simple-good-llm-memory-mcp-server
Enables LLMs to store, search, and manage memories with hybrid semantic and keyword search using ChromaDB and Neo4j for persistent memory and knowledge graph capabilities.
README
Quick Start
1. Start Services
docker-compose up -d
This starts:
- ChromaDB (port 8000) - Vector storage for semantic search
- Neo4j (ports 7474, 7687) - Knowledge graph for relationships
2. Configure in llm client
Type /mcp in llm client and add:
{
"mcpServers": {
"skynet-memory": {
"command": "node",
"args": ["/Users/PROECT_DIRECTORY/dist/index.js"],
"env": {
"CHROMA_URL": "http://localhost:8000",
"NEO4J_URI": "bolt://localhost:7687",
"NEO4J_USER": "neo4j",
"NEO4J_PASSWORD": "password123"
}
}
}
}
3. Restart llm client
Changes take effect after restart.
Features
Dual Storage Architecture
- ChromaDB: Vector embeddings for semantic search
- Neo4j: Graph relationships between memories, tags, sessions
Hybrid Search
- Semantic search via embeddings (Google text-embedding-004)
- Keyword fallback when semantic results are poor
- Intelligent merging and ranking
Memory Operations
- Save: Store with tags, importance (1-10), context, session
- Search: Semantic + keyword hybrid with filters
- Update: Modify existing memories
- Delete: Remove with automatic graph cleanup
- Related: Find semantically similar memories
- Stats: Analytics and tag distribution
- Time Range: Temporal queries with pagination
Resilience
- Automatic retry queues for failed syncs
- Health checks and graceful degradation
- Falls back to hash-based embeddings if API unavailable
Architecture
┌─────────────────────────────────────────┐
│ MCP Server (index.js) │
│ - 8 Tools exposed via MCP protocol │
└────────────┬────────────────────────────┘
│
┌────────────┴────────────────────────────┐
│ Conscious Memory Service │
│ - Hybrid search logic │
│ - Importance scoring │
│ - Session management │
└────────┬─────────────┬──────────────────┘
│ │
▼ ▼
┌─────────────┐ ┌──────────────────┐
│ ChromaDB │ │ Neo4j KG │
│ Vectors │ │ Relationships │
└─────────────┘ └──────────────────┘
8 MCP Tools
- save_memory - Store information
- search_memories - Search with filters
- search_memories_by_time_range - Temporal queries
- update_memory - Modify memories
- delete_memory - Remove memories
- get_memory_tags - List all tags
- get_related_memories - Find similar
- get_memory_stats - Analytics
Environment Variables
# ChromaDB
CHROMA_URL=http://localhost:8000
MEMORY_COLLECTION_NAME=skynet_memories
# Neo4j
NEO4J_URI=bolt://localhost:7687
NEO4J_USER=neo4j
NEO4J_PASSWORD=password123
# Google Embeddings (optional)
GOOGLE_API_KEY=your_key_here
Development
# Install dependencies
npm install
# Build
npm run build
# Type check
npm run type-check
License
MIT
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.