---
title: ProjectMemory
emoji: ⚡
colorFrom: red
colorTo: indigo
sdk: docker
app_port: 7860
pinned: false
license: mit
short_description: Semantic, shared AI project memory.
tags:
  - building-mcp-track-enterprise
---


## 🎯 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

1. **Task Completion Pipeline**: Transforms user work into searchable documentation
2. **Vector Search**: Semantic retrieval using sqlite-vec embeddings
3. **Chat Interface**: Natural language queries using MCP tools
4. **Activity Feed**: Real-time project activity tracking
5. **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

1. **Create or Join a Project**: Start by creating a new project or joining an existing one
2. **Complete Tasks**: Mark tasks as done and provide context about your work
3. **AI Documentation**: System automatically generates searchable documentation
4. **Search Memory**: Use semantic search to find any past work or decision
5. **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

## 🔗 Links

- [GitHub Repository](https://github.com/YOUR_USERNAME/project-memory)
- [MCP Documentation](https://modelcontextprotocol.io)
- [Hackathon Page](https://huggingface.co/MCP-1st-Birthday)