Memdocs

Name: Memdocs
Author: Smart-AI-Memory

作者：Smart-AI-Memory版本 2.1.4i★ –i在你的电脑上运行

注册名称io.github.silversurfer562/memdocs

安装

概览

作者提供的说明（英文原文）

Git-native project memory for AI assistants with enterprise-grade audit compliance

PopNx MCP 的三大支柱

这三项永远不会合并成一个分数：意见、测量和人气是不同的东西。

用户评价i暂无评价评价功能将在后续阶段与账户功能一起开放
我们的基础检查i1 项中通过 0 项依据公开信息检查；尚未扫描代码
使用情况i尚未统计★ –

它能帮你做什么

我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。

连接方式: 在你的电脑上运行
传输方式: stdio
分类: 搜索与 AI 记忆
你需要填写：ANTHROPIC_API_KEY

安装

选择你使用的 AI 应用，然后复制设置。

安装之前

基础检查：1 项中通过 0 项
在你的电脑上运行，拥有你的用户账户的权限（文件、网络、应用）。
需要你自己填写： PROJECT_ROOT, ANTHROPIC_API_KEY

标为机密的值是你账户的钥匙。千万不要分享给别人，也不要粘贴到聊天里。

你需要

PyPI已安装 Python 的 uv（用于 uvx 命令）

你用的是哪个应用？

ClaudeClaude CodeCursorVS CodeChatGPT

在上方选择你的应用。我们会记住它，下次按钮会显示“安装到 …”。

打开 Claude Desktop → Settings → Developer → Edit Config，把下面的内容粘贴到 claude_desktop_config.json

{
  "mcpServers": {
    "memdocs": {
      "command": "uvx",
      "args": [
        "memdocs"
      ],
      "env": {
        "PROJECT_ROOT": "your-value",
        "ANTHROPIC_API_KEY": "your-value"
      }
    }
  }
}

在终端中运行这条命令

claude mcp add memdocs --env PROJECT_ROOT=your-value --env ANTHROPIC_API_KEY=your-value -- uvx memdocs

运行前请先填写空着的值。

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json，或 ~/.cursor/mcp.json

{
  "mcpServers": {
    "memdocs": {
      "command": "uvx",
      "args": [
        "memdocs"
      ],
      "env": {
        "PROJECT_ROOT": "your-value",
        "ANTHROPIC_API_KEY": "your-value"
      }
    }
  }
}

浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

{
  "servers": {
    "memdocs": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "memdocs"
      ],
      "env": {
        "PROJECT_ROOT": "your-value",
        "ANTHROPIC_API_KEY": "your-value"
      }
    }
  }
}

浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。

这个服务器在你的电脑上运行，而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。

你的选择通过 Cookie 保存在这台设备上，无需账户。

需要你填写的值

PROJECT_ROOT env
Root directory of the project to track
ANTHROPIC_API_KEY 机密必填 env
API key for Claude AI operations (summarization, analysis)

这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同，请以 README 为准。

它能做什么

它在你的电脑上运行；安装后，工具列表会显示在你的 AI 应用中。

基础检查i

我们检查的是任何人都能看到的信息；我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月6日

! 注意
作者与仓库一致
发布者（silversurfer562）不是仓库所有者（Smart-AI-Memory）；可能是其他人代为上架的
… 尚未检查
仍在维护
我们还没有从 GitHub 读取这个仓库
… 尚未检查
有许可证
我们还没有从 GitHub 读取这个仓库
– 不适用
端点有响应
它在你的电脑上运行，没有互联网端点
– 不适用
使用 OAuth 登录
在你的电脑上运行，改用本地设置
… 尚未检查
工具描述中没有隐藏指令
它在你的电脑上运行；我们只能读取互联网端点的工具列表

与“搜索与 AI 记忆”中的其他服务器比较i

星标数多于此分类中其他 7241 个服务器的 0%
发布到注册中心的时间比此分类中其他 7241 个服务器的 1% 更新
基础检查结果等于或优于此分类中其他 7241 个服务器的 35%

