{ "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": 53.5, "grade": "D", "agentReady": false, "rank": 840, "ranked": true, "rankOf": 1239, "categoryRank": 14, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 46, "maintenance": 78, "payments": 60, "reliability": 67, "schema": 57, "security": 38, "transparency": 62 }, "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": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 53.5 } ], "editorialScores": { "ergonomics": 46, "maintenance": 78, "payments": 60, "reliability": 67, "schema": 57, "security": 38, "transparency": 58 }, "provenanceScore": 66, "checked": { "categories": { "ergonomics": "2026-10-03", "maintenance": "2026-10-03", "payments": "2026-10-03", "reliability": "2026-10-03", "schema": "2026-10-03", "security": "2026-10-03", "transparency": "2026-10-03" }, "last": "2026-10-03", "lastMoved": "2026-10-27", "oldest": "2026-10-03", "stale": false, "staleAfterDays": 90 } }, "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": 66 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/anythingllm.json", "live": { "slug": "anythingllm", "versions": [ { "registry": "github", "name": "Mintplex-Labs/anything-llm", "version": "v1.19.0", "released": "2026-10-26", "seenAt": "2026-10-30T13:57:17.257047939Z" } ], "githubStars": 67748, "securityTxt": { "url": "https://anythingllm.com/.well-known/security.txt", "state": "none", "checkedAt": "2026-10-30T15:37:42.963885248Z" }, "domain": { "domain": "anythingllm.com", "registered": "2023-06-08", "source": "https://rdap.verisign.com/com/v1/domain/anythingllm.com", "checkedAt": "2026-10-25T14:03:09.415637117Z" }, "pages": [ { "url": "https://docs.anythingllm.com/installation-desktop/privacy", "kind": "privacy", "status": 304, "checkedAt": "2026-10-30T05:36:19.783970192Z", "changedAt": "2026-10-27T03:43:12.316803363Z", "fingerprint": "0c687507c778" }, { "url": "https://docs.anythingllm.com/installation-desktop/terms", "kind": "terms", "status": 304, "checkedAt": "2026-10-30T05:36:21.868887675Z", "changedAt": "2026-10-27T03:43:14.432642242Z", "fingerprint": "d88a86a051d7" } ], "github": { "repo": "mintplex-labs/anything-llm", "checkedAt": "2026-10-26T07:46:43.817140901Z", "stars": 67570, "archived": false, "issuesEnabled": true, "pushedAt": "2026-10-25T18:20:45Z", "openIssues": 320, "openPRs": 31, "issuesOpened90d": 360, "issuesClosed90d": 336, "prsMerged90d": 299, "ci": { "branch": "master", "runs": 50, "success": 21, "failure": 2, "other": 27, "latest": "success", "latestAt": "2026-10-25T18:23:09Z" }, "advisories": [ { "ghsa": "GHSA-p9pc-ccw4-fxc5", "severity": "low", "published": "2026-09-21T18:19:57Z" }, { "ghsa": "GHSA-3f4c-xj8r-h4cr", "severity": "medium", "published": "2026-09-21T18:08:52Z" }, { "ghsa": "GHSA-8wh8-3v68-whwj", "severity": "medium", "published": "2026-09-14T16:42:35Z" }, { "ghsa": "GHSA-rh3m-xv7m-9jhf", "cve": "CVE-2026-88055", "severity": "medium", "published": "2026-09-01T16:29:20Z" }, { "ghsa": "GHSA-vv8w-wg6r-hq56", "cve": "CVE-2026-72917", "severity": "medium", "published": "2026-07-15T17:25:36Z" }, { "ghsa": "GHSA-r872-gr59-vf5w", "cve": "CVE-2026-55611", "severity": "low", "published": "2026-06-14T23:17:22Z" }, { "ghsa": "GHSA-j4m9-wwcq-m868", "cve": "CVE-2026-48789", "severity": "medium", "published": "2026-06-01T23:27:21Z" }, { "ghsa": "GHSA-6hrp-7mw6-8v59", "cve": "CVE-2026-48116", "severity": "high", "published": "2026-05-21T00:02:49Z" }, { "ghsa": "GHSA-h349-hp2v-8rhw", "cve": "CVE-2026-47713", "severity": "low", "published": "2026-05-19T18:28:03Z" }, { "ghsa": "GHSA-vjrp-43mm-j7vw", "cve": "CVE-2026-45403