AITerm

🌐 Official Website | English | 繁體中文 | ☕ Buy Me a Coffee

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AITerm (English)

A powerful, cross-platform AI-enhanced terminal built with Tauri 2, React 19, and Rust. AITerm seamlessly integrates the traditional command-line experience with advanced AI capabilities, autonomous agents, a task board that dispatches work to Claude Code on your behalf, and project requirement management.

Beyond cloud providers, AITerm connects to local models (Ollama, LM Studio, oMLX, and other OpenAI-compatible endpoints) so inference runs on your own machine — no API cost and nothing sent off-box — which pays off for high-volume automation. It can also bridge the Claude Code CLI to any provider you have configured, so you run Claude Code on your OpenAI, Gemini, or local models.

✨ Key Features

• Integrated AI Providers: Out-of-the-box support for multiple LLM providers including OpenAI, Anthropic, Google, Ollama (local inference), and OpenAI-compatible endpoints. You can also sign in with a subscription account — Claude, ChatGPT, Gemini, or GitHub Copilot — with no API key to obtain.
• AI Command Flow (/ai): Type /ai <query> directly in the terminal to generate and preview commands with risk-level assessment before execution.
• Local Models · Save on Tokens: Connect to Ollama, LM Studio, oMLX, and other local / OpenAI-compatible endpoints — inference runs on your own machine with no API cost and nothing sent off-box. Or use the Claude Code Bridge to run the Claude Code CLI on any provider you have configured.
• Structured Command Blocks: Every command and its output render as a distinct card instead of raw scrollback. The live terminal pane auto-expands only while a command is running and shrinks back down when idle, keeping the screen uncluttered.
• Autonomous Agent Loop (/agent): Multi-step, goal-driven agentic execution for complex tasks, with built-in guardrails and fallback to manual confirmation for dangerous operations.
• LoopStudio (Loop Engineering): A visual multi-agent orchestration workbench — an orchestrator breaks a goal down and dispatches sub-agents to work in parallel, with safety gates and a live execution trace, running autonomously until the goal is verified done instead of requiring back-and-forth confirmation.
• Task Board: A kanban board that hands work to Claude Code for you. Write what you want done as cards — title, which folder to work in, detailed instructions, attachments if needed — and drag one to Queued: AITerm opens a tab, launches Claude Code, delivers your instructions, and moves the card to Done with a success or failure outcome when it finishes. Queue up an evening's work instead of driving each task by hand. Cards live in projects (self-contained folders you can copy to move or share), several projects open as tabs at once, and a global concurrency cap keeps the machine sane. Interactive mode marks work you intend to discuss with Claude Code yourself, exempt from the cap and from stall detection. Finished work keeps a clean transcript you can reread, and "Requeue" clones a card to run the same thing again. Search filters all four columns by title, body or folder.
• AI Work Report: Turn a project's board into a formatted document. Pick a style, choose a model, and AITerm reads every card — including the full transcripts of finished work — and writes an HTML report that is saved into the project folder and kept as history. A finished card's summary is written once and cached, so a second report only processes what is new.
• Archive: Finished cards would otherwise pile up forever. Archive them one at a time or a whole column at once and the board keeps only what is in flight. Nothing is deleted — cards, transcripts and attachments stay put, the archive view searches and pages through them, and any card can be restored to the board.
• AI Refine: Jot a rough note into a task's body, press Refine, and the AI rewrites it into a brief Claude Code can execute — goal, scope, constraints, acceptance criteria — without inventing work you did not ask for. One click restores your original.
• Code Assistant: A dedicated tab where the AI answers questions about any project directory — it scans the file tree, searches and reads source files on its own, can render Mermaid diagrams, and lets you export the conversation to Markdown.
• Multi-turn Chat Sidebar: Persistent, context-aware AI chat directly beside your terminal for troubleshooting, code generation, and brainstorming.
• AI Document & Chart Panel: Reports, comparison tables, and charts the AI produces open in a formatted panel beside the conversation instead of raw markup — bar/line/pie charts render directly, switch to a table view for exact numbers, and export to an HTML file in one click. Available across the terminal and remote AI panels, Code Assistant, Knowledge Base, and database / cross-database Q&A.
• Knowledge Base: Create a notebook pointed at a folder and AITerm chunks, embeds, and indexes every document inside it, so you can ask natural-language questions with answers that cite the source documents. Works with Ollama or any OpenAI-compatible embedding provider.
• Multi-Database Connections: Native support for PostgreSQL, MySQL, SQLite, and ODBC. DB2 works on both macOS and Windows via a built-in Java JDBC sidecar — no driver installation required.
• File Explorer: A built-in sidebar file browser kept in two-way sync with the terminal's working directory — navigate in the terminal and the explorer follows, or click the "cd" button (or the folder picker built into the command input) to jump the terminal to any folder instantly.
• Command Search & Bookmarks: Ctrl+F searches across the entire block history with live scroll-to-match; Ctrl+Shift+R opens a bookmarks picker to save and re-fill frequently used commands.
• Multilingual UI: Full support for English and Traditional Chinese, switchable at runtime with no restart required.
• Remote Terminal AI Panel: When connected to a colleague's shared terminal, Ask AI is the same full chat panel as local — free-form Q&A, provider switching, and browsing or continuing past conversations, all through your own configured provider so the other machine needs no AI setup; /agent works in a remote tab too.
• Telegram Remote Control: Send commands to your terminal from your phone via a Telegram bot, with results streamed back to the same chat.
• Requirement Management System: AI-assisted discussion for feature planning, automatically generating and saving specifications (SDD) into structured, project-managed directories.
• MCP Tools: Connect any MCP (Model Context Protocol) server and the AI can read and write files, query data, and call external services directly — no terminal command in between. Every tool call and its result stays visible and expandable.
• Claude Code Bridge: Point the Claude Code CLI at any provider you have configured in AITerm — run Claude Code on your OpenAI, Gemini, or local models, and switch between them at any time.
• Robust Terminal Engine: Powered by xterm.js and portable-pty for a fast, native-feeling shell experience across Windows, macOS, and Linux.