与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时，星标数才计入。

常见问题

Memdocs 安全吗？

它在 1 项适用的基础检查中通过了 0 项。我们没有阅读代码。在添加密钥或令牌之前，请先看看这个服务器要求哪些权限，并使用只具备所需权限的密钥。

它需要哪些密钥或代码？

你需要填写：ANTHROPIC_API_KEY

它在我的电脑上运行还是在云端？

它在你的电脑上运行。你的 AI 应用会在需要时启动它，数据不会经过作者的服务器（除非它自己调用在线服务）。

它还在维护吗？

GitHub 仓库最后更新于–，最新版本（2.1.4）于11个月前进入注册中心。

哪些应用可以使用它？

支持 MCP 的应用，例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Python (uv)。

有什么可以替代它？

同一分类中有：Context7, Codebase Memory, Exa

替代选择

同一分类中其他作者的服务器，按星标数排序。

如何看卡片：★ GitHub 星标i三大支柱：评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i

Context7

作者：upstash

Up-to-date docs for AI agents

电脑或云端★ 64,224MIT3天前更新

安装

Codebase Memory

作者：DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

在你的电脑上运行★ 48,148MIT20天前更新

安装

Exa

作者：exa-labs

Connect AI agents to Exa for web search, content fetching, and multi-step research.

在云端运行★ 5,213MIT44天前更新

安装

作者的 README

打开 README

按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。

MemDocs 🧠

Persistent Memory for AI Projects

Features • Quick Start • Complete Stack • Empathy Integration • Documentation • Examples • Contributing

🚀 The Complete Stack for 10x+ Productivity

`

VS Code + Claude Code (latest) + MemDocs + Empathy = 10x+ Productivity

`

Documented user experience: Transformational productivity through Level 4-5 AI collaboration

📖 Learn More:

- [Quick Start: Blog Post](docs/EMPATHY_MEMDOCS_BLOG_POST.md) - Engaging narrative explaining the five levels

- [Executive Summary](docs/EMPATHY_MEMDOCS_EXECUTIVE_SUMMARY.md) - 1-page overview for teams & investors

- [Technical Deep-Dive](docs/EMPATHY_MEMDOCS_SYNERGY.md) - 21-page analysis with code examples

🎯 What is MemDocs?

MemDocs is a git-native memory management system that gives AI assistants persistent, project-specific memory. It generates structured, machine-readable documentation that lives in your repository—no cloud services, no recurring costs, just local/git-based storage that enhances AI context and team collaboration.

💡 The Problem

AI assistants like ChatGPT and GitHub Copilot have no memory between sessions. Every conversation starts from scratch, forcing you to repeatedly explain your codebase, architecture decisions, and project context.

Result: AI stuck at Level 1-2 (Reactive) - can only respond after being asked, can't predict future needs, can't learn from patterns.

✨ The Solution

MemDocs creates a persistent memory layer that unlocks Level 4-5 AI collaboration:

🧠 Remembers your project across sessions (via .memdocs/ directory)
🔮 Enables predictions 30-90 days ahead (Level 4 Anticipatory Empathy)
👥 Shares memory with your team (committed to git)
💰 2000x cost savings vs full repo reviews ($0.03 vs $60)
⚡ Works offline (no cloud dependencies for retrieval)
🤝 Integrates with Empathy Framework (Level 4 Anticipatory Intelligence)
🔒 Privacy-first (optional PHI/PII detection and redaction)

Enterprise ROI: 6,000% return on investment (documented across 10-1,000 developer teams)

🚀 Quick Start

Installation

# From PyPI (recommended)

pip install memdocs

With optional features

pip install memdocs[embeddings] # Local vector search pip install memdocs[all] # All features

From source

git clone https://github.com/Smart-AI-Memory/memdocs.git cd memdocs pip install -e ".[dev,embeddings]"

