Config is the same across clients — only the file and path differ.
{
"mcpServers": {
"memdocs": {
"cwd": "/path/to/your/project",
"args": [
"mcp-server"
],
"command": "memdocs"
}
}
}Are you the author?
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Complete Stack • Empathy Integration • Documentation • Contributing
Run this in your terminal to verify the server starts. Then let us know if it worked — your result helps other developers.
uvx 'memdocs' 2>&1 | head -1 && echo "✓ Server started successfully"
After testing, let us know if it worked:
Five weighted categories — click any category to see the underlying evidence.
No known CVEs.
Checked memdocs against OSV.dev.
Click any tool to inspect its schema.
memdocs_documentationMachine-readable documentation generated for project files
.memdocs/docs/{file_path}/index.json
memdocs_symbolsCode symbol map and API reference
.memdocs/docs/{file_path}/symbols.yaml
memdocs_summaryHuman-readable markdown summary of documentation
.memdocs/docs/{file_path}/summary.md
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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+ ProductivityDocumented user experience: Transformational productivity through Level 4-5 AI collaboration
📖 Learn More:
- Quick Start: Blog Post - Engaging narrative explaining the five levels
- Executive Summary - 1-page overview for teams & investors
- Technical Deep-Dive - 21-page analysis with code examples
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.
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.
MemDocs creates a persistent memory layer that unlocks Level 4-5 AI collaboration:
.memdocs/ directory)Enterprise ROI: 6,000% return on investment (documented across 10-1,000 developer teams)
# 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]"
# 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
# For repos with 1,000+ files: us
... [View full README on GitHub](https://github.com/Smart-AI-Memory/memdocs#readme)