{ "data": { "a": { "slug": "docker-model-runner", "name": "Docker Model Runner", "vendor": "Docker, Inc.", "vendorUrl": "https://www.docker.com", "kind": "http-api", "category": "local-ai", "summary": "Docker's open-source tool for pulling and running open models from Docker Hub, OCI registries or Hugging Face. It runs through Docker Desktop, Docker Engine or a standalone `dmr` binary, with local OpenAI-, Anthropic- and Ollama-compatible APIs.", "url": "https://www.anchorterminal.com/tools/docker-model-runner", "markdownUrl": "https://www.anchorterminal.com/tools/docker-model-runner.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/docker-model-runner.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/docker-model-runner.json", "dns": "docker-model-runner.grade.anchorterminal.com", "repo": "https://github.com/docker/model-runner", "license": "Apache-2.0 (server, CLI plugin and `dmr` binary). Docker Desktop, which bundles it, is closed software under Docker's subscription agreement, and each model carries its own licence", "transports": [ "http" ], "packages": [ { "registry": "oci", "name": "docker.io/docker/model-runner" } ], "auth": "none", "authNotes": "The API takes no credential, and the docs say it ignores any key sent. Per the docs, any client that can reach it, including other containers on the same Docker network, can pull, load and run models. In Docker Desktop, host-side TCP is off until enabled in settings or with `docker desktop enable model-runner --tcp \u003cport\u003e`, and containers reach the API at model-runner.docker.internal. In Docker Engine, TCP is on by default on port 12434. Cross-origin requests get 403 unless the origin is localhost, 127.0.0.1, 0.0.0.0 or listed in `DMR_ORIGINS` (https://docs.docker.com/ai/model-runner/; https://github.com/docker/model-runner/blob/main/pkg/envconfig/envconfig.go).", "pricing": "free", "pricingNotes": "Free under Apache-2.0, with no account needed for the Docker Engine plugin or the standalone `dmr` binary. On macOS and Windows it also ships inside Docker Desktop, which is free for personal use, non-commercial open-source projects and businesses with fewer than 250 employees and under US $10,000,000 in annual revenue. Larger organisations need a paid Docker plan for Desktop (https://www.docker.com/legal/docker-subscription-service-agreement/; https://www.docker.com/pricing/, checked 2026-10-08).", "priceSummary": "Free · OSS", "where": "local", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in the docs or the source (checked 2026-10-08).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 656, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-08" }, "docsUrl": "https://docs.docker.com/ai/model-runner/", "llmsTxt": "https://docs.docker.com/llms.txt", "capabilities": [ "inference.local", "inference.open-weights", "inference.llm", "embed.text", "rerank", "image.generate" ], "tags": [ "open-source", "local", "self-hosted", "free", "no-card", "openai-compatible", "llms-txt", "docker", "go", "no-auth" ], "lastRelease": "2026-10-21", "graded": true, "anchor": { "graded": true, "score": 53.8, "grade": "D", "agentReady": false, "rank": 940, "ranked": true, "rankOf": 1510, "categoryRank": 10, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 58, "maintenance": 85, "payments": 60, "reliability": 75, "schema": 49, "security": 40, "transparency": 74 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-11-04" }, "negative": -7, "negativeNotes": [ "2026-02-27. GHSA-m456-c56c-hh5c (CVE-2026-28400, High 7.5). The unauthenticated `/engines/_configure` route accepted arbitrary runtime flags, so a caller, including a container on Docker Desktop, could overwrite files the runner could reach. Fixed in Model Runner 1.0.16 and Docker Desktop 4.61.0 and published by Docker, over two months ago, -1 at this run's value for a fixed High. https://osv.dev/vulnerability/CVE-2026-28400", "2026-04-01. GHSA-x2f5-332j-9xwq (CVE-2026-33990, Medium 6.8 in OSV). A malicious OCI registry could point the token exchange at an internal URL. Fixed in 1.1.25 and Docker Desktop 4.67.0 and published by Docker, -1 at this run's value for a fixed vendor-published Moderate. https://osv.dev/vulnerability/CVE-2026-33990", "2026-08-11. A released fix of the project's own described flaw with no advisory. Commit 60c8865 in v1.2.7 says CVE-2026-33990 \"was fixed only in the hand-rolled Exchange() used by the push flow\" and that the pull path still let \"a malicious registry ... drive a token fetch at an internal address and turn Model Runner into an SSRF proxy\". OSV holds no advisory for it, -3. https://github.com/docker/model-runner/commit/60c8865", "Misleading claim, carried from the 8 October docs reading. The docs' data-collection section says no personal data is collected and describes a HEAD request carrying the model name and user agent. The tracker (pkg/metrics/metrics.go) sends that request over HTTPS on each inference and backend configure call to the registry named in the model's tag (registry-1.docker.io for Docker Hub models), with the credentials stored for that registry in the user's keychain, as a Basic username and password or a Bearer registry token. That lets the registry tie each model use to the account. The credentials go to no host other than that registry. -2. https://github.com/docker/model-runner/blob/main/pkg/metrics/metrics.go" ], "verdict": "OpenAI-, Anthropic- and Ollama-compatible routes on one local port, three releases since August and CI that runs race-detector tests. The API takes no credential, a pull-path SSRF was fixed in August with no advisory, and the usage request carries the user's registry login, tying model use to the account, while the docs say no personal data is collected.", "bestFor": "A team that already runs Docker and wants local models served to containers and Compose services through OpenAI-, Anthropic- or Ollama-compatible routes, with models stored as OCI artefacts.", "strengths": [ "OpenAI-, Anthropic- and Ollama-compatible routes on one local port, so existing clients for those three APIs work with a changed base URL", "CI runs lint, race-detector tests and validation on every push to main, with separate end-to-end and integration workflows", "Three releases in the 90 days to 4 November 2026, v1.2.7, v1.2.8 and v1.2.9 (21 October)", "Apache-2.0 source, and Docker's security policy promises a response to reports within 72 hours", "Host-side TCP is off by default in Docker Desktop, and inference engines run sandboxed on macOS and Windows or in a container on Linux" ], "weaknesses": [ "No credential on the API. The docs say any client that can reach it, including other containers, can pull, load and run models", "No OpenAPI file, no error reference and no rate-limit or retry guidance in the reviewed documentation", "v1.2.7 fixed an SSRF left open on the pull path by the CVE-2026-33990 fix, with no advisory published", "The tracking request signs in to the model's registry with the user's stored credentials, tying each use to the account, while the docs say no personal data is collected", "v1.2.7's SSRF guard broke model pulls behind a proxy until v1.2.8 the next day" ], "agentNotes": [ "Use base URL `http://localhost:12434/engines/v1` for OpenAI clients and `http://localhost:12434` for Anthropic and Ollama clients. Any API key value is accepted", "In Docker Desktop, run `docker desktop enable model-runner --tcp 12434` first. Host-side TCP is off by default", "From a container, call `http://model-runner.docker.internal` on Docker Desktop or `http://172.17.0.1:12434` on Docker Engine", "Raise the context before agent work with `docker model configure --context-size \u003cn\u003e \u003cmodel\u003e`. The llama.cpp default is 4,096 tokens", "Name models with their namespace, such as `ai/smollm2`, and expect plain-text error bodies with a 400, 404, 500 or 503 status" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "D", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 53.8 } ], "editorialScores": { "ergonomics": 58, "maintenance": 85, "payments": 60, "reliability": 75, "schema": 49, "security": 40, "transparency": 62 }, "provenanceScore": 86, "checked": { "categories": { "ergonomics": "2026-11-04", "maintenance": "2026-11-04", "payments": "2026-11-04", "reliability": "2026-11-04", "schema": "2026-11-04", "security": "2026-11-04", "transparency": "2026-11-04" }, "last": "2026-11-04", "lastMoved": "2026-11-04", "oldest": "2026-11-04", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "sudo apt-get update \u0026\u0026 sudo apt-get install docker-model-plugin # Docker Engine on Ubuntu or Debian; Docker Desktop: docker desktop enable model-runner --tcp 12434\ndocker model pull ai/smollm2", "http": "curl http://localhost:12434/engines/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"model\": \"ai/smollm2\",\n \"messages\": [{\"role\": \"user\", \"content\": \"Say hello in one sentence.