Basic Usage

# 1. Set your Claude API key

export ANTHROPIC_API_KEY="your-key-here"

2. Initialize MemDocs in your project (MCP enabled by default!)

cd your-project memdocs init

3. Set up automatic updates (recommended)

memdocs setup-hooks --post-commit

4. Document changed files

memdocs review --changed

5. Search your project memory

memdocs query "payment processing"

6. Show memory stats

memdocs stats

Large Repository Workflow

# For repos with 1,000+ files: use git integration

memdocs init memdocs setup-hooks --post-commit # Auto-review on every commit

Work normally - memory updates automatically!

git add file.py git commit -m "refactor: improve performance"

MemDocs reviews changed files automatically (5-15 seconds)

Or manually review only changes

memdocs review --changed # Modified files only memdocs review --since main # Your branch changes memdocs review --since HEAD~10 # Last 10 commits

Your First Documentation

# Document a specific file

memdocs review --path src/main.py

Output:

✨ Analyzing src/main.py...

📝 Generating documentation with Claude Sonnet 4.5...

✅ Documentation saved to .memdocs/docs/main/

- index.json (machine-readable)

- symbols.yaml (code map)

- summary.md (human-readable)

✨ Key Features

🧠 Git-Native Memory

All documentation stored in .memdocs/ directory
Committed alongside your code (same git workflow)
Version controlled memory (track how project evolves)
Team collaboration built-in (push/pull memory with code)

🎯 Smart Scoping

File-level (default): Document individual files
Module-level: Document entire directories
Repo-level: Full codebase overview
Auto-escalation: Automatically increases scope for important changes

🤖 AI-Powered Summarization

Claude Sonnet 4.5: Latest and most capable model
Intelligent extraction: Symbols, APIs, architecture decisions
Multi-format output: JSON, YAML, Markdown
Token-efficient: Only summarizes, doesn't embed

🔍 Semantic Search (Optional)

Local embeddings: sentence-transformers (no API costs)
Vector search: FAISS for fast similarity search
Automatic indexing: Updates as you document
No cloud lock-in: Everything runs locally

📈 Enterprise Scale - Large Repository Support

MemDocs scales to codebases of any size through intelligent git integration:

Review only what changed: memdocs review --changed reviews modified files only
Branch-aware: memdocs review --since main reviews your branch changes
Automatic updates: Git hooks keep memory current on every commit
Cost-effective: 2000x cheaper than full repo reviews ($0.03 vs $60)
Lightning fast: 15 seconds instead of hours

Perfect for large repos (1,000+ files):

# One-time setup

memdocs init memdocs setup-hooks --post-commit

Every commit after: automatic memory updates!

git commit -m "fix: bug in auth" # Reviews 5 files, takes 15s, costs $0.03

Cost comparison:

Repo Size	Full Review	Changed Files	Savings
10,000 files	$60 + 2-4 hours	$0.03 + 15 seconds	2000x
5,000 files	$30 + 1-2 hours	$0.02 + 10 seconds	1500x
1,000 files	$6 + 15 minutes	$0.01 + 5 seconds	600x

🔌 MCP Server (Model Context Protocol)

Real-time memory serving: Serve memory to AI assistants via MCP
Claude Desktop integration: Auto-loaded context in Claude Desktop
Cursor/Continue.dev support: Works with MCP-compatible tools
Query-based context: AI requests exactly what it needs
Auto-start: Automatically detect and serve memory when opening projects

Quick setup for Claude Desktop:

# Start MCP server

memdocs serve --mcp

Or auto-start in VS Code (add to .vscode/tasks.json)

See docs/guides/mcp-setup.md for details

🚀 The Complete Stack: Transformational Productivity

When you combine the right tools, productivity isn't linear—it's exponential.

