专项更强Offers industry-leading reasoning and artifact generation, but lacks direct terminal-first integration and local preference memory.
#1CodeMoss 记录了 0 月访问量,月度环比增长为 —,定价为免费增值。Command Code 记录了 2,358,177 月访问量,增长率为 -12.1%,定价为免费增值,定价请咨询。

CodeMoss is an AI programming assistant delivered as a Chrome extension. It adds an AI side panel to the browser so developers can ask coding questions, get optimization suggestions and have code polished in real time, all without leaving the page they are working on.
| 维度 / 指标 | ![]() | ![]() |
|---|---|---|
| 官方域名 | chromewebstore.google.com | commandcode.ai |
| 定价模式 | 免费增值 | 免费增值,定价请咨询 |
| 月访问量 | 0 (0) | 235.82万 (2,358,177) |
| 月度环比流量变化 |
| 0 (—) |
| -32.57万 (-12.1%) |
| 主要市场与份额 | 🌐 全球 (—) | 🇨🇳 中国 (47%) |
|---|
| 直接访问 / 月 | 0 | 174.96万 |
|---|
| 用户评分 | 4.1 | 4.3 |
|---|
| 支持的平台 | 浏览器扩展 | 网站 |
|---|

CodeMoss is an AI programming assistant delivered as a Chrome extension. It adds an AI side panel to the browser so developers can ask coding questions, get optimization suggestions and have code polished in real time, all without leaving the page they are working on.
Open an assistant panel on any page to ask programming questions and get explanations without switching to a separate site or app.
Select code on a page and let the assistant rewrite, tidy up or clarify it so the snippet is cleaner and easier to read.
Request a review of a snippet and receive concrete suggestions for improving structure, readability or efficiency.
Pairs with web browsing so results and technical pages are easier to interpret when you are researching a fix or a new API.

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。
专项更强Offers industry-leading reasoning and artifact generation, but lacks direct terminal-first integration and local preference memory.
#1免费 / 开源Orchestrates multiple CLI coding agents in parallel git worktrees, but is less focused on single-user preference learning.