\"}]\n }'", "claudeCode": "docker model launch claude" }, "letme": { "capability": "https://letme.dev/inference.local", "tool": "https://letme.dev/docker-model-runner" }, "sameCompany": [ "docker-agent" ], "area": "models", "provenance": { "legalEntity": "Docker, Inc.", "domain": "docker.com", "domainRegistered": "1995-01-25", "endpointOnVendorDomain": null, "terms": "https://www.docker.com/legal/docker-subscription-service-agreement/", "privacy": "https://www.docker.com/legal/privacy/", "statusPage": "", "changelog": "https://github.com/docker/model-runner/releases", "securityTxt": "valid", "checked": "2026-10-08", "notes": [ "The repository is under GitHub's docker organisation and SECURITY.md sends reports to security@docker.com. The subscription agreement and privacy policy both name Docker, Inc.", "Docker publishes no terms written for Model Runner. The Docker Subscription Service Agreement (last updated 26 August 2026) governs Docker Desktop, which bundles it, and the privacy policy carries the same date. The Engine plugin and the `dmr` binary are under Apache-2.0 only.", "www.docker.com/.well-known/security.txt gives security@docker.com, a policy URL and an expiry of 1 January 2030.", "No status page is listed because the software runs on the owner's machine.", "RDAP for docker.com gives a registration date of 1995-01-25." ], "score": 86 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/docker-model-runner.json", "live": { "slug": "docker-model-runner", "versions": [ { "registry": "github", "name": "docker/model-runner", "version": "v1.2.9", "released": "2026-10-22", "seenAt": "2026-11-06T23:21:49.576371059Z" } ], "githubStars": 658, "securityTxt": { "url": "https://docker.com/.well-known/security.txt", "state": "valid", "expires": "2030-01-01T05:00:00.000Z", "checkedAt": "2026-11-07T16:00:06.40302104Z" }, "llmsTxt": { "url": "https://docs.docker.com/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-11-07T15:32:18.148175068Z" }, "domain": { "domain": "docker.com", "registered": "1995-01-25", "source": "https://rdap.verisign.com/com/v1/domain/docker.com", "checkedAt": "2026-11-01T14:14:21.327350802Z" }, "pages": [ { "url": "https://www.docker.com/pricing/", "kind": "pricing", "status": 200, "checkedAt": "2026-11-07T14:28:04.2303934Z", "changedAt": "2026-11-07T14:28:04.2303934Z", "fingerprint": "4fa0bc9e38a0" }, { "url": "https://www.docker.com/legal/docker-subscription-service-agreement/", "kind": "terms", "status": 200, "checkedAt": "2026-11-07T14:27:58.080672554Z", "changedAt": "2026-10-29T05:21:57.976837404Z", "fingerprint": "dc1264d42246" } ], "github": { "repo": "docker/model-runner", "checkedAt": "2026-11-02T08:44:46.43502733Z", "stars": 659, "archived": false, "issuesEnabled": true, "pushedAt": "2026-11-02T06:59:40Z", "openIssues": 41, "openPRs": 26, "issuesOpened90d": 16, "issuesClosed90d": 16, "prsMerged90d": 34, "ci": { "branch": "main", "runs": 50, "success": 50, "failure": 0, "other": 0, "latest": "success", "latestAt": "2026-10-26T09:44:01Z" }, "advisories": [ { "ghsa": "GHSA-x2f5-332j-9xwq", "cve": "CVE-2026-33990", "severity": "high", "published": "2026-03-30T10:26:25Z" }, { "ghsa": "GHSA-m456-c56c-hh5c", "cve": "CVE-2026-28400", "severity": "high", "published": "2026-02-27T16:33:09Z" } ], "replies": { "sampled": 25, "unanswered": 22, "medianDays": 6 } }, "updatedAt": "2026-11-07T16:00:06.40302104Z" } }, "answer": "Docker Model Runner and PrivateGPT score within a point of each other on agent readiness, 53.8 (D) and 53.2 (D). PrivateGPT leads on schema \u0026 documentation and agent ergonomics.", "b": { "slug": "privategpt", "name": "PrivateGPT", "vendor": "Zylon (PriBAI Technology Spain S.L.)", "vendorUrl": "https://docs.privategpt.dev", "kind": "http-api", "category": "local-ai", "summary": "PrivateGPT is Zylon's open-source server that puts an Anthropic-style messages API, document ingestion, retrieval with citations, built-in tools and MCP connectors in front of any OpenAI-compatible local model server. The owner runs it.", "url": "https://www.anchorterminal.com/tools/privategpt", "markdownUrl": "https://www.anchorterminal.com/tools/privategpt.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/privategpt.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/privategpt.json", "dns": "privategpt.grade.anchorterminal.com", "repo": "https://github.com/zylon-ai/private-gpt", "license": "Apache-2.0", "transports": [ "http" ], "packages": [ { "registry": "oci", "name": "zylonai/private-gpt" } ], "auth": "none", "authNotes": "Authentication is off by default. When `server.auth.enabled` is set, every request must carry one static secret in the `Authorization` header, compared with the whole header value. There are no per-user keys, scopes or rotation, and the docs show it as a Bearer token, a Basic value and an Anthropic API key in different places (checked 2026-11-06).", "pricing": "free", "pricingNotes": "Free under Apache-2.0 with no account or card. The cost is the owner's hardware and model server. Zylon sells a separate enterprise platform built on PrivateGPT through demos, with no public price (checked 2026-11-06).", "priceSummary": "Free · OSS", "where": "local", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in the source, the OpenAPI file or the docs (checked 2026-11-06).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 57600, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-06" }, "docsUrl": "https://docs.privategpt.dev/introduction", "llmsTxt": "https://docs.privategpt.dev/llms.txt", "openapi": "https://raw.githubusercontent.com/zylon-ai/private-gpt/main/fern/openapi/openapi.json", "capabilities": [ "inference.local", "memory.search", "agent.mcp-client" ], "tags": [ "open-source", "local", "self-hosted", "docker", "openapi", "llms-txt", "rag", "mcp-client", "anthropic-compatible", "python", "free" ], "lastRelease": "2026-06-18", "graded": true, "anchor": { "graded": true, "score": 53.2, "grade": "D", "agentReady": false, "rank": 960, "ranked": true, "rankOf": 1510, "categoryRank": 11, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 64, "maintenance": 41, "payments": 60, "reliability": 70, "schema": 83, "security": 32, "transparency": 64 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-11-06" }, "negative": -7, "negativeNotes": [ "Issue #2269, opened 11 June 2026, reports that the async ingestion API reads any local path given as a URI artifact. The stale bot closed it as not planned on 5 July with no maintainer reply, and `load_file_from_uri` in the source of 29 October 2026 still falls back to reading a local file for any value that isn't a URL, s3 or base64. With authentication off and a 0.0.0.0 bind by default, that is an open file-read path on a default install. Read from the source, not run (-7). https://github.com/zylon-ai/private-gpt/issues/2269" ], "verdict": "PrivateGPT 1.0 is an Apache-2.0 server that gives local models an Anthropic-style messages API with retrieval, citations, tools and MCP, documented by an OpenAPI file and llms.txt. A default start has authentication off, listens on every interface and reads local paths given as URIs, and a public report of that was closed without a reply.", "bestFor": "A local backend for apps and agents that want an Anthropic-style API with document retrieval, citations and tools over local models.", "strengths": [ "Messages API modelled on Anthropic's, so Claude Code and Anthropic SDKs work by changing the base URL", "OpenAPI file of 38 paths, llms.txt and Markdown copies of every docs page", "Retrieval with citations, async ingestion, web, database and tabular tools, and remote MCP servers in one API", "Apache-2.0, with no telemetry to Zylon found in the source and OpenTelemetry tracing off by default", "Works with Ollama, llama.cpp, vLLM, LM Studio or any OpenAI-compatible server" ], "weaknesses": [ "Authentication is off by default, the server binds 0.0.0.0 and CORS allows every origin", "When on, authentication is one shared static secret, with no per-user keys or scopes", "A June 2026 report of local file reads through URI artifacts was closed by a stale bot, and the source still reads local paths", "No release since 1.0.1 in June 2026 while main has moved on, including a breaking change", "Code execution runs as a host subprocess with resource limits, not in a container" ], "agentNotes": [ "Turn on `server.auth` and bind to 127.0.0.1 before exposing an instance, since a default start answers any caller", "Point Anthropic SDKs or Claude Code at the server with `ANTHROPIC_BASE_URL=http://localhost:8080`", "Start an OpenAI-compatible model server and an embedding server first, and set `OPENAI_API_BASE` and `OPENAI_EMBEDDING_API_BASE`", "Pass ingested documents through `tool_context` to get answers with citations", "Treat web fetch, search and MCP results as untrusted, since PrivateGPT documents no prompt-injection handling" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "D", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 53.2 } ], "editorialScores": { "ergonomics": 64, "maintenance": 41, "payments": 60, "reliability": 70, "schema": 83, "security": 32, "transparency": 67 }, "provenanceScore": 61, "checked": { "categories": { "ergonomics": "2026-11-06", "maintenance": "2026-11-06", "payments": "2026-11-06", "reliability": "2026-11-06", "schema": "2026-11-06", "security": "2026-11-06", "transparency": "2026-11-06" }, "last": "2026-11-06", "oldest": "2026-11-06", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "brew tap zylon-ai/tap\nbrew install private-gpt", "http": "curl http://localhost:8080/v1/messages \\\n -H \"Content-Type: application/json\" \\\n -d '{\"model\": \"qwen3.5:35b\", \"messages\": [{\"role\": \"user\", \"content\": \"Explain retrieval-augmented generation.