GraphMemory-IDE: AI-Powered Collaborative Memory Platform
About
AI-assisted development MCP providing long-term, on-device "AI memory" for IDEs. Powered by Kuzu GraphDB and exposed via MCP server
Details
- Author
- elementalcollision
- GitHub stars
- 15
- Downloads
- 346
- Categories
- Developer Tools, Database
Jump to
- Graph-based memory storage with Kuzu and HNSW vector indexes
- Codon-accelerated graph algorithms with 10–100x speedups
- FastAPI backend with JWT authentication and rate limiting
- Real-time analytics via WebSocket and SSE streaming
- Multi-IDE plugin support (VSCode, Cursor, Windsurf)
- Production-ready Docker deployment with monitoring stack
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
GraphMemory-IDE: AI-Powered Collaborative Memory PlatformCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
Deploy with Docker (recommended): clone the repository, navigate to docker/, and run docker compose up -d. For local development, install dependencies with pip install -r requirements.txt, start the FastAPI server with uvicorn server.main:app --host 0.0.0.0 --port 8080 --reload, and optionally launch the Streamlit dashboard separately. Required environment variables include JWT_SECRET_KEY, DATABASE_URL, REDIS_URL, and KUZU_DB_PATH.
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"graphmemory-ide: ai-powered collaborative memory platform": {
"GraphMemory-IDE": {
"command": "docker",
"args": [
"compose",
"up",
"-d"
]
}
}
}
}McpServers
{
"GraphMemory-IDE": {
"command": "docker",
"args": [
"compose",
"up",
"-d"
]
}
}GraphMemory-IDE
An AI-assisted, long-term memory system for IDEs, powered by Kuzu graph database. GraphMemory-IDE is an MCP (Model Context Protocol) server that provides semantic vector search, graph-based knowledge storage, and real-time analytics. It integrates with VSCode, Cursor, and Windsurf through dedicated IDE plugins.
Features
- Graph-based memory storage — Kuzu native graph database with semantic vector search (HNSW indexes, sentence-transformers embeddings)
- Codon-accelerated graph algorithms — Optional native compilation via Codon for 10-100x speedups on centrality, community detection, path analysis, and similarity computations, with automatic Python/NetworkX fallback
- FastAPI backend — Async API with JWT authentication (EdDSA/Ed25519), rate limiting, and security middleware
- Real-time analytics — WebSocket and SSE streaming for live telemetry dashboards
- Streamlit dashboard — Interactive visualization of graph metrics, user activity, and system health
- Multi-IDE plugin support — Extensions for VSCode, Cursor, and Windsurf
- Full observability — Prometheus metrics, Grafana dashboards, health checks, and alert correlation
- Production-ready Docker deployment — Multi-service Docker Compose with Nginx, PostgreSQL, Redis, and monitoring stack
Quick Start
Docker (recommended)
git clone https://github.com/elementalcollision/GraphMemory-IDE.git
cd GraphMemory-IDE/docker
docker compose up -d
Services will be available at:
- MCP Server: http://localhost:8080/docs
- Kestra (workflow orchestration): http://localhost:8081
Local Development
```bash
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