VS Code + Claude Code (latest) + MemDocs + Empathy = 10x+ Productivity

The four components work synergistically:

Component	Role	What It Enables
VS Code	Professional IDE	Tested environment, task automation, MCP auto-start
Claude Code (VS Code extension)	AI pair programming	Multi-file editing, command execution, real-time assistance
MemDocs	Persistent memory layer	Pattern detection, trajectory tracking, cross-session learning
Empathy Framework	5-level maturity model	Level 4-5 anticipatory suggestions, structural design

Real-world results:

10x+ efficiency improvement (documented user experience)
Lower cost: 2000x cheaper than full repo reviews
Higher quality: Problems predicted and prevented
Faster delivery: Anticipatory design eliminates bottlenecks

Quick setup (5 minutes):

# Install VS Code: https://code.visualstudio.com

Install Claude Code extension in VS Code: https://claude.ai/claude-code

pip install empathy-framework[full]>=1.6.0 # Empathy 1.6.0+ includes MemDocs cd your-project/ memdocs init # Auto-configures MCP for Claude Code empathy-os configure code . # Open in VS Code - MCP server auto-starts!

Result: Claude Code in VS Code operates at Level 4-5 (anticipatory) instead of Level 1-2 (reactive)

🔗 Empathy Framework Integration: Level 4-5 AI Collaboration

MemDocs unlocks Level 4 Anticipatory Empathy when integrated with the Empathy Framework.

The Five Levels of AI Collaboration:

Level	Name	Behavior	Memory Required	Example
1	Reactive	Help after being asked	None	ChatGPT: "You asked, here it is"
2	Guided	Collaborative exploration	Session only	"Let me ask clarifying questions"
3	Proactive	Act before being asked	MemDocs patterns	"I pre-fetched what you usually need"
4	Anticipatory	Predict future needs (30-90 days)	MemDocs trajectory	"Next week's audit—docs ready"
5	Systems	Design structural solutions	MemDocs cross-project	"I built a framework for all cases"

Why MemDocs is Essential:

🔄 Level 3 (Proactive): MemDocs stores user patterns across sessions
🔮 Level 4 (Anticipatory): MemDocs tracks system trajectory for predictions
🏗️ Level 5 (Systems): MemDocs identifies leverage points across projects

Without persistent memory, AI is stuck at Level 1-2 forever.

📚 Deep Dive Resources:

[Blog Post](docs/EMPATHY_MEMDOCS_BLOG_POST.md): Why your AI can't predict tomorrow's problems (and how to fix it)
[Executive Summary](docs/EMPATHY_MEMDOCS_EXECUTIVE_SUMMARY.md): 1-page overview for teams & investors
[Technical Analysis](docs/EMPATHY_MEMDOCS_SYNERGY.md): 21-page deep-dive comparing five empathy frameworks

Integration features:

✅ Works seamlessly with Empathy framework (1.6.0+)
✅ Supports Level 4 Anticipatory Empathy workflows
✅ Bidirectional sync (MemDocs ↔ Empathy)
✅ Trust-building behaviors powered by persistent memory
✅ 16 software development wizards (security, performance, testing, etc.)
✅ 18 healthcare documentation wizards (SOAP notes, SBAR, assessments, etc.)

🔒 Privacy & Security

PHI/PII detection: Automatic sensitive data detection
Redaction: Optional redaction modes (off, standard, strict)
HIPAA/GDPR aware: Configurable privacy settings
Local-first: No required cloud dependencies

📖 Documentation

Configuration

Create .memdocs.yml in your project root:

version: 1

Scope policy (controls memory granularity)

policies: default_scope: file # file | module | repo max_files_without_force: 150

Auto-escalate for important changes

escalate_on:

cross_module_changes # Multi-module = bigger context
security_sensitive_paths # auth/*, security/* = thorough docs
public_api_signatures # API changes = team awareness

Output configuration (git-committed memory)

outputs: docs_dir: .memdocs/docs # Committed to git memory_dir: .memdocs/memory # Committed to gi

只显示了开头部分；其余内容请在仓库中阅读