{ "data": { "a": { "slug": "lemonade", "name": "Lemonade", "vendor": "AMD and the Lemonade community", "vendorUrl": "https://lemonade-server.ai", "kind": "http-api", "category": "local-ai", "summary": "Open-source local AI server from AMD and community contributors. It runs text, speech and image models on the owner's CPU, GPU or NPU behind OpenAI-, Anthropic- and Ollama-compatible APIs and an MCP endpoint on port 13305.", "url": "https://www.anchorterminal.com/tools/lemonade", "markdownUrl": "https://www.anchorterminal.com/tools/lemonade.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/lemonade.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/lemonade.json", "repo": "https://github.com/lemonade-sdk/lemonade", "license": "Apache 2.0. Each backend (llama.cpp, whisper.cpp, stable-diffusion.cpp, FastFlowLM and others) is downloaded separately under its own licence", "transports": [ "http" ], "packages": [ { "registry": "oci", "name": "ghcr.io/lemonade-sdk/lemonade-server" } ], "auth": "api-key", "authNotes": "Off by default. With no key set, every endpoint answers without authentication, on a default bind of localhost. `LEMONADE_API_KEY` sets one bearer key for the regular API (`/api/*`, `/v0/*`, `/v1/*`, `/mcp` and `/metrics`). `LEMONADE_ADMIN_API_KEY` sets a second key for the internal control endpoints (`/internal/*`), and without it the regular key reaches those too. Both are environment variables, so there are no per-user keys. The docs tell WebSocket clients to pass `?api_key=KEY` in the URL.", "pricing": "free", "pricingNotes": "Free and Apache 2.0 with nothing to buy and no account. You run it on your own hardware. Cloud Offload, which is optional and experimental, bills through the owner's own keys at whichever cloud provider they add.", "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": 6, "popularity": { "githubStars": 5800, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-08" }, "docsUrl": "https://lemonade-server.ai/docs/", "capabilities": [ "inference.local", "inference.open-weights", "embed.text", "rerank", "speech.stt", "speech.tts", "image.generate" ], "tags": [ "open-source", "local", "self-hosted", "free", "no-card", "openai-compatible", "mcp", "streaming", "open-weights", "amd", "npu", "docker" ], "lastRelease": "2026-10-07", "graded": true, "anchor": { "graded": true, "score": 63.8, "grade": "B", "agentReady": false, "rank": 336, "ranked": true, "rankOf": 842, "categoryRank": 2, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 70, "maintenance": 76, "payments": 60, "reliability": 75, "schema": 70, "security": 36, "transparency": 64 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-08" }, "negative": 0, "verdict": "Apache 2.0, with weekly releases, installers for Windows, macOS and five Linux routes, and a six-tool MCP endpoint whose descriptions steer a caller away from accidental multi-gigabyte downloads. Authentication is off by default, the repository has no security policy, and the docs tell WebSocket clients to pass the key in the URL.", "bestFor": "An owner with AMD hardware (Ryzen AI NPUs, Radeon or Strix Halo) who wants one local server for chat, speech and images that existing OpenAI, Anthropic or Ollama clients can call, and for MCP clients that want local models as tools.", "strengths": [ "Apache 2.0, with OpenAI, Anthropic Messages, Ollama and llama.cpp-compatible routes on one port (13305)", "`POST /mcp` exposes six tools over Streamable HTTP, and omitting `model` reuses a loaded or downloaded model before any download", "Every running server returns its own Markdown API reference at `GET /v1/docs` and through the `lemonade_docs` tool, so it matches the installed version", "Stable release v2026.41.1 on 7 October 2026 on a weekly cadence, each with a Breaking Changes section in its notes", "Telemetry is off by default and exports OTLP traces only to an endpoint the operator sets, with switches to redact prompts and outputs" ], "weaknesses": [ "No authentication by default. With no key set, every route answers, including `/internal/*` shutdown and configuration", "GitHub reports no `SECURITY.md`, and we found no security.txt, advisory or disclosure address", "The docs tell WebSocket clients to send the key as `?api_key=KEY` in the URL", "No OpenAPI file in the repository, and no `readOnlyHint` or `destructiveHint` on the MCP tools", "The main build and test workflow's badge read failing on main on 8 October 2026, with 411 issues open" ], "agentNotes": [ "Call `lemonade_list_models` (or `GET /v1/models`) before naming a model. A wrong name with `allow_download: true` can start a multi-gigabyte download", "Send `Authorization: Bearer \u003ckey\u003e` when the operator has set `LEMONADE_API_KEY`. `/internal/*` needs the admin key when one is set", "Use `POST /v1/chat/completions` for streamed tokens, embeddings and speech. The MCP endpoint ignores `stream` and has no embeddings or text-to-speech tool", "Pass `output_dir` to `lemonade_generate_image` and `lemonade_omni` to get file paths in place of inline base64. Writes stay inside the MCP media sandbox", "Read `GET /v1/docs` on the running server for the reference that matches its version. Weekly releases change behaviour" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "B", "methodology": "0.4", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 63.8 } ], "editorialScores": { "ergonomics": 70, "maintenance": 76, "payments": 60, "reliability": 75, "schema": 70, "security": 36, "transparency": 70 }, "provenanceScore": 57 }, "connect": { "install": "winget install --id AMD.LemonadeServer -e", "http": "curl http://localhost:13305/api/v1/chat/completions -H \"Content-Type: application/json\" -d '{\"model\": \"your-model-name\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello\"}]}'", "claudeCode": "lemonade launch claude" }, "letme": { "capability": "https://letme.dev/inference.local", "tool": "https://letme.dev/lemonade" }, "area": "models", "provenance": { "legalEntity": "Advanced Micro Devices, Inc.", "domain": "lemonade-server.ai", "domainRegistered": "2025-05-12", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://github.com/lemonade-sdk/lemonade/releases", "securityTxt": "none", "checked": "2026-10-08", "notes": [ "The website footer reads \"© 2026 AMD. Licensed under Apache 2.0\", and `contrib/debian/copyright` in the repository names Advanced Micro Devices, Inc. The code lives in the lemonade-sdk organisation on GitHub, and the README calls it a community project with optimisations by AMD engineers.", "We found no terms of service and no privacy policy for the software or the website. The home page and its footer link to neither, so both fields are empty.", "lemonade-server.ai/.well-known/security.txt, /security.txt and /llms.txt return 404.", "RDAP gives a registration date of 2025-05-12 for lemonade-server.ai, with Porkbun LLC as registrar and the registrant behind a privacy service.", "There's no hosted endpoint. Each instance answers on the owner's own machine, by default localhost port 13305." ], "score": 57 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/lemonade.json" }, "answer": "Lemonade scores 63.8 (B) on agent readiness against MLX LM's 52.2 (D), and leads in 5 of 7 scored categories.", "b": { "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", "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": 139915, "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": "2026-10-01", "graded": true, "anchor": { "graded": true, "score": 52.2, "grade": "D", "agentReady": false, "rank": 657, "ranked": true, "rankOf": 842, "categoryRank": 12, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 54, "maintenance": 61, "payments": 60, "reliability": 66, "schema": 37, "security": 32, "transparency": 66 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-08" }, "negative": 0, "verdict": "MIT, with no telemetry found in the source, and the tests passed on the last eight pushes to main. `mlx_lm.server` has no API key option, answers any origin by default and loads whichever model a request names, and its own 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` 0.32.0, Python 3.11 or later) and conda-forge, with releases published to PyPI by trusted publishing from a GitHub workflow", "The Build and Test workflow passed on the last eight pushes to main, with 21 test files run on a macOS runner", "`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", "127 commits from 82 authors on main in the 90 days to 8 October 2026" ], "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)", "The docs and a start-up warning say the server is not recommended for production because it has only basic security checks", "No OpenAPI file or llms.txt, and `SERVER.md` leaves out `tools`, `seed`, `/health` and the error responses", "One PyPI release in 90 days (0.32.0 on 1 October 2026, the first since 0.31.3 on 22 April), and the version is still 0.x" ], "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. The `model` and `adapters` request fields accept any Hugging Face repository or