Command Code 记录了 2,358,177 月访问量，月度环比增长为 -12.1%，定价为免费增值，定价请咨询。Joxo 记录了 0 月访问量，增长率为 0%，定价为免费增值。
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.
| 维度 / 指标 |  | |
|---|---|---|
| 官方域名 | commandcode.ai | joxo.ai |
| 定价模式 | 免费增值，定价请咨询 | 免费增值 |
| 月访问量 | 235.82万 (2,358,177) | 0 (0) |
| 月度环比流量变化 | -32.57万 (-12.1%) | 0 (0%) |
| 主要市场与份额 | 🇨🇳 中国 (47%) | 🌐 全球 (—) |
| 直接访问 / 月 | 174.96万 | 0 |
| 用户评分 | 4.3 | 4.2 |
| 支持的平台 | 网站 | 网站 |
会学习你编码品味的 AI 编程智能体
持续强化学习层把每一次采纳、拒绝和手动编辑变成信号，再把反复出现的模式转化为可复用的项目级技能。
自动修复格式错误的工具调用，让较弱的开放权重模型也能可靠完成智能体循环，接近前沿模型的水平。
交互式 CLI 用于结对，无头标志用于脚本化，后台沙箱智能体则用于更长时间的自主任务。
自定义 /agents 与持久化 /memory 跨会话携带项目上下文，让你不必反复解释代码库与规范。
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.
专项更强Offers industry-leading reasoning and artifact generation, but lacks direct terminal-first integration and local preference memory.
免费 / 开源Orchestrates multiple CLI coding agents in parallel git worktrees, but is less focused on single-user preference learning.
