{ "data": { "a": { "slug": "mlx-lm", "name": "MLX LM", "vendor": "Apple Inc.", "vendorUrl": "https://opensource.apple.com/projects/mlx/", "kind": "http-api", "category": "local-ai", "summary": "Open-source Python package and command-line tools from Apple's MLX team for running, quantising and fine-tuning language models on Apple silicon. `mlx_lm.server` exposes a local HTTP API modelled on OpenAI's chat completions.", "url": "https://www.anchorterminal.com/tools/mlx-lm", "markdownUrl": "https://www.anchorterminal.com/tools/mlx-lm.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/mlx-lm.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/mlx-lm.json", "dns": "mlx-lm.grade.anchorterminal.com", "repo": "https://github.com/ml-explore/mlx-lm", "license": "MIT", "transports": [ "http" ], "packages": [ { "registry": "pypi", "name": "mlx-lm" } ], "auth": "none", "authNotes": "No credential, and no option to add one. `mlx_lm.server` binds 127.0.0.1:8080 by default, and `--allowed-origins` defaults to `*`, so any origin's requests are answered. Access control is left to the network or a proxy in front (https://github.com/ml-explore/mlx-lm/blob/main/mlx_lm/SERVER.md).", "pricing": "free", "pricingNotes": "Free under MIT, with no account, key or card. Nothing is sold. The owner pays for the hardware and electricity.", "priceSummary": "Free · OSS", "where": "local", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in the docs or the source (checked 2026-10-08).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 7300, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-08" }, "docsUrl": "https://github.com/ml-explore/mlx-lm/blob/main/mlx_lm/SERVER.md", "capabilities": [ "inference.local", "inference.open-weights" ], "tags": [ "open-source", "local", "self-hosted", "free", "no-card", "openai-compatible", "python", "pre-1.0", "no-auth", "no-telemetry" ], "lastRelease": "2026-10-16", "graded": true, "anchor": { "graded": true, "score": 50.7, "grade": "D", "agentReady": false, "rank": 1052, "ranked": true, "rankOf": 1510, "categoryRank": 14, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 54, "maintenance": 81, "payments": 60, "reliability": 56, "schema": 37, "security": 29, "transparency": 69 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-11-04" }, "negative": -1, "negativeNotes": [ "2026-05-22. CVE-2026-5843 (High, CVSS 3.1 8.2 and 4.0 8.8), published on 22 May 2026 by Docker as the assigning authority, against Docker Desktop from 4.56.0 to before 4.71.0 on macOS with Model Runner's MLX backend (GitHub advisory record GHSA-9m9w-53g9-47c4). It describes MLX-LM importing and running any Python file a model's `config.json` names in `model_file`, with no `trust_remote_code` gate, and the code in MLX-LM 0.31.3 does so. Commit bfa25a1 of 11 June 2026, titled as the fix for this CVE, gated it behind `trust_remote_code`, and the fix first shipped in v0.32.0 on 30 September 2026. A fixed third-party High over two months old, -1. https://github.com/CVEProject/cvelistV5/blob/main/cves/2026/5xxx/CVE-2026-5843.json" ], "verdict": "MIT, with no telemetry found in the source, three tagged releases since 30 September and tests run on every push to main. `mlx_lm.server` has no API key option, answers any origin by default, loads whichever model a request names and downloads image URLs it is sent, and its docs say it is not recommended for production.", "bestFor": "An owner with an Apple silicon Mac who wants MLX-format models, local fine-tuning and quantisation from Python or the command line, with a simple local chat completions server.", "strengths": [ "MIT, with no telemetry, analytics or update check found in the source", "Installs from PyPI (`mlx-lm`, Python 3.11 or later) and conda-forge, and each version tag is published to PyPI by trusted publishing", "The Build and Test workflow runs a lint check and 21 test files on every pull request and push to main", "`mlx_lm.server` binds 127.0.0.1:8080 by default, caps output at 512 tokens unless told otherwise and validates field types and ranges with a 400", "Three tagged releases from 30 September to 16 October 2026, and 146 commits from 87 authors on main since 6 August" ], "weaknesses": [ "`mlx_lm.server` has no API key or other credential option, and `--allowed-origins` defaults to `*`", "A request's `model` and `adapters` fields make the server download or load any Hugging Face repository or local path, with no allow-list (open issue #1892)", "Since v0.32.1 the chat route downloads any http or https image URL in a request, with no allow-list, and `SERVER.md` does not say so", "No OpenAPI file or llms.txt, and `SERVER.md` leaves