{
  "data": {
    "a": {
      "slug": "anythingllm",
      "name": "AnythingLLM",
      "vendor": "Mintplex Labs",
      "vendorUrl": "https://anythingllm.com",
      "kind": "platform",
      "category": "local-ai",
      "summary": "Open-source app for chatting with documents using local or hosted models. Available as a desktop app or a self-hosted server.",
      "url": "https://www.anchorterminal.com/tools/anythingllm",
      "markdownUrl": "https://www.anchorterminal.com/tools/anythingllm.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/anythingllm.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/anythingllm.json",
      "repo": "https://github.com/Mintplex-Labs/anything-llm",
      "license": "MIT (server, document collector, frontend and Docker image). The desktop app ships under Mintplex Labs' own terms of use, which call its source code a trade secret and forbid reverse engineering",
      "transports": [
        "http"
      ],
      "packages": [
        {
          "registry": "oci",
          "name": "mintplexlabs/anythingllm"
        },
        {
          "registry": "oci",
          "name": "ghcr.io/mintplex-labs/anything-llm"
        }
      ],
      "auth": "api-key",
      "authNotes": "The developer API under /api/v1 takes a key in `Authorization: Bearer`. An admin creates keys in the UI. SECURITY.md says each key has full, unrestricted access to the whole /v1 surface, equivalent to admin, and the database stores keys in plain text with no scopes or expiry. A key is revoked by deleting it. The instance itself runs with no password, one password or multi-user accounts, chosen at onboarding, and the desktop backend listens on 127.0.0.1:3001 unless network discovery is switched on.",
      "pricing": "freemium",
      "pricingNotes": "The desktop app and the Docker server are free, with no account. AnythingLLM Cloud, a private managed instance on AWS, costs $50 a month (Basic, bring your own model key) or $99 a month (Pro, 72-hour support SLA), with Enterprise on request. The pricing page shows no trial or free tier for Cloud, and checkout goes through my.mintplexlabs.com (checked 2026-10-03).",
      "priceSummary": "$50 / mo",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "No x402, MPP or L402 in the docs, the pricing page or the source (checked 2026-10-03).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": 66600,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-10-03"
      },
      "docsUrl": "https://docs.anythingllm.com",
      "openapi": "https://raw.githubusercontent.com/Mintplex-Labs/anything-llm/master/server/swagger/openapi.json",
      "capabilities": [
        "inference.local",
        "memory.search",
        "agent.mcp-client",
        "memory.user",
        "inference.open-weights"
      ],
      "tags": [
        "open-source",
        "local",
        "self-hosted",
        "hosted",
        "desktop",
        "docker",
        "openapi",
        "openai-compatible",
        "rag",
        "mcp-client",
        "freemium",
        "telemetry-default-on"
      ],
      "lastRelease": "2026-10-01",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 53.3,
        "grade": "D",
        "agentReady": false,
        "rank": 709,
        "ranked": true,
        "rankOf": 950,
        "categoryRank": 11,
        "methodology": "0.4",
        "run": "2026-10-01",
        "scores": {
          "ergonomics": 46,
          "maintenance": 78,
          "payments": 60,
          "reliability": 67,
          "schema": 57,
          "security": 38,
          "transparency": 59
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "medium",
          "date": "2026-10-03"
        },
        "negative": -3,
        "negativeNotes": [
          "2026-04-15 to 2026-05-21. Ten advisories published in the last year, all fixed, among them CVE-2026-48116 (GHSA-6hrp-7mw6-8v59, CVSS 7.5), code execution through a `--pre` argument passed to ripgrep by the filesystem-search-files agent skill in 1.12.1 and earlier, fixed on 20 May 2026 (commit 94ed62d3) and published on 21 May, and GHSA-4q6m-qh3w-9gf5 (15 April 2026), a DOM XSS in chart rendering that prompt injection could trigger. Fixed and published inside six months, so a small deduction, -3. https://github.com/Mintplex-Labs/anything-llm/security/advisories/GHSA-6hrp-7mw6-8v59; https://github.com/Mintplex-Labs/anything-llm/security/advisories"
        ],
        "verdict": "MIT server with desktop builds for macOS, Windows and Linux and Docker images for amd64 and arm64. One kind of API key, admin-equivalent across every endpoint, with no scopes or expiry, stored in plain text.",
