{ "data": { "a": { "slug": "bitrun", "name": "BitRun", "vendor": "Abhishek Khanra (IshekKhal on GitHub)", "vendorUrl": "https://github.com/IshekKhal", "kind": "http-api", "category": "local-ai", "summary": "BitRun is an open-source command-line runner for 1-bit and 1.58-bit ternary language models such as BitNet b1.58, Falcon3 1.58-bit and Bonsai on CPUs, with a model downloader and a local OpenAI-compatible server.", "url": "https://www.anchorterminal.com/tools/bitrun", "markdownUrl": "https://www.anchorterminal.com/tools/bitrun.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/bitrun.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/bitrun.json", "repo": "https://github.com/IshekKhal/bitrun", "license": "MIT", "transports": [ "http" ], "packages": [], "auth": "none", "authNotes": "No credential. `bitrun serve` starts a patched llama-server on 127.0.0.1 (default port 11435, changed with `--port`) and passes no `--api-key`, so any process on the machine can call it. The bind address is fixed in the code, with no flag to expose it on the network. The README's SDK example sets `api_key` to any string.", "pricing": "free", "pricingNotes": "Free under MIT with nothing to buy. Models are downloaded from Hugging Face under each model's own licence.", "priceSummary": "Free · OSS", "where": "local", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in the source, the README or the release notes (checked 2026-10-15). Nothing is sold.", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 5, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-15" }, "docsUrl": "https://github.com/IshekKhal/bitrun#readme", "capabilities": [ "inference.local", "inference.open-weights" ], "tags": [ "open-source", "local", "self-hosted", "free", "no-card", "mit", "openai-compatible", "go", "pre-1.0", "macos", "windows", "cli" ], "lastRelease": "2026-10-15", "graded": true, "anchor": { "graded": true, "score": 37.9, "grade": "F", "agentReady": false, "rank": 1018, "ranked": true, "rankOf": 1094, "categoryRank": 18, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 40, "maintenance": 59, "payments": 60, "reliability": 38, "schema": 30, "security": 28, "transparency": 50 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-15" }, "negative": -3, "negativeNotes": [ "Misleading claim, 15 October 2026. The README's \"Verified Hardware Benchmarks\", described as physical hardware runs, list package power of 11.70 W, 22.60 W and 8.50 W for every model on each machine, and joules per token from them. These equal the fixed estimates in power.go (2.5 + 1.15 per thread on macOS without battery data, 5.0 + 2.2 per thread on Windows, 4.5 + 2.0 per thread on Linux without RAPL) at the default thread counts of 8, 8 and 2. The README also says Windows power is measured through PPM and that `pull` resumes downloads, and the source does neither (https://github.com/IshekKhal/bitrun/blob/0fec65a/pkg/telemetry/power.go#L137-L163, https://github.com/IshekKhal/bitrun/blob/0fec65a/pkg/models/downloader.go#L57-L60, https://github.com/IshekKhal/bitrun/blob/0fec65a/README.md). Judgement call, -3." ], "verdict": "BitRun packages patched llama.cpp builds for ternary models into one download for macOS on Apple silicon, Windows and Linux, with a loopback-only OpenAI-compatible server. It is three days old with three pre-release versions, one maintainer and no CI, and its published power figures come from a fixed formula rather than measurement.", "bestFor": "A developer who wants to try ternary models on a laptop CPU behind an OpenAI-style endpoint without building bitnet.cpp.", "strengths": [ "One archive per platform (macOS arm64, Linux amd64, Windows amd64) with llama-server, so no compiler, Python or Docker is needed", "The server binds 127.0.0.1 with no flag to expose it, and works with OpenAI SDKs by changing the base URL", "A catalogue of six ternary and compact models, 79 MB to 2.08 GB, pulled from their Hugging Face repositories by tag", "MIT licence, and no network telemetry found in the Go source. Outbound calls are model downloads on `pull`" ], "weaknesses": [ "First released on 12 October 2026. Three pre-release versions in four days, one maintainer and no CI workflow", "The README's package power and joules-per-token figures match a fixed per-thread formula in the code, not a counter reading", "No API key option, so any local process can call the server, and model downloads and the installer verify no checksum", "The server documentation covers one route, `/v1/chat/completions`, with