ETOS-LLM-Studio — security grade SAFE, quality 60/100

Security audit verdict: SAFE · quality 60/100

No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

by Eric-Terminal · MCP Server · ★ 190

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

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About ETOS-LLM-Studio

ETOS LLM Studio 一个运行在 iOS 和 Apple Watch 上的原生 AI 客户端。支持 OpenAI、Anthropic Claude、Google Gemini 与本机 GGUF / llama.cpp 模型,内置 MCP 工具调用、Agent Skills 技能包、本地 RAG 记忆、世界书、每日脉冲、应用锁与 SQLCipher 全盘加密、CloudKit / WatchConnectivity 双端同步以及 Siri 快捷指令。 English Русский 📸 截图 👋 写在前面 在学校的日子挺无聊的,平时又总会冒出很多想问 AI 的问题。当时我嫌 App Store 上的 AI 应用要么贵得离谱,要么功能太残废(尤其是手表端),索性就自己动手搓了一个。 从最初那个只有 1,800 行代码、API Key 还要硬编码的简陋版本,到现在 641 个 Swift 源文件、226,381 行 Swift 代码(仅计算项目内 Swift,不把 llama.cpp 子模块和 VitePress 文档站依赖算进来)的工程,它确实已经长大了不少。虽然名字叫 "ETOS LLM Studio" 听着有点唬人,但它本质上还是我拿来探索大模型应用边界的试验场。 现在它已经不只是一个手表端 App 了:我把 iOS 端也一点点补成了完整版本,方便在手机上管理云端模型、本地 GGUF 权重、工具、记忆、世界书和每日脉冲;两端数据还能通过内置同步引擎自动互通。 因为我平时主要还是用 Mac 和 Watch,iPhone 端偶尔会有一些还在继续打磨的边角,不过我会继续慢慢补齐。 主要功能 聊天与模型 双端原生体验:iOS 和 Apple Watch 原生适配,两端界面风格统一,但会针对不同屏幕尺寸分别优化交互;iOS 会话列表采用卡片样式,

ai-assistantapple-watchdaily-pulseggufiosllama-cppllm-clientmcpragswiftui

Quick Facts

Stars190
Forks21
LanguageSwift
CategoryMCP Server
LicenseGPL-3.0
Quality Score60.2380037253084/100
Open Issues12
Last Updated2026-08-19
Created2025-09-06
Platformsclaude-code, cli, gemini, mcp
Est. Tokens~18k

Compatible Skills

These tools work well together with ETOS-LLM-Studio for enhanced workflows:

  • maclocal-api — semantic(0.24)+complementary+shared_fw(openai)+same_lang+similar_pop+shared_platform (62%)
  • swift-ios-skills — semantic(0.31)+complementary+shared_fw(openai)+similar_pop+shared_platform (59%)
  • claude-code-apple-skills — semantic(0.15)+complementary+same_lang+similar_pop+shared_platform (50%)

ETOS-LLM-Studio alternative? Top 6 similar tools

Looking for a ETOS-LLM-Studio alternative? If you're comparing ETOS-LLM-Studio with other mcp server tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.

  • GalwayBus by joreilly · ⭐ 588

    Galway Bus Kotlin Multiplatform project using Jetpack Compose and SwiftUI

  • InferrLM by sbhjt-gr · ⭐ 100

    InferrLM - On-device AI for iOS & Android

  • awesome-ios-ai by Techopolis · ⭐ 97

    AI agent skills, agent teams, MCP servers, and tools that make AI coding assistants better at Swift and iOS de

  • pdf-reader-mcp by SylphxAI · ⭐ 894

    Give your AI agent eyes for PDFs — structured text, tables, OCR, visual evidence, and page-level citations via

  • VectorCode by Davidyz · ⭐ 872

    A code repository indexing tool to supercharge your LLM experience.

  • argo by xark-argo · ⭐ 814

    ARGO is an open-source AI Agent platform that brings Local Manus to your desktop. With one-click model downloa

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Frequently Asked Questions

What is ETOS-LLM-Studio?

ETOS-LLM-Studio is A native LLM client for iOS & Apple Watch. Run local GGUF models offline via llama.cpp, or connect to OpenAI/Claude/Gemini. Features local RAG, Model Context Protocol (MCP) tools, Siri Shortcuts, and . It is categorized as a MCP Server with 190 GitHub stars.

What programming language is ETOS-LLM-Studio written in?

ETOS-LLM-Studio is primarily written in Swift. It covers topics such as ai-assistant, apple-watch, daily-pulse.

How do I install or use ETOS-LLM-Studio?

You can find installation instructions and usage details in the ETOS-LLM-Studio GitHub repository at github.com/Eric-Terminal/ETOS-LLM-Studio. The project has 190 stars and 21 forks, indicating an active community.

What license does ETOS-LLM-Studio use?

ETOS-LLM-Studio is released under the GPL-3.0 license, making it free to use and modify according to the license terms.

What are the best alternatives to ETOS-LLM-Studio?

The top alternatives to ETOS-LLM-Studio on Agent Skills Hub include GalwayBus, InferrLM, awesome-ios-ai. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.

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