        "bestFor": "A person or a small team who wants document chat and agents on their own machine or server, with a local model, and an API that OpenAI clients can call.",
        "strengths": [
          "MIT server with desktop builds for macOS, Windows and Linux and Docker images for amd64 and arm64",
          "63 developer API operations in OpenAPI 3.0, served at /api/docs on every instance",
          "OpenAI-compatible chat, embeddings and model endpoints that treat each workspace as a model",
          "38 model providers, including Ollama, LM Studio and a built-in local engine on the desktop, and LanceDB on disk by default",
          "v1.17.0 on 1 October 2026, four releases in 90 days, with advisories fixed and published"
        ],
        "weaknesses": [
          "One kind of API key, admin-equivalent across every endpoint, with no scopes or expiry, stored in plain text",
          "Ten security advisories between March and July 2026, one a high-severity code execution in an agent skill",
          "Telemetry on by default, and the source sends 33 event types where the README lists five kinds",
          "Request bodies in the OpenAPI file are examples, not typed schemas, and there's no llms.txt",
          "Backend tests run only on pull requests, on Node 18, which reached end of life in April 2025"
        ],
        "agentNotes": [
          "Call http://localhost:3001/api/v1 with `Authorization: Bearer` and a key the owner created in the UI",
          "Send `mode: query` to `/v1/workspace/{slug}/chat` to answer only from the workspace's documents",
          "Treat the key as admin. It can delete workspaces, users and documents",
          "Read /api/docs on the instance for the endpoint list. Request bodies there are examples, not schemas",
          "Pass a `sessionId` with each chat to keep your conversation apart from other API callers"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 2,
        "avgRating": 2,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "medium",
            "grade": "D",
            "methodology": "0.4",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 53.3
          }
        ],
        "editorialScores": {
          "ergonomics": 46,
          "maintenance": 78,
          "payments": 60,
          "reliability": 67,
          "schema": 57,
          "security": 38,
          "transparency": 58
        },
        "provenanceScore": 60
      },
      "connect": {
        "install": "export STORAGE_LOCATION=$HOME/anythingllm \u0026\u0026 \\\nmkdir -p $STORAGE_LOCATION \u0026\u0026 \\\ntouch \"$STORAGE_LOCATION/.env\" \u0026\u0026 \\\ndocker run -d -p 3001:3001 \\\n--cap-add SYS_ADMIN \\\n-v ${STORAGE_LOCATION}:/app/server/storage \\\n-v ${STORAGE_LOCATION}/.env:/app/server/.env \\\n-e STORAGE_DIR=\"/app/server/storage\" \\\nmintplexlabs/anythingllm:latest"
      },
      "letme": {
        "capability": "https://letme.dev/inference.local",
        "tool": "https://letme.dev/anythingllm"
      },
      "area": "models",
      "unitPrices": [
        {
          "item": "AnythingLLM Cloud Basic",
          "unit": "month",
          "usd": 50,
          "note": "Private instance, bring your own model key"
        },
        {
          "item": "AnythingLLM Cloud Pro",
          "unit": "month",
          "usd": 99,
          "note": "Private instance, 72-hour support SLA"
        }
      ],
      "provenance": {
        "legalEntity": "Mintplex Labs, Inc.",
        "domain": "anythingllm.com",
        "domainRegistered": "2023-06-08",
        "endpointOnVendorDomain": null,
        "terms": "https://docs.anythingllm.com/installation-desktop/terms",
        "privacy": "https://docs.anythingllm.com/installation-desktop/privacy",
        "statusPage": "",
        "changelog": "https://github.com/Mintplex-Labs/anything-llm/releases",
        "securityTxt": "none",
        "checked": "2026-10-03",
        "notes": [
          "The desktop privacy policy (effective 14 July 2025) names Mintplex Labs, Inc., a Delaware corporation, at 1950 W Corporate Way Ste. 25340, Anaheim, CA 92801.",
          "There's no shared hosted endpoint. Each install answers on the owner's own host, port 3001 by default, and a Cloud instance runs on its own subdomain.",
          "anythingllm.com/.well-known/security.txt returns 404. SECURITY.md takes reports only through GitHub security advisories.",
          "RDAP for anythingllm.com gives a registration date of 2023-06-08. Self-hosted terms are in TERMS_SELF_HOSTED.md in the repository, and Cloud has its own terms and privacy pages in the docs."