", "severity": "low", "published": "2026-05-10T20:12:20Z" }, { "ghsa": "GHSA-jwqg-jfg3-x5vv", "cve": "CVE-2026-42456", "severity": "medium", "published": "2026-04-22T23:46:30Z" }, { "ghsa": "GHSA-4q6m-qh3w-9gf5", "cve": "CVE-2026-41318", "severity": "medium", "published": "2026-04-15T17:38:37Z" }, { "ghsa": "GHSA-2qmm-82f7-8qj5", "severity": "medium", "published": "2026-03-13T23:51:21Z" }, { "ghsa": "GHSA-p5rf-8p88-979c", "severity": "medium", "published": "2026-03-13T22:22:27Z" }, { "ghsa": "GHSA-rh66-4w74-cf4m", "cve": "CVE-2026-32719", "severity": "medium", "published": "2026-03-13T17:18:38Z" }, { "ghsa": "GHSA-7754-8jcc-2rg3", "cve": "CVE-2026-32717", "severity": "low", "published": "2026-03-13T17:05:31Z" }, { "ghsa": "GHSA-wfq3-65gm-3g2p", "cve": "CVE-2026-32715", "severity": "low", "published": "2026-03-13T16:46:57Z" }, { "ghsa": "GHSA-jwjx-mw2p-5wc7", "cve": "CVE-2026-32628", "severity": "high", "published": "2026-03-13T04:57:39Z" }, { "ghsa": "GHSA-rrmw-2j6x-4mf2", "cve": "CVE-2026-32626", "severity": "critical", "published": "2026-03-13T03:50:32Z" }, { "ghsa": "GHSA-24qj-pw4h-3jmm", "cve": "CVE-2026-32617", "severity": "high", "published": "2026-03-12T22:42:05Z" }, { "ghsa": "GHSA-jp2f-99h9-7vjv", "cve": "CVE-2026-24478", "severity": "high", "published": "2026-01-24T01:08:03Z" }, { "ghsa": "GHSA-gm94-qc2p-xcwf", "cve": "CVE-2026-24477", "severity": "high", "published": "2026-01-24T00:05:36Z" }, { "ghsa": "GHSA-47vr-w3vm-69ch", "cve": "CVE-2026-21484", "severity": "medium", "published": "2026-01-02T23:51:01Z" }, { "ghsa": "GHSA-7hpg-6pc7-cx86", "severity": "high", "published": "2025-05-07T16:38:51Z" }, { "ghsa": "GHSA-xmj6-g32r-fc5q", "cve": "CVE-2024-22422", "severity": "high", "published": "2024-01-18T21:57:52Z" } ], "replies": { "sampled": 25, "unanswered": 2, "medianDays": 0.5 } }, "updatedAt": "2026-10-30T15:37:42.963885248Z" } }, "answer": "HyperQwen scores 56.9 (C) on agent readiness against AnythingLLM's 53.5 (D), and leads in 2 of 7 scored categories. AnythingLLM leads on reliability, maintenance \u0026 community and transparency \u0026 trust.", "b": { "slug": "hyperqwen", "name": "HyperQwen", "vendor": "syv.ai (syv-ai on GitHub)", "vendorUrl": "https://syv.ai", "kind": "http-api", "category": "local-ai", "summary": "HyperQwen is syv.ai's open-source serving setup for Qwen3.8-27B on one 24 GB NVIDIA GPU. A patch series against a pinned vLLM, a requantisation pipeline and Docker Compose profiles run vLLM's OpenAI-compatible API on port 18020.", "url": "https://www.anchorterminal.com/tools/hyperqwen", "markdownUrl": "https://www.anchorterminal.com/tools/hyperqwen.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/hyperqwen.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/hyperqwen.json", "repo": "https://github.com/syv-ai/HyperQwen", "license": "Apache-2.0", "transports": [ "http" ], "packages": [ { "registry": "oci", "name": "ghcr.io/syv-ai/hyperqwen" } ], "auth": "none", "authNotes": "No credential by default. `VLLM_API_KEY` in `.env` (or an `api_key.txt` file) turns on vLLM's Bearer check. With no key, the launcher binds 127.0.0.1 outside a container, and Docker Compose publishes the port on 127.0.0.1 unless `BIND` is changed. With a key set, the launcher binds every interface. The key is vLLM's own, one static key with no scopes, which vLLM checks only under `/v1`, `/v2`, `/inference` and `/cohere` (per the vLLM listing's check of 9 October 2026, 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. The README links a Ko-fi page for donations towards GPU time. The owner pays for a 24 GB NVIDIA GPU, about 40 