🏗️ Architecture

AITerm uses a modern Desktop architecture communicating via Tauri IPC (invoke + events):

• Frontend (src/): React 19 shell, xterm.js terminal views, component-local state management, and an intelligent frontend router for handling AI commands and streams.
• Backend (src-tauri/src/): Rust-powered high-performance core handling PTY lifecycles, AI provider routing, SQLx-backed database connections (including a Java JDBC sidecar for DB2), Telegram bot integration, and secure local configuration (OS keyring).

🚀 Getting Started

Prerequisites

• Rust 1.78+ (rustup show)
• Node.js 20+ (node -v)
• Windows only: Windows 11 with WebView2 runtime and MSVC build tools.

Development

npm install
npm run tauri:dev

Note: The first build will take a few minutes as Rust dependencies are compiled. Subsequent runs are much faster.

Testing

# Frontend tests (Vitest + React Testing Library)
npm run test

# Rust unit & integration tests
# --workspace is required: src-tauri/Cargo.toml is both a package and the
# workspace root, so a bare `cargo test` runs only the `app` package and
# silently skips aiterm-core and aiterm-host.
cd src-tauri && cargo test --workspace

# Type checking
# NOT `tsc --noEmit`: the root tsconfig.json is a solution file ("files": []),
# so it checks nothing and always exits 0.
npx tsc -b

🖥️ aiterm-host (remote host)

Run a shell on a machine with no desktop — a cloud VM, a container, a NAS — and connect to it from AITerm to drive it with AI. The AI runs on your desktop, so the host needs no AI configuration or API keys.

Install

# macOS / Linux
curl -fsSL https://raw.githubusercontent.com/jamesju9999/AITERM/master/scripts/install.sh | sh

# Homebrew (trust the tap first — see below)
brew trust jamesju9999/tap
brew install jamesju9999/tap/aiterm-host

# npm (or run without installing via npx)
npx aiterm-host --print-connection

# Container
docker run -it --rm -p 8022:8022 -e AITERM_HOST_KEY=<your-key> \
  ghcr.io/jamesju9999/aiterm-host:latest

# Windows
irm https://raw.githubusercontent.com/jamesju9999/AITERM/master/scripts/install.ps1 | iex

Homebrew: invalid syntax in tap! is not a syntax error. Recent Homebrew refuses to tap a third-party repository you have not trusted, and the message it prints — Cannot tap jamesju9999/tap: invalid syntax in tap! — points at the formula rather than at the trust check. Run brew trust jamesju9999/tap once and the install proceeds.