        ],
        "score": 60
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/anythingllm.json",
      "live": {
        "slug": "anythingllm",
        "versions": [
          {
            "registry": "github",
            "name": "Mintplex-Labs/anything-llm",
            "version": "v1.17.0",
            "released": "2026-10-01",
            "seenAt": "2026-10-09T16:39:38.499161322Z"
          }
        ],
        "githubStars": 66864,
        "securityTxt": {
          "url": "https://anythingllm.com/.well-known/security.txt",
          "state": "none",
          "checkedAt": "2026-10-09T15:40:33.307015767Z"
        },
        "domain": {
          "domain": "anythingllm.com",
          "registered": "2023-06-08",
          "source": "https://rdap.verisign.com/com/v1/domain/anythingllm.com",
          "checkedAt": "2026-10-04T13:06:56.741891994Z"
        },
        "pages": [
          {
            "url": "https://docs.anythingllm.com/installation-desktop/privacy",
            "kind": "privacy",
            "status": 304,
            "checkedAt": "2026-10-09T18:36:30.399019142Z",
            "changedAt": "0001-01-01T00:00:00Z",
            "fingerprint": "d60b6740a520"
          },
          {
            "url": "https://docs.anythingllm.com/installation-desktop/terms",
            "kind": "terms",
            "status": 304,
            "checkedAt": "2026-10-09T18:36:32.506511957Z",
            "changedAt": "0001-01-01T00:00:00Z",
            "fingerprint": "fbedb30fe013"
          }
        ],
        "updatedAt": "2026-10-09T18:36:32.506511957Z"
      }
    },
    "answer": "vLLM scores 57.7 (C) on agent readiness against AnythingLLM's 53.3 (D), and leads in 5 of 7 scored categories. AnythingLLM leads on reliability.",
    "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": "Mintplex Labs",
        "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": "API key",
        "b": "None",
        "name": "Auth"
      },
      {
        "a": "Freemium",
        "b": "Free",
        "name": "Pricing"
      },
      {
        "a": "no",
        "b": "no",
        "name": "x402"
      },
      {
        "a": "MIT (server, document collector, frontend and Docker image). The desktop app ships under Mintplex Labs' own terms of use, which call its source code a trade secret and forbid reverse engineering",
        "b": "Apache-2.0",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-10-01",
        "b": "2026-10-02",
        "name": "Last release"
      },
      {
        "a": "no date given",
        "b": "no document linked",
        "name": "Terms last updated"
      },
      {
        "a": "no date given",
        "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": "67k stars",
        "b": "93k stars",
        "name": "Popularity"
      },
      {
        "a": "2/5 (2)",
        "b": "none",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "vLLM scores 57.7 (C) on agent readiness against AnythingLLM's 53.3 (D), and leads in 5 of 7 scored categories. AnythingLLM leads on reliability.",
        "question": "Which is better for AI agents, AnythingLLM or vLLM?"
      },
      {
        "answer": "No hosted endpoint is listed for AnythingLLM. No hosted endpoint is listed for vLLM.",
        "question": "Can an agent call AnythingLLM and vLLM without installing anything?"
