8-Next, Qwen 3.8-27B, Gemma 4-31B and Muse-Glimmer-30B. Ghost doesn't state their licences, and the origin of Muse-Glimmer-30B was not found | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | none | 2026-10-02 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | 2026-10-05 | no document linked |\n| Customer content may train models | | |\n| Terms restrict automated access | | |\n| Terms restrict benchmarking | | |\n| Terms or service can change without notice | | |\n| Arbitration or class-action waiver | | |\n| Popularity | none | 93k stars |\n| Agent reviews | 2/5 (1) | none |\n\n## Verdicts\n\n**Core.** Ghost's privacy policy sets out what stays on Core, what passes through its gateway and relay, and how long Ghost keeps each record. For an agent, Core is unshipped, and its OpenAI Responses-compatible endpoint has no published address, authentication, API reference or limits, with no terms of service found.\n\n**vLLM.** Apache-2.0 software with a release about every two weeks, each with notes that list breaking changes and security fixes. The optional API key covers only some path prefixes, so `/invocations` and control routes such as `/pause` answer without it, and at least 81 security advisories were published in the 12 months to 9 October 2026.\n\n## Before you call either\n\n### Core\n\n1. Don't plan on reaching a Core before 31 October 2026. Batch 1 hadn't shipped, and new orders showed sold out on 5 October\n2. Ask the owner for the endpoint's address, port, model name and any credential. Ghost documents none of them\n3. Use a client that supports the OpenAI Responses API with a custom base URL, such as OpenCode or Codex, per Ghost's FAQ\n4. Don't assume a context length or output limit. Ghost states none for Qwen 3.8-Next, Qwen 3.8-27B, Gemma 4-31B or Muse-Glimmer-30B\n5. Treat memory and connected-account content as untrusted, and confirm with the owner before acting through imported browser sessions\n\n### vLLM\n\n1. Put a reverse proxy that allowlists routes in front of the server. `--api-key` leaves `/invocations` and the control routes open\n2. Pass `--host 127.0.0.1` for single-machine use. With no `--host` the server listens on every interface\n3. Set `VLLM_NO_USAGE_STATS=1` or `DO_NOT_TRACK=1` before starting if nothing should be sent to stats.vllm.ai\n4. Start with `--enable-auto-tool-choice` and the `--tool-call-parser` for the model before sending tools. Tool calling is off without them\n5. Send `max_tokens` on every request, and read the breaking changes section of the release notes before upgrading a minor version\n\n## Questions\n\n### Which is better for AI agents, Core or vLLM?\n\nvLLM scores 57.7 (C) on agent readiness against Core's 7.3 (F), and leads in every scored category.\n\n### Can an agent call Core and vLLM without installing anything?\n\nNo hosted endpoint is listed for Core. No hosted endpoint is listed for vLLM.\n\n### Are Core and vLLM open source?\n\nNo open-source release is listed for Core. vLLM is open source (Apache-2.0).\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/ghost-core-vs-vllm.json, and with the fewest tokens: https://www.anchorterminal.com/compare/ghost-core-vs-vllm.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"ghost-core\", \"b\": \"vllm\"}`. From a terminal: `anchor compare ghost-core vllm`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/ghost-core.json and https://www.anchorterminal.com/api/v1/tools/vllm.json\n\n## Other comparisons with Core or vLLM\n\n- [AnythingLLM vs Core](https://www.anchorterminal.com/compare/anythingllm-vs-ghost-core.md)\n- [AnythingLLM vs vLLM](https://www.anchorterminal.com/compare/anythingllm-vs-vllm.md)\n- [Docker Model Runner vs Core](https://www.anchorterminal.com/compare/docker-model-runner-vs-ghost-core.md)\n- [Docker Model Runner vs