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title: Project Memory - MCP-Powered Team Memory System
emoji: 🧠
colorFrom: blue
colorTo: purple
sdk: docker
app_port: 7860
pinned: false
license: mit
short_description: AI-powered shared memory for development teams using MCP
tags:
  - building-mcp-track-enterprise

ProjectMemory - MCP 1st Birthday Hackathon Submission

Check out the Hackathon details at: https://huggingface.co/MCP-1st-Birthday

🎯 Track 1: Building MCP - Enterprise Category

Project Memory is a multi-user, multi-project AI memory system powered by MCP (Model Context Protocol). It creates shared project memory where every action gets logged and becomes searchable via semantic search and AI chat.

🚀 What We Built

An MCP server that extends LLM capabilities for enterprise teams by:

Persistent Project Memory: Every task completion generates AI documentation that becomes searchable knowledge
Semantic Search: Vector-based memory retrieval across all project activities
MCP Tool Integration: Exposes project management capabilities as MCP tools
Multi-User Collaboration: Teams can share and search collective knowledge

🛠️ MCP Tools Exposed

Our MCP server provides these tools:

create_project: Initialize a new project workspace
list_projects: View all available projects
join_project: Join an existing project
list_tasks: Get project tasks with status
complete_task: Mark task as done with AI-generated documentation
memory_search: Semantic search across project history
list_activity: View project activity feed

📹 Demo Video

Watch our 3-minute demo showing MCP integration with Claude Desktop (link to be added)

🏗️ Architecture

┌─────────────────┐     ┌─────────────────┐
│   Web Frontend  │────▶│  FastAPI Backend │
│     (React)     │     │   (MCP Client)   │
└─────────────────┘     └─────────────────┘
                               │
                               ▼
                        ┌─────────────────┐
                        │   MCP Server    │
                        │  (TypeScript)   │
                        └─────────────────┘
                               │
                               ▼
                        ┌─────────────────┐
                        │  SQLite + Vec   │
                        │   (Embeddings)  │
                        └─────────────────┘

💡 Key Features

Task Completion Pipeline: Transforms user work into searchable documentation
Vector Search: Semantic retrieval using sqlite-vec embeddings
Chat Interface: Natural language queries using MCP tools
Activity Feed: Real-time project activity tracking
Multi-Project Support: Manage multiple projects with isolated memory

🔧 Technical Stack

MCP Server: TypeScript with @modelcontextprotocol/sdk
Backend: FastAPI (Python) as MCP client
Frontend: React + Vite + Tailwind CSS
Database: SQLite with sqlite-vec for embeddings
AI: Google Generative AI (Gemini) for documentation generation
Deployment: Docker container for Hugging Face Spaces

🎮 How to Use

Create or Join a Project: Start by creating a new project or joining an existing one
Complete Tasks: Mark tasks as done and provide context about your work
AI Documentation: System automatically generates searchable documentation
Search Memory: Use semantic search to find any past work or decision
Chat with Memory: Ask questions about project history using natural language

🚢 Deployment

This Space runs as a Docker container combining:

FastAPI backend serving as MCP client
React frontend for user interface
MCP server handling all tool operations
SQLite database with vector search capabilities

🔐 Environment Variables

Configure in Space settings:

GOOGLE_API_KEY: For Gemini AI integration
DATABASE_URL: (Optional) Custom database connection

👥 Team

Add team member names here

📝 License

MIT License - See LICENSE file for details