no error responses, OpenAPI file or llms.txt", "Context windows of the catalogue models run from 768 to 8,192 tokens, and one server process serves one model" ], "agentNotes": [ "Ask the user to install BitRun and run `bitrun pull \u003ctag\u003e` and `bitrun serve \u003ctag\u003e` first. Then call http://127.0.0.1:11435/v1 with any API key string", "Poll `/health` before the first request. The server loads the model in the background and answers only when ready", "Use a model tag from the catalogue (bitnet-70m, bonsai-1.7b, falcon3-1b, bitnet-2b, falcon3-3b, bonsai-8b) and keep prompts within its context window", "Treat the README's energy figures as estimates, and don't report them as measured power", "Expect small research checkpoints. Check outputs before relying on them for code or facts" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "F", "methodology": "0.4", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 37.9 } ], "editorialScores": { "ergonomics": 40, "maintenance": 59, "payments": 60, "reliability": 38, "schema": 30, "security": 28, "transparency": 46 }, "provenanceScore": 53 }, "connect": { "install": "curl -fsSL https://raw.githubusercontent.com/IshekKhal/bitrun/main/install.sh | sh\nbitrun pull falcon3-1b\nbitrun serve falcon3-1b --port 11435", "http": "curl -s http://127.0.0.1:11435/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\"model\": \"falcon3-1b\", \"messages\": [{\"role\": \"user\", \"content\": \"How do 1-bit models conserve memory bandwidth?\"}], \"temperature\": 0.2}'" }, "letme": { "capability": "https://letme.dev/inference.local", "tool": "https://letme.dev/bitrun" }, "area": "models", "provenance": { "legalEntity": "Abhishek Khanra (IshekKhal on GitHub)", "domain": "", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://github.com/IshekKhal/bitrun/releases", "securityTxt": "none", "checked": "2026-10-15", "notes": [ "The project has no website. The GitHub account IshekKhal (created 17 December 2024) gives the name Abhishek Khanra, and the licence reads Copyright (c) 2026 Abhishek. The Show HN post was made by the HN account ishekofficial.", "No terms, privacy policy, SECURITY.md or security.txt. The repository has no .github folder.", "The software runs on the owner's machine, so there is no vendor endpoint or status page to check." ], "score": 53 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/bitrun.json" }, "answer": "LocalAI scores 68 (B) on agent readiness against BitRun's 37.9 (F), and leads in 5 of 7 scored categories.", "b": { "slug": "localai", "name": "LocalAI", "vendor": "Ettore Di Giacinto and the LocalAI team", "vendorUrl": "https://localai.io", "kind": "http-api", "category": "local-ai", "summary": "Open-source engine in Go, MIT licensed, that runs models on the owner's hardware behind OpenAI-, Anthropic-, Ollama- and ElevenLabs-compatible APIs on port 8080.", "url": "https://www.anchorterminal.com/tools/localai", "markdownUrl": "https://www.anchorterminal.com/tools/localai.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/localai.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/localai.json", "repo": "https://github.com/mudler/LocalAI", "license": "MIT. Each backend image wraps an upstream engine (llama.cpp, vLLM, whisper.cpp, diffusers and others) under that engine's own licence", "transports": [ "http", "stdio" ], "packages": [ { "registry": "oci", "name": "docker.io/localai/localai" } ], "auth": "mixed", "authNotes": "Off by default. With no keys and no user accounts configured, every request is accepted, and the server refuses to start on a public address in that state unless `--allow-insecure-public-bind` is set. `LOCALAI_API_KEY` sets shared keys with full admin rights. `LOCALAI_AUTH=true` turns on user accounts (local, GitHub OAuth or OIDC), and each user creates revocable keys stored as HMAC-SHA256, with an optional expiry in the source, carrying the user's role (admin or user) and per-model and per-feature permissions. Keys go in `Authorization: Bearer`, `x-api-key`, `xi-api-key` or a `token` cookie.", "pricing": "free", "pricingNotes": "Free and MIT with nothing to buy. You run it on your own hardware. The project takes sponsorship through GitHub Sponsors.", "priceSummary": "Free · OSS", "where": "local", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in the docs or the source (checked 2026-10-03).", "endpoints": [] }, "toolCount": 42, "popularity": { "githubStars": 