askimo

askimo-aiAGPL-3.0活跃维护

一款桌面端 AI 客户端,把多模型对话、RAG 检索、Skills 技能、MCP 工具调用和 Agent 编排整合到一个应用里,适合想统一管理各家 LLM 的用户。

AI desktop app for chat, RAG, Skills, MCP tools, and agents. Support multiple LLMs (Anthropic, OpenAI, VertexAI, vLLM, Nvidia NIM, Gemini, OpenRouter)

v1.5.4发布于 2026-10-19 04:22 UTC542star +1 / 7 天114fork同步于 2026-11-10 06:01 UTC

怎么上手

由 README 提炼,执行前请确认
git clone https://github.com/askimo-ai/askimo.git
cd askimo
./gradlew :desktop:run
硬件要求按 README 说明

README 未说明显存要求;内存占用 50–300 MB,AI 模型按所选提供商另需内存,磁盘 250 MB;支持 macOS 11+、Windows 10+、Linux(Ubuntu 20.04+、Debian 11+、Fedora 35+)。

下载:源码 zip·GitHub Releases(安装包)本站不镜像安装包

项目说明

由 README 自动翻译提炼

本地优先的 AI 桌面应用,统一接入多家大模型并支持 RAG、MCP 与智能体

一款桌面端 AI 客户端,把多模型对话、RAG 检索、Skills 技能、MCP 工具调用和 Agent 编排整合到一个应用里,适合想统一管理各家 LLM 的用户。

核心能力

  • 按会话切换 OpenAI、Claude、Gemini、Ollama 等多家模型
  • 本地索引文件夹与网页,用 BM25 加向量混合检索做 RAG
  • 用表单式 UI 串联多步 AI 流程,支持 YAML 自定义
  • 通过 stdio 或 HTTP 接入 MCP 兼容服务器
  • 把技能物化到 Claude Code、Codex 等智能体的原生目录

容易踩的坑

从源码构建需要 JDK 25+;需自行准备各 AI 提供商的 API Key 或本地 Ollama 实例;语音转写与朗读的本地离线模式依赖内置 Whisper 与 Piper 端点。

内容由模型根据 README 生成,可能不完整或不准确,以官方文档为准。看原文 README

最近发版

5 个版本v1.5.4
  1. v1.5.5-beta2pre-release2026-11-02 · 2026-11-02 03:22 UTC
    查看更新说明

    What's Changed

    New Contributors

    Full Changelog: https://github.com/askimo-ai/askimo/compare/v1.5.5-beta1...v1.5.5-beta2

  2. v1.5.5-beta1pre-release2026-10-26 · 2026-10-26 21:06 UTC
    查看更新说明

    What's Changed

    New Contributors

    Full Changelog: https://github.com/askimo-ai/askimo/compare/v1.5.4...v1.5.5-beta1

  3. v1.5.4最新正式版2026-10-19 · 2026-10-19 04:22 UTC

    Summary

    This release focused on infrastructure modernization, migrating core components to newer technologies including SQLDelight for database management, cache4k for caching, and Kermit for logging. New features expand the platform's AI capabilities by integrating local Large Language Model (LLM) engines and enhancing voice processing with Whisper support. Additionally, several refactors improve system stability, standardize time handling, and update platform compatibility across various OS environments.

    What's Changed

    • Database module now utilizes SQLDelight for schema definition and query handling, abstracting database operations for improved type safety and portability. (#742)
    • Configuration management was updated to migrate legacy Docker provider settings and standardize model request timeout parameters for API calls, ensuring consistent resource allocation. (#741, #746)
    • Caching implementation in the shared module was refactored to use the cache4k library, standardizing memory management and improving cache efficiency. (#744)
    • Logging infrastructure was updated to migrate the backend to Kermit logging and deprecate legacy event logging mechanisms, centralizing event capture and simplifying the logging pipeline. (#747, #751)
    • Project schema and Project structure were refined by removing redundant space fields, streamlining data storage and improving schema integrity. (#748)
    • MCP Instance handling was introduced to track and broadcast changes in the Model Control Plane (MCP) instance, enhancing system observability. (#745)
    • Time Handling was standardized across the application using Instant and Clock, resolving potential inconsistencies related to time zone management. (#752)
    • Voice Integration capabilities were expanded to include whispercpp for voice processing and model management, supporting bidirectional voice communication. (#756, #758, #774)
    • Local LLM functionality was added, providing integration support for external local Large Language Model engines, increasing deployment flexibility. (#762, #764)
    • Suggestions module received enhancements, implementing typing suggestion features for inline autocomplete and refining suggestion matching logic alongside usage history management. (#759, #761)
    • Core functionalities were enhanced by improving exception mapping and integrating detailed analytics reporting, enhancing system resilience and data capture. (#760)
    • Tool Handling was updated to manage the tool approval timeout state, preventing service blockages and ensuring robust tool lifecycle management. (#755)
    • UI/UX components received adjustments, including improvements to the chat interface, keymap handling, and integrating community forum links alongside improved error reporting. (#753, #773)
    • Networking/OpenRouter services were updated to load embedding models from a dedicated catalog, ensuring proper resource resolution. ([#763](https://github.com/askimo-ai/askimo/pull/
  4. v1.5.32026-09-28 · 2026-09-28 23:31 UTC
    查看更新说明

