Command Code 每月有 2,358,177 次造訪，月增率 -12.1%，定價模式為免費增值、需聯絡報價。Joxo 每月有 0 次造訪，成長率 0%，定價模式為免費增值。
Joxo is a shared workspace for software teams that use AI coding agents such as Claude Code, Codex, Cursor, Gemini CLI and OpenCode. It puts every teammate and their agent on one project, lets agents hand work to each other, and surfaces the decisions that need a human — including one-tap approvals from an iPhone app.
| 面向／指標 |  | |
|---|---|---|
| 官方網域 | commandcode.ai | joxo.ai |
| 定價模式 | 免費增值、需聯絡報價 | 免費增值 |
| 每月造訪次數 | 235.82萬 (2,358,177) | 0 (0) |
| 月流量變化量 | -32.57萬 (-12.1%) | 0 (0%) |
| 主要市場與市佔 | 🇨🇳 中國 (47%) | 🌐 全球 (—) |
| 每月直接造訪 | 174.96萬 | 0 |
| 使用者評分 | 4.3 | 4.2 |
| 支援平台 | 網站 | 網站 |
會學習你編碼品味的 AI 編程代理
持續強化學習層把每一次採納、拒絕與手動編輯變成訊號，再把反覆出現的模式轉化為可重複使用的專案層級技能。
自動修復格式錯誤的工具呼叫，讓較弱的開放權重模型也能可靠完成代理循環，接近前沿模型的水準。
互動式 CLI 用於結對，無頭旗標用於腳本化，背景沙箱代理則用於更長時間的自主任務。
自訂 /agents 與持久化 /memory 跨工作階段攜帶專案上下文，讓你不必反覆解釋程式庫與規範。
Joxo is a shared workspace for software teams that use AI coding agents such as Claude Code, Codex, Cursor, Gemini CLI and OpenCode. It puts every teammate and their agent on one project, lets agents hand work to each other, and surfaces the decisions that need a human — including one-tap approvals from an iPhone app.
Your agent can ask a teammate's agent for the answer it is missing, then apply the result in your own session — so knowledge from one person's work benefits the whole project.
A single notes file teaches every agent connected to the project how your team builds software, and it compounds the longer the team uses it.
Group Claude Code, Codex, Cursor, Gemini and OpenCode sessions by project in one window, with anything waiting on you pinned to the top.
One task board shows who is on what, team chat mixes people and agents, and a session view reports what is running on every team computer and what finished.
專項更強Offers industry-leading reasoning and artifact generation, but lacks direct terminal-first integration and local preference memory.
免費 / 開源Orchestrates multiple CLI coding agents in parallel git worktrees, but is less focused on single-user preference learning.