GB of disk and electricity.", "priceSummary": "Free · OSS", "where": "local", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in the README, the docs or the launch scripts (checked 2026-10-30). Nothing is sold.", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 2400, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-30" }, "docsUrl": "https://github.com/syv-ai/HyperQwen#readme", "capabilities": [ "inference.local", "inference.open-weights" ], "tags": [ "open-source", "local", "self-hosted", "free", "no-card", "apache-2.0", "openai-compatible", "docker", "tool-calling", "reasoning", "pre-1.0" ], "graded": true, "anchor": { "graded": true, "score": 56.9, "grade": "C", "agentReady": false, "rank": 738, "ranked": true, "rankOf": 1239, "categoryRank": 11, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 65, "maintenance": 68, "payments": 60, "reliability": 59, "schema": 56, "security": 41, "transparency": 55 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-30" }, "negative": 0, "verdict": "HyperQwen packages a patched vLLM 0.32.0 and requantised Qwen3.8-27B weights into one container with tool calling preset and a loopback bind when no key is set. It runs one model family, its speed figures come from one RTX 3090, and it has no versioned releases, changelog or security policy.", "bestFor": "An operator with one 24 GB NVIDIA card who wants Qwen3.8-27B behind an OpenAI-compatible endpoint for a coding or tool-calling agent, with long context.", "strengths": [ "Prebuilt image on ghcr.io with an immutable tag per commit, built by CI that applies all 60 patches and runs the build-time tests", "Tool calling on by default with the `qwen3_coder` parser and argument schemas enforced (`TOOL_STRICT=parameter`)", "With no API key the server listens on 127.0.0.1 only, and a CI check tests that default", "Five documented setups from 64k to 240k context, with the measured cost of each knob", "Apache-2.0 throughout, with vendored kernels and patches carrying their upstream references" ], "weaknesses": [ "Serves Qwen3.8-27B and checkpoints derived from it only. Other models are on the roadmap", "No tags, releases or changelog. Changes reach users through `latest` on every push to main", "No SECURITY.md or disclosure route, and the base weights come from a third-party repository with no pinned revision", "vLLM's key covers only `/v1`-style prefixes, so control routes such as `/pause` answer without it", "First start pulls a 9.5 GB image and about 20 GB of weights and can take over 30 minutes" ], "agentNotes": [ "Call http://127.0.0.1:18020/v1 with model `qwen3.8-27b` and the `VLLM_API_KEY` value, or any string if no key was set", "Poll `/health` before the first request. A first start downloads and prepares the model and compiles kernels, which can take over 30 minutes", "Declare the context window your setup uses (65,536 by default, 131,072 for `CTX=long`, 245,760 for `CTX=huge`) so the client doesn't truncate", "Ask the owner to set `VLLM_API_KEY` before setting `BIND=0.0.0.0`, and to front the port with a proxy that allowlists routes", "Treat the README's tokens-per-second figures as the project's measurements on one RTX 3090 at 250 W" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 56.9 } ], "editorialScores": { "ergonomics": 65, "maintenance": 68, "payments": 60, "reliability": 59, "schema": 56, "security": 41, "transparency": 60 }, "provenanceScore": 49, "checked": { "categories": { "ergonomics": "2026-10-30", "maintenance": "2026-10-30", "payments": "2026-10-30", "reliability": "2026-10-30", "schema": "2026-10-30", "security": "2026-10-30", "transparency": "2026-10-30" }, "last": "2026-10-30", "oldest": "2026-10-30", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "git clone https://github.com/syv-ai/HyperQwen \u0026\u0026 cd HyperQwen\ncp .env.example .env\n\ndocker compose --profile single up -d # one or a few people chatting\ndocker compose --profile batch up -d # API backend, many concurrent requests" }, "letme": { "capability": "https://letme.dev/inference.local", "tool": "https://letme.dev/hyperqwen" }, "area": "models", "provenance": { "legalEntity": "syv.ai (Copenhagen, CVR DK44671247)", "domain": "syv.ai", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "https://syv.ai/privatlivspolitik", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-30", "notes": [ "The Hugging Face organisation syvai, full name syv.ai, links https://syv.ai and github.com/syv-ai, and hosts the drafter files HyperQwen downloads. syv.ai's own pages don't mention HyperQwen.", "syv.ai's contact page gives the address in Copenhagen and CVR DK44671247. The Danish CVR register answered with a bot check and wasn't read.", "No terms, SECURITY.md or changelog for HyperQwen. The Apache-2.0 licence stands in for terms. The privacy policy listed is syv.ai's website policy and doesn't cover the software, which runs on the owner's machine.", "syv.ai answers 404 at /.well-known/security.txt and /security.txt." ], "score": 49 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/hyperqwen.json" }, "facts": [ { "a": "Platform", "b": "HTTP API", "name": "Kind" }, { "a": "Mintplex Labs", "b": "syv.ai (syv-ai on GitHub)", "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": "none", "name": "Last release" }, { "a": "no date given", "b": "no document linked", "name": "Terms last updated" }, { "a": "no date given", "b": "no date given", "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": "2.4k stars", "name": "Popularity" }, { "a": "2/5 (2)", "b": "none", "name": "Agent reviews" } ], "faq": [ { "answer": "HyperQwen scores 56.9 (C) on agent readiness against AnythingLLM's 53.5 (D), and leads in 2 of 7 scored categories. AnythingLLM leads on reliability, maintenance \u0026 community and transparency \u0026 trust.", "question": "Which is better for AI agents, AnythingLLM or HyperQwen?" }, { "answer": "No hosted endpoint is listed for AnythingLLM. No hosted endpoint is listed for HyperQwen.", "question": "Can an agent call AnythingLLM and HyperQwen 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). HyperQwen is open source (Apache-2.0).", "question": "Are AnythingLLM and HyperQwen open source?" } ], "goodFor": [ { "aheadOn": [ "Reliability, 67 against 59", "Maintenance \u0026 community, 78 against 68", "Transparency \u0026 trust, 62 against 55" ], "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": [ "Agent ergonomics, 65 against 46" ], "also": [ "No key needed to call it", "Free to start without a card", "No incidents deducted, where AnythingLLM loses 3 points for them" ], "goodFor": "An operator with one 24 GB NVIDIA card who wants Qwen3.8-27B behind an OpenAI-compatible endpoint for a coding or tool-calling agent, with long context.", "slug": "hyperqwen", "watchFor": "Serves Qwen3.8-27B and checkpoints derived from it only. Other models are on the roadmap" } ], "job": { "capability": "inference.local", "name": "Local inference" }, "others": [ { "json": "https://www.anchorterminal.com/compare/anythingllm-vs-berth.json", "title": "AnythingLLM vs Berth", "url": "https://www.anchorterminal.com/compare/anythingllm-vs-berth" }, { "json": "https://www.anchorterminal.com/compare/anythingllm-vs-bitrun.json", "title": "AnythingLLM