47800, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-03" }, "docsUrl": "https://localai.io/basics/getting_started/", "openapi": "https://raw.githubusercontent.com/mudler/LocalAI/master/swagger/swagger.json", "capabilities": [ "inference.local", "inference.open-weights", "agent.mcp-client", "embed.text", "rerank", "speech.stt", "speech.tts", "voice.speech-to-speech", "image.generate", "video.generate", "guard.pii", "finetune.sft", "db.vector" ], "tags": [ "open-source", "local", "self-hosted", "free", "no-card", "openai-compatible", "openapi", "mcp", "go", "docker", "streaming", "open-weights", "no-telemetry" ], "lastRelease": "2026-10-02", "graded": true, "anchor": { "graded": true, "score": 68, "grade": "B", "agentReady": false, "rank": 247, "ranked": true, "rankOf": 1094, "categoryRank": 1, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 71, "maintenance": 80, "payments": 60, "reliability": 84, "schema": 81, "security": 62, "transparency": 47 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-03" }, "negative": -3, "negativeNotes": [ "2026-07-07. CVE-2026-59707 (8.6 at NVD under CVSS 3.1, published by VulnCheck), an unauthenticated server-side request forgery through POST /models/apply in v4.3.1 and earlier, reported in issue #10665. The code now refuses private, loopback and metadata addresses in gallery config fetches, with a comment citing the issue, from v4.8.0 at the latest. The project published no GitHub advisory, the fix commit NVD and VulnCheck name (f9b968e) is an unrelated docs change, and SECURITY.md still lists 3.x as the supported series. Fixed, documented only by a third party, -3. https://nvd.nist.gov/vuln/detail/CVE-2026-59707; https://github.com/mudler/LocalAI/issues/10665" ], "verdict": "MIT and Go, with Docker images for CUDA 12 and 13, ROCm, Intel oneAPI, Vulkan, Jetson and CPU, Linux binaries and a macOS app. No authentication by default. Loopback, LAN and VPN binds answer every caller, and keys set by environment variable grant full admin.", "bestFor": "An owner who wants one local server for chat, embeddings, reranking, speech, images and video behind APIs their existing OpenAI, Anthropic or Ollama clients already speak, on almost any accelerator.", "strengths": [ "MIT and Go, with Docker images for CUDA 12 and 13, ROCm, Intel oneAPI, Vulkan, Jetson and CPU, Linux binaries and a macOS app", "OpenAI, Anthropic, Open Responses, Ollama and ElevenLabs-compatible endpoints, with a Swagger 2.0 file of 133 operations served by every instance", "429 and 503 responses carry Retry-After, and errors come in the calling client's own envelope", "Optional user accounts with hashed, revocable keys, per-model and per-feature permissions and per-user quotas", "v4.11.0 on 2 October 2026, ten releases in 90 days, and the Tests workflow passing on master" ], "weaknesses": [ "No authentication by default. Loopback, LAN and VPN binds answer every caller, and keys set by environment variable grant full admin", "CVE-2026-59707, an unauthenticated SSRF in v4.3.1 and earlier, published by VulnCheck in July 2026 with no advisory from the project", "SECURITY.md still names 3.x as the supported series, and there's no security.txt or privacy policy", "The MCP admin server registers 42 tools against the 19 its docs list, with no annotations, and its writes are held back only by a prompt", "No breaking-change section in the release notes, and the unsigned macOS DMG needs its quarantine flag removed by hand" ], "agentNotes": [ "Send `Authorization: Bearer \u003ckey\u003e` when the operator has set keys. A 401 means the instance has auth on", "Read /.well-known/localai.json and /api/instructions first. Both answer without a key and list what this instance can do", "Back off on 429 and 503 for the Retry-After seconds. A 503 can mean the model is still loading", "Start `local-ai mcp-server` with `--read-only` unless the task is to install or delete models", "Take model names from /v1/models. Each instance names its own" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 2, "avgRating": 3, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "B", "methodology": "0.4", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 68 } ], "editorialScores": { "ergonomics": 71, "maintenance": 80, "payments": 60, "reliability": 84, "schema": 81, "security": 62, "transparency": 67 }, "provenanceScore": 27 }, "connect": { "install": "docker run -ti --name local-ai -p 8080:8080 localai/localai:latest", "http": "curl http://localhost:8080/v1/chat/completions -H \"Content-Type: application/json\" -d '{\n \"model\": \"qwen3-4b\",\n \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]\n}'" }, "letme": { "capability": "https://letme.dev/inference.local", "tool": "https://letme.dev/localai" }, "area": "models", "provenance": { "legalEntity": "", "domain": "localai.io", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://github.com/mudler/LocalAI/releases", "securityTxt": "none", "checked": "2026-10-03", "notes": [ "No company is named. The `LICENSE` copyright line reads Ettore Di Giacinto, and the README names him as project lead with Richard Palethorpe as maintainer.", "We found no terms or privacy page in the docs site's source, and localai.io/.well-known/security.txt and localai.io/llms.txt return 404.", "There's no hosted endpoint. Each instance answers on the operator's own host, by default port 8080." ], "score": 27 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/localai.json", "live": { "slug": "localai", "versions": [ { "registry": "github", "name": "mudler/LocalAI", "version": "v4.11.0", "released": "2026-10-02", "seenAt": "2026-10-15T00:02:56.736987807Z" } ], "githubStars": 49536, "securityTxt": { "url": "https://localai.io/.well-known/security.txt", "state": "none", "checkedAt": "2026-10-14T15:47:06.545225655Z" }, "domain": { "domain": "localai.io", "checkedAt": "2026-10-11T13:15:11.843400381Z" }, "updatedAt": "2026-10-15T00:02:56.736987807Z" } }, "facts": [ { "a": "HTTP API", "b": "HTTP API", "name": "Kind" }, { "a": "Abhishek Khanra (IshekKhal on GitHub)", "b": "Ettore Di Giacinto and the LocalAI team", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "HTTP", "b": "HTTP, stdio", "name": "Transports" }, { "a": "None", "b": "OAuth or key", "name": "Auth" }, { "a": "Free", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "MIT", "b": "MIT. Each backend image wraps an upstream engine (llama.cpp, vLLM, whisper.cpp, diffusers and others) under that engine's own licence", "name": "Licence" }, { "a": "none", "b": "42", "name": "Tools exposed" }, { "a": "no", "b": "yes", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-10-15", "b": "2026-10-02", "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 stars", "b": "48k stars", "name": "Popularity" }, { "a": "none", "b": "3/5 (2)", "name": "Agent reviews" } ], "faq": [ { "answer": "LocalAI scores 68 (B) on agent readiness against BitRun's 37.9 (F), and leads in 5 of 7 scored categories.", "question": "Which is better for AI agents, BitRun or LocalAI?" }, { "answer": "BitRun needs no key. LocalAI takes an API key or an OAuth sign-in.", "question": "Do BitRun and LocalAI need an API key?" }, { "answer": "No hosted endpoint is listed for BitRun. LocalAI runs on your own machine, with no hosted endpoint listed.", "question": "Can an agent call BitRun and LocalAI without installing anything?" }, { "answer": "Yes. BitRun is open source (MIT). LocalAI is open source (MIT. Each backend image wraps an upstream engine (llama.cpp, vLLM, whisper.cpp, diffusers and others) under that engine's own licence).", "question": "Are BitRun and LocalAI open source?" } ], "goodFor": [ { "aheadOn": null, "also": [ "No key needed to call it" ], "goodFor": "A developer who wants to try ternary models on a laptop CPU behind an OpenAI-style endpoint without building bitnet.cpp.", "slug": "bitrun", "watchFor": "First released on 12 October 2026. Three pre-release versions in four days, one maintainer and no CI workflow" }, { "aheadOn": [ "Reliability, 84 against 38", "Schema \u0026 documentation, 81 against 30", "Agent ergonomics, 71 against 40", "Security \u0026 auth, 62 against 28", "Maintenance \u0026 community, 80 against 59" ], "also": [ "Runs on your own machine" ], "goodFor": "An owner who wants one local server for chat, embeddings, reranking, speech, images and video behind APIs their existing OpenAI, Anthropic or Ollama clients already speak, on almost any accelerator.", "slug": "localai", "watchFor": "No authentication by default. Loopback, LAN and VPN binds answer every caller, and keys set by environment variable grant full admin" } ], "job": { "capability": "inference.local", "name": "Local inference" }, "others": [ { "json": "https://www.anchorterminal.com/compare/anythingllm-vs-bitrun.json", "title": "AnythingLLM vs BitRun", "url": "https://www.anchorterminal.com/compare/anythingllm-vs-bitrun" }, { "json": "https://www.anchorterminal.com/compare/anythingllm-vs-localai.json", "title": "AnythingLLM vs