out `tools`, `seed`, image parts, `/health` and the error responses", "CVE-2026-5843 (High), running a model's `model_file` code, was fixed on main on 11 June 2026 and reached a release only on 30 September" ], "agentNotes": [ "Keep `mlx_lm.server` on 127.0.0.1 and pass `--allowed-origins` with the origins you trust. There is no API key, and the default answers every origin", "Treat any caller as able to load any model and make the server fetch any web address, through the `model`, `adapters` and image URL fields", "Upgrade to 0.32.0 or later before loading third-party models. Earlier versions run a model's `model_file` code without `--trust-remote-code`", "Send `max_tokens` or `max_completion_tokens` when you need more than 512 tokens, the server default", "Read errors as `{\"error\": \"\u003ctext\u003e\"}` with 400 for a bad field and 404 for a model that failed to load. They are not OpenAI error objects" ], "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": 50.7 } ], "editorialScores": { "ergonomics": 54, "maintenance": 81, "payments": 60, "reliability": 56, "schema": 37, "security": 29, "transparency": 65 }, "provenanceScore": 72, "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": "pip install mlx-lm\nmlx_lm.server --model mlx-community/Mistral-7B-Instruct-v0.3-4bit # listens on 127.0.0.1:8080", "http": "curl localhost:8080/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"messages\": [{\"role\": \"user\", \"content\": \"Say this is a test!\"}],\n \"temperature\": 0.7\n }'" }, "letme": { "capability": "https://letme.dev/inference.local", "tool": "https://letme.dev/mlx-lm" }, "sameCompany": [ "apple-ads" ], "area": "models", "provenance": { "legalEntity": "Apple Inc.", "domain": "apple.com", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://github.com/ml-explore/mlx-lm/releases", "securityTxt": "valid", "checked": "2026-10-08", "notes": [ "The `LICENSE` file reads Copyright 2023 Apple Inc., and the package author on PyPI is MLX Contributors at a group.apple.com address. The repository sits in GitHub's ml-explore organisation and has no website of its own.", "opensource.apple.com/projects/mlx describes the MLX framework and does not name MLX LM. Its footer links Apple's website terms and general privacy policy, which do not govern this software, so terms and privacy are left empty.", "www.apple.com/.well-known/security.txt is valid until 6 October 2027 and is Apple's corporate file. The repository's own policy takes reports through GitHub private vulnerability reporting.", "There is no shared hosted endpoint. The server runs on the owner's machine." ], "score": 72 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/mlx-lm.json", "live": { "slug": "mlx-lm", "versions": [ { "registry": "github", "name": "ml-explore/mlx-lm", "version": "v0.32.2", "released": "2026-10-16", "seenAt": "2026-11-06T23:46:55.418819154Z" }, { "registry": "pypi", "name": "mlx-lm", "version": "0.32.2", "released": "2026-10-16", "seenAt": "2026-11-06T23:46:55.195772733Z" } ], "githubStars": 7577, "pypiWeekly": 135499, "securityTxt": { "url": "https://apple.com/.well-known/security.txt", "state": "valid", "expires": "2027-10-09T01:00:00.000Z", "checkedAt": "2026-11-07T16:01:42.074844316Z" }, "domain": { "domain": "apple.com", "registered": "1987-02-19", "source": "https://rdap.verisign.com/com/v1/domain/apple.com", "checkedAt": "2026-11-01T14:24:46.055603274Z" }, "github": { "repo": "ml-explore/mlx-lm", "checkedAt": "2026-11-02T09:07:57.406357661Z", "stars": 7532, "archived": false, "issuesEnabled": true, "pushedAt": "2026-10-30T09:28:48Z", "openIssues": 156, "openPRs": 99, "issuesOpened90d": 92, "issuesClosed90d": 147, "prsMerged90d": 147, "ci": { "branch": "main", "runs": 50, "success": 47, "failure": 3, "other": 0, "latest": "success", "latestAt": "2026-10-30T09:33:08Z" }, "replies": { "sampled": 25, "unanswered": 15, "medianDays": 5.4 } }, "updatedAt": "2026-11-07T16:01:42.074844316Z" } }, "answer": "PrivateGPT scores 53.2 (D) on agent readiness against MLX LM's 50.7 (D), and leads in 4 of 7 scored categories. MLX LM leads on maintenance \u0026 community and transparency \u0026 trust.", "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": 46, "category": "schema", "leads": "privategpt", "name": "Schema \u0026 documentation", "reasons": { "a": "Read for the HTTP server, the surface an agent calls. No OpenAPI file or other machine-readable contract. `SERVER.md` says the API is intended to be similar to OpenAI's chat API (5 of 25). No llms.txt. The docs are Markdown files in the repository, 176 lines for the server (5 of 10). Request and response fields each have a one-line purpose, the page opens with the production warning, and `stop` gained a fuller description in October, but `tools`, `seed`, `max_completion_tokens`, `chat_template_kwargs`, image content parts, the `/v1/completions` route and `/health` are in the code and not in the docs (9 of 20). Types and defaults are given in prose, and the server checks types and ranges in code. We found no `response_format` or JSON schema output in `server.py` (6 of 15). Two curl examples and no documented error responses (5 of 15). Version tags (v0.32.0 to v0.32.2 since 30 September) and GitHub release notes, with no changelog file and no version on the API itself beyond the `/v1` path (7 of 15). 