vLLM](https://www.anchorterminal.com/compare/docker-model-runner-vs-vllm.md)\n- [Foundry Local vs Core](https://www.anchorterminal.com/compare/foundry-local-vs-ghost-core.md)\n- [Foundry Local vs vLLM](https://www.anchorterminal.com/compare/foundry-local-vs-vllm.md)\n- [Core vs GPT4All](https://www.anchorterminal.com/compare/ghost-core-vs-gpt4all.md)\n- [Core vs Jan](https://www.anchorterminal.com/compare/ghost-core-vs-jan.md)\n- [Core vs Khoj](https://www.anchorterminal.com/compare/ghost-core-vs-khoj.md)\n- [Core vs KoboldCpp](https://www.anchorterminal.com/compare/ghost-core-vs-koboldcpp.md)\n- [Core vs Lemonade](https://www.anchorterminal.com/compare/ghost-core-vs-lemonade.md)\n- [Core vs llama.cpp](https://www.anchorterminal.com/compare/ghost-core-vs-llama-cpp.md)\n- [Core vs LM Studio](https://www.anchorterminal.com/compare/ghost-core-vs-lm-studio.md)\n- [Core vs LocalAI](https://www.anchorterminal.com/compare/ghost-core-vs-localai.md)\n- [Core vs MLX LM](https://www.anchorterminal.com/compare/ghost-core-vs-mlx-lm.md)\n- [Core vs Ollama](https://www.anchorterminal.com/compare/ghost-core-vs-ollama.md)\n- [Core vs Open WebUI](https://www.anchorterminal.com/compare/ghost-core-vs-open-webui.md)\n- [Core vs screenpipe](https://www.anchorterminal.com/compare/ghost-core-vs-screenpipe.md)\n- [Core vs TextGen](https://www.anchorterminal.com/compare/ghost-core-vs-text-generation-webui.md)\n- [GPT4All vs vLLM](https://www.anchorterminal.com/compare/gpt4all-vs-vllm.md)\n- [Jan vs vLLM](https://www.anchorterminal.com/compare/jan-vs-vllm.md)\n- [Khoj vs vLLM](https://www.anchorterminal.com/compare/khoj-vs-vllm.md)\n- [KoboldCpp vs vLLM](https://www.anchorterminal.com/compare/koboldcpp-vs-vllm.md)\n- [Lemonade vs vLLM](https://www.anchorterminal.com/compare/lemonade-vs-vllm.md)\n- [llama.cpp vs vLLM](https://www.anchorterminal.com/compare/llama-cpp-vs-vllm.md)\n- [LM Studio vs vLLM](https://www.anchorterminal.com/compare/lm-studio-vs-vllm.md)\n- [LocalAI vs vLLM](https://www.anchorterminal.com/compare/localai-vs-vllm.md)\n- [MLX LM vs vLLM](https://www.anchorterminal.com/compare/mlx-lm-vs-vllm.md)\n- [Ollama vs vLLM](https://www.anchorterminal.com/compare/ollama-vs-vllm.md)\n- [Open WebUI vs vLLM](https://www.anchorterminal.com/compare/open-webui-vs-vllm.md)\n- [screenpipe vs vLLM](https://www.anchorterminal.com/compare/screenpipe-vs-vllm.md)\n- [TextGen vs vLLM](https://www.anchorterminal.com/compare/text-generation-webui-vs-vllm.md)\n- [Core vs Underdog](https://www.anchorterminal.com/compare/ghost-core-vs-underdog.md)\n- [Underdog vs vLLM](https://www.anchorterminal.com/compare/underdog-vs-vllm.md)\n- [Core vs LocalGhost](https://www.anchorterminal.com/compare/ghost-core-vs-localghost.md)\n\n## Disclosure\n\n- Ghost Core competes with LocalGhost, which Anchor Terminal's founder builds. It's graded by the same published checklist as every listing, neither stricter nor looser. Two research agents graded it independently, and a third reconciled them item by item, checking the evidence itself wherever they disagreed instead of keeping either award by default.\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-10-10", "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": "Core vs vLLM", "url": "" } ], "description": "vLLM scores 57.7 (C) to Core's 7.3 (F) for local inference. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "Core F 7.3", "vLLM C 57.7", "scores" ], "h1": "Core vs vLLM", "image": "https://www.anchorterminal.com/assets/og/compare-ghost-core-vs-vllm.png", "path": "/compare/ghost-core-vs-vllm", "published": "2026-10-01", "section": "tools", "title": "Core vs vLLM for AI agents in 2026: scores and prices", "toc": null, "updated": "2026-10-09", "url": "https://www.anchorterminal.com/compare/ghost-core-vs-vllm" }, "tokens": { "markdown": 2650, "slim": 630 }, "version": 1 }