      },
      {
        "answer": "Yes. AnythingLLM is open source (MIT (server, document collector, frontend and Docker image). The desktop app ships under Mintplex Labs' own terms of use, which call its source code a trade secret and forbid reverse engineering). vLLM is open source (Apache-2.0).",
        "question": "Are AnythingLLM and vLLM open source?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reliability, 67 against 62"
        ],
        "also": null,
        "goodFor": "A person or a small team who wants document chat and agents on their own machine or server, with a local model, and an API that OpenAI clients can call.",
        "slug": "anythingllm",
        "watchFor": "One kind of API key, admin-equivalent across every endpoint, with no scopes or expiry, stored in plain text"
      },
      {
        "aheadOn": [
          "Schema \u0026 documentation, 68 against 57",
          "Agent ergonomics, 64 against 46",
          "Security \u0026 auth, 50 against 38",
          "Maintenance \u0026 community, 88 against 78",
          "Transparency \u0026 trust, 67 against 59"
        ],
        "also": [
          "No key needed to call it",
          "Free to start without a card"
        ],
        "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-docker-model-runner.json",
        "title": "AnythingLLM vs Docker Model Runner",
        "url": "https://www.anchorterminal.com/compare/anythingllm-vs-docker-model-runner"
      },
      {
        "json": "https://www.anchorterminal.com/compare/anythingllm-vs-foundry-local.json",
        "title": "AnythingLLM vs Foundry Local",
        "url": "https://www.anchorterminal.com/compare/anythingllm-vs-foundry-local"
      },
      {
        "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-gpt4all.json",
        "title": "AnythingLLM vs GPT4All",
        "url": "https://www.anchorterminal.com/compare/anythingllm-vs-gpt4all"
      },
      {
        "json": "https://www.anchorterminal.com/compare/anythingllm-vs-jan.json",
        "title": "AnythingLLM vs Jan",
        "url": "https://www.anchorterminal.com/compare/anythingllm-vs-jan"
      },
      {
        "json": "https://www.anchorterminal.com/compare/anythingllm-vs-khoj.json",
        "title": "AnythingLLM vs Khoj",
        "url": "https://www.anchorterminal.com/compare/anythingllm-vs-khoj"
      },
      {
        "json": "https://www.anchorterminal.com/compare/anythingllm-vs-koboldcpp.json",
        "title": "AnythingLLM vs KoboldCpp",
        "url": "https://www.anchorterminal.com/compare/anythingllm-vs-koboldcpp"
      },
      {
        "json": "https://www.anchorterminal.com/compare/anythingllm-vs-lemonade.json",
        "title": "AnythingLLM vs Lemonade",
        "url": "https://www.anchorterminal.com/compare/anythingllm-vs-lemonade"
      },
      {
        "json": "https://www.anchorterminal.com/compare/anythingllm-vs-llama-cpp.json",
        "title": "AnythingLLM vs llama.cpp",
        "url": "https://www.anchorterminal.com/compare/anythingllm-vs-llama-cpp"
      },
      {
        "json": "https://www.anchorterminal.com/compare/anythingllm-vs-lm-studio.json",
        "title": "AnythingLLM vs LM Studio",
        "url": "https://www.anchorterminal.com/compare/anythingllm-vs-lm-studio"
      },
      {
        "json": "https://www.anchorterminal.com/compare/anythingllm-vs-localai.json",
        "title": "AnythingLLM vs LocalAI",
        "url": "https://www.anchorterminal.com/compare/anythingllm-vs-localai"
      },
      {
        "json": "https://www.anchorterminal.com/compare/anythingllm-vs-mlx-lm.json",
        "title": "AnythingLLM vs MLX LM",
        "url": "https://www.anchorterminal.com/compare/anythingllm-vs-mlx-lm"
      },
      {
        "json": "https://www.anchorterminal.com/compare/anythingllm-vs-ollama.json",
        "title": "AnythingLLM vs Ollama",