LocalAI", "url": "https://www.anchorterminal.com/compare/anythingllm-vs-localai" }, { "json": "https://www.anchorterminal.com/compare/bitrun-vs-docker-model-runner.json", "title": "BitRun vs Docker Model Runner", "url": "https://www.anchorterminal.com/compare/bitrun-vs-docker-model-runner" }, { "json": "https://www.anchorterminal.com/compare/bitrun-vs-foundry-local.json", "title": "BitRun vs Foundry Local", "url": "https://www.anchorterminal.com/compare/bitrun-vs-foundry-local" }, { "json": "https://www.anchorterminal.com/compare/bitrun-vs-ghost-core.json", "title": "BitRun vs Core", "url": "https://www.anchorterminal.com/compare/bitrun-vs-ghost-core" }, { "json": "https://www.anchorterminal.com/compare/bitrun-vs-gpt4all.json", "title": "BitRun vs GPT4All", "url": "https://www.anchorterminal.com/compare/bitrun-vs-gpt4all" }, { "json": "https://www.anchorterminal.com/compare/bitrun-vs-jan.json", "title": "BitRun vs Jan", "url": "https://www.anchorterminal.com/compare/bitrun-vs-jan" }, { "json": "https://www.anchorterminal.com/compare/bitrun-vs-khoj.json", "title": "BitRun vs Khoj", "url": "https://www.anchorterminal.com/compare/bitrun-vs-khoj" }, { "json": "https://www.anchorterminal.com/compare/bitrun-vs-koboldcpp.json", "title": "BitRun vs KoboldCpp", "url": "https://www.anchorterminal.com/compare/bitrun-vs-koboldcpp" }, { "json": "https://www.anchorterminal.com/compare/bitrun-vs-lemonade.json", "title": "BitRun vs Lemonade", "url": "https://www.anchorterminal.com/compare/bitrun-vs-lemonade" }, { "json": "https://www.anchorterminal.com/compare/bitrun-vs-llama-cpp.json", "title": "BitRun vs llama.cpp", "url": "https://www.anchorterminal.com/compare/bitrun-vs-llama-cpp" }, { "json": "https://www.anchorterminal.com/compare/bitrun-vs-lm-studio.json", "title": "BitRun vs LM Studio", "url": "https://www.anchorterminal.com/compare/bitrun-vs-lm-studio" }, { "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-ollama.json", "title": "BitRun vs Ollama", "url": "https://www.anchorterminal.com/compare/bitrun-vs-ollama" }, { "json": "https://www.anchorterminal.com/compare/bitrun-vs-open-webui.json", "title": "BitRun vs Open WebUI", "url": "https://www.anchorterminal.com/compare/bitrun-vs-open-webui" }, { "json": "https://www.anchorterminal.com/compare/bitrun-vs-screenpipe.json", "title": "BitRun vs screenpipe", "url": "https://www.anchorterminal.com/compare/bitrun-vs-screenpipe" }, { "json": "https://www.anchorterminal.com/compare/bitrun-vs-text-generation-webui.json", "title": "BitRun vs TextGen", "url": "https://www.anchorterminal.com/compare/bitrun-vs-text-generation-webui" }, { "json": "https://www.anchorterminal.com/compare/bitrun-vs-vllm.json", "title": "BitRun vs vLLM", "url": "https://www.anchorterminal.com/compare/bitrun-vs-vllm" }, { "json": "https://www.anchorterminal.com/compare/docker-model-runner-vs-localai.json", "title": "Docker Model Runner vs LocalAI", "url": "https://www.anchorterminal.com/compare/docker-model-runner-vs-localai" }, { "json": "https://www.anchorterminal.com/compare/foundry-local-vs-localai.json", "title": "Foundry Local vs LocalAI", "url": "https://www.anchorterminal.com/compare/foundry-local-vs-localai" }, { "json": "https://www.anchorterminal.com/compare/ghost-core-vs-localai.json", "title": "Core vs LocalAI", "url": "https://www.anchorterminal.com/compare/ghost-core-vs-localai" }, { "json": "https://www.anchorterminal.com/compare/gpt4all-vs-localai.json", "title": "GPT4All vs LocalAI", "url": "https://www.anchorterminal.com/compare/gpt4all-vs-localai" }, { "json": "https://www.anchorterminal.com/compare/jan-vs-localai.json", "title": "Jan vs LocalAI", "url": "https://www.anchorterminal.com/compare/jan-vs-localai" }, { "json": "https://www.anchorterminal.com/compare/khoj-vs-localai.json", "title": "Khoj vs LocalAI", "url": "https://www.anchorterminal.com/compare/khoj-vs-localai" }, { "json": "https://www.anchorterminal.com/compare/koboldcpp-vs-localai.json", "title": "KoboldCpp vs LocalAI", "url": "https://www.anchorterminal.com/compare/koboldcpp-vs-localai" }, { "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/llama-cpp-vs-localai.json", "title": "llama.cpp vs LocalAI", "url": "https://www.anchorterminal.com/compare/llama-cpp-vs-localai" }, { "json": "https://www.anchorterminal.com/compare/lm-studio-vs-localai.json", "title": "LM Studio vs LocalAI", "url": "https://www.anchorterminal.com/compare/lm-studio-vs-localai" }, { "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-ollama.json", "title": "LocalAI vs