vs BitRun", "url": "https://www.anchorterminal.com/compare/anythingllm-vs-bitrun" }, { "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/anythingllm-vs-vllm.json", "title": "AnythingLLM vs vLLM", "url": "https://www.anchorterminal.com/compare/anythingllm-vs-vllm" }, { "json": "https://www.anchorterminal.com/compare/berth-vs-hyperqwen.json", "title": "Berth vs HyperQwen", "url": "https://www.anchorterminal.com/compare/berth-vs-hyperqwen" }, { "json": "https://www.anchorterminal.com/compare/bitrun-vs-hyperqwen.json", "title": "BitRun vs HyperQwen", "url": "https://www.anchorterminal.com/compare/bitrun-vs-hyperqwen" }, { "json": "https://www.anchorterminal.com/compare/docker-model-runner-vs-hyperqwen.json", "title": "Docker Model Runner vs HyperQwen", "url": "https://www.anchorterminal.com/compare/docker-model-runner-vs-hyperqwen" }, { "json": "https://www.anchorterminal.com/compare/foundry-local-vs-hyperqwen.json", "title": "Foundry Local vs HyperQwen", "url": "https://www.anchorterminal.com/compare/foundry-local-vs-hyperqwen" }, { "json": "https://www.anchorterminal.com/compare/ghost-core-vs-hyperqwen.json", "title": "Core vs HyperQwen", "url": "https://www.anchorterminal.com/compare/ghost-core-vs-hyperqwen" }, { "json": "https://www.anchorterminal.com/compare/gpt4all-vs-hyperqwen.json", "title": "GPT4All vs HyperQwen", "url": "https://www.anchorterminal.com/compare/gpt4all-vs-hyperqwen" }, { "json": "https://www.anchorterminal.com/compare/hyperqwen-vs-jan.json", "title": "HyperQwen vs Jan", "url": "https://www.anchorterminal.com/compare/hyperqwen-vs-jan" }, { "json": "https://www.anchorterminal.com/compare/hyperqwen-vs-khoj.json", "title": "HyperQwen vs Khoj", "url": "https://www.anchorterminal.com/compare/hyperqwen-vs-khoj" }, { "json": "https://www.anchorterminal.com/compare/hyperqwen-vs-koboldcpp.json", "title": "HyperQwen vs KoboldCpp", "url": "https://www.anchorterminal.com/compare/hyperqwen-vs-koboldcpp" }, { "json": "https://www.anchorterminal.com/compare/hyperqwen-vs-lemonade.json", "title": "HyperQwen vs Lemonade", "url": "https://www.anchorterminal.com/compare/hyperqwen-vs-lemonade" }, { "json": "https://www.anchorterminal.com/compare/hyperqwen-vs-llama-cpp.json", "title": "HyperQwen vs llama.cpp", "url": "https://www.anchorterminal.com/compare/hyperqwen-vs-llama-cpp" }, { "json": "https://www.anchorterminal.com/compare/hyperqwen-vs-lm-studio.json", "title": "HyperQwen vs LM Studio", "url": "https://www.anchorterminal.com/compare/hyperqwen-vs-lm-studio" }, { "json": "https://www.anchorterminal.com/compare/hyperqwen-vs-localai.json", "title": "HyperQwen vs LocalAI", "url": "https://www.anchorterminal.com/compare/hyperqwen-vs-localai" }, { "json": "https://www.anchorterminal.com/compare/hyperqwen-vs-mlx-lm.json", "title": "HyperQwen vs MLX LM", "url": "https://www.anchorterminal.com/compare/hyperqwen-vs-mlx-lm" }, { "json": "https://www.anchorterminal.com/compare/hyperqwen-vs-ollama.json", "title": "HyperQwen vs Ollama", "url": "https://www.anchorterminal.com/compare/hyperqwen-vs-ollama" }, { "json": "https://www.anchorterminal.com/compare/hyperqwen-vs-open-webui.json", "title": "HyperQwen vs Open WebUI", "url": "https://www.anchorterminal.com/compare/hyperqwen-vs-open-webui" }, { "json": "https://www.anchorterminal.com/compare/hyperqwen-vs-screenpipe.json", "title": "HyperQwen vs screenpipe", "url": "https://www.anchorterminal.com/compare/hyperqwen-vs-screenpipe" }, { "json": "https://www.anchorterminal.com/compare/hyperqwen-vs-text-generation-webui.json", "title": "HyperQwen