Memdocs

Name: Memdocs
Author: Smart-AI-Memory

by Smart-AI-Memoryversion 2.1.4i★ –iRuns on your computer

Registry nameio.github.silversurfer562/memdocs

Install

Overview

Description from the maker (original English)

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

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi0 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Search & AI memory
You need to fill in: ANTHROPIC_API_KEY

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 0 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
You fill in yourself: PROJECT_ROOT, ANTHROPIC_API_KEY

Values marked secret are keys to your account. Never share them or paste them into a chat.

You need

PyPIPython’s uv installed (for the uvx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

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

Run this in your terminal

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

Fill in the empty values before running it.

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

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

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

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

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

Values you fill in

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

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 6, 2026

! Caution
Maker matches the repo
The publisher (silversurfer562) is not the repo owner (Smart-AI-Memory); someone else may have listed it
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Search & AI memoryi

More stars than 0% of the other 7241 servers in this category
Published to the registry more recently than 1% of the other 7241 servers in this category
Basic checks equal to or better than 35% of the other 7241 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Memdocs safe?

It passed 0 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

You need to fill in: ANTHROPIC_API_KEY

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (2.1.4) reached the registry 11 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Context7, Codebase Memory, Exa

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Context7

by upstash

Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.

Computer or cloud★ 64,224MITUpdated 3 days ago

Install

Codebase Memory

by DeusData

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

Runs on your computer★ 48,148MITUpdated 20 days ago

Install

Exa

by exa-labs

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

Runs in the cloud★ 5,213MITUpdated 44 days ago

Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

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

Only the beginning is shown; read the rest in the repo

Memdocs

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

Values you fill in

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Search & AI memoryi

Common questions

Alternatives

Context7

Codebase Memory

Exa

README from the maker

MemDocs 🧠

🎯 What is MemDocs?

💡 The Problem

✨ The Solution

🚀 Quick Start

Installation

With optional features

From source

Basic Usage

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

3. Set up automatic updates (recommended)

4. Document changed files

5. Search your project memory

6. Show memory stats

Large Repository Workflow

Work normally - memory updates automatically!

MemDocs reviews changed files automatically (5-15 seconds)

Or manually review only changes

Your First Documentation

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

🎯 Smart Scoping

🤖 AI-Powered Summarization

🔍 Semantic Search (Optional)

📈 Enterprise Scale - Large Repository Support

Every commit after: automatic memory updates!

🔌 MCP Server (Model Context Protocol)

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

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

🚀 The Complete Stack: Transformational Productivity

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

🔗 Empathy Framework Integration: Level 4-5 AI Collaboration

🔒 Privacy & Security

📖 Documentation

Configuration

Scope policy (controls memory granularity)

Auto-escalate for important changes

Output configuration (git-committed memory)