\"}]}'" }, "letme": { "capability": "https://letme.dev/inference.local", "tool": "https://letme.dev/privategpt" }, "area": "models", "provenance": { "legalEntity": "PriBAI Technology Spain S.L.", "domain": "privategpt.dev", "domainRegistered": "2023-10-16", "endpointOnVendorDomain": null, "terms": "https://github.com/zylon-ai/private-gpt/blob/main/LICENSE", "privacy": "", "statusPage": "", "changelog": "https://github.com/zylon-ai/private-gpt/blob/main/CHANGELOG.md", "securityTxt": "none", "checked": "2026-11-06", "notes": [ "Self-hosted software with no hosted endpoint, so the Apache-2.0 licence stands in for terms and no privacy policy is scored.", "The zylon.ai terms name PriBAI Technology Spain S.L. (VAT ESB56550130) under Spanish law and cover the website and Zylon services. They forbid access through bots, scripts or scrapers, outside standard search engine or browser use. We read the terms and two public pages.", "RDAP gives privategpt.dev a registration date of 16 October 2023.", "docs.privategpt.dev/.well-known/security.txt and www.zylon.ai/.well-known/security.txt answer 404. SECURITY.md in the repository is the reporting route.", "docs.privategpt.dev and www.zylon.ai robots.txt allow our reader. wheels.privategpt.dev has no robots.txt (404)." ], "score": 61 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/privategpt.json", "live": { "slug": "privategpt", "versions": [ { "registry": "github", "name": "zylon-ai/private-gpt", "version": "v1.0.1", "released": "2026-06-18", "seenAt": "2026-11-06T23:58:40.643071731Z" } ], "githubStars": 57632, "securityTxt": { "url": "https://privategpt.dev/.well-known/security.txt", "state": "none", "checkedAt": "2026-11-07T15:59:53.351491852Z" }, "llmsTxt": { "url": "https://docs.privategpt.dev/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-11-07T15:33:41.730243326Z" }, "pages": [ { "url": "https://raw.githubusercontent.com/zylon-ai/private-gpt/main/CHANGELOG.md", "kind": "changelog", "status": 200, "checkedAt": "2026-11-07T14:24:15.016082629Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "767d5dd99544" }, { "url": "https://raw.githubusercontent.com/zylon-ai/private-gpt/main/LICENSE", "kind": "terms", "status": 200, "checkedAt": "2026-11-07T14:24:17.075442465Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "839fbbcddc72" } ], "updatedAt": "2026-11-07T15:59:53.351491852Z" } }, "edges": [ { "by": 44, "category": "maintenance", "leads": "docker-model-runner", "name": "Maintenance \u0026 community", "reasons": { "a": "v1.2.9 was tagged on 21 October 2026, 14 days before the check (30 of 30, from 20). Three releases in the 90 days to 4 November 2026, v1.2.7 (11 August, an SSRF fix on the pull path and a llama.cpp bump), v1.2.8 (12 August, a fix for proxied token fetches) and v1.2.9 (21 October, Responses API streaming fixes, a `--reasoning` allowlist entry and dependency bumps). Each changes the server's code, so all count in full (20 of 20, from 0). 49 commits on main since 6 August, the newest on 2 November. Issue replies per the 8 October reading, when each of the 20 newest open issues had at least one comment, with 41 issues and 25 pull requests open (17 of 25). No SDK of its own. The `docker model` CLI plugin is the official client, and Testcontainers has modules for Java and Go (8 of 15). Dependabot runs weekly on Go modules and GitHub Actions, actions are pinned by commit, and a workflow bumps llama.cpp. CI runs the tests on main, pass state per 8 October (10). Total 85.", "b": "The last release is 1.0.1, dated 16 June 2026 in the changelog and tagged 18 June, 141 days before the check (10 of 30). No release or dated changelog entry in the 90 days since 8 August (0 of 20). Maintainers commit most days and outside pull requests merged in October, but 23 pull requests are open and the stale bot closes issues, including two security reports, with no maintainer reply (13 of 25). Homebrew tap, wheel index and Docker images ship with releases, and Anthropic SDKs work against it. No product MCP server of its own, though it connects to remote MCP servers (10 of 15). Dependabot, uv.lock, actions pinned by commit since 19 October and dependency CVE upgrades in June, July and August (8 of 10). 10 + 0 + 13 + 10 + 8 = 41." }, "scores": { "a": 85, "b": 41 } }, { "by": 34, "category": "schema", "leads": "privategpt", "name": "Schema \u0026 documentation", "reasons": { "a": "Read for an API. No OpenAPI or similar file was found in the repository or the docs. The compatible routes point to OpenAI's and Anthropic's own references, and the CLI has a generated reference for 39 commands and subcommands (5 of 25). docs.docker.com has llms.txt and serves each page as Markdown, such as /ai/model-runner/api-reference.md (10). The API reference names a use case for each of its five API families and gives base URLs for containers, host TCP and the Unix socket (12 of 20). Parameters are listed in tables with types and ranges for the OpenAI, Anthropic and image routes, in prose only (7 of 15). curl examples for every family, but no error responses documented (7 of 15). Dated GitHub releases with notes. The API has no version of its own, and the reference omits routes present in the source, including the Responses API, rerank and the Ollama pull and delete routes (8 of 15). Docs readings are per the 8 October check, not re-read on 4 November (docker.com hosts paused under Docker's Terms of Use).", "b": "An OpenAPI file of 38 paths in the repository (fern/openapi/openapi.json, version 1.0.1), also served by each instance at /openapi.json, with the messages API modelled on Anthropic's (25). docs.privategpt.dev serves llms.txt, a Markdown copy of every page and a docs MCP server (10). The docs explain each endpoint group, startup and model discovery, and a compatibility table says which parts of the Claude API are supported, partial or missing, such as prompt caching and OAuth (15 of 20). Pydantic models with literal type discriminators and required fields, while settings and tool context are wide objects (12 of 15). curl examples for each guide and a `/v1/messages/validate` route, with error shapes not documented as a list (9 of 15). `/v1` paths and a dated changelog, though releases trail main by more than four months (12 of 15). 25 + 10 + 15 + 12 + 9 + 12 = 83." }, "scores": { "a": 49, "b": 83 } }, { "by": 10, "category": "transparency", "leads": "docker-model-runner", "name": "Transparency \u0026 trust", "reasons": { "a": "The editorial half. Apache-2.0 for the server, the CLI and the `dmr` binary in a public repository. Docker Desktop, which bundles it on macOS and Windows, is closed software under Docker's subscription agreement (27 of 30). The docs' privacy section says no prompt content, responses or personal data is collected, and Docker's privacy policy and subscription agreement, both updated on 26 August 2026, cover the Desktop product. No retention period specific to Model Runner was found (18 of 30). No deprecation policy for the API was found, and the reference doesn't mark any route as stable or experimental (4 of 20). Telemetry is disclosed with a link to the source. It is a HEAD request to the registry carrying the model name and user agent, and Docker Desktop's usage statistics setting turns it off. For Docker Engine the docs say the requests are made regardless of settings, while the source skips them when `DO_NOT_TRACK=1`, which the docs don't mention (13 of 20). Docs readings are per the 8 October check, not re-read on 4 November. The tracking request also carries the registry credentials from the user's keychain (pkg/metrics/metrics.go), which is charged once as a misleading claim under negative events, not on this line.", "b": "Apache-2.0 (30). The OpenAPI description says no data leaves the execution environment, but the docs list cloud gateways as model providers and web search goes to outside services when enabled, and no privacy statement covers the software. Zylon's privacy policy covers its website (15 of 30). The 1.0 rewrite was marked as a breaking change in the changelog, and there is no deprecation policy (6 of 20). No telemetry to Zylon found in the source. OpenTelemetry tracing is off by default and its exporters write local files or to the owner's collector, documented on the observability page (16 of 20). 