local path", "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", "Poll `GET /health` before the first request. It answers 503 with `unavailable` when the generation thread has stopped" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "D", "methodology": "0.4", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 52.2 } ], "editorialScores": { "ergonomics": 54, "maintenance": 61, "payments": 60, "reliability": 66, "schema": 37, "security": 32, "transparency": 65 }, "provenanceScore": 67 }, "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" }, "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": 67 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/mlx-lm.json" }, "facts": [ { "a": "HTTP API", "b": "HTTP API", "name": "Kind" }, { "a": "AMD and the Lemonade community", "b": "Apple Inc.", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "HTTP", "b": "HTTP", "name": "Transports" }, { "a": "API key", "b": "None", "name": "Auth" }, { "a": "Free", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Apache 2.0. Each backend (llama.cpp, whisper.cpp, stable-diffusion.cpp, FastFlowLM and others) is downloaded separately under its own licence", "b": "MIT", "name": "Licence" }, { "a": "6", "b": "none", "name": "Tools exposed" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-10-07", "b": "2026-10-01", "name": "Last release" }, { "a": "no document linked", "b": "no document linked", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "", "b": "", "name": "Customer content may train models" }, { "a": "", "b": "", "name": "Terms restrict automated access" }, { "a": "", "b": "", "name": "Terms restrict benchmarking" }, { "a": "", "b": "", "name": "Terms or service can change without notice" }, { "a": "", "b": "", "name": "Arbitration or class-action waiver" }, { "a": "5.8k stars", "b": "7.3k stars, 140k PyPI/wk", "name": "Popularity" } ], "faq": [ { "answer": "Lemonade scores 63.8 (B) on agent readiness against MLX LM's 52.2 (D), and leads in 5 of 7 scored categories.", "question": "Which is better for AI agents, Lemonade or MLX LM?" }, { "answer": "Lemonade needs an API key. MLX LM needs no key.", "question": "Do Lemonade and MLX LM need an API key?" }, { "answer": "No hosted endpoint is listed for Lemonade. No hosted endpoint is listed for MLX LM.", "question": "Can an agent call Lemonade and MLX LM without installing anything?" }, { "answer": "Yes. Lemonade is open source (Apache 2.0. Each backend (llama.cpp, whisper.cpp, stable-diffusion.cpp, FastFlowLM and others) is downloaded separately under its own licence). MLX LM is open source (MIT).", "question": "Are Lemonade and MLX LM open source?" } ], "goodFor": [ { "aheadOn": [ "Reliability, 75 against 66", "Schema \u0026 documentation, 70 against 37", "Agent ergonomics, 70 against 54", "Maintenance \u0026 community, 76 against 61" ], "also": null, "goodFor": "An owner with AMD hardware (Ryzen AI NPUs, Radeon or Strix Halo) who wants one local server for chat, speech and images that existing OpenAI, Anthropic or Ollama clients can call, and for MCP clients that want local models as tools.", "slug": "lemonade", "watchFor": "No authentication by default. With no key set, every route answers, including `/internal/*` shutdown and configuration" }, { "aheadOn": null, "also": [ "No key needed to call it" ], "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 `*`" } ], "job": { "capability": "inference.local", "name": "Local inference" }, "others": [ { "json": "https://www.anchorterminal.com/compare/anythingllm-vs-lemonade.json", "title": "AnythingLLM vs Lemonade", "url": "https://www.anchorterminal.com/compare/anythingllm-vs-lemonade" }, { "json": "https://www.anchorterminal.com/compare/anythingllm-vs-mlx-lm.json", "title": "AnythingLLM vs MLX LM", "url": "https://www.anchorterminal.com/compare/anythingllm-vs-mlx-lm" }, { "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-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/foundry-local-vs-lemonade.json", "title": "Foundry Local vs Lemonade", "url": "https://www.anchorterminal.com/compare/foundry-local-vs-lemonade" }, { "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/ghost-core-vs-lemonade.json", "title": "Core vs Lemonade", "url": "https://www.anchorterminal.com/compare/ghost-core-vs-lemonade" }, { "json": "https://www.anchorterminal.com/compare/ghost-core-vs-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/gpt4all-vs-lemonade.json", "title": "GPT4All vs