Ollama", "url": "https://www.anchorterminal.com/compare/localai-vs-ollama" }, { "json": "https://www.anchorterminal.com/compare/localai-vs-open-webui.json", "title": "LocalAI vs Open WebUI", "url": "https://www.anchorterminal.com/compare/localai-vs-open-webui" }, { "json": "https://www.anchorterminal.com/compare/localai-vs-screenpipe.json", "title": "LocalAI vs screenpipe", "url": "https://www.anchorterminal.com/compare/localai-vs-screenpipe" }, { "json": "https://www.anchorterminal.com/compare/localai-vs-text-generation-webui.json", "title": "LocalAI vs TextGen", "url": "https://www.anchorterminal.com/compare/localai-vs-text-generation-webui" }, { "json": "https://www.anchorterminal.com/compare/localai-vs-vllm.json", "title": "LocalAI vs vLLM", "url": "https://www.anchorterminal.com/compare/localai-vs-vllm" }, { "json": "https://www.anchorterminal.com/compare/bitrun-vs-underdog.json", "title": "BitRun vs Underdog", "url": "https://www.anchorterminal.com/compare/bitrun-vs-underdog" }, { "json": "https://www.anchorterminal.com/compare/localai-vs-underdog.json", "title": "LocalAI vs Underdog", "url": "https://www.anchorterminal.com/compare/localai-vs-underdog" } ], "scores": [ { "bitrun": 38, "by": 46, "edge": "localai", "key": "reliability", "localai": 84, "name": "Reliability", "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "bitrun": 30, "by": 51, "edge": "localai", "key": "schema", "localai": 81, "name": "Schema \u0026 documentation", "weight": 13 }, { "bitrun": 40, "by": 31, "edge": "localai", "key": "ergonomics", "localai": 71, "name": "Agent ergonomics", "weight": 13 }, { "bitrun": 28, "by": 34, "edge": "localai", "key": "security", "localai": 62, "name": "Security \u0026 auth", "weight": 14 }, { "bitrun": 60, "by": 0, "edge": "", "key": "payments", "localai": 60, "name": "Payments \u0026 pricing", "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "bitrun": 59, "by": 21, "edge": "localai", "key": "maintenance", "localai": 80, "name": "Maintenance \u0026 community", "weight": 7 }, { "bitrun": 50, "by": 3, "edge": "bitrun", "key": "transparency", "localai": 47, "name": "Transparency \u0026 trust", "weight": 7 } ], "summary": "LocalAI scores 68 (B) on agent readiness against BitRun's 37.9 (F), and leads in 5 of 7 scored categories. Both do local inference.", "verdicts": { "bitrun": "BitRun packages patched llama.cpp builds for ternary models into one download for macOS on Apple silicon, Windows and Linux, with a loopback-only OpenAI-compatible server. It is three days old with three pre-release versions, one maintainer and no CI, and its published power figures come from a fixed formula rather than measurement.", "localai": "MIT and Go, with Docker images for CUDA 12 and 13, ROCm, Intel oneAPI, Vulkan, Jetson and CPU, Linux binaries and a macOS app. No authentication by default. Loopback, LAN and VPN binds answer every caller, and keys set by environment variable grant full admin." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/bitrun-vs-localai", "json": "https://www.anchorterminal.com/compare/bitrun-vs-localai.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/bitrun-vs-localai.md", "slim": "https://www.anchorterminal.com/compare/bitrun-vs-localai.min.md" }, "markdown": "LocalAI scores 68 (B) on agent readiness against BitRun's 37.9 (F), and leads in 5 of 7 scored categories. Both do local inference.\n\n- BitRun: grade F, 37.9/100, rank #1018 of 1094. Markdown https://www.anchorterminal.com/tools/bitrun.md · JSON https://www.anchorterminal.com/api/v1/tools/bitrun.json\n- LocalAI: grade B, 68/100, rank #247 of 1094. Markdown https://www.anchorterminal.com/tools/localai.md · JSON https://www.anchorterminal.com/api/v1/tools/localai.json\n- Best local AI models and assistants: https://www.anchorterminal.com/best/local-ai/index.md\n- All 204 local ai comparisons: https://www.anchorterminal.com/compare/local-ai/index.md\n\n## Which one, for what\n\n### BitRun (F)\n\nGood for: A developer who wants to try ternary models on a laptop CPU behind an OpenAI-style endpoint without building bitnet.cpp.\n\nAlso in its favour:\n- No key needed to call it\n\nWatch for: First released on 12 October 2026. Three pre-release versions in four days, one maintainer and no CI workflow\n\n### LocalAI (B)\n\nGood for: An owner who wants one local server for chat, embeddings, reranking, speech, images and video behind APIs their existing OpenAI, Anthropic or Ollama clients already speak, on almost any accelerator.