30 + 15 + 6 + 16 = 67." }, "scores": { "a": 74, "b": 64 } }, { "by": 8, "category": "security", "leads": "docker-model-runner", "name": "Security \u0026 auth", "reasons": { "a": "Read with the tool checklist, for the local API. No credential, by design. The docs say the API is not authenticated and that any client that can reach it, including other containers on the same Docker network, can pull, load and run models. Host-side TCP is off by default in Docker Desktop and on by default on port 12434 in Docker Engine, and cross-origin requests are refused with 403 unless the origin is localhost, 127.0.0.1, 0.0.0.0 or listed in `DMR_ORIGINS` (7 of 30). No read-only mode or per-caller limit. Runtime flags pass an allowlist, engines run sandboxed on macOS and Windows and in a container on Linux, and Enhanced Container Isolation, a Docker Business control, blocks container access (6 of 20). The API returns model output, with no injection guidance in the docs (6 of 15). `docker model requests` and the Requests tab show recent requests and responses, the source keeps the last 10 per model, and `/metrics` exposes Prometheus counters. No caller identity (8 of 15). www.docker.com has a valid security.txt with a disclosure policy, Docker's organisation-wide SECURITY.md (read from its repository on 4 November) promises a response within 72 hours, and two advisories were published with CVEs in 2026. No monetary bounty for this project (13 of 20).", "b": "Read with the tool checklist. Authentication is off by default, and when on it is one static secret compared with the whole `Authorization` header, with a sample `secret:key` value in settings.yaml. There are no per-user keys or scopes, `private-gpt serve` binds 0.0.0.0 by default and CORS allows every origin, so a default start answers anyone on the network. The docs disagree on the header (Bearer, Basic or an Anthropic key) (6 of 30). Tools are chosen per request and code execution has internet off by default, but bash runs as a host subprocess with resource limits, not in a container, and nothing asks for approval (6 of 20). Web fetch, web search, documents and MCP servers bring untrusted text to the model. A public prompt-injection report (#2270) was closed by the stale bot, and no guidance was found beyond a 19 October change that bounds tool output (3 of 15). OpenTelemetry traces cover every request, model call, tool and sandbox step, off by default, with local JSON lines exporters (10 of 15). SECURITY.md since 23 July 2026 asks for private reports through GitHub or Discord. No bounty, no certification, and the June and July security reports in issues were closed by a bot with no reply (7 of 20). 6 + 6 + 3 + 10 + 7 = 32." }, "scores": { "a": 40, "b": 32 } }, { "by": 6, "category": "ergonomics", "leads": "privategpt", "name": "Agent ergonomics", "reasons": { "a": "Read for an API. Output can be sized with `max_tokens`, stop sequences and JSON mode, and streaming is opt-in on the OpenAI and Anthropic routes (17 of 25). Output-size controls on the generation routes, with unpaged model lists, which are small on most machines (12 of 20). Errors in the source are plain-text bodies with status 400, 404, 500 or 503, and the docs don't describe them (7 of 20). Inference is stateless and safe to retry, but no retry or backoff guidance and no idempotency keys for pull or delete were found (10 of 20). `model` is the only required field beyond the messages or prompt. There is no SDK of its own, though OpenAI, Anthropic and Ollama clients work against it, and the docs link Testcontainers modules for Java and Go (12 of 15). Per the 8 October check, not re-read on 4 November.", "b": "Read for an HTTP API an agent calls on a local instance. Token counting, `top_k` style retrieval settings, citation output and response byte caps let a caller size context (18 of 25). Artifact listing by namespace and retrieval filters, with no documented cursor pagination (12 of 20). FastAPI validation errors name the field and the validate route checks a request before running it, but there is no error catalogue (12 of 20). Async messages and ingestion return task ids with status, cancel and delete routes. No idempotency keys were found (10 of 20). Anthropic SDKs and Claude Code work by pointing `ANTHROPIC_BASE_URL` at the server, but a model server and an embedding server have to be configured first (12 of 15). 18 + 12 + 12 + 10 + 12 = 64." }, "scores": { "a": 58, "b": 64 } }, { "by": 5, "category": "reliability", "leads": "docker-model-runner", "name": "Reliability", "reasons": { "a": "Read with the local-software lines, since the API runs on the owner's machine. Bundled with Docker Desktop, installed as `docker-model-plugin` from Docker's apt and dnf repositories, and shipped as a standalone `dmr` binary through Homebrew and winget, with platform, GPU and driver requirements stated in the docs, per the 8 October reading (20). The CI workflow runs lint, `go test -race ./...` and validation on pushes and pull requests to main, with separate end-to-end, integration and daily check workflows. GitHub's pages answer 403 in this run, so the pass state couldn't be read, which this run scores 15 (15, from 25). 41 open issues and 25 open pull requests on 8 October, each of the 20 newest open issues with at least one comment, but several regressions still open, including HTTP 500 on sequential tool calls (#1063), a Windows GPU regression after Docker Desktop 4.82.0 (#1054) and a context-size setting applied nondeterministically (#1025). Not re-read on 4 November (17 of 25). Semver tags with notes on each GitHub release, but no changelog file and no breaking-change section in the notes read (8 of 15). Version 1.2.9 (15). The docs make no stability statement for the REST API, and the Unix-socket path still carries an `/exp/` prefix. Total 75.", "b": "Read with the local-software lines, since PrivateGPT runs on the owner's hardware with no hosted service behind it. Homebrew tap, `uv tool install` from Zylon's own wheel index (not PyPI) and Docker Hub images `zylonai/private-gpt` 1.0.1 of 18 June 2026. Python 3.11 only, stated in pyproject.toml, and it needs a separate OpenAI-compatible model server (17 of 20). tests.yml runs format, lint, typecheck and the tests affected by each push on main, with the full suite of 217 test files on release pull requests. We couldn't see run results because the GitHub API refused us, and issue #2441 of 1 November reports the Claude spec update workflow failing (15 of 25). GitHub shows 3 open issues of about 1,250, because a stale bot closes quiet issues, two security reports among them, with no maintainer reply (12 of 25). release-please keeps a changelog with breaking changes marked, but no release has been cut since 1.0.1 while main carries 51 commits since 8 August, among them a breaking observability change of 23 October (11 of 15). Version 1.0.1 (15). 17 + 15 + 12 + 11 + 15 = 70." }, "scores": { "a": 75, "b": 70 } } ], "facts": [ { "a": "HTTP API", "b": "HTTP API", "name": "Kind" }, { "a": "Docker, Inc.", "b": "Zylon (PriBAI Technology Spain S.L.)", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "HTTP", "b": "HTTP", "name": "Transports" }, { "a": "None", "b": "None", "name": "Auth" }, { "a": "Free", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Apache-2.0 (server, CLI plugin and `dmr` binary). Docker Desktop, which bundles it, is closed software under Docker's subscription agreement, and each model carries its own licence", "b": "Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "yes", "name": "llms.txt" }, { "a": "2026-10-21", "b": "2026-06-18", "name": "Last release" }, { "a": "2026-08-26", "b": "no date given", "name": "Terms last updated" }, { "a": "2026-08-26", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "not found in the text", "b": "not found in the text", "name": "Customer content may train models" }, { "a": "yes", "b": "not found in the text", "name": "Terms restrict automated access" }, { "a": "yes", "b": "not found in the text", "name": "Terms restrict benchmarking" }, { "a": "not found in the text", "b": "not found in the text", "name": "Terms or service can change without notice" }, { "a": "yes", "b": "not found in the text", "name": "Arbitration or class-action waiver" }, { "a": "656 stars", "b": "58k stars", "name": "Popularity" } ], "faq": [ { "answer": "Docker Model Runner and PrivateGPT score within a point of each other on agent readiness, 53.8 (D) and 53.2 (D). PrivateGPT leads on schema \u0026 documentation and agent ergonomics.", "question": "Which is better for AI agents, Docker Model Runner or PrivateGPT?" }, { "answer": "Neither needs a key.", "question": "Do Docker Model Runner and PrivateGPT need an API key?" }, { "answer": "No hosted endpoint is listed for Docker Model Runner. No hosted endpoint is listed for PrivateGPT.", "question": "Can an agent call Docker Model Runner and PrivateGPT without installing anything?" }, { "answer": "Yes. Docker Model Runner is open source (Apache-2.0 (server, CLI plugin and `dmr` binary). Docker Desktop, which bundles it, is closed software under Docker's subscription agreement, and each model carries its own licence). PrivateGPT is open source (Apache-2.0).", "question": "Are Docker Model Runner and PrivateGPT open source?" } ], "goodFor": [ { "aheadOn": [ "Reliability, 75 against 