5 + 5 + 9 + 6 + 5 + 7 = 37.", "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": 37, "b": 83 } }, { "by": 40, "category": "maintenance", "leads": "mlx-lm", "name": "Maintenance \u0026 community", "reasons": { "a": "Tag v0.32.2 on 16 October 2026, 19 days before this check (30). Three tagged releases in the 90 days from 6 August, v0.32.0 (30 September), v0.32.1 (15 October) and v0.32.2 (16 October), each with code changes, and each tag push publishes to PyPI through the release workflow. PyPI itself could not be read on 4 November (20). 146 commits from 87 authors on main since 6 August, the newest on 30 October. Recent bug reports mostly had no reply or one comment on 8 October, not re-read, and reply times were not measured (15 of 25). The package is the Python library, current with the release. No other official client (8 of 15). CI on every push, a pre-commit lint job, PyPI publishing by trusted publishing, and dependency floors on recent versions (`mlx\u003e=0.32.3`, `transformers\u003e=5.7.0`). No Dependabot file (8 of 10). 30 + 20 + 15 + 8 + 8 = 81.", "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": 81, "b": 41 } }, { "by": 14, "category": "reliability", "leads": "privategpt", "name": "Reliability", "reasons": { "a": "Read with the local-software lines, since the package and its server run on the owner's machine. `pyproject.toml` states Python 3.11 or later with macOS and Linux classifiers, each version tag is published to PyPI by trusted publishing from `release.yml`, and conda-forge carries it per the 8 October check (20). The Build and Test workflow runs a lint check and the unit tests (21 test files) on every pull request and push to main, on self-hosted macOS runners since 30 October. Run results on GitHub could not be read on 4 November, so the line takes 15, the value for public CI that runs the tests with an unreadable result (15 of 25). 151 open issues and 74 open pull requests, with no reply or one comment on the newest bug reports, among them server bugs #1946 and #1909, per the 8 October check, not re-read (15 of 25). Version tags with GitHub release notes and no changelog file. The notes could not be read on 4 November, and none of the ten read on 8 October called out a breaking change (6 of 15). Version 0.32.2, pre-1.0, and the server docs still say it is not recommended for production (0). 20 + 15 + 15 + 6 + 0 = 56.", "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": 56, "b": 70 } }, { "by": 10, "category": "ergonomics", "leads": "privategpt", "name": "Agent ergonomics", "reasons": { "a": "Read for an API. `max_tokens` defaults to 512, `stop` and `logprobs` shape the output and `usage` reports token counts, but there is no field selection and no structured output mode (15 of 25). `GET /v1/models` lists the MLX models in the Hugging Face cache and can be narrowed to one repository by path. No paging and no token-counting route (10 of 20). Errors are `{\"error\": \"\u003ctext\u003e\"}` with 400 and a specific message for a bad field, 411 for a missing Content-Length, and 404 for any failure while loading a model or building the prompt. There are no codes and none of it is documented (8 of 20). Generation is stateless and safe to retry, `seed` is accepted, a prompt cache holds ten entries by default and `/health` answers 503 when the generation thread has stopped. No retry guidance (12 of 20). Only `messages` is required and the defaults are conservative. OpenAI clients can call the chat route, and the Python API (`load`, `generate`, `stream_generate`) ships in the same package. No client library in a second language in this repository (9 of 15).", "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": 54, "b": 64 } }, { "by": 5, "category": "transparency", "leads": "mlx-lm", "name": "Transparency \u0026 trust", "reasons": { "a": "The editorial half. MIT, copyright Apple Inc., all of it public (30). No privacy statement covers the software and nothing states what it sends. In the source the outbound calls are model downloads from Hugging Face (or ModelScope when `MLXLM_USE_MODELSCOPE` is set), image URLs named in a chat request (since v0.32.1), uploads only on command, and a calibration text fetched from a GitHub gist by the quantisation tools (12 of 30). Runtime messages say `python -m mlx_lm.