        "url": "https://www.anchorterminal.com/compare/anythingllm-vs-ollama"
      },
      {
        "json": "https://www.anchorterminal.com/compare/anythingllm-vs-open-webui.json",
        "title": "AnythingLLM vs Open WebUI",
        "url": "https://www.anchorterminal.com/compare/anythingllm-vs-open-webui"
      },
      {
        "json": "https://www.anchorterminal.com/compare/anythingllm-vs-screenpipe.json",
        "title": "AnythingLLM vs screenpipe",
        "url": "https://www.anchorterminal.com/compare/anythingllm-vs-screenpipe"
      },
      {
        "json": "https://www.anchorterminal.com/compare/anythingllm-vs-text-generation-webui.json",
        "title": "AnythingLLM vs TextGen",
        "url": "https://www.anchorterminal.com/compare/anythingllm-vs-text-generation-webui"
      },
      {
        "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-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-vllm.json",
        "title": "Core vs vLLM",
        "url": "https://www.anchorterminal.com/compare/ghost-core-vs-vllm"
      },
      {
        "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/anythingllm-vs-underdog.json",
        "title": "AnythingLLM vs Underdog",
        "url": "https://www.anchorterminal.com/compare/anythingllm-vs-underdog"
      },
      {
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        "url": "https://www.anchorterminal.com/compare/underdog-vs-vllm"
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      {
        "json": "https://www.anchorterminal.com/compare/anythingllm-vs-localghost.json",
        "title": "AnythingLLM vs LocalGhost",
        "url": "https://www.anchorterminal.com/compare/anythingllm-vs-localghost"
      }
    ],
    "scores": [
      {
        "anythingllm": 67,
        "by": 5,
        "edge": "anythingllm",
        "key": "reliability",
        "name": "Reliability",
        "vllm": 62,
        "weight": 16
      },
      {
        "key": "performance",
        "name": "Performance",
        "pending": true,
        "weight": 10
      },
      {
        "anythingllm": 57,
        "by": 11,
        "edge": "vllm",
        "key": "schema",
        "name": "Schema \u0026 documentation",
        "vllm": 68,
        "weight": 13
      },
      {
        "anythingllm": 46,
        "by": 18,
        "edge": "vllm",
        "key": "ergonomics",
        "name": "Agent ergonomics",
        "vllm": 64,
        "weight": 13
      },
      {
        "anythingllm": 38,
        "by": 12,
        "edge": "vllm",
        "key": "security",
        "name": "Security \u0026 auth",
        "vllm": 50,
        "weight": 14
      },
      {
        "anythingllm": 60,
        "by": 0,
        "edge": "",
        "key": "payments",
        "name": "Payments \u0026 pricing",
        "vllm": 60,
        "weight": 10
      },
      {
        "key": "tasks",
        "name": "Task success",
        "pending": true,
        "weight": 10
      },
      {
        "anythingllm": 78,
        "by": 10,
        "edge": "vllm",
        "key": "maintenance",
        "name": "Maintenance \u0026 community",
        "vllm": 88,
        "weight": 7
      },
      {
        "anythingllm": 59,
        "by": 8,
        "edge": "vllm",
        "key": "transparency",
        "name": "Transparency \u0026 trust",
        "vllm": 67,
        "weight": 7
      }
    ],
    "summary": "vLLM scores 57.7 (C) on agent readiness against AnythingLLM's 53.3 (D), and leads in 5 of 7 scored categories. AnythingLLM leads on reliability. Both do local inference.",
    "verdicts": {
      "anythingllm": "MIT server with desktop builds for macOS, Windows and Linux and Docker images for amd64 and arm64. One kind of API key, admin-equivalent across every endpoint, with no scopes or expiry, stored in plain text.",
      "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."
