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--- |
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title: ProjectMemory |
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emoji: ⚡ |
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colorFrom: red |
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colorTo: indigo |
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sdk: docker |
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app_port: 7860 |
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pinned: false |
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license: mit |
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short_description: Semantic, shared AI project memory. |
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tags: |
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- building-mcp-track-enterprise |
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--- |
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## 🎯 Track 1: Building MCP - Enterprise Category |
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**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. |
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## 🚀 What We Built |
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An MCP server that extends LLM capabilities for enterprise teams by: |
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- **Persistent Project Memory**: Every task completion generates AI documentation that becomes searchable knowledge |
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- **Semantic Search**: Vector-based memory retrieval across all project activities |
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- **MCP Tool Integration**: Exposes project management capabilities as MCP tools |
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- **Multi-User Collaboration**: Teams can share and search collective knowledge |
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## 🛠️ MCP Tools Exposed |
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Our MCP server provides these tools: |
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- `create_project`: Initialize a new project workspace |
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- `list_projects`: View all available projects |
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- `join_project`: Join an existing project |
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- `list_tasks`: Get project tasks with status |
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- `complete_task`: Mark task as done with AI-generated documentation |
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- `memory_search`: Semantic search across project history |
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- `list_activity`: View project activity feed |
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## 📹 Demo Video |
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[Watch our 3-minute demo showing MCP integration with Claude Desktop](#) *(link to be added)* |
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## 🏗️ Architecture |
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``` |
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┌─────────────────┐ ┌─────────────────┐ |
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│ Web Frontend │────▶│ FastAPI Backend │ |
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│ (React) │ │ (MCP Client) │ |
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└─────────────────┘ └─────────────────┘ |
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│ |
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▼ |
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┌─────────────────┐ |
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│ MCP Server │ |
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│ (TypeScript) │ |
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└─────────────────┘ |
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│ |
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▼ |
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┌─────────────────┐ |
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│ SQLite + Vec │ |
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│ (Embeddings) │ |
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└─────────────────┘ |
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``` |
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## 💡 Key Features |
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1. **Task Completion Pipeline**: Transforms user work into searchable documentation |
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2. **Vector Search**: Semantic retrieval using sqlite-vec embeddings |
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3. **Chat Interface**: Natural language queries using MCP tools |
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4. **Activity Feed**: Real-time project activity tracking |
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5. **Multi-Project Support**: Manage multiple projects with isolated memory |
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## 🔧 Technical Stack |
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- **MCP Server**: TypeScript with @modelcontextprotocol/sdk |
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- **Backend**: FastAPI (Python) as MCP client |
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- **Frontend**: React + Vite + Tailwind CSS |
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- **Database**: SQLite with sqlite-vec for embeddings |
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- **AI**: Google Generative AI (Gemini) for documentation generation |
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- **Deployment**: Docker container for Hugging Face Spaces |
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## 🎮 How to Use |
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1. **Create or Join a Project**: Start by creating a new project or joining an existing one |
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2. **Complete Tasks**: Mark tasks as done and provide context about your work |
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3. **AI Documentation**: System automatically generates searchable documentation |
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4. **Search Memory**: Use semantic search to find any past work or decision |
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5. **Chat with Memory**: Ask questions about project history using natural language |
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## 🚢 Deployment |
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This Space runs as a Docker container combining: |
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- FastAPI backend serving as MCP client |
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- React frontend for user interface |
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- MCP server handling all tool operations |
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- SQLite database with vector search capabilities |
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## 🔐 Environment Variables |
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Configure in Space settings: |
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- `GOOGLE_API_KEY`: For Gemini AI integration |
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- `DATABASE_URL`: (Optional) Custom database connection |
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## 👥 Team |
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*Add team member names here* |
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## 📝 License |
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MIT License - See LICENSE file for details |
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## 🔗 Links |
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- [GitHub Repository](https://github.com/YOUR_USERNAME/project-memory) |
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- [MCP Documentation](https://modelcontextprotocol.io) |
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- [Hackathon Page](https://huggingface.co/MCP-1st-Birthday) |