\u003ccommand\u003e` is deprecated, with no date and no written policy (5 of 20). No telemetry, analytics or update check found in the source at the 30 October head, so nothing to opt out of, though the README does not say so (18 of 20). 30 + 12 + 5 + 18 = 65.", "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": 69, "b": 64 } } ], "facts": [ { "a": "HTTP API", "b": "HTTP API", "name": "Kind" }, { "a": "Apple 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": "MIT", "b": "Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "yes", "name": "llms.txt" }, { "a": "2026-10-16", "b": "2026-06-18", "name": "Last release" }, { "a": "no document linked", "b": "no date given", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "not read yet", "b": "not found in the text", "name": "Customer content may train models" }, { "a": "not read yet", "b": "not found in the text", "name": "Terms restrict automated access" }, { "a": "not read yet", "b": "not found in the text", "name": "Terms restrict benchmarking" }, { "a": "not read yet", "b": "not found in the text", "name": "Terms or service can change without notice" }, { "a": "not read yet", "b": "not found in the text", "name": "Arbitration or class-action waiver" }, { "a": "7.3k stars", "b": "58k stars", "name": "Popularity" } ], "faq": [ { "answer": "PrivateGPT scores 53.2 (D) on agent readiness against MLX LM's 50.7 (D), and leads in 4 of 7 scored categories. MLX LM leads on maintenance \u0026 community and transparency \u0026 trust.", "question": "Which is better for AI agents, MLX LM or PrivateGPT?" }, { "answer": "Neither needs a key.", "question": "Do MLX LM and PrivateGPT need an API key?" }, { "answer": "No hosted endpoint is listed for MLX LM. No hosted endpoint is listed for PrivateGPT.", "question": "Can an agent call MLX LM and PrivateGPT without installing anything?" }, { "answer": "Yes. MLX LM is open source (MIT). PrivateGPT is open source (Apache-2.0).", "question": "Are MLX LM and PrivateGPT open source?" } ], "goodFor": [ { "aheadOn": [ "Maintenance \u0026 community, 81 against 41", "Transparency \u0026 trust, 69 against 64" ], "also": [ "Free to start without a card" ], "goodFor": "An owner with an Apple silicon Mac who wants MLX-format models, local fine-tuning and quantisation from Python or the command line, with a simple local chat completions server.", "slug": "mlx-lm", "watchFor": "`mlx_lm.server` has no API key or other credential option, and `--allowed-origins` defaults to `*`" }, { "aheadOn": [ "Reliability, 70 against 56", "Schema \u0026 documentation, 83 against 37", "Agent ergonomics, 64 against 54" ], "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": { "mlx-lm": [ { "causes": [ "security", "product", "correction" ], "date": "2026-11-04", "delta": -1.8, "from": 52.5, "fromGrade": "D", "to": 50.7, "toGrade": "D" }, { "causes": [ "method" ], "date": "2026-10-27", "delta": 0.3, "from": 52.2, "fromGrade": "D", "to": 52.5, "toGrade": "D" } ], "privategpt": null }, "job": { "capability": "inference.local", "name": "Local inference" }, "others": [ { "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-privategpt.json", "title": "AnythingLLM vs PrivateGPT", "url": "https://www.anchorterminal.com/compare/anythingllm-vs-privategpt" }, { "json": "https://www.anchorterminal.com/compare/berth-vs-mlx-lm.json", "title": "Berth vs MLX LM", "url": "https://www.anchorterminal.com/compare/berth-vs-mlx-lm" }, { "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-mlx-lm.json", "title": "BitRun vs MLX LM", "url": "https://www.anchorterminal.com/compare/bitrun-vs-mlx-lm" }, { "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-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-privategpt.json", "title": "Docker Model Runner vs PrivateGPT", "url": "https://www.anchorterminal.com/compare/docker-model-runner-vs-privategpt" }, { "json": "https://www.anchorterminal.com/compare/foundry-local-vs-mlx-lm.json", "title": "Foundry Local vs MLX LM", "url": "https://www.anchorterminal.com/compare/foundry-local-vs-mlx-lm" }, { "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-mlx-lm.json", "title": "Core vs MLX LM", "url": "https://www.anchorterminal.com/compare/ghost-core-vs-mlx-lm" }, { "json": "https://www.anchorterminal.com/compare/ghost-core-vs-privategpt.json", "title": "Core