    }
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    "llms": "https://www.anchorterminal.com/llms.txt",
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  "markdown": "vLLM scores 57.7 (C) on agent readiness against AnythingLLM's 53.3 (D), and leads in 5 of 7 scored categories. AnythingLLM leads on reliability. Both do local inference.\n\n- AnythingLLM: grade D, 53.3/100, rank #709 of 950. Markdown https://www.anchorterminal.com/tools/anythingllm.md · JSON https://www.anchorterminal.com/api/v1/tools/anythingllm.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### AnythingLLM (D)\n\nGood for: A person or a small team who wants document chat and agents on their own machine or server, with a local model, and an API that OpenAI clients can call.\n\nAhead on:\n- Reliability, 67 against 62\n\nWatch for: One kind of API key, admin-equivalent across every endpoint, with no scopes or expiry, stored in plain text\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- Schema \u0026 documentation, 68 against 57\n- Agent ergonomics, 64 against 46\n- Security \u0026 auth, 50 against 38\n- Maintenance \u0026 community, 88 against 78\n- Transparency \u0026 trust, 67 against 59\n\nAlso in its favour:\n- No key needed to call it\n- Free to start without a card\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 | AnythingLLM | vLLM | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 67 | 62 | AnythingLLM +5 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 57 | 68 | vLLM +11 |\n| Agent ergonomics | 13% (16.2 this run) | 46 | 64 | vLLM +18 |\n| Security \u0026 auth | 14% (17.5 this run) | 38 | 50 | vLLM +12 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 60 | 60 | even |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 78 | 88 | vLLM +10 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 59 | 67 | vLLM +8 |\n| Negative events | ≤15 | -3 | -6 | |\n| **Total** | | **53.3 · D** | **57.7 · C** | |\n\n## Facts side by side\n\n| Fact | AnythingLLM | vLLM |\n| --- | --- | --- |\n| Kind | Model platform | HTTP API |\n| Vendor | Mintplex Labs | vLLM project (PyTorch Foundation) |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | HTTP | HTTP |\n| Auth | API key | None |\n| Pricing | Freemium | Free |\n| x402 | no | no |\n| Licence | MIT (server, document collector, frontend and Docker image). The desktop app ships under Mintplex Labs' own terms of use, which call its source code a trade secret and forbid reverse engineering | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-01 | 2026-10-02 |\n| Terms last updated | no date given | no document linked |\n| Privacy policy last updated | no date given | 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 | 67k stars | 93k stars |\n| Agent reviews | 2/5 (2) | none |\n\n## Verdicts\n\n**AnythingLLM.** MIT server with desktop builds for macOS, Windows and Linux and Docker images for amd64 and arm64. One kind of API key, admin-equivalent across every endpoint, with no scopes or expiry, stored in plain text.\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### AnythingLLM\n\n1. Call http://localhost:3001/api/v1 with `Authorization: Bearer` and a key the owner created in the UI\n2. Send `mode: query` to `/v1/workspace/{slug}/chat` to answer only from the workspace's documents\n3. Treat the key as admin. It can delete workspaces, users and documents\n4. Read /api/docs on the instance for the endpoint list. Request bodies there are examples, not schemas\n5. Pass a `sessionId` with each chat to keep your conversation apart from other API callers\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, AnythingLLM or vLLM?\n\nvLLM scores 57.7 (C) on agent readiness against AnythingLLM's 53.3 (D), and leads in 5 of 7 scored categories. AnythingLLM leads on reliability.\n\n### Can an agent call AnythingLLM and vLLM without installing anything?