    Summary

    The release introduces a core API fix, adds persistent storage for chat attachments, updates the navigation sidebar layout, and enhances chat view functionality and UI.

    What's Changed

    • Fixed the missing argument in the Time and fetch mcp functions that prevented tool loading in the core module. (#733)
    • Implemented a persistent storage path for chat attachments in the core module, enabling attachments to be saved and retrieved across sessions. (#735)
    • Updated padding, imports, and icon usage in the navigation sidebar component, reducing layout drift and simplifying the component’s import tree. (#736) (#737)
    • Enhanced the chat view UI: added an empty state placeholder, updated project navigation, simplified state observation logic, and introduced an editing workflow for messages. (#738) (#739) (#740)

    Contributors

    • @cdrozs
    • @haiphucnguyen

    Full Changelog: https://github.com/askimo-ai/askimo/compare/v1.5.2...v1.5.3

  5. v1.5.22026-09-24 · 2026-09-24 00:57 UTC
    查看更新说明

    Summary

    The release refactors core timeline deduplication logic and consolidates UI theme configuration. Agent execution handling is decoupled from view lifecycle, and a new resource collections feature is added with UI, persistence, and indexing support. The UI layer receives several updates, including renaming skills to agents, and migrating Compose Material3 usage. Error handling is enhanced with custom messages for max tool calls, MCP command failures, and missing embedding model configuration.

    What's Changed

    • Refactor timeline deduplication logic and consolidate UI theme configuration in the core timeline module and UI theme subsystem. (#723)
    • Decouple agent run handling from view lifecycle to prevent premature termination in the agent runtime subsystem. (#724)
    • Add custom error message for exceeding maximum tool calling round trips in core error handling logic. (#725)
    • Introduce resource collections support with UI components, persistence layer, and indexing functionality. (#727)
    • Rename UI components from skills to agents, update related UI and i18n resources. (#728)
    • Update Compose Material3 usage across desktop‑shared and shared modules and fix type inference errors. (#729)
    • Add user‑friendly error messages for MCP command errors. (#730)
    • Add power‑user share prompt for sessions with more than 100 messages. (#731)
    • Add banner notification for missing embedding model configuration. (#732)

    Contributors

    • @haiphucnguyen
    • @totodev90
    • @hiennguyen142
    • @minhnguyen-ai

    Full Changelog: https://github.com/askimo-ai/askimo/compare/v1.5.1...v1.5.2

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    README 原文

    未翻译

    Why Askimo?

    You shouldn't have to choose between the best AI model, your privacy, and getting real work done.

    • One app, every model. Stop juggling browser tabs. Chat with OpenAI, Claude, Gemini, Grok, or a local Ollama model, switch in seconds, no copy-pasting.
    • Built as a native desktop app. Not a web wrapper. Starts fast, runs lean, and stays responsive even after hours of use and thousands of messages in a single conversation.
    • Long conversations that actually work. No crashes, no tab reloads, no lost context. Askimo handles deep, extended sessions the way a real desktop app should.
    • More than just chat. Delegate real work to autonomous agent CLIs (Claude Code, Codex, Antigravity), chain multi-step AI Plans from a form UI, and connect MCP tools, all from the same app.
    • Skills, managed once, used everywhere. Define a skill independently of any single agent, then run it with whichever agent CLI fits the job. No per-agent duplication or lock-in.
    • Talk to it, not just type. Dictate messages and have responses read back to you. Fully local/offline speech-to-text and text-to-speech options included.