70", "Security \u0026 auth, 40 against 32", "Maintenance \u0026 community, 85 against 41", "Transparency \u0026 trust, 74 against 64" ], "also": [ "Free to start without a card" ], "goodFor": "A team that already runs Docker and wants local models served to containers and Compose services through OpenAI-, Anthropic- or Ollama-compatible routes, with models stored as OCI artefacts.", "slug": "docker-model-runner", "watchFor": "No credential on the API. The docs say any client that can reach it, including other containers, can pull, load and run models" }, { "aheadOn": [ "Schema \u0026 documentation, 83 against 49", "Agent ergonomics, 64 against 58" ], "also": null, "goodFor": "A local backend for apps and agents that want an Anthropic-style API with document retrieval, citations and tools over local models.", "slug": "privategpt", "watchFor": "Authentication is off by default, the server binds 0.0.0.0 and CORS allows every origin" } ], "history": { "docker-model-runner": [ { "causes": [ "security", "product", "correction" ], "date": "2026-11-04", "delta": -3.4, "from": 57.2, "fromGrade": "C", "to": 53.8, "toGrade": "D" }, { "causes": [ "method" ], "date": "2026-10-27", "delta": 0.1, "from": 57.1, "fromGrade": "C", "to": 57.2, "toGrade": "C" } ], "privategpt": null }, "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-privategpt.json", "title": "AnythingLLM vs PrivateGPT", "url": "https://www.anchorterminal.com/compare/anythingllm-vs-privategpt" }, { "json": "https://www.anchorterminal.com/compare/berth-vs-docker-model-runner.json", "title": "Berth vs Docker Model Runner", "url": "https://www.anchorterminal.com/compare/berth-vs-docker-model-runner" }, { "json": "https://www.anchorterminal.com/compare/berth-vs-privategpt.json", "title": "Berth vs PrivateGPT", "url": "https://www.anchorterminal.com/compare/berth-vs-privategpt" }, { "json": "https://www.anchorterminal.com/compare/bitrun-vs-docker-model-runner.json", "title": "BitRun vs Docker Model Runner", "url": "https://www.anchorterminal.com/compare/bitrun-vs-docker-model-runner" }, { "json": "https://www.anchorterminal.com/compare/bitrun-vs-privategpt.json", "title": "BitRun vs PrivateGPT", "url": "https://www.anchorterminal.com/compare/bitrun-vs-privategpt" }, { "json": "https://www.anchorterminal.com/compare/docker-model-runner-vs-foundry-local.json", "title": "Docker Model Runner vs Foundry Local", "url": "https://www.anchorterminal.com/compare/docker-model-runner-vs-foundry-local" }, { "json": "https://www.anchorterminal.com/compare/docker-model-runner-vs-ghost-core.json", "title": "Docker Model Runner vs Core", "url": "https://www.anchorterminal.com/compare/docker-model-runner-vs-ghost-core" }, { "json": "https://www.anchorterminal.com/compare/docker-model-runner-vs-gpt4all.json", "title": "Docker Model Runner vs GPT4All", "url": "https://www.anchorterminal.com/compare/docker-model-runner-vs-gpt4all" }, { "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/docker-model-runner-vs-jan.json", "title": "Docker Model Runner vs Jan", "url": "https://www.anchorterminal.com/compare/docker-model-runner-vs-jan" }, { "json": "https://www.anchorterminal.com/compare/docker-model-runner-vs-khoj.json", "title": "Docker Model Runner vs Khoj", "url": "https://www.anchorterminal.com/compare/docker-model-runner-vs-khoj" }, { "json": "https://www.anchorterminal.com/compare/docker-model-runner-vs-koboldcpp.json", "title": "Docker Model Runner vs KoboldCpp", "url": "https://www.anchorterminal.com/compare/docker-model-runner-vs-koboldcpp" }, { "json": "https://www.anchorterminal.com/compare/docker-model-runner-vs-lemonade.json", "title": "Docker Model Runner vs Lemonade", "url": "https://www.anchorterminal.com/compare/docker-model-runner-vs-lemonade" }, { "json": "https://www.anchorterminal.com/compare/docker-model-runner-vs-llama-cpp.json", "title": "Docker Model Runner vs llama.cpp", "url": "https://www.anchorterminal.com/compare/docker-model-runner-vs-llama-cpp" }, { "json": "https://www.anchorterminal.com/compare/docker-model-runner-vs-lm-studio.json", "title": "Docker Model Runner vs LM Studio", "url": "https://www.anchorterminal.com/compare/docker-model-runner-vs-lm-studio" }, { "json": "https://www.anchorterminal.com/compare/docker-model-runner-vs-localai.json", "title": "Docker Model Runner vs LocalAI", "url": "https://www.anchorterminal.com/compare/docker-model-runner-vs-localai" }, { "json": "https://www.anchorterminal.com/compare/docker-model-runner-vs-mlx-lm.json", "title": "Docker Model Runner vs MLX LM", "url": "https://www.anchorterminal.com/compare/docker-model-runner-vs-mlx-lm" }, { "json": "https://www.anchorterminal.com/compare/docker-model-runner-vs-ollama.json", "title": "Docker Model Runner vs Ollama", "url": "https://www.anchorterminal.com/compare/docker-model-runner-vs-ollama" }, { "json": "https://www.anchorterminal.com/compare/docker-model-runner-vs-open-webui.json", "title": "Docker Model Runner vs Open WebUI", "url": "https://www.anchorterminal.com/compare/docker-model-runner-vs-open-webui" }, { "json": "https://www.anchorterminal.com/compare/docker-model-runner-vs-screenpipe.json", "title": "Docker Model Runner vs screenpipe", "url": "https://www.anchorterminal.com/compare/docker-model-runner-vs-screenpipe" }, { "json": "https://www.anchorterminal.com/compare/docker-model-runner-vs-text-generation-webui.json", "title": "Docker Model Runner vs TextGen", "url": "https://www.anchorterminal.com/compare/docker-model-runner-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-privategpt.json", "title": "Foundry Local vs PrivateGPT", "url": "https://www.anchorterminal.com/compare/foundry-local-vs-privategpt" }, { "json": "https://www.anchorterminal.com/compare/ghost-core-vs-privategpt.json", "title": "Core vs PrivateGPT", "url": "https://www.anchorterminal.com/compare/ghost-core-vs-privategpt" }, { "json": "https://www.anchorterminal.com/compare/gpt4all-vs-privategpt.json", "title": "GPT4All vs PrivateGPT", "url": "https://www.anchorterminal.com/compare/gpt4all-vs-privategpt" }, { "json": "https://www.anchorterminal.com/compare/hyperqwen-vs-privategpt.json", "title": "HyperQwen vs PrivateGPT", "url": "https://www.anchorterminal.com/compare/hyperqwen-vs-privategpt" }, { "json": "https://www.anchorterminal.com/compare/jan-vs-privategpt.json", "title": "Jan vs PrivateGPT", "url": "https://www.anchorterminal.com/compare/jan-vs-privategpt" }, { "json": "https://www.anchorterminal.com/compare/koboldcpp-vs-privategpt.json", "title": "KoboldCpp vs PrivateGPT", "url": "https://www.anchorterminal.com/compare/koboldcpp-vs-privategpt" }, { "json": "https://www.anchorterminal.com/compare/lemonade-vs-privategpt.json", "title": "Lemonade vs PrivateGPT", "url": "https://www.anchorterminal.com/compare/lemonade-vs-privategpt" }, { "json": "https://www.anchorterminal.com/compare/llama-cpp-vs-privategpt.json", "title": "llama.cpp vs PrivateGPT", "url": "https://www.anchorterminal.com/compare/llama-cpp-vs-privategpt" }, { "json": "https://www.anchorterminal.com/compare/lm-studio-vs-privategpt.json", "title": "LM Studio vs PrivateGPT", "url": "https://www.anchorterminal.com/compare/lm-studio-vs-privategpt" }, { "json": "https://www.anchorterminal.com/compare/localai-vs-privategpt.json", "title": "LocalAI vs PrivateGPT", "url": "https://www.anchorterminal.com/compare/localai-vs-privategpt" }, { "json": "https://www.anchorterminal.com/compare/mlx-lm-vs-privategpt.json", "title": "MLX LM vs PrivateGPT", "url": "https://www.anchorterminal.com/compare/mlx-lm-vs-privategpt" }, { "json": "https://www.anchorterminal.com/compare/ollama-vs-privategpt.json", "title": "Ollama vs PrivateGPT", "url": "https://www.anchorterminal.com/compare/ollama-vs-privategpt" }, { "json": "https://www.anchorterminal.com/compare/open-webui-vs-privategpt.json", "title": "Open WebUI vs PrivateGPT", "url": "https://www.anchorterminal.com/compare/open-webui-vs-privategpt" }, { "json": "https://www.anchorterminal.com/compare/privategpt-vs-screenpipe.json", "title": "PrivateGPT vs screenpipe", "url": "https://www.anchorterminal.com/compare/privategpt-vs-screenpipe" }, { "json": "https://www.anchorterminal.com/compare/privategpt-vs-text-generation-webui.json", "title": "PrivateGPT vs TextGen", "url": "https://www.anchorterminal.com/compare/privategpt-vs-text-generation-webui" }, { "json": "https://www.anchorterminal.com/compare/privategpt-vs-vllm.json", "title": "PrivateGPT vs vLLM", "url": "https://www.anchorterminal.com/compare/privategpt-vs-vllm" }, { "json": "https://www.anchorterminal.com/compare/khoj-vs-privategpt.json", "title": "Khoj vs PrivateGPT", "url": "https://www.anchorterminal.com/compare/khoj-vs-privategpt" }, { "json": "https://www.anchorterminal.com/compare/localghost-vs-privategpt.json", "title": "LocalGhost vs PrivateGPT", "url": "https://www.anchorterminal.com/compare/localghost-vs-privategpt" }, { "json": "https://www.anchorterminal.com/compare/docker-model-runner-vs-underdog.json", "title": "Docker Model Runner vs Underdog", "url": "https://www.anchorterminal.com/compare/docker-model-runner-vs-underdog" } ], "panel": null, "scores": [ { "by": 5, "docker-model-runner": 75, "edge": "docker-model-runner", "key": "reliability", "name": "Reliability", "privategpt": 70, "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 34, "docker-model-runner": 49, "edge": "privategpt", "key": "schema", "name": "Schema \u0026 documentation", "privategpt": 83, "weight": 13 }, { "by": 6, "docker-model-runner": 58, "edge": "privategpt", "key": "ergonomics", "name": "Agent ergonomics", "privategpt": 64, "weight": 13 }, { "by": 8, "docker-model-runner": 40, "edge": "docker-model-runner", "key": "security", "name": "Security \u0026 auth", "privategpt": 32, "weight": 14 }, { "by": 0, "docker-model-runner": 60, "edge": "", "key": "payments", "name": "Payments \u0026 pricing", "privategpt": 60, "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 44, "docker-model-runner": 85, "edge": "docker-model-runner", "key": "maintenance", "name": "Maintenance \u0026 community", "privategpt": 41, "weight": 7 }, { "by": 10, "docker-model-runner": 74, "edge": "docker-model-runner", "key": "transparency", "name": "Transparency \u0026 trust", "privategpt": 64, "weight": 7 } ], "summary": "Docker Model Runner and PrivateGPT score within a point of each other on agent readiness, 53.8 (D) and 53.2 (D). PrivateGPT leads on schema \u0026 documentation and agent ergonomics. Both do local inference.", "tasks": null, "tutorials": [], "verdicts": { "docker-model-runner": "OpenAI-, Anthropic- and Ollama-compatible routes on one local port, three releases since August and CI that runs race-detector tests. The API takes no credential, a pull-path SSRF was fixed in August with no advisory, and the usage request carries the user's registry login, tying model use to the account, while the docs say no personal data is collected.", "privategpt": "PrivateGPT 1.0 is an Apache-2.0 server that gives local models an Anthropic-style messages API with retrieval, citations, tools and MCP, documented by an OpenAPI file and llms.txt. A default start has authentication off, listens on every interface and reads local paths given as URIs, and a public report of that was closed without a reply." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/docker-model-runner-vs-privategpt", "json": "https://www.anchorterminal.com/compare/docker-model-runner-vs-privategpt.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/docker-model-runner-vs-privategpt.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/docker-model-runner-vs-privategpt.min.md" }, "markdown": "Docker Model Runner and PrivateGPT score within a point of each other on agent readiness, 53.8 (D) and 53.2 (D). PrivateGPT leads on schema \u0026 documentation and agent ergonomics. Both do local inference.\n\n- Docker Model Runner: grade D, 53.8/100, rank #940 of 1510. Markdown https://www.anchorterminal.com/tools/docker-model-runner.md · JSON https://www.anchorterminal.com/api/v1/tools/docker-model-runner.json\n- PrivateGPT: grade D, 53.2/100, rank #960 of 1510. Markdown https://www.anchorterminal.com/tools/privategpt.md · JSON https://www.anchorterminal.com/api/v1/tools/privategpt.json\n- Best local AI models and assistants: https://www.anchorterminal.com/best/local-ai/index.md\n- All 280 local ai comparisons: https://www.anchorterminal.com/compare/local-ai/index.md\n\n## Which one, for what\n\n### Docker Model Runner (D)\n\nGood for: A team that already runs Docker and wants local models served to containers and Compose services through OpenAI-, Anthropic- or Ollama-compatible routes, with models stored as OCI artefacts.\n\nAhead on:\n- Reliability, 75 against 70\n- Security \u0026 auth, 40 against 32\n- Maintenance \u0026 community, 85 against 41\n- Transparency \u0026 trust, 74 against 64\n\nAlso in its favour:\n- Free to start without a card\n\nWatch for: No credential on the API. The docs say any client that can reach it, including other containers, can pull, load and run models\n\n### PrivateGPT (D)\n\nGood for: A local backend for apps and agents that want an Anthropic-style API with document retrieval, citations and tools over local models.\n\nAhead on:\n- Schema \u0026 documentation, 83 against 49\n- Agent ergonomics, 64 against 58\n\nWatch for: Authentication is off by default, the server binds 0.0.0.0 and CORS allows every origin\n\n\n## Why the edge\n\n### Maintenance \u0026 community: Docker Model Runner +44\n\n- Docker Model Runner (85): v1.2.9 was tagged on 21 October 2026, 14 days before the check (30 of 30, from 20). Three releases in the 90 days to 4 November 2026, v1.2.7 (11 August, an SSRF fix on the pull path and a llama.cpp bump), v1.2.8 (12 August, a fix for proxied token fetches) and v1.2.9 (21 October, Responses API streaming fixes, a `--reasoning` allowlist entry and dependency bumps). Each changes the server's code, so all count in full (20 of 20, from 0). 49 commits on main since 6 August, the newest on 2 November. Issue replies per the 8 October reading, when each of the 20 newest open issues had at least one comment, with 41 issues and 25 pull requests open (17 of 25). No SDK of its own. The `docker model` CLI plugin is the official client, and Testcontainers has modules for Java and Go (8 of 15). Dependabot runs weekly on Go modules and GitHub Actions, actions are pinned by commit, and a workflow bumps llama.cpp. CI runs the tests on main, pass state per 8 October (10). Total 85.\n- PrivateGPT (41): The last release is 1.0.1, dated 16 June 2026 in the changelog and tagged 18 June, 141 days before the check (10 of 30). No release or dated changelog entry in the 90 days since 8 August (0 of 20). Maintainers commit most days and outside pull requests merged in October, but 23 pull requests are open and the stale bot closes issues, including two security reports, with no maintainer reply (13 of 25). Homebrew tap, wheel index and Docker images ship with releases, and Anthropic SDKs work against it. No product MCP server of its own, though it connects to remote MCP servers (10 of 15). Dependabot, uv.lock, actions pinned by commit since 19 October and dependency CVE upgrades in June, July and August (8 of 10). 10 + 0 + 13 + 10 + 8 = 41.\n\n### Schema \u0026 documentation: PrivateGPT +34\n\n- Docker Model Runner (49): Read for an API. No OpenAPI or similar file was found in the repository or the docs. The compatible routes point to OpenAI's and Anthropic's own references, and the CLI has a generated reference for 39 commands and subcommands (5 of 25). docs.docker.com has llms.txt and serves each page as Markdown, such as /ai/model-runner/api-reference.md (10). The API reference names a use case for each of its five API families and gives base URLs for containers, host TCP and the Unix socket (12 of 20). Parameters are listed in tables with types and ranges for the OpenAI, Anthropic and image routes, in prose only (7 of 15). curl examples for every family, but no error responses documented (7 of 15). Dated GitHub releases with notes. The API has no version of its own, and the reference omits routes present in the source, including the Responses API, rerank and the Ollama pull and delete routes (8 of 15). Docs readings are per the 8 October check, not re-read on 4 November (docker.com hosts paused under Docker's Terms of Use).\n- PrivateGPT (83): An OpenAPI file of 38 paths in the repository (fern/openapi/openapi.json, version 1.0.1), also served by each instance at /openapi.json, with the messages API modelled on Anthropic's (25). docs.privategpt.dev serves llms.txt, a Markdown copy of every page and a docs MCP server (10). The docs explain each endpoint group, startup and model discovery, and a compatibility table says which parts of the Claude API are supported, partial or missing, such as prompt caching and OAuth (15 of 20). Pydantic models with literal type discriminators and required fields, while settings and tool context are wide objects (12 of 15). curl examples for each guide and a `/v1/messages/validate` route, with error shapes not documented as a list (9 of 15). `/v1` paths and a dated changelog, though releases trail main by more than four months (12 of 15). 25 + 10 + 15 + 12 + 9 + 12 = 83.\n\n### Transparency \u0026 trust: Docker Model Runner +10\n\n- Docker Model Runner (74): The editorial half. Apache-2.0 for the server, the CLI and the `dmr` binary in a public repository. Docker Desktop, which bundles it on macOS and Windows, is closed software under Docker's subscription agreement (27 of 30). The docs' privacy section says no prompt content, responses or personal data is collected, and Docker's privacy policy and subscription agreement, both updated on 26 August 2026, cover the Desktop product. No retention period specific to Model Runner was found (18 of 30). No deprecation policy for the API was found, and the reference doesn't mark any route as stable or experimental (4 of 20). Telemetry is disclosed with a link to the source. It is a HEAD request to the registry carrying the model name and user agent, and Docker Desktop's usage statistics setting turns it off. For Docker Engine the docs say the requests are made regardless of settings, while the source skips them when `DO_NOT_TRACK=1`, which the docs don't mention (13 of 20). Docs readings are per the 8 October check, not re-read on 4 November. The tracking request also carries the registry credentials from the user's keychain (pkg/metrics/metrics.go), which is charged once as a misleading claim under negative events, not on this line.