vs TextGen", "url": "https://www.anchorterminal.com/compare/hyperqwen-vs-text-generation-webui" }, { "json": "https://www.anchorterminal.com/compare/hyperqwen-vs-vllm.json", "title": "HyperQwen vs vLLM", "url": "https://www.anchorterminal.com/compare/hyperqwen-vs-vllm" }, { "json": "https://www.anchorterminal.com/compare/anythingllm-vs-localghost.json", "title": "AnythingLLM vs LocalGhost", "url": "https://www.anchorterminal.com/compare/anythingllm-vs-localghost" }, { "json": "https://www.anchorterminal.com/compare/anythingllm-vs-underdog.json", "title": "AnythingLLM vs Underdog", "url": "https://www.anchorterminal.com/compare/anythingllm-vs-underdog" }, { "json": "https://www.anchorterminal.com/compare/hyperqwen-vs-underdog.json", "title": "HyperQwen vs Underdog", "url": "https://www.anchorterminal.com/compare/hyperqwen-vs-underdog" } ], "scores": [ { "anythingllm": 67, "by": 8, "edge": "anythingllm", "hyperqwen": 59, "key": "reliability", "name": "Reliability", "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "anythingllm": 57, "by": 1, "edge": "anythingllm", "hyperqwen": 56, "key": "schema", "name": "Schema \u0026 documentation", "weight": 13 }, { "anythingllm": 46, "by": 19, "edge": "hyperqwen", "hyperqwen": 65, "key": "ergonomics", "name": "Agent ergonomics", "weight": 13 }, { "anythingllm": 38, "by": 3, "edge": "hyperqwen", "hyperqwen": 41, "key": "security", "name": "Security \u0026 auth", "weight": 14 }, { "anythingllm": 60, "by": 0, "edge": "", "hyperqwen": 60, "key": "payments", "name": "Payments \u0026 pricing", "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "anythingllm": 78, "by": 10, "edge": "anythingllm", "hyperqwen": 68, "key": "maintenance", "name": "Maintenance \u0026 community", "weight": 7 }, { "anythingllm": 62, "by": 7, "edge": "anythingllm", "hyperqwen": 55, "key": "transparency", "name": "Transparency \u0026 trust", "weight": 7 } ], "summary": "HyperQwen scores 56.9 (C) on agent readiness against AnythingLLM's 53.5 (D), and leads in 2 of 7 scored categories. AnythingLLM leads on reliability, maintenance \u0026 community and transparency \u0026 trust. Both do local inference.", "tutorials": [], "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.", "hyperqwen": "HyperQwen packages a patched vLLM 0.32.0 and requantised Qwen3.8-27B weights into one container with tool calling preset and a loopback bind when no key is set. It runs one model family, its speed figures come from one RTX 3090, and it has no versioned releases, changelog or security policy." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/anythingllm-vs-hyperqwen", "json": "https://www.anchorterminal.com/compare/anythingllm-vs-hyperqwen.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/anythingllm-vs-hyperqwen.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/anythingllm-vs-hyperqwen.min.md" }, "markdown": "HyperQwen scores 56.9 (C) on agent readiness against AnythingLLM's 53.5 (D), and leads in 2 of 7 scored categories. AnythingLLM leads on reliability, maintenance \u0026 community and transparency \u0026 trust. Both do local inference.\n\n- AnythingLLM: grade D, 53.5/100, rank #840 of 1239. Markdown https://www.anchorterminal.com/tools/anythingllm.md · JSON https://www.anchorterminal.com/api/v1/tools/anythingllm.json\n- HyperQwen: grade C, 56.9/100, rank #738 of 1239. Markdown https://www.anchorterminal.com/tools/hyperqwen.md · JSON https://www.anchorterminal.com/api/v1/tools/hyperqwen.json\n- Best local AI models and assistants: https://www.anchorterminal.com/best/local-ai/index.md\n- All 253 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 59\n- Maintenance \u0026 community, 78 against 68\n- Transparency \u0026 trust, 62 against 55\n\nWatch for: One kind of API key, admin-equivalent across every endpoint, with no scopes or expiry, stored in plain text\n\n### HyperQwen (C)\n\nGood for: An operator with one 24 GB NVIDIA card who wants Qwen3.8-27B behind an OpenAI-compatible endpoint for a coding or tool-calling agent, with long context.