vs PrivateGPT", "url": "https://www.anchorterminal.com/compare/ghost-core-vs-privategpt" }, { "json": "https://www.anchorterminal.com/compare/gpt4all-vs-mlx-lm.json", "title": "GPT4All vs MLX LM", "url": "https://www.anchorterminal.com/compare/gpt4all-vs-mlx-lm" }, { "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-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-privategpt.json", "title": "HyperQwen vs PrivateGPT", "url": "https://www.anchorterminal.com/compare/hyperqwen-vs-privategpt" }, { "json": "https://www.anchorterminal.com/compare/jan-vs-mlx-lm.json", "title": "Jan vs MLX LM", "url": "https://www.anchorterminal.com/compare/jan-vs-mlx-lm" }, { "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/khoj-vs-mlx-lm.json", "title": "Khoj vs MLX LM", "url": "https://www.anchorterminal.com/compare/khoj-vs-mlx-lm" }, { "json": "https://www.anchorterminal.com/compare/koboldcpp-vs-mlx-lm.json", "title": "KoboldCpp vs MLX LM", "url": "https://www.anchorterminal.com/compare/koboldcpp-vs-mlx-lm" }, { "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-mlx-lm.json", "title": "Lemonade vs MLX LM", "url": "https://www.anchorterminal.com/compare/lemonade-vs-mlx-lm" }, { "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-mlx-lm.json", "title": "llama.cpp vs MLX LM", "url": "https://www.anchorterminal.com/compare/llama-cpp-vs-mlx-lm" }, { "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-mlx-lm.json", "title": "LM Studio vs MLX LM", "url": "https://www.anchorterminal.com/compare/lm-studio-vs-mlx-lm" }, { "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-mlx-lm.json", "title": "LocalAI vs MLX LM", "url": "https://www.anchorterminal.com/compare/localai-vs-mlx-lm" }, { "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-ollama.json", "title": "MLX LM vs Ollama", "url": "https://www.anchorterminal.com/compare/mlx-lm-vs-ollama" }, { "json": "https://www.anchorterminal.com/compare/mlx-lm-vs-open-webui.json", "title": "MLX LM vs Open WebUI", "url": "https://www.anchorterminal.com/compare/mlx-lm-vs-open-webui" }, { "json": "https://www.anchorterminal.com/compare/mlx-lm-vs-screenpipe.json", "title": "MLX LM vs screenpipe", "url": "https://www.anchorterminal.com/compare/mlx-lm-vs-screenpipe" }, { "json": "https://www.anchorterminal.com/compare/mlx-lm-vs-text-generation-webui.json", "title": "MLX LM vs TextGen", "url": "https://www.anchorterminal.com/compare/mlx-lm-vs-text-generation-webui" }, { "json": "https://www.anchorterminal.com/compare/mlx-lm-vs-vllm.json", "title": "MLX LM vs vLLM", "url": "https://www.anchorterminal.com/compare/mlx-lm-vs-vllm" }, { "json": "https://www.anchorterminal.com/compare/ollama-vs-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/mlx-lm-vs-underdog.json", "title": "MLX LM vs Underdog", "url": "https://www.anchorterminal.com/compare/mlx-lm-vs-underdog" } ], "panel": null, "scores": [ { "by": 14, "edge": "privategpt", "key": "reliability", "mlx-lm": 56, "name": "Reliability", "privategpt": 70, "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 46, "edge": "privategpt", "key": "schema", "mlx-lm": 37, "name": "Schema \u0026 documentation", "privategpt": 83, "weight": 13 }, { "by": 10, "edge": "privategpt", "key": "ergonomics", "mlx-lm": 54, "name": "Agent ergonomics", "privategpt": 64, "weight": 13 }, { "by": 3, "edge": "privategpt", "key": "security", "mlx-lm": 29, "name": "Security \u0026 auth", "privategpt": 32, "weight": 14 }, { "by": 0, "edge": "", "key": "payments", "mlx-lm": 60, "name": "Payments \u0026 pricing", "privategpt": 60, "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 40, "edge": "mlx-lm", "key": "maintenance", "mlx-lm": 81, "name": "Maintenance \u0026 community", "privategpt": 41, "weight": 7 }, { "by": 5, "edge": "mlx-lm", "key": "transparency", "mlx-lm": 69, "name": "Transparency \u0026 trust", "privategpt": 64, "weight": 7 } ], "summary": "PrivateGPT scores 53.2 (D) on agent readiness against MLX LM's 50.7 (D), and leads in 4 of 7 scored categories. MLX LM leads on maintenance \u0026 community and transparency \u0026 trust. Both do local inference.", "tasks": null, "tutorials": [], "verdicts": { "mlx-lm": "MIT, with no telemetry found in the source, three tagged releases since 30 September and tests run on every push to main. `mlx_lm.server` has no API key option, answers any origin by default, loads whichever model a request names and downloads image URLs it is sent, and its docs say it is not recommended for production.", "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/mlx-lm-vs-privategpt", "json": "https://www.anchorterminal.com/compare/mlx-lm-vs-privategpt.