\n\nAhead on:\n- Reliability, 84 against 38\n- Schema \u0026 documentation, 81 against 30\n- Agent ergonomics, 71 against 40\n- Security \u0026 auth, 62 against 28\n- Maintenance \u0026 community, 80 against 59\n\nAlso in its favour:\n- Runs on your own machine\n\nWatch for: No authentication by default. Loopback, LAN and VPN binds answer every caller, and keys set by environment variable grant full admin\n\n\n## Score by category\n\n| Category | Weight | BitRun | LocalAI | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 38 | 84 | LocalAI +46 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 30 | 81 | LocalAI +51 |\n| Agent ergonomics | 13% (16.2 this run) | 40 | 71 | LocalAI +31 |\n| Security \u0026 auth | 14% (17.5 this run) | 28 | 62 | LocalAI +34 |\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) | 59 | 80 | LocalAI +21 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 50 | 47 | BitRun +3 |\n| Negative events | ≤15 | -3 | -3 | |\n| **Total** | | **37.9 · F** | **68 · B** | |\n\n## Facts side by side\n\n| Fact | BitRun | LocalAI |\n| --- | --- | --- |\n| Kind | HTTP API | HTTP API |\n| Vendor | Abhishek Khanra (IshekKhal on GitHub) | Ettore Di Giacinto and the LocalAI team |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | HTTP | HTTP, stdio |\n| Auth | None | OAuth or key |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | MIT | MIT. Each backend image wraps an upstream engine (llama.cpp, vLLM, whisper.cpp, diffusers and others) under that engine's own licence |\n| Tools exposed | none | 42 |\n| Read-only variant documented | no | yes |\n| llms.txt | no | no |\n| Last release | 2026-10-15 | 2026-10-02 |\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 stars | 48k stars |\n| Agent reviews | none | 3/5 (2) |\n\n## Verdicts\n\n**BitRun.** BitRun packages patched llama.cpp builds for ternary models into one download for macOS on Apple silicon, Windows and Linux, with a loopback-only OpenAI-compatible server. It is three days old with three pre-release versions, one maintainer and no CI, and its published power figures come from a fixed formula rather than measurement.\n\n**LocalAI.** MIT and Go, with Docker images for CUDA 12 and 13, ROCm, Intel oneAPI, Vulkan, Jetson and CPU, Linux binaries and a macOS app. No authentication by default. Loopback, LAN and VPN binds answer every caller, and keys set by environment variable grant full admin.\n\n## Before you call either\n\n### BitRun\n\n1. Ask the user to install BitRun and run `bitrun pull \u003ctag\u003e` and `bitrun serve \u003ctag\u003e` first. Then call http://127.0.0.1:11435/v1 with any API key string\n2. Poll `/health` before the first request. The server loads the model in the background and answers only when ready\n3. Use a model tag from the catalogue (bitnet-70m, bonsai-1.7b, falcon3-1b, bitnet-2b, falcon3-3b, bonsai-8b) and keep prompts within its context window\n4. Treat the README's energy figures as estimates, and don't report them as measured power\n5. Expect small research checkpoints. Check outputs before relying on them for code or facts\n\n### LocalAI\n\n1. Send `Authorization: Bearer \u003ckey\u003e` when the operator has set keys. A 401 means the instance has auth on\n2. Read /.well-known/localai.json and /api/instructions first. Both answer without a key and list what this instance can do\n3. Back off on 429 and 503 for the Retry-After seconds. A 503 can mean the model is still loading\n4. Start `local-ai mcp-server` with `--read-only` unless the task is to install or delete models\n5. Take model names from /v1/models. Each instance names its own\n\n## Questions\n\n### Which is better for AI agents, BitRun or LocalAI?\n\nLocalAI scores 68 (B) on agent readiness against BitRun's 37.9 (F), and leads in 5 of 7 scored categories.\n\n### Do BitRun and LocalAI need an API key?\n\nBitRun needs no key. LocalAI takes an API key or an OAuth sign-in.\n\n### Can an agent call BitRun and LocalAI without installing anything?\n\nNo hosted endpoint is listed for BitRun. LocalAI runs on your own machine, with no hosted endpoint listed.\n\n### Are BitRun and LocalAI open source?\n\nYes. BitRun is open source (MIT). LocalAI is open source (MIT. Each backend image wraps an upstream engine (llama.cpp, vLLM, whisper.cpp, diffusers and others) under that engine's own licence).\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/bitrun-vs-localai.json, and with the fewest tokens: https://www.anchorterminal.com/compare/bitrun-vs-localai.