Lemonade", "url": "https://www.anchorterminal.com/compare/gpt4all-vs-lemonade" }, { "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/jan-vs-lemonade.json", "title": "Jan vs Lemonade", "url": "https://www.anchorterminal.com/compare/jan-vs-lemonade" }, { "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/khoj-vs-lemonade.json", "title": "Khoj vs Lemonade", "url": "https://www.anchorterminal.com/compare/khoj-vs-lemonade" }, { "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-lemonade.json", "title": "KoboldCpp vs Lemonade", "url": "https://www.anchorterminal.com/compare/koboldcpp-vs-lemonade" }, { "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/lemonade-vs-llama-cpp.json", "title": "Lemonade vs llama.cpp", "url": "https://www.anchorterminal.com/compare/lemonade-vs-llama-cpp" }, { "json": "https://www.anchorterminal.com/compare/lemonade-vs-lm-studio.json", "title": "Lemonade vs LM Studio", "url": "https://www.anchorterminal.com/compare/lemonade-vs-lm-studio" }, { "json": "https://www.anchorterminal.com/compare/lemonade-vs-localai.json", "title": "Lemonade vs LocalAI", "url": "https://www.anchorterminal.com/compare/lemonade-vs-localai" }, { "json": "https://www.anchorterminal.com/compare/lemonade-vs-ollama.json", "title": "Lemonade vs Ollama", "url": "https://www.anchorterminal.com/compare/lemonade-vs-ollama" }, { "json": "https://www.anchorterminal.com/compare/lemonade-vs-open-webui.json", "title": "Lemonade vs Open WebUI", "url": "https://www.anchorterminal.com/compare/lemonade-vs-open-webui" }, { "json": "https://www.anchorterminal.com/compare/lemonade-vs-screenpipe.json", "title": "Lemonade vs screenpipe", "url": "https://www.anchorterminal.com/compare/lemonade-vs-screenpipe" }, { "json": "https://www.anchorterminal.com/compare/lemonade-vs-text-generation-webui.json", "title": "Lemonade vs TextGen", "url": "https://www.anchorterminal.com/compare/lemonade-vs-text-generation-webui" }, { "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/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/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/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/lemonade-vs-underdog.json", "title": "Lemonade vs Underdog", "url": "https://www.anchorterminal.com/compare/lemonade-vs-underdog" }, { "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" } ], "scores": [ { "by": 9, "edge": "lemonade", "key": "reliability", "lemonade": 75, "mlx-lm": 66, "name": "Reliability", "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 33, "edge": "lemonade", "key": "schema", "lemonade": 70, "mlx-lm": 37, "name": "Schema \u0026 documentation", "weight": 13 }, { "by": 16, "edge": "lemonade", "key": "ergonomics", "lemonade": 70, "mlx-lm": 54, "name": "Agent ergonomics", "weight": 13 }, { "by": 4, "edge": "lemonade", "key": "security", "lemonade": 36, "mlx-lm": 32, "name": "Security \u0026 auth", "weight": 14 }, { "by": 0, "edge": "", "key": "payments", "lemonade": 60, "mlx-lm": 60, "name": "Payments \u0026 pricing", "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 15, "edge": "lemonade", "key": "maintenance", "lemonade": 76, "mlx-lm": 61, "name": "Maintenance \u0026 community", "weight": 7 }, { "by": 2, "edge": "mlx-lm", "key": "transparency", "lemonade": 64, "mlx-lm": 66, "name": "Transparency \u0026 trust", "weight": 7 } ], "summary": "Lemonade scores 63.8 (B) on agent readiness against MLX LM's 52.2 (D), and leads in 5 of 7 scored categories. Both do local inference.", "verdicts": { "lemonade": "Apache 2.0, with weekly releases, installers for Windows, macOS and five Linux routes, and a six-tool MCP endpoint whose descriptions steer a caller away from accidental multi-gigabyte downloads. Authentication is off by default, the repository has no security policy, and the docs tell WebSocket clients to pass the key in the URL.", "mlx-lm": "MIT, with no telemetry found in the source, and the tests passed on the last eight pushes to main. `mlx_lm.server` has no API key option, answers any origin by default and loads whichever model a request names, and its own docs say it is not recommended for production." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/lemonade-vs-mlx-lm", "json": "https://www.anchorterminal.com/compare/lemonade-vs-mlx-lm.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/lemonade-vs-mlx-lm.md", "slim": "https://www.anchorterminal.com/compare/lemonade-vs-mlx-lm.min.md" }, "markdown": "Lemonade scores 63.8 (B) on agent readiness against MLX LM's 52.2 (D), and leads in 5 of 7 scored categories. Both do local inference.