\n\nAhead on:\n- Agent ergonomics, 65 against 46\n\nAlso in its favour:\n- No key needed to call it\n- Free to start without a card\n- No incidents deducted, where AnythingLLM loses 3 points for them\n\nWatch for: Serves Qwen3.8-27B and checkpoints derived from it only. Other models are on the roadmap\n\n\n## Score by category\n\n| Category | Weight | AnythingLLM | HyperQwen | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 67 | 59 | AnythingLLM +8 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 57 | 56 | AnythingLLM +1 |\n| Agent ergonomics | 13% (16.2 this run) | 46 | 65 | HyperQwen +19 |\n| Security \u0026 auth | 14% (17.5 this run) | 38 | 41 | HyperQwen +3 |\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 | 68 | AnythingLLM +10 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 62 | 55 | AnythingLLM +7 |\n| Negative events | ≤15 | -3 | 0 | |\n| **Total** | | **53.5 · D** | **56.9 · C** | |\n\n## Facts side by side\n\n| Fact | AnythingLLM | HyperQwen |\n| --- | --- | --- |\n| Kind | Platform | HTTP API |\n| Vendor | Mintplex Labs | syv.ai (syv-ai on GitHub) |\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 | none |\n| Terms last updated | no date given | no document linked |\n| Privacy policy last updated | no date given | no date given |\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 | 2.4k 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**HyperQwen.** HyperQwen packages a patched vLLM 0.32.0 and requantised Qwen3.8-27B weights into one container with tool calling preset and a loopback bind when no key is set. It runs one model family, its speed figures come from one RTX 3090, and it has no versioned releases, changelog or security policy.\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### HyperQwen\n\n1. Call http://127.0.0.1:18020/v1 with model `qwen3.8-27b` and the `VLLM_API_KEY` value, or any string if no key was set\n2. Poll `/health` before the first request. A first start downloads and prepares the model and compiles kernels, which can take over 30 minutes\n3. Declare the context window your setup uses (65,536 by default, 131,072 for `CTX=long`, 245,760 for `CTX=huge`) so the client doesn't truncate\n4. Ask the owner to set `VLLM_API_KEY` before setting `BIND=0.0.0.0`, and to front the port with a proxy that allowlists routes\n5. Treat the README's tokens-per-second figures as the project's measurements on one RTX 3090 at 250 W\n\n## Questions\n\n### Which is better for AI agents, AnythingLLM or HyperQwen?\n\nHyperQwen scores 56.9 (C) on agent readiness against AnythingLLM's 53.5 (D), and leads in 2 of 7 scored categories. AnythingLLM leads on reliability, maintenance \u0026 community and transparency \u0026 trust.\n\n### Can an agent call AnythingLLM and HyperQwen without installing anything?\n\nNo hosted endpoint is listed for AnythingLLM. No hosted endpoint is listed for HyperQwen.\n\n### Are AnythingLLM and HyperQwen 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). HyperQwen is open source (Apache-2.0).\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/anythingllm-vs-hyperqwen.json, and with the fewest tokens: https://www.anchorterminal.com/compare/anythingllm-vs-hyperqwen.