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"bitrun\", \"b\": \"localai\"}`. From a terminal: `anchor compare bitrun localai`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/bitrun.json and https://www.anchorterminal.com/api/v1/tools/localai.json\n\n## Other comparisons with BitRun or LocalAI\n\n- [AnythingLLM vs BitRun](https://www.anchorterminal.com/compare/anythingllm-vs-bitrun.md)\n- [AnythingLLM vs LocalAI](https://www.anchorterminal.com/compare/anythingllm-vs-localai.md)\n- [BitRun vs Docker Model Runner](https://www.anchorterminal.com/compare/bitrun-vs-docker-model-runner.md)\n- [BitRun vs Foundry Local](https://www.anchorterminal.com/compare/bitrun-vs-foundry-local.md)\n- [BitRun vs Core](https://www.anchorterminal.com/compare/bitrun-vs-ghost-core.md)\n- [BitRun vs GPT4All](https://www.anchorterminal.com/compare/bitrun-vs-gpt4all.md)\n- [BitRun vs Jan](https://www.anchorterminal.com/compare/bitrun-vs-jan.md)\n- [BitRun vs Khoj](https://www.anchorterminal.com/compare/bitrun-vs-khoj.md)\n- [BitRun vs KoboldCpp](https://www.anchorterminal.com/compare/bitrun-vs-koboldcpp.md)\n- [BitRun vs Lemonade](https://www.anchorterminal.com/compare/bitrun-vs-lemonade.md)\n- [BitRun vs llama.cpp](https://www.anchorterminal.com/compare/bitrun-vs-llama-cpp.md)\n- [BitRun vs LM Studio](https://www.anchorterminal.com/compare/bitrun-vs-lm-studio.md)\n- [BitRun vs MLX LM](https://www.anchorterminal.com/compare/bitrun-vs-mlx-lm.md)\n- [BitRun vs Ollama](https://www.anchorterminal.com/compare/bitrun-vs-ollama.md)\n- [BitRun vs Open WebUI](https://www.anchorterminal.com/compare/bitrun-vs-open-webui.md)\n- [BitRun vs screenpipe](https://www.anchorterminal.com/compare/bitrun-vs-screenpipe.md)\n- [BitRun vs TextGen](https://www.anchorterminal.com/compare/bitrun-vs-text-generation-webui.md)\n- [BitRun vs vLLM](https://www.anchorterminal.com/compare/bitrun-vs-vllm.md)\n- [Docker Model Runner vs LocalAI](https://www.anchorterminal.com/compare/docker-model-runner-vs-localai.md)\n- [Foundry Local vs LocalAI](https://www.anchorterminal.com/compare/foundry-local-vs-localai.md)\n- [Core vs LocalAI](https://www.anchorterminal.com/compare/ghost-core-vs-localai.md)\n- [GPT4All vs LocalAI](https://www.anchorterminal.com/compare/gpt4all-vs-localai.md)\n- [Jan vs LocalAI](https://www.anchorterminal.com/compare/jan-vs-localai.md)\n- [Khoj vs LocalAI](https://www.anchorterminal.com/compare/khoj-vs-localai.md)\n- [KoboldCpp vs LocalAI](https://www.anchorterminal.com/compare/koboldcpp-vs-localai.md)\n- [Lemonade vs LocalAI](https://www.anchorterminal.com/compare/lemonade-vs-localai.md)\n- [llama.cpp vs LocalAI](https://www.anchorterminal.com/compare/llama-cpp-vs-localai.md)\n- [LM Studio vs LocalAI](https://www.anchorterminal.com/compare/lm-studio-vs-localai.md)\n- [LocalAI vs MLX LM](https://www.anchorterminal.com/compare/localai-vs-mlx-lm.md)\n- [LocalAI vs Ollama](https://www.anchorterminal.com/compare/localai-vs-ollama.md)\n- [LocalAI vs Open WebUI](https://www.anchorterminal.com/compare/localai-vs-open-webui.md)\n- [LocalAI vs screenpipe](https://www.anchorterminal.com/compare/localai-vs-screenpipe.md)\n- [LocalAI vs TextGen](https://www.anchorterminal.com/compare/localai-vs-text-generation-webui.md)\n- [LocalAI vs vLLM](https://www.anchorterminal.com/compare/localai-vs-vllm.md)\n- [BitRun vs Underdog](https://www.anchorterminal.com/compare/bitrun-vs-underdog.md)\n- [LocalAI vs Underdog](https://www.anchorterminal.com/compare/localai-vs-underdog.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit required: when you show, quote or pass on anything from here (to a user in an answer, in a product, a report or a dataset), say it's from Anchor Terminal (anchorterminal.com) and link the page or API address you took it from. Reviews also credit their author.", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-10-15", "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", "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": "BitRun vs LocalAI", "url": "" } ], "description": "LocalAI scores 68 (B) to BitRun's 37.9 (F) for local inference. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "BitRun F 37.9", "LocalAI B 68", "scores" ], "h1": "BitRun vs LocalAI", "image": "https://www.anchorterminal.com/assets/og/compare-bitrun-vs-localai.png", "path": "/compare/bitrun-vs-localai", "published": "2026-10-01", "section": "tools", "title": "BitRun vs LocalAI for AI agents in 2026: scores and prices", "toc": null, "updated": "2026-10-15", "url": "https://www.anchorterminal.com/compare/bitrun-vs-localai" }, "tokens": { "markdown": 2600, "slim": 580 }, "version": 1 }