\n- PrivateGPT (64): Apache-2.0 (30). The OpenAPI description says no data leaves the execution environment, but the docs list cloud gateways as model providers and web search goes to outside services when enabled, and no privacy statement covers the software. Zylon's privacy policy covers its website (15 of 30). The 1.0 rewrite was marked as a breaking change in the changelog, and there is no deprecation policy (6 of 20). No telemetry to Zylon found in the source. OpenTelemetry tracing is off by default and its exporters write local files or to the owner's collector, documented on the observability page (16 of 20). 30 + 15 + 6 + 16 = 67.\n\n### Security \u0026 auth: Docker Model Runner +8\n\n- Docker Model Runner (40): Read with the tool checklist, for the local API. No credential, by design. The docs say the API is not authenticated and that any client that can reach it, including other containers on the same Docker network, can pull, load and run models. Host-side TCP is off by default in Docker Desktop and on by default on port 12434 in Docker Engine, and cross-origin requests are refused with 403 unless the origin is localhost, 127.0.0.1, 0.0.0.0 or listed in `DMR_ORIGINS` (7 of 30). No read-only mode or per-caller limit. Runtime flags pass an allowlist, engines run sandboxed on macOS and Windows and in a container on Linux, and Enhanced Container Isolation, a Docker Business control, blocks container access (6 of 20). The API returns model output, with no injection guidance in the docs (6 of 15). `docker model requests` and the Requests tab show recent requests and responses, the source keeps the last 10 per model, and `/metrics` exposes Prometheus counters. No caller identity (8 of 15). www.docker.com has a valid security.txt with a disclosure policy, Docker's organisation-wide SECURITY.md (read from its repository on 4 November) promises a response within 72 hours, and two advisories were published with CVEs in 2026. No monetary bounty for this project (13 of 20).\n- PrivateGPT (32): Read with the tool checklist. Authentication is off by default, and when on it is one static secret compared with the whole `Authorization` header, with a sample `secret:key` value in settings.yaml. There are no per-user keys or scopes, `private-gpt serve` binds 0.0.0.0 by default and CORS allows every origin, so a default start answers anyone on the network. The docs disagree on the header (Bearer, Basic or an Anthropic key) (6 of 30). Tools are chosen per request and code execution has internet off by default, but bash runs as a host subprocess with resource limits, not in a container, and nothing asks for approval (6 of 20). Web fetch, web search, documents and MCP servers bring untrusted text to the model. A public prompt-injection report (#2270) was closed by the stale bot, and no guidance was found beyond a 19 October change that bounds tool output (3 of 15). OpenTelemetry traces cover every request, model call, tool and sandbox step, off by default, with local JSON lines exporters (10 of 15). SECURITY.md since 23 July 2026 asks for private reports through GitHub or Discord. No bounty, no certification, and the June and July security reports in issues were closed by a bot with no reply (7 of 20). 6 + 6 + 3 + 10 + 7 = 32.\n\n### Agent ergonomics: PrivateGPT +6\n\n- Docker Model Runner (58): Read for an API. Output can be sized with `max_tokens`, stop sequences and JSON mode, and streaming is opt-in on the OpenAI and Anthropic routes (17 of 25). Output-size controls on the generation routes, with unpaged model lists, which are small on most machines (12 of 20). Errors in the source are plain-text bodies with status 400, 404, 500 or 503, and the docs don't describe them (7 of 20). Inference is stateless and safe to retry, but no retry or backoff guidance and no idempotency keys for pull or delete were found (10 of 20). `model` is the only required field beyond the messages or prompt. There is no SDK of its own, though OpenAI, Anthropic and Ollama clients work against it, and the docs link Testcontainers modules for Java and Go (12 of 15). Per the 8 October check, not re-read on 4 November.\n- PrivateGPT (64): Read for an HTTP API an agent calls on a local instance. Token counting, `top_k` style retrieval settings, citation output and response byte caps let a caller size context (18 of 25). Artifact listing by namespace and retrieval filters, with no documented cursor pagination (12 of 20). FastAPI validation errors name the field and the validate route checks a request before running it, but there is no error catalogue (12 of 20). Async messages and ingestion return task ids with status, cancel and delete routes. No idempotency keys were found (10 of 20). Anthropic SDKs and Claude Code work by pointing `ANTHROPIC_BASE_URL` at the server, but a model server and an embedding server have to be configured first (12 of 15). 18 + 12 + 12 + 10 + 12 = 64.\n\n### Reliability: Docker Model Runner +5\n\n- Docker Model Runner (75): Read with the local-software lines, since the API runs on the owner's machine. Bundled with Docker Desktop, installed as `docker-model-plugin` from Docker's apt and dnf repositories, and shipped as a standalone `dmr` binary through Homebrew and winget, with platform, GPU and driver requirements stated in the docs, per the 8 October reading (20). The CI workflow runs lint, `go test -race ./...` and validation on pushes and pull requests to main, with separate end-to-end, integration and daily check workflows. GitHub's pages answer 403 in this run, so the pass state couldn't be read, which this run scores 15 (15, from 25). 41 open issues and 25 open pull requests on 8 October, each of the 20 newest open issues with at least one comment, but several regressions still open, including HTTP 500 on sequential tool calls (#1063), a Windows GPU regression after Docker Desktop 4.82.0 (#1054) and a context-size setting applied nondeterministically (#1025). Not re-read on 4 November (17 of 25). Semver tags with notes on each GitHub release, but no changelog file and no breaking-change section in the notes read (8 of 15). Version 1.2.9 (15). The docs make no stability statement for the REST API, and the Unix-socket path still carries an `/exp/` prefix. Total 75.\n- PrivateGPT (70): Read with the local-software lines, since PrivateGPT runs on the owner's hardware with no hosted service behind it. Homebrew tap, `uv tool install` from Zylon's own wheel index (not PyPI) and Docker Hub images `zylonai/private-gpt` 1.0.1 of 18 June 2026. Python 3.11 only, stated in pyproject.toml, and it needs a separate OpenAI-compatible model server (17 of 20). tests.yml runs format, lint, typecheck and the tests affected by each push on main, with the full suite of 217 test files on release pull requests. We couldn't see run results because the GitHub API refused us, and issue #2441 of 1 November reports the Claude spec update workflow failing (15 of 25). GitHub shows 3 open issues of about 1,250, because a stale bot closes quiet issues, two security reports among them, with no maintainer reply (12 of 25). release-please keeps a changelog with breaking changes marked, but no release has been cut since 1.0.1 while main carries 51 commits since 8 August, among them a breaking observability change of 23 October (11 of 15). Version 1.0.1 (15). 17 + 15 + 12 + 11 + 15 = 70.\n\n## Score by category\n\n| Category | Weight | Docker Model Runner | PrivateGPT | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 75 | 70 | Docker Model Runner +5 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 49 | 83 | PrivateGPT +34 |\n| Agent ergonomics | 13% (16.2 this run) | 58 | 64 | PrivateGPT +6 |\n| Security \u0026 auth | 14% (17.5 this run) | 40 | 32 | Docker Model Runner +8 |\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) | 85 | 41 | Docker Model Runner +44 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 74 | 64 | Docker Model Runner +10 |\n| Negative events | ≤15 | -7 | -7 | |\n| **Total** | | **53.8 · D** | **53.2 · D** | |\n\n## Facts side by side\n\n| Fact | Docker Model Runner | PrivateGPT |\n| --- | --- | --- |\n| Kind | HTTP API | HTTP API |\n| Vendor | Docker, Inc. | Zylon (PriBAI Technology Spain S.L.) |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | HTTP | HTTP |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | Apache-2.0 (server, CLI plugin and `dmr` binary). Docker Desktop, which bundles it, is closed software under Docker's subscription agreement, and each model carries its own licence | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | yes | yes |\n| Last release | 2026-10-21 | 2026-06-18 |\n| Terms last updated | 2026-08-26 | no date given |\n| Privacy policy last updated | 2026-08-26 | no document linked |\n| Customer content may train models | not found in the text | not found in the text |\n| Terms restrict automated access | yes | not found in the text |\n| Terms restrict benchmarking | yes | not found in the text |\n| Terms or service can change without notice | not found in the text | not found in the text |\n| Arbitration or class-action waiver | yes | not found in the text |\n| Popularity | 656 stars | 58k stars |\n\n## How the scores have moved\n\n### Docker Model Runner\n\n- -3.4 on 4 November 2026 (C to D): security, product, correction\n- +0.1 on 27 October 2026: checklist\n\n### PrivateGPT\n\nNo score change since it was listed.