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"anythingllm\", \"b\": \"hyperqwen\"}`. From a terminal: `anchor compare anythingllm hyperqwen`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/anythingllm.json and https://www.anchorterminal.com/api/v1/tools/hyperqwen.json\n\n## Other comparisons with AnythingLLM or HyperQwen\n\n- [AnythingLLM vs Berth](https://www.anchorterminal.com/compare/anythingllm-vs-berth.md)\n- [AnythingLLM vs BitRun](https://www.anchorterminal.com/compare/anythingllm-vs-bitrun.md)\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- [AnythingLLM vs vLLM](https://www.anchorterminal.com/compare/anythingllm-vs-vllm.md)\n- [Berth vs HyperQwen](https://www.anchorterminal.com/compare/berth-vs-hyperqwen.md)\n- [BitRun vs HyperQwen](https://www.anchorterminal.com/compare/bitrun-vs-hyperqwen.md)\n- [Docker Model Runner vs HyperQwen](https://www.anchorterminal.com/compare/docker-model-runner-vs-hyperqwen.md)\n- [Foundry Local vs HyperQwen](https://www.anchorterminal.com/compare/foundry-local-vs-hyperqwen.md)\n- [Core vs HyperQwen](https://www.anchorterminal.com/compare/ghost-core-vs-hyperqwen.md)\n- [GPT4All vs HyperQwen](https://www.anchorterminal.com/compare/gpt4all-vs-hyperqwen.md)\n- [HyperQwen vs Jan](https://www.anchorterminal.com/compare/hyperqwen-vs-jan.md)\n- [HyperQwen vs Khoj](https://www.anchorterminal.com/compare/hyperqwen-vs-khoj.md)\n- [HyperQwen vs KoboldCpp](https://www.anchorterminal.com/compare/hyperqwen-vs-koboldcpp.md)\n- [HyperQwen vs Lemonade](https://www.anchorterminal.com/compare/hyperqwen-vs-lemonade.md)\n- [HyperQwen vs llama.cpp](https://www.anchorterminal.com/compare/hyperqwen-vs-llama-cpp.md)\n- [HyperQwen vs LM Studio](https://www.anchorterminal.com/compare/hyperqwen-vs-lm-studio.md)\n- [HyperQwen vs LocalAI](https://www.anchorterminal.com/compare/hyperqwen-vs-localai.md)\n- [HyperQwen vs MLX LM](https://www.anchorterminal.com/compare/hyperqwen-vs-mlx-lm.md)\n- [HyperQwen vs Ollama](https://www.anchorterminal.com/compare/hyperqwen-vs-ollama.md)\n- [HyperQwen vs Open WebUI](https://www.anchorterminal.com/compare/hyperqwen-vs-open-webui.md)\n- [HyperQwen vs screenpipe](https://www.anchorterminal.com/compare/hyperqwen-vs-screenpipe.md)\n- [HyperQwen vs TextGen](https://www.anchorterminal.com/compare/hyperqwen-vs-text-generation-webui.md)\n- [HyperQwen vs vLLM](https://www.anchorterminal.com/compare/hyperqwen-vs-vllm.md)\n- [AnythingLLM vs LocalGhost](https://www.anchorterminal.com/compare/anythingllm-vs-localghost.md)\n- [AnythingLLM vs Underdog](https://www.anchorterminal.com/compare/anythingllm-vs-underdog.md)\n- [HyperQwen vs Underdog](https://www.anchorterminal.com/compare/hyperqwen-vs-underdog.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-10-31", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.7", "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": "AnythingLLM vs HyperQwen", "url": "" } ], "description": "HyperQwen scores 56.9 (C) to AnythingLLM's 53.5 (D) for local inference. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "AnythingLLM D 53.5", "HyperQwen C 56.9", "scores" ], "h1": "AnythingLLM vs HyperQwen", "image": "https://www.anchorterminal.com/assets/og/compare-anythingllm-vs-hyperqwen.png", "path": "/compare/anythingllm-vs-hyperqwen", "published": "2026-10-01", "section": "tools", "title": "AnythingLLM vs HyperQwen for AI agents in 2026: scores and prices", "toc": null, "updated": "2026-10-30", "url": "https://www.anchorterminal.com/compare/anythingllm-vs-hyperqwen" }, "tokens": { "markdown": 2850, "slim": 730 }, "version": 1 }