The install scripts verify every download against the release's SHA256SUMS and abort on a mismatch — there is no "install anyway" option.

aiterm-host has its own version numbers and release notes (CHANGELOG-host.md), published from host-v* tags independently of the desktop app.

Use

aiterm-host --bind 0.0.0.0 --port 8022

It prints an address, a port and a key. In AITerm, open Connect to a remote terminal, expand the manual-address section, and enter the address, port and key (leave the 6-digit code blank).

The key lives in ~/.config/aiterm-host/key and survives restarts, so a saved connection keeps working. Across networks, use an address the viewer can reach (Tailscale, a VPN, or an SSH tunnel with --bind 127.0.0.1).

📂 Project Layout

src-tauri/       # Rust backend (Tauri + PTY + AI Router + DB)
  src/pty/       # PTY lifecycle and streaming
  src/ai/        # Multi-provider AI router
  tests/         # Rust integration tests
src/             # React 19 frontend
  components/    # TerminalView, ChatSidebar, etc.
  ipc/           # Typed Tauri invoke + event wrappers
docs/            # Specs and implementation plans

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AITerm (繁體中文)

一個強大且跨平台的 AI 智慧終端機，基於 Tauri 2、React 19 和 Rust 打造。AITerm 將傳統的命令列體驗與先進的 AI 能力、自主代理（Agent）、可自動把工作派給 Claude Code 執行的工作看板，以及專案需求管理完美結合。

除了雲端供應商，AITerm 也能串接本地端模型（Ollama、LM Studio、oMLX 及其他 OpenAI 相容端點），推論在自己機器上跑——不花 API 費用、資料不外送——量大的自動化任務尤其划算。也可以用 Claude Code 橋接，讓 Claude Code CLI 跑在你已設定的任何供應商上（OpenAI、Gemini 或本地模型皆可）。