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/mlx-lm-vs-privategpt.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/mlx-lm-vs-privategpt.min.md" }, "markdown": "PrivateGPT scores 53.2 (D) on agent readiness against MLX LM's 50.7 (D), and leads in 4 of 7 scored categories. MLX LM leads on maintenance \u0026 community and transparency \u0026 trust. Both do local inference.\n\n- MLX LM: grade D, 50.7/100, rank #1052 of 1510. Markdown https://www.anchorterminal.com/tools/mlx-lm.md · JSON https://www.anchorterminal.com/api/v1/tools/mlx-lm.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### MLX LM (D)\n\nGood for: An owner with an Apple silicon Mac who wants MLX-format models, local fine-tuning and quantisation from Python or the command line, with a simple local chat completions server.\n\nAhead on:\n- Maintenance \u0026 community, 81 against 41\n- Transparency \u0026 trust, 69 against 64\n\nAlso in its favour:\n- Free to start without a card\n\nWatch for: `mlx_lm.server` has no API key or other credential option, and `--allowed-origins` defaults to `*`\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- Reliability, 70 against 56\n- Schema \u0026 documentation, 83 against 37\n- Agent ergonomics, 64 against 54\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### Schema \u0026 documentation: PrivateGPT +46\n\n- MLX LM (37): Read for the HTTP server, the surface an agent calls. No OpenAPI file or other machine-readable contract. `SERVER.md` says the API is intended to be similar to OpenAI's chat API (5 of 25). No llms.txt. The docs are Markdown files in the repository, 176 lines for the server (5 of 10). Request and response fields each have a one-line purpose, the page opens with the production warning, and `stop` gained a fuller description in October, but `tools`, `seed`, `max_completion_tokens`, `chat_template_kwargs`, image content parts, the `/v1/completions` route and `/health` are in the code and not in the docs (9 of 20). Types and defaults are given in prose, and the server checks types and ranges in code. We found no `response_format` or JSON schema output in `server.py` (6 of 15). Two curl examples and no documented error responses (5 of 15). Version tags (v0.32.0 to v0.32.2 since 30 September) and GitHub release notes, with no changelog file and no version on the API itself beyond the `/v1` path (7 of 15). 5 + 5 + 9 + 6 + 5 + 7 = 37.\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### Maintenance \u0026 community: MLX LM +40\n\n- MLX LM (81): Tag v0.32.2 on 16 October 2026, 19 days before this check (30). Three tagged releases in the 90 days from 6 August, v0.32.0 (30 September), v0.32.1 (15 October) and v0.32.2 (16 October), each with code changes, and each tag push publishes to PyPI through the release workflow. PyPI itself could not be read on 4 November (20). 146 commits from 87 authors on main since 6 August, the newest on 30 October. Recent bug reports mostly had no reply or one comment on 8 October, not re-read, and reply times were not measured (15 of 25). The package is the Python library, current with the release. No other official client (8 of 15). CI on every push, a pre-commit lint job, PyPI publishing by trusted publishing, and dependency floors on recent versions (`mlx\u003e=0.32.3`, `transformers\u003e=5.7.0`). No Dependabot file (8 of 10). 30 + 20 + 15 + 8 + 8 = 81.\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### Reliability: PrivateGPT +14\n\n- MLX LM (56): Read with the local-software lines, since the package and its server run on the owner's machine. `pyproject.toml` states Python 3.11 or later with macOS and Linux classifiers, each version tag is published to PyPI by trusted publishing from `release.yml`, and conda-forge carries it per the 8 October check (20). The Build and Test workflow runs a lint check and the unit tests (21 test files) on every pull request and push to main, on self-hosted macOS runners since 30 October. Run results on GitHub could not be read on 4 November, so the line takes 15, the value for public CI that runs the tests with an unreadable result (15 of 25). 151 open issues and 74 open pull requests, with no reply or one comment on the newest bug reports, among them server bugs #1946 and #1909, per the 8 October check, not re-read (15 of 25). Version tags with GitHub release notes and no changelog file. The notes could not be read on 4 November, and none of the ten read on 8 October called out a breaking change (6 of 15). Version 0.32.2, pre-1.0, and the server docs still say it is not recommended for production (0). 20 + 15 + 15 + 6 + 0 = 56.