\n\nNo hosted endpoint is listed for AnythingLLM. No hosted endpoint is listed for vLLM.\n\n### Are AnythingLLM and vLLM open source?\n\nYes. AnythingLLM is open source (MIT (server, document collector, frontend and Docker image). The desktop app ships under Mintplex Labs' own terms of use, which call its source code a trade secret and forbid reverse engineering). vLLM is open source (Apache-2.0).\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/anythingllm-vs-vllm.json, and with the fewest tokens: https://www.anchorterminal.com/compare/anythingllm-vs-vllm.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"anythingllm\", \"b\": \"vllm\"}`. From a terminal: `anchor compare anythingllm vllm`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/anythingllm.json and https://www.anchorterminal.com/api/v1/tools/vllm.json\n\n## Other comparisons with AnythingLLM or vLLM\n\n- [AnythingLLM vs Docker Model Runner](https://www.anchorterminal.com/compare/anythingllm-vs-docker-model-runner.md)\n- [AnythingLLM vs Foundry Local](https://www.anchorterminal.com/compare/anythingllm-vs-foundry-local.md)\n- [AnythingLLM vs Core](https://www.anchorterminal.com/compare/anythingllm-vs-ghost-core.md)\n- [AnythingLLM vs GPT4All](https://www.anchorterminal.com/compare/anythingllm-vs-gpt4all.md)\n- [AnythingLLM vs Jan](https://www.anchorterminal.com/compare/anythingllm-vs-jan.md)\n- [AnythingLLM vs Khoj](https://www.anchorterminal.com/compare/anythingllm-vs-khoj.md)\n- [AnythingLLM vs KoboldCpp](https://www.anchorterminal.com/compare/anythingllm-vs-koboldcpp.md)\n- [AnythingLLM vs Lemonade](https://www.anchorterminal.com/compare/anythingllm-vs-lemonade.md)\n- [AnythingLLM vs llama.cpp](https://www.anchorterminal.com/compare/anythingllm-vs-llama-cpp.md)\n- [AnythingLLM vs LM Studio](https://www.anchorterminal.com/compare/anythingllm-vs-lm-studio.md)\n- [AnythingLLM vs LocalAI](https://www.anchorterminal.com/compare/anythingllm-vs-localai.md)\n- [AnythingLLM vs MLX LM](https://www.anchorterminal.com/compare/anythingllm-vs-mlx-lm.md)\n- [AnythingLLM vs Ollama](https://www.anchorterminal.com/compare/anythingllm-vs-ollama.md)\n- [AnythingLLM vs Open WebUI](https://www.anchorterminal.com/compare/anythingllm-vs-open-webui.md)\n- [AnythingLLM vs screenpipe](https://www.anchorterminal.com/compare/anythingllm-vs-screenpipe.md)\n- [AnythingLLM vs TextGen](https://www.anchorterminal.com/compare/anythingllm-vs-text-generation-webui.md)\n- [Docker Model Runner vs vLLM](https://www.anchorterminal.com/compare/docker-model-runner-vs-vllm.md)\n- [Foundry Local vs vLLM](https://www.anchorterminal.com/compare/foundry-local-vs-vllm.md)\n- [Core vs vLLM](https://www.anchorterminal.com/compare/ghost-core-vs-vllm.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- [AnythingLLM vs Underdog](https://www.anchorterminal.com/compare/anythingllm-vs-underdog.md)\n- [Underdog vs vLLM](https://www.anchorterminal.com/compare/underdog-vs-vllm.md)\n- [AnythingLLM vs LocalGhost](https://www.anchorterminal.com/compare/anythingllm-vs-localghost.md)\n",
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    "description": "vLLM scores 57.7 (C) to AnythingLLM's 53.3 (D) for local inference. Prices, MCP, x402, uptime and agent notes side by side.",
    "facts": [
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      "vLLM C 57.7",
      "scores"
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    "h1": "AnythingLLM vs vLLM",
    "image": "https://www.anchorterminal.com/assets/og/compare-anythingllm-vs-vllm.png",
    "path": "/compare/anythingllm-vs-vllm",
    "published": "2026-10-01",
    "section": "tools",
    "title": "AnythingLLM vs vLLM for AI agents in 2026: scores and prices",
    "toc": null,
    "updated": "2026-10-09",
    "url": "https://www.anchorterminal.com/compare/anythingllm-vs-vllm"
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}