✨ 核心功能

• 多重 AI 供應商整合：開箱即支援 OpenAI、Anthropic、Google、Ollama（本地端推論）以及 OpenAI 相容端點等多種語言模型。也可以直接用訂閱帳號登入——Claude、ChatGPT、Gemini、GitHub Copilot 皆可，不必另外申請 API Key。
• AI 指令工作流 (/ai)：直接在終端機輸入 /ai <查詢> 即可生成指令，並在執行前提供風險等級評估與預覽。
• 本地模型・省 token 費用：接 Ollama、LM Studio、oMLX 這類本地 / OpenAI 相容端點——推論在自己機器上跑，不花 API 費用、資料不外送。或用 Claude Code 橋接，讓 Claude Code CLI 跑在你手邊任何一個供應商上。
• 結構化指令區塊：每個指令與其輸出都會渲染成獨立卡片，而非傳統終端機的原始捲動畫面；即時終端機視窗只在指令執行中才自動展開，閒置時自動收合，畫面保持乾淨。
• 自主代理循環 (/agent)：針對複雜任務提供多步驟、目標導向的代理執行能力，內建安全防護機制，並在遇到危險操作時自動退回手動確認模式。
• LoopStudio（循環工程）：可視化的多代理協同工作台——主控代理拆解任務並分派子代理平行執行，搭配安全閘門與即時執行追蹤，自主循環直到目標驗證完成，不需要一問一答反覆確認。
• 工作看板：把工作排進去，AITerm 自動派給 Claude Code 執行。把想做的事寫成卡片（標題、要在哪個資料夾做、詳細指示，需要的話還能附上檔案），拖到「待執行」，AITerm 就會開一個分頁、啟動 Claude Code、把指示送過去，做完自動搬到「已完成」並標上成功或失敗——一次排好幾件晚上再看結果，不必守在電腦前一件一件下指令。卡片歸屬於專案（自成一體的磁碟資料夾，複製即可搬移或分享），可同時開多個專案分頁，並行上限為全域設定。「互動模式」用於你打算自己跟 Claude Code 討論的工作，不佔並行額度也不會被判定卡住。完成的工作留有乾淨的對話記錄可回顧，「重新派工」會複製一張新卡片重跑同一件事。搜尋可依標題、內容或工作目錄即時過濾四個欄位。
• AI 工作報告：把一個專案的看板整理成一份文件。選風格、選模型，AITerm 會讀過每一張卡片（含已完成工作的完整對話記錄），產出排版好的 HTML 報告，存進專案資料夾累積成歷史。已完成的工作摘要只整理一次就快取起來，第二次產報告只需處理新完成的部分。
• 封存：完成的卡片不會無止境堆積。可單張封存或整欄一次收起，看板只留下正在進行的事。封存不是刪除——卡片、對話記錄、附件全部留著，封存清單可搜尋、分頁翻閱，任何一張都能放回看板。
• AI 潤飾：工作內容隨手打幾句，按下「AI 潤飾」，AI 會改寫成一份 Claude Code 能執行的完整指示（目標、範圍、限制、驗收標準），且不會自己加上你沒要求的工作。不滿意按「還原」即換回原文。
• 程式庫協助：獨立分頁，讓 AI 針對任意專案目錄回答問題——自動掃描檔案樹、搜尋並讀取原始碼，可繪製 Mermaid 圖表，對話還能匯出成 Markdown。
• 多輪對話側邊欄：終端機旁內建具備上下文記憶的 AI 對話視窗，方便進行問題排解、程式碼生成與靈感發想。
• AI 文件與圖表面板：AI 產生的報告、比較表格與圖表會在對話右側開一個排版好的面板，而不是塞成原始碼——長條圖、折線圖、圓餅圖直接畫出來，可切換成表格檢視看確切數字，也能一鍵存成 HTML 檔；終端機與遠端 AI 面板、程式庫協助、知識庫、資料庫與跨資料庫問答皆適用。
• 知識庫：建立筆記本並指定一個資料夾，AITerm 會自動切割、嵌入（Embedding）並索引其中的所有文件，之後即可用自然語言提問，回答附上原始文件引用來源；支援 Ollama 與任何 OpenAI 相容的 Embedding 供應商。
• 多資料庫連線：原生支援 PostgreSQL、MySQL、SQLite、ODBC。DB2 透過內建 Java JDBC Sidecar 在 macOS 與 Windows 均可使用，無需安裝驅動程式。
• 檔案總管：內建側邊欄檔案瀏覽器，與終端機工作目錄雙向同步——終端機切換路徑會即時反映在檔案總管，點擊「cd」按鈕或輸入框內建的資料夾選單也能立即把終端機切換到該目錄。
• 指令搜尋與書籤：Ctrl+F 可跨所有區塊歷史搜尋指令並即時捲動至符合項目；Ctrl+Shift+R 開啟書籤選單，收藏常用指令並一鍵帶入輸入框。
• 多語言介面：完整支援英文與繁體中文，可於執行時即時切換，不需重啟。
• 遠端終端機 AI 面板：連進同事分享的終端機後，Ask AI 是跟本機一樣的完整對話面板——自由問答、切換 AI 供應商、瀏覽並接續歷史對話，全部走觀看端自己設定的供應商，對方那台不需要有 AI；遠端分頁也能用 /agent。
• Telegram 遠端控制：透過 Telegram Bot 從手機傳送指令至終端機，執行結果即時回傳至同一對話。
• 需求管理系統：提供 AI 輔助的系統功能討論，可自動生成規格文件 (SDD) 並將其儲存到專案管理的結構化目錄中。
• MCP 工具：接上任何 MCP（Model Context Protocol）伺服器，AI 就能直接讀寫檔案、查詢資料、呼叫外部服務，不必經過終端機指令；工具的每一次呼叫與回傳都看得到、展得開。
• Claude Code 橋接：把 Claude Code CLI 接到 AITerm 已設定的任何供應商——用你手邊的 OpenAI、Gemini 或本地模型跑 Claude Code，模型可隨時切換。
• 強大的終端機引擎：基於 xterm.js 與 portable-pty，在 Windows、macOS 與 Linux 上提供快速、原生的 Shell 體驗。