\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### Agent ergonomics: PrivateGPT +10\n\n- MLX LM (54): Read for an API. `max_tokens` defaults to 512, `stop` and `logprobs` shape the output and `usage` reports token counts, but there is no field selection and no structured output mode (15 of 25). `GET /v1/models` lists the MLX models in the Hugging Face cache and can be narrowed to one repository by path. No paging and no token-counting route (10 of 20). Errors are `{\"error\": \"\u003ctext\u003e\"}` with 400 and a specific message for a bad field, 411 for a missing Content-Length, and 404 for any failure while loading a model or building the prompt. There are no codes and none of it is documented (8 of 20). Generation is stateless and safe to retry, `seed` is accepted, a prompt cache holds ten entries by default and `/health` answers 503 when the generation thread has stopped. No retry guidance (12 of 20). Only `messages` is required and the defaults are conservative. OpenAI clients can call the chat route, and the Python API (`load`, `generate`, `stream_generate`) ships in the same package. No client library in a second language in this repository (9 of 15).\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### Transparency \u0026 trust: MLX LM +5\n\n- MLX LM (69): The editorial half. MIT, copyright Apple Inc., all of it public (30). No privacy statement covers the software and nothing states what it sends. In the source the outbound calls are model downloads from Hugging Face (or ModelScope when `MLXLM_USE_MODELSCOPE` is set), image URLs named in a chat request (since v0.32.1), uploads only on command, and a calibration text fetched from a GitHub gist by the quantisation tools (12 of 30). Runtime messages say `python -m mlx_lm.\u003ccommand\u003e` is deprecated, with no date and no written policy (5 of 20). No telemetry, analytics or update check found in the source at the 30 October head, so nothing to opt out of, though the README does not say so (18 of 20). 30 + 12 + 5 + 18 = 65.\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## Score by category\n\n| Category | Weight | MLX LM | PrivateGPT | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 56 | 70 | PrivateGPT +14 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 37 | 83 | PrivateGPT +46 |\n| Agent ergonomics | 13% (16.2 this run) | 54 | 64 | PrivateGPT +10 |\n| Security \u0026 auth | 14% (17.5 this run) | 29 | 32 | PrivateGPT +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) | 81 | 41 | MLX LM +40 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 69 | 64 | MLX LM +5 |\n| Negative events | ≤15 | -1 | -7 | |\n| **Total** | | **50.7 · D** | **53.2 · D** | |\n\n## Facts side by side\n\n| Fact | MLX LM | PrivateGPT |\n| --- | --- | --- |\n| Kind | HTTP API | HTTP API |\n| Vendor | Apple 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 | MIT | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | yes |\n| Last release | 2026-10-16 | 2026-06-18 |\n| Terms last updated | no document linked | no date given |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models | not read yet | not found in the text |\n| Terms restrict automated access | not read yet | not found in the text |\n| Terms restrict benchmarking | not read yet | not found in the text |\n| Terms or service can change without notice | not read yet | not found in the text |\n| Arbitration or class-action waiver | not read yet | not found in the text |\n| Popularity | 7.3k stars | 58k stars |\n\n## How the scores have moved\n\n### MLX LM\n\n- -1.8 on 4 November 2026: security, product, correction\n- +0.3 on 27 October 2026: checklist\n\n### PrivateGPT\n\nNo score change since it was listed.\n\n## Verdicts\n\n**MLX LM.** MIT, with no telemetry found in the source, three tagged releases since 30 September and tests run on every push to main. `mlx_lm.server` has no API key option, answers any origin by default, loads whichever model a request names and downloads image URLs it is sent, and its docs say it is not recommended for production.\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### MLX LM\n\n1. Keep `mlx_lm.server` on 127.0.0.1 and pass `--allowed-origins` with the origins you trust. There is no API key, and the default answers every origin\n2. Treat any caller as able to load any model and make the server fetch any web address, through the `model`, `adapters` and image URL fields\n3. Upgrade to 0.32.0 or later before loading third-party models. Earlier versions run a model's `model_file` code without `--trust-remote-code`\n4. Send `max_tokens` or `max_completion_tokens` when you need more than 512 tokens, the server default\n5. Read errors as `{\"error\": \"\u003ctext\u003e\"}` with 400 for a bad field and 404 for a model that failed to load. They are not OpenAI error objects\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, MLX LM or PrivateGPT?