\n\n- Lemonade: grade B, 63.8/100, rank #336 of 842. Markdown https://www.anchorterminal.com/tools/lemonade.md · JSON https://www.anchorterminal.com/api/v1/tools/lemonade.json\n- MLX LM: grade D, 52.2/100, rank #657 of 842. Markdown https://www.anchorterminal.com/tools/mlx-lm.md · JSON https://www.anchorterminal.com/api/v1/tools/mlx-lm.json\n\n## Which one, for what\n\n### Lemonade (B)\n\nGood for: An owner with AMD hardware (Ryzen AI NPUs, Radeon or Strix Halo) who wants one local server for chat, speech and images that existing OpenAI, Anthropic or Ollama clients can call, and for MCP clients that want local models as tools.\n\nAhead on:\n- Reliability, 75 against 66\n- Schema \u0026 documentation, 70 against 37\n- Agent ergonomics, 70 against 54\n- Maintenance \u0026 community, 76 against 61\n\nWatch for: No authentication by default. With no key set, every route answers, including `/internal/*` shutdown and configuration\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\nAlso in its favour:\n- No key needed to call it\n\nWatch for: `mlx_lm.server` has no API key or other credential option, and `--allowed-origins` defaults to `*`\n\n\n## Score by category\n\n| Category | Weight | Lemonade | MLX LM | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 75 | 66 | Lemonade +9 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 70 | 37 | Lemonade +33 |\n| Agent ergonomics | 13% (16.2 this run) | 70 | 54 | Lemonade +16 |\n| Security \u0026 auth | 14% (17.5 this run) | 36 | 32 | Lemonade +4 |\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) | 76 | 61 | Lemonade +15 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 64 | 66 | MLX LM +2 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **63.8 · B** | **52.2 · D** | |\n\n## Facts side by side\n\n| Fact | Lemonade | MLX LM |\n| --- | --- | --- |\n| Kind | HTTP API | HTTP API |\n| Vendor | AMD and the Lemonade community | Apple Inc. |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | HTTP | HTTP |\n| Auth | API key | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | Apache 2.0. Each backend (llama.cpp, whisper.cpp, stable-diffusion.cpp, FastFlowLM and others) is downloaded separately under its own licence | MIT |\n| Tools exposed | 6 | none |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-07 | 2026-10-01 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models | | |\n| Terms restrict automated access | | |\n| Terms restrict benchmarking | | |\n| Terms or service can change without notice | | |\n| Arbitration or class-action waiver | | |\n| Popularity | 5.8k stars | 7.3k stars, 140k PyPI/wk |\n\n## Verdicts\n\n**Lemonade.** Apache 2.0, with weekly releases, installers for Windows, macOS and five Linux routes, and a six-tool MCP endpoint whose descriptions steer a caller away from accidental multi-gigabyte downloads. Authentication is off by default, the repository has no security policy, and the docs tell WebSocket clients to pass the key in the URL.\n\n**MLX LM.** MIT, with no telemetry found in the source, and the tests passed on the last eight pushes to main. `mlx_lm.server` has no API key option, answers any origin by default and loads whichever model a request names, and its own docs say it is not recommended for production.\n\n## Before you call either\n\n### Lemonade\n\n1. Call `lemonade_list_models` (or `GET /v1/models`) before naming a model. A wrong name with `allow_download: true` can start a multi-gigabyte download\n2. Send `Authorization: Bearer \u003ckey\u003e` when the operator has set `LEMONADE_API_KEY`. `/internal/*` needs the admin key when one is set\n3. Use `POST /v1/chat/completions` for streamed tokens, embeddings and speech. The MCP endpoint ignores `stream` and has no embeddings or text-to-speech tool\n4. Pass `output_dir` to `lemonade_generate_image` and `lemonade_omni` to get file paths in place of inline base64. Writes stay inside the MCP media sandbox\n5. Read `GET /v1/docs` on the running server for the reference that matches its version. Weekly releases change behaviour\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. The `model` and `adapters` request fields accept any Hugging Face repository or local path\n3. Send `max_tokens` or `max_completion_tokens` when you need more than 512 tokens, the server default\n4. 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\n5. Poll `GET /health` before the first request. It answers 503 with `unavailable` when the generation thread has stopped\n\n## Questions\n\n### Which is better for AI agents, Lemonade or MLX LM?