    🔒 Privacy by design. Your files, RAG index, conversation history, and telemetry all stay on your machine, nothing is uploaded, ever. Local RAG, local SQLite storage, local usage/cost tracking. The only network calls are the ones you configure (your chosen AI provider).


    See It in Action

    Agents - select the installed agent CLI and delegate a goal:

    Askimo Agents Demo

    RAG - search and chat with your local files:

    Askimo RAG Demo

    MCP tools - connect any MCP-compatible server:

    Askimo MCP Demo


    Quick Start

    Download for macOS, Windows, or Linux →

    1. Install and open Askimo
    2. Add a provider - paste an API key (OpenAI, Claude, Gemini…) or point it at a running Ollama instance
    3. Start chatting

    Full setup guide →

    System Requirements

    OSmacOS 11+, Windows 10+, Linux (Ubuntu 20.04+, Debian 11+, Fedora 35+)
    Memory50–300 MB (AI models require additional memory depending on provider)
    Disk250 MB

    Features

    AI & Providers

    • Multi-provider - Switch between OpenAI, Claude, Gemini, Grok, Ollama, LM Studio, Docker AI, OpenRouter, NVIDIA NIM, Together AI, vLLM Server, or any OpenAI-compatible endpoint per session
    • Vision - Attach images to conversations; works with any multimodal model

    Search & Data

    • Web search (multiple backends) - Search the web with DuckDuckGo (no API key), Brave Search API, Tavily, Serply, or your own SearxNG instance
    • Local RAG - Index local folders, files, and web URLs. Hybrid BM25 + vector retrieval with an AI classifier that skips retrieval when the query doesn't need it. Your data never leaves your machine.

    Workflows & Extensibility

    • Plans (agentic workflows) - Chain multi-step AI pipelines from a form UI. Each step builds on the previous; progress shown live. Export as PDF or Word. Define your own plans in YAML or generate them by describing your workflow in plain English.
    • Script runner - Execute Python, Bash, and JavaScript from chat. Python runs in an auto-managed virtualenv with automatic dependency installation.
    • MCP tool integration - Connect MCP-compatible servers via stdio or HTTP

    AI Agents (CLI)

    • Run autonomous coding agents - Delegate a goal to Claude Code, OpenAI Codex, or Google Antigravity directly from Askimo, with live streamed tool calls, thinking, and status
    • Reusable skills - Define a skill once and materialize it into any supported agent's native skill-discovery folder, so it's just as invocable there as in Askimo's own chat

    Voice

    • Speech-to-text - Dictate messages via OpenAI's transcription API or a fully local/offline built-in Whisper engine (whisper.cpp)
    • Text-to-speech - Have AI responses read aloud via OpenAI's TTS API or a fully local/offline Piper endpoint, with optional auto-play for hands-free "conversation mode"

    Reliability & Privacy

    • Persistent sessions - Conversations stored in a local SQLite database, restored on restart
    • Local telemetry - Token usage, cost estimates, RAG performance per provider. Nothing uploaded.
    • i18n - English, Chinese (Simplified & Traditional), Japanese, Korean, French, Spanish, German, Portuguese, Vietnamese

    Building from Source

    Prerequisites

    • JDK 25+
    • Git
    git clone https://github.com/askimo-ai/askimo.git
    cd askimo
    
    # Run the desktop app
    ./gradlew :desktop:run
    
    # Build native installers
    ./gradlew :desktop:package
    

    Project Structure

    ModuleDescription
    desktop/Compose Multiplatform desktop application
    desktop-shared/Shared UI components
    shared/Kotlin Multiplatform common module: domain models/DTOs, chat types, event bus, and shared utils used by both JVM and other targets
    shared-jvm/Core: providers, RAG, MCP, memory, tools, database, plans engine, skills & agent runtimes

    See CONTRIBUTING.md for development guidelines and DCO requirements, or the Development Getting Started Guide.


    Localization

    English · 中文 (简体/繁體) · 日本語 · 한국어 · Français · Español · Deutsch · Português · Tiếng Việt

    Translations are managed on Crowdin. Contributions welcome - no coding required.

    Crowdin

    Help translate Askimo →


    License

    AGPLv3. See LICENSE.


    Contributing

    Bug reports, feature requests, and pull requests are welcome. See CONTRIBUTING.md for details.


    Star History


    Contributors