\n\nPrivateGPT scores 53.2 (D) on agent readiness against MLX LM's 50.7 (D), and leads in 4 of 7 scored categories. MLX LM leads on maintenance \u0026 community and transparency \u0026 trust.\n\n### Do MLX LM and PrivateGPT need an API key?\n\nNeither needs a key.\n\n### Can an agent call MLX LM and PrivateGPT without installing anything?\n\nNo hosted endpoint is listed for MLX LM. No hosted endpoint is listed for PrivateGPT.\n\n### Are MLX LM and PrivateGPT open source?\n\nYes. MLX LM is open source (MIT). PrivateGPT is open source (Apache-2.0).\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/mlx-lm-vs-privategpt.json, and with the fewest tokens: https://www.anchorterminal.com/compare/mlx-lm-vs-privategpt.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"mlx-lm\", \"b\": \"privategpt\"}`. From a terminal: `anchor compare mlx-lm privategpt`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/mlx-lm.json and https://www.anchorterminal.com/api/v1/tools/privategpt.json\n\n## Other comparisons with MLX LM or PrivateGPT\n\n- [AnythingLLM vs MLX LM](https://www.anchorterminal.com/compare/anythingllm-vs-mlx-lm.md)\n- [AnythingLLM vs PrivateGPT](https://www.anchorterminal.com/compare/anythingllm-vs-privategpt.md)\n- [Berth vs MLX LM](https://www.anchorterminal.com/compare/berth-vs-mlx-lm.md)\n- [Berth vs PrivateGPT](https://www.anchorterminal.com/compare/berth-vs-privategpt.md)\n- [BitRun vs MLX LM](https://www.anchorterminal.com/compare/bitrun-vs-mlx-lm.md)\n- [BitRun vs PrivateGPT](https://www.anchorterminal.com/compare/bitrun-vs-privategpt.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 PrivateGPT](https://www.anchorterminal.com/compare/docker-model-runner-vs-privategpt.md)\n- [Foundry Local vs MLX LM](https://www.anchorterminal.com/compare/foundry-local-vs-mlx-lm.md)\n- [Foundry Local vs PrivateGPT](https://www.anchorterminal.com/compare/foundry-local-vs-privategpt.md)\n- [Core vs MLX LM](https://www.anchorterminal.com/compare/ghost-core-vs-mlx-lm.md)\n- [Core vs PrivateGPT](https://www.anchorterminal.com/compare/ghost-core-vs-privategpt.md)\n- [GPT4All vs MLX LM](https://www.anchorterminal.com/compare/gpt4all-vs-mlx-lm.md)\n- [GPT4All vs PrivateGPT](https://www.anchorterminal.com/compare/gpt4all-vs-privategpt.md)\n- [HyperQwen vs MLX LM](https://www.anchorterminal.com/compare/hyperqwen-vs-mlx-lm.md)\n- [HyperQwen vs PrivateGPT](https://www.anchorterminal.com/compare/hyperqwen-vs-privategpt.md)\n- [Jan vs MLX LM](https://www.anchorterminal.com/compare/jan-vs-mlx-lm.md)\n- [Jan vs PrivateGPT](https://www.anchorterminal.com/compare/jan-vs-privategpt.md)\n- [Khoj vs MLX LM](https://www.anchorterminal.com/compare/khoj-vs-mlx-lm.md)\n- [KoboldCpp vs MLX LM](https://www.anchorterminal.com/compare/koboldcpp-vs-mlx-lm.md)\n- [KoboldCpp vs PrivateGPT](https://www.anchorterminal.com/compare/koboldcpp-vs-privategpt.md)\n- [Lemonade vs MLX LM](https://www.anchorterminal.com/compare/lemonade-vs-mlx-lm.md)\n- [Lemonade vs PrivateGPT](https://www.anchorterminal.com/compare/lemonade-vs-privategpt.md)\n- [llama.cpp vs MLX LM](https://www.anchorterminal.com/compare/llama-cpp-vs-mlx-lm.md)\n- [llama.cpp vs PrivateGPT](https://www.anchorterminal.com/compare/llama-cpp-vs-privategpt.md)\n- [LM Studio vs MLX LM](https://www.anchorterminal.com/compare/lm-studio-vs-mlx-lm.md)\n- [LM Studio vs PrivateGPT](https://www.anchorterminal.com/compare/lm-studio-vs-privategpt.md)\n- [LocalAI vs MLX LM](https://www.anchorterminal.com/compare/localai-vs-mlx-lm.md)\n- [LocalAI vs PrivateGPT](https://www.anchorterminal.com/compare/localai-vs-privategpt.md)\n- [MLX LM vs Ollama](https://www.anchorterminal.com/compare/mlx-lm-vs-ollama.md)\n- [MLX LM vs Open WebUI](https://www.anchorterminal.com/compare/mlx-lm-vs-open-webui.md)\n- [MLX LM vs screenpipe](https://www.anchorterminal.com/compare/mlx-lm-vs-screenpipe.md)\n- [MLX LM vs TextGen](https://www.anchorterminal.com/compare/mlx-lm-vs-text-generation-webui.md)\n- [MLX LM vs vLLM](https://www.anchorterminal.com/compare/mlx-lm-vs-vllm.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- [MLX LM vs Underdog](https://www.anchorterminal.com/compare/mlx-lm-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": "2026-11-07", "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": "MLX LM vs PrivateGPT", "url": "" } ], "description": "PrivateGPT scores 53.2 (D) to MLX LM's 50.7 (D) for local inference. 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