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Macro is a unified workspace for teams: email, chat, docs, tasks, agents, calls, and CRM — @-linked together with shared AI memory.
这个仓库详情页已经具备足够的原创分析、来源证据和同类对比，可以作为 Git-Stars 的可索引评测页。
100
评测分
解决的问题
Macro solves the problem of tool fragmentation and data silos in modern teams, where information is scattered across multiple apps and held together by brittle integrations. It provides a unified graph-based data model with bidirectional links, so that context is preserved and AI agents can act across all surfaces without switching tools.
部署现实
当前可用的安装信号是：Sign up at macro.com/app or self-host from the GitHub repo (macro-inc/macro) following the docs at docs.macro.com.。这只能作为起点，正式使用前建议让 Codex 或 Claude 检查 README、环境变量、运行时版本和部署目标。
商业可用性
当前记录的许可证是 AGPL-3.0。这是重要的商业使用信号，但仍应核验许可证全文、依赖许可证、模型/API 条款和商标限制。
能力上限
Developers can build a complete team operating system with email triage, real-time collaborative docs, task management, CRM, and AI agents that have shared memory and can take actions (e.g., send emails, update tasks). The ceiling includes creating custom agents that leverage unified search across all data (including parsed PDFs), automating workflows across email, chat, and docs, and building a fully integrated productivity suite that scales to small companies or teams.
Git-Stars 是独立产品，不隶属于 GitHub 或该项目。 分析可能由 AI 辅助生成，依据公开仓库元数据和 README 的短摘要。 我们不镜像完整 README、文档、Issue 或社媒评论。
macro-inc/macro 被追踪为 Rust 项目，主要属于 AI Agent, MCP Server, Automation 方向。这个评估结合公开 GitHub 元数据、分类信号、短来源摘要和 Git-Stars 编辑规则，而不是复制项目文档。
增长检查：该仓库目前有 3.3k Star，今日 +0，本周 +2.4k，本月 +0。这些窗口用于区分持续采用信号和短期曝光峰值。
维护检查：当前活跃度为 一般；最近一次推送距今 59 天，未关闭 Issue 为 60，约占总 Star 的 1.83%。这只是采用信号，不替代工程尽调。
采用检查：326 Fork 和 3.3k Watcher 反映项目被复用和关注的程度。许可证信号：AGPL-3.0。商业或内部使用前请核验许可证兼容性。
适用判断：当你需要「AI 原型、LLM 工作流和 Agent 类应用」时，这个项目更值得评估；如果「需要法律审查、安全审计或生产 SLA 保证」，则需要谨慎。
来源检查：Git-Stars 当前为这份报告保留了 2 个明确来源引用，近期增长信号为 2.4k。最终安装、安全和版本信息仍应以原始 GitHub 仓库为准。
原创分析
充分已经具备问题、能力和结论层面的原创判断。
来源
充分已附带仓库元数据和 README/来源引用。
许可证
充分已记录 AGPL-3.0，可作为评估依据。
维护
充分仓库元数据中可以看到近期活跃信号。
同类对比
充分已有足够相近项目用于对比。
热度
3.3k 星标
复用
326 复刻
关注
3.3k 关注者
维护
moderate
许可证
AGPL-3.0
未解决 Issue
60
Macro is an all-in-one workspace that unifies email, messages, docs, tasks, agents, and CRM into a single fast interface with shared team-level memory. It aims to replace the need for multiple separate tools by providing a complete redesign of work software as one integrated system.
核心特性
- Unified inbox for email, messages, @mentions, and tasks with keyboard shortcuts. - Native CRM with contact/company objects and email aggregation. - Built-in window manager for multitasking with multiple splits.
AI Agent
Agent frameworks, autonomous workflows, and tool-use systems
MCP Server
Model Context Protocol servers, clients, and integrations
Automation
Workflow automation, integration glue, and orchestration
Sign up at macro.com/app or self-host from the GitHub repo (macro-inc/macro) following the docs at docs.macro.com.63
健康评分
一般
提交活跃度
Nov 8, 2025
创建于
Aug 15, 2026
最近提交
每日同步积累数据后，Star 历史将在此显示。
复刻
未解决
关注者
macro-inc
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Fork 情况
~ 一般
维护情况
解决的问题
Macro solves the problem of tool fragmentation and data silos in modern teams, where information is scattered across multiple apps and held together by brittle integrations. It provides a unified graph-based data model with bidirectional links, so that context is preserved and AI agents can act across all surfaces without switching tools.
能力范围
Developers can build a complete team operating system with email triage, real-time collaborative docs, task management, CRM, and AI agents that have shared memory and can take actions (e.g., send emails, update tasks). The ceiling includes creating custom agents that leverage unified search across all data (including parsed PDFs), automating workflows across email, chat, and docs, and building a fully integrated productivity suite that scales to small companies or teams.
结论
Macro is ideal for small teams or startups that want to consolidate their tool stack and leverage AI with shared context. It may be overkill for individuals or teams heavily invested in existing tools that don't want to migrate. The key trade-off is adopting a new, opinionated platform versus the flexibility of best-of-breed tools.