🏗️ 系統架構

AITerm 採用現代化桌面應用架構，透過 Tauri IPC（invoke + events）進行通訊：

• 前端 (src/)：使用 React 19，包含 xterm.js 終端機視圖、元件狀態管理，以及處理 AI 指令與資料流的智慧前端邏輯。
• 後端 (src-tauri/src/)：由 Rust 驅動的高效能核心，負責處理 PTY 生命週期、AI 路由、基於 SQLx 的資料庫連線（含 DB2 專用的 Java JDBC Sidecar）、Telegram Bot 整合，以及安全的本地配置（OS Keyring）。

🚀 快速開始

環境要求

• Rust 1.78+ (rustup show)
• Node.js 20+ (node -v)
• 僅限 Windows：Windows 11（包含 WebView2 執行階段與 MSVC 建置工具）。

開發測試

npm install
npm run tauri:dev

提示：首次建置需要編譯 Rust 依賴套件，會花費幾分鐘的時間，之後的執行速度將大幅提升。

執行測試

# 前端測試 (Vitest + React Testing Library)
npm run test

# Rust 單元測試與整合測試
# 一定要加 --workspace：src-tauri/Cargo.toml 同時是 package 與 workspace root，
# bare `cargo test` 只會跑 `app`，aiterm-core 與 aiterm-host 會被整批跳過。
cd src-tauri && cargo test --workspace

# 型別檢查
# 不可用 `tsc --noEmit`：根目錄的 tsconfig.json 是 solution file（"files": []），
# 那樣什麼都不會檢查，而且永遠回傳 0。
npx tsc -b

🖥️ aiterm-host（遠端主機端）

在一台沒有桌面環境的機器上——雲端主機、容器、NAS——開一個 shell，讓 AITerm 連進去用 AI 操作它。AI 跑在你自己的桌面上，主機端不需要任何 AI 設定或 API key。

安裝

# macOS / Linux
curl -fsSL https://raw.githubusercontent.com/jamesju9999/AITERM/master/scripts/install.sh | sh

# Homebrew（要先信任這個 tap，見下方說明）
brew trust jamesju9999/tap
brew install jamesju9999/tap/aiterm-host

# npm（不想安裝的話直接用 npx）
npx aiterm-host --print-connection

# 容器
docker run -it --rm -p 8022:8022 -e AITERM_HOST_KEY=<你的金鑰> \
  ghcr.io/jamesju9999/aiterm-host:latest

# Windows
irm https://raw.githubusercontent.com/jamesju9999/AITERM/master/scripts/install.ps1 | iex

Homebrew 的 invalid syntax in tap! 不是語法錯誤。 新版 Homebrew 會擋下 你還沒信任過的第三方 tap，但它印出來的訊息 Cannot tap jamesju9999/tap: invalid syntax in tap! 指向 formula， 而不是指向信任檢查。執行一次 brew trust jamesju9999/tap 就能繼續安裝。

安裝腳本會用 release 的 SHA256SUMS 驗證每一個下載，不符就中止安裝， 沒有「照樣安裝」的選項。

aiterm-host 有自己的版本號與更新記錄（CHANGELOG-host.md）， 從 host-v* tag 發佈，跟桌面版各走各的。

使用

aiterm-host --bind 0.0.0.0 --port 8022

會印出位址、埠與一組金鑰。在 AITerm 開「連線到遠端終端機」，展開手動位址那一區， 填入位址、埠與金鑰（6 位短碼留空）即可。

金鑰存在 ~/.config/aiterm-host/key，重啟不變，所以存下來的連線會一直有效。 跨網段時請填觀看端連得到的位址（Tailscale、VPN，或配合 --bind 127.0.0.1 走 SSH tunnel）。

📂 專案結構

src-tauri/       # Rust 後端 (Tauri + PTY + AI Router + DB)
  src/pty/       # PTY 生命週期與資料流處理
  src/ai/        # 多重 AI 供應商路由
  tests/         # Rust 整合測試
src/             # React 19 前端
  components/    # TerminalView、對話側邊欄等元件
  ipc/           # 型別安全的 Tauri IPC 封裝
docs/            # 規格文件與實作計畫