\n\n## Verdicts\n\n**Docker Model Runner.** OpenAI-, Anthropic- and Ollama-compatible routes on one local port, three releases since August and CI that runs race-detector tests. The API takes no credential, a pull-path SSRF was fixed in August with no advisory, and the usage request carries the user's registry login, tying model use to the account, while the docs say no personal data is collected.\n\n**PrivateGPT.** PrivateGPT 1.0 is an Apache-2.0 server that gives local models an Anthropic-style messages API with retrieval, citations, tools and MCP, documented by an OpenAPI file and llms.txt. A default start has authentication off, listens on every interface and reads local paths given as URIs, and a public report of that was closed without a reply.\n\n## Before you call either\n\n### Docker Model Runner\n\n1. Use base URL `http://localhost:12434/engines/v1` for OpenAI clients and `http://localhost:12434` for Anthropic and Ollama clients. Any API key value is accepted\n2. In Docker Desktop, run `docker desktop enable model-runner --tcp 12434` first. Host-side TCP is off by default\n3. From a container, call `http://model-runner.docker.internal` on Docker Desktop or `http://172.17.0.1:12434` on Docker Engine\n4. Raise the context before agent work with `docker model configure --context-size \u003cn\u003e \u003cmodel\u003e`. The llama.cpp default is 4,096 tokens\n5. Name models with their namespace, such as `ai/smollm2`, and expect plain-text error bodies with a 400, 404, 500 or 503 status\n\n### PrivateGPT\n\n1. Turn on `server.auth` and bind to 127.0.0.1 before exposing an instance, since a default start answers any caller\n2. Point Anthropic SDKs or Claude Code at the server with `ANTHROPIC_BASE_URL=http://localhost:8080`\n3. Start an OpenAI-compatible model server and an embedding server first, and set `OPENAI_API_BASE` and `OPENAI_EMBEDDING_API_BASE`\n4. Pass ingested documents through `tool_context` to get answers with citations\n5. Treat web fetch, search and MCP results as untrusted, since PrivateGPT documents no prompt-injection handling\n\n## Questions\n\n### Which is better for AI agents, Docker Model Runner or PrivateGPT?\n\nDocker Model Runner and PrivateGPT score within a point of each other on agent readiness, 53.8 (D) and 53.2 (D). PrivateGPT leads on schema \u0026 documentation and agent ergonomics.\n\n### Do Docker Model Runner and PrivateGPT need an API key?\n\nNeither needs a key.\n\n### Can an agent call Docker Model Runner and PrivateGPT without installing anything?\n\nNo hosted endpoint is listed for Docker Model Runner. No hosted endpoint is listed for PrivateGPT.\n\n### Are Docker Model Runner and PrivateGPT open source?\n\nYes. Docker Model Runner is open source (Apache-2.0 (server, CLI plugin and `dmr` binary). Docker Desktop, which bundles it, is closed software under Docker's subscription agreement, and each model carries its own licence). PrivateGPT is open source (Apache-2.0).\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/docker-model-runner-vs-privategpt.json, and with the fewest tokens: https://www.anchorterminal.com/compare/docker-model-runner-vs-privategpt.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"docker-model-runner\", \"b\": \"privategpt\"}`. From a terminal: `anchor compare docker-model-runner privategpt`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/docker-model-runner.json and https://www.anchorterminal.com/api/v1/tools/privategpt.json\n\n## Other comparisons with Docker Model Runner or PrivateGPT\n\n- [AnythingLLM vs Docker Model Runner](https://www.anchorterminal.com/compare/anythingllm-vs-docker-model-runner.md)\n- [AnythingLLM vs PrivateGPT](https://www.anchorterminal.com/compare/anythingllm-vs-privategpt.md)\n- [Berth vs Docker Model Runner](https://www.anchorterminal.com/compare/berth-vs-docker-model-runner.md)\n- [Berth vs PrivateGPT](https://www.anchorterminal.com/compare/berth-vs-privategpt.md)\n- [BitRun vs Docker Model Runner](https://www.anchorterminal.com/compare/bitrun-vs-docker-model-runner.md)\n- [BitRun vs PrivateGPT](https://www.anchorterminal.com/compare/bitrun-vs-privategpt.md)\n- [Docker Model Runner vs Foundry Local](https://www.anchorterminal.com/compare/docker-model-runner-vs-foundry-local.md)\n- [Docker Model Runner vs Core](https://www.anchorterminal.com/compare/docker-model-runner-vs-ghost-core.md)\n- [Docker Model Runner vs GPT4All](https://www.anchorterminal.com/compare/docker-model-runner-vs-gpt4all.md)\n- [Docker Model Runner vs HyperQwen](https://www.anchorterminal.com/compare/docker-model-runner-vs-hyperqwen.md)\n- [Docker Model Runner vs Jan](https://www.anchorterminal.com/compare/docker-model-runner-vs-jan.md)\n- [Docker Model Runner vs Khoj](https://www.anchorterminal.com/compare/docker-model-runner-vs-khoj.md)\n- [Docker Model Runner vs KoboldCpp](https://www.anchorterminal.com/compare/docker-model-runner-vs-koboldcpp.md)\n- [Docker Model Runner vs Lemonade](https://www.anchorterminal.com/compare/docker-model-runner-vs-lemonade.md)\n- [Docker Model Runner vs llama.cpp](https://www.anchorterminal.com/compare/docker-model-runner-vs-llama-cpp.md)\n- [Docker Model Runner vs LM Studio](https://www.anchorterminal.com/compare/docker-model-runner-vs-lm-studio.md)\n- [Docker Model Runner vs LocalAI](https://www.anchorterminal.com/compare/docker-model-runner-vs-localai.md)\n- [Docker Model Runner vs MLX LM](https://www.anchorterminal.com/compare/docker-model-runner-vs-mlx-lm.md)\n- [Docker Model Runner vs Ollama](https://www.anchorterminal.com/compare/docker-model-runner-vs-ollama.md)\n- [Docker Model Runner vs Open WebUI](https://www.anchorterminal.com/compare/docker-model-runner-vs-open-webui.md)\n- [Docker Model Runner vs screenpipe](https://www.anchorterminal.com/compare/docker-model-runner-vs-screenpipe.md)\n- [Docker Model Runner vs TextGen](https://www.anchorterminal.com/compare/docker-model-runner-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 PrivateGPT](https://www.anchorterminal.com/compare/foundry-local-vs-privategpt.md)\n- [Core vs PrivateGPT](https://www.anchorterminal.com/compare/ghost-core-vs-privategpt.md)\n- [GPT4All vs PrivateGPT](https://www.anchorterminal.com/compare/gpt4all-vs-privategpt.md)\n- [HyperQwen vs PrivateGPT](https://www.anchorterminal.com/compare/hyperqwen-vs-privategpt.md)\n- [Jan vs PrivateGPT](https://www.anchorterminal.com/compare/jan-vs-privategpt.md)\n- [KoboldCpp vs PrivateGPT](https://www.anchorterminal.com/compare/koboldcpp-vs-privategpt.md)\n- [Lemonade vs PrivateGPT](https://www.anchorterminal.com/compare/lemonade-vs-privategpt.md)\n- [llama.cpp vs PrivateGPT](https://www.anchorterminal.com/compare/llama-cpp-vs-privategpt.md)\n- [LM Studio vs PrivateGPT](https://www.anchorterminal.com/compare/lm-studio-vs-privategpt.md)\n- [LocalAI vs PrivateGPT](https://www.anchorterminal.com/compare/localai-vs-privategpt.md)\n- [MLX LM vs PrivateGPT](https://www.anchorterminal.com/compare/mlx-lm-vs-privategpt.md)\n- [Ollama vs PrivateGPT](https://www.anchorterminal.com/compare/ollama-vs-privategpt.md)\n- [Open WebUI vs PrivateGPT](https://www.anchorterminal.com/compare/open-webui-vs-privategpt.md)\n- [PrivateGPT vs screenpipe](https://www.anchorterminal.com/compare/privategpt-vs-screenpipe.md)\n- [PrivateGPT vs TextGen](https://www.anchorterminal.com/compare/privategpt-vs-text-generation-webui.md)\n- [PrivateGPT vs vLLM](https://www.anchorterminal.com/compare/privategpt-vs-vllm.md)\n- [Khoj vs PrivateGPT](https://www.anchorterminal.com/compare/khoj-vs-privategpt.md)\n- [LocalGhost vs PrivateGPT](https://www.anchorterminal.com/compare/localghost-vs-privategpt.md)\n- [Docker Model Runner vs Underdog](https://www.anchorterminal.com/compare/docker-model-runner-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 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Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "Docker Model Runner D 53.8", "PrivateGPT D 53.2", "scores" ], "h1": "Docker Model Runner vs PrivateGPT", "image": "https://www.anchorterminal.com/assets/og/default.png", "path": "/compare/docker-model-runner-vs-privategpt", "published": "2026-10-01", "section": "tools", "title": "Docker Model Runner vs PrivateGPT for AI agents (2026)", "toc": null, "updated": "2026-11-07", "url": "https://www.anchorterminal.com/compare/docker-model-runner-vs-privategpt" }, "tokens": { "markdown": 6250, "slim": 3780 }, "version": 1 }