\n\nLemonade scores 63.8 (B) on agent readiness against MLX LM's 52.2 (D), and leads in 5 of 7 scored categories.\n\n### Do Lemonade and MLX LM need an API key?\n\nLemonade needs an API key. MLX LM needs no key.\n\n### Can an agent call Lemonade and MLX LM without installing anything?\n\nNo hosted endpoint is listed for Lemonade. No hosted endpoint is listed for MLX LM.\n\n### Are Lemonade and MLX LM open source?\n\nYes. Lemonade is open source (Apache 2.0. Each backend (llama.cpp, whisper.cpp, stable-diffusion.cpp, FastFlowLM and others) is downloaded separately under its own licence). MLX LM is open source (MIT).\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/lemonade-vs-mlx-lm.json, and with the fewest tokens: https://www.anchorterminal.com/compare/lemonade-vs-mlx-lm.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"lemonade\", \"b\": \"mlx-lm\"}`. From a terminal: `anchor compare lemonade mlx-lm`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/lemonade.json and https://www.anchorterminal.com/api/v1/tools/mlx-lm.json\n\n## Other comparisons with Lemonade or MLX LM\n\n- [AnythingLLM vs Lemonade](https://www.anchorterminal.com/compare/anythingllm-vs-lemonade.md)\n- [AnythingLLM vs MLX LM](https://www.anchorterminal.com/compare/anythingllm-vs-mlx-lm.md)\n- [Docker Model Runner vs Lemonade](https://www.anchorterminal.com/compare/docker-model-runner-vs-lemonade.md)\n- [Docker Model Runner vs MLX LM](https://www.anchorterminal.com/compare/docker-model-runner-vs-mlx-lm.md)\n- [Foundry Local vs Lemonade](https://www.anchorterminal.com/compare/foundry-local-vs-lemonade.md)\n- [Foundry Local vs MLX LM](https://www.anchorterminal.com/compare/foundry-local-vs-mlx-lm.md)\n- [Core vs Lemonade](https://www.anchorterminal.com/compare/ghost-core-vs-lemonade.md)\n- [Core vs MLX LM](https://www.anchorterminal.com/compare/ghost-core-vs-mlx-lm.md)\n- [GPT4All vs Lemonade](https://www.anchorterminal.com/compare/gpt4all-vs-lemonade.md)\n- [GPT4All vs MLX LM](https://www.anchorterminal.com/compare/gpt4all-vs-mlx-lm.md)\n- [Jan vs Lemonade](https://www.anchorterminal.com/compare/jan-vs-lemonade.md)\n- [Jan vs MLX LM](https://www.anchorterminal.com/compare/jan-vs-mlx-lm.md)\n- [Khoj vs Lemonade](https://www.anchorterminal.com/compare/khoj-vs-lemonade.md)\n- [Khoj vs MLX LM](https://www.anchorterminal.com/compare/khoj-vs-mlx-lm.md)\n- [KoboldCpp vs Lemonade](https://www.anchorterminal.com/compare/koboldcpp-vs-lemonade.md)\n- [KoboldCpp vs MLX LM](https://www.anchorterminal.com/compare/koboldcpp-vs-mlx-lm.md)\n- [Lemonade vs llama.cpp](https://www.anchorterminal.com/compare/lemonade-vs-llama-cpp.md)\n- [Lemonade vs LM Studio](https://www.anchorterminal.com/compare/lemonade-vs-lm-studio.md)\n- [Lemonade vs LocalAI](https://www.anchorterminal.com/compare/lemonade-vs-localai.md)\n- [Lemonade vs Ollama](https://www.anchorterminal.com/compare/lemonade-vs-ollama.md)\n- [Lemonade vs Open WebUI](https://www.anchorterminal.com/compare/lemonade-vs-open-webui.md)\n- [Lemonade vs screenpipe](https://www.anchorterminal.com/compare/lemonade-vs-screenpipe.md)\n- [Lemonade vs TextGen](https://www.anchorterminal.com/compare/lemonade-vs-text-generation-webui.md)\n- [llama.cpp vs MLX LM](https://www.anchorterminal.com/compare/llama-cpp-vs-mlx-lm.md)\n- [LM Studio vs MLX LM](https://www.anchorterminal.com/compare/lm-studio-vs-mlx-lm.md)\n- [LocalAI vs MLX LM](https://www.anchorterminal.com/compare/localai-vs-mlx-lm.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- [Lemonade vs Underdog](https://www.anchorterminal.com/compare/lemonade-vs-underdog.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)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-10-09", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.4", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "Lemonade vs MLX LM", "url": "" } ], "description": "Lemonade scores 63.8 (B) on agent readiness against MLX LM's 52.2 (D), and leads in 5 of 7 scored categories. Both do local inference. Category scores, facts, verdicts and agent notes side by side.", "facts": [ "Lemonade B 63.8", "MLX LM D 52.2", "scores" ], "h1": "Lemonade vs MLX LM", "image": "https://www.anchorterminal.com/assets/og/compare-lemonade-vs-mlx-lm.png", "path": "/compare/lemonade-vs-mlx-lm", "published": "2026-10-01", "section": "tools", "title": "Lemonade vs MLX LM for AI agents, B 63.8 vs D 52.2 | Anchor Terminal", "toc": null, "updated": "2026-10-09", "url": "https://www.anchorterminal.com/compare/lemonade-vs-mlx-lm" }, "tokens": { "markdown": 2550, "slim": 530 }, "version": 1 }