MCP Neo4j Memory Server
About
# Neo4j Memory Server A Model Context Protocol (MCP) server that provides AI assistants with persistent, intelligent memory capabilities using Neo4j's graph database with unified architecture ## What it does This server enables AI assistants to: - **Remember** - Store memories as interconnected knowledge nodes with…
Details
- License
- MIT
Explore
- 🧠 Graph Memory - Memories as nodes, relationships as edges, observations as content
- 🔍 Unified Search - Semantic vectors, exact matching, wildcards, and graph traversal in one tool
- 🔗 Smart Relations - Typed connections with strength, source tracking, and temporal metadata
- 📊 Multi-Database - Isolated project contexts with instant switching
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
MCP Neo4j Memory ServerCommand (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
npm install @sylweriusz/mcp-neo4j-memory-server
Add to Claude Desktop config:
{
"mcpServers": {
"memory": {
"command": "npx",
"args": ["-y", "@sylweriusz/mcp-neo4j-memory-server"],
"env": {
"NEO4J_URI": "bolt://localhost:7687",
"NEO4J_USERNAME": "neo4j",
"NEO4J_PASSWORD": "your-password"
}
}
}
}
For the database, use DozerDB with the Graph Data Science plug-in, GDS is not only recommended but necessary:
For current installation instructions, see: https://dozerdb.org/
Example setup:
- Store all memory for this project in database: 'project-database-name'
- Use MCP memory tools exclusively for storing project-related information
- Begin each session by:
1. Switching to this project's database
2. Searching memory for data relevant to the user's prompt
memory_store
Create memories with observations and immediate relations in ONE operation
memory_find
Unified search/retrieval with semantic search, direct ID lookup, date filtering, and graph traversal
memory_modify
Comprehensive modification operations (update, delete, observations, relations)
database_switch
Switch database context for isolated environments
The server provides 4 unified MCP tools that integrate automatically with Claude:
- memory_store - Create memories with observations and immediate relations in ONE operation
- memory_find - Unified search/retrieval with semantic search, direct ID lookup, date filtering, and graph traversal
- memory_modify - Comprehensive modification operations (update, delete, observations, relations)
- database_switch - Switch database context for isolated environments
- Store all memory for this project in database: 'project-database-name'
- Use MCP memory tools exclusively for storing project-related information
- Begin each session by:
1. Switching to this project's database
2. Searching memory for data relevant to the user's prompt
```
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"mcp neo4j memory server": {
"mcp-neo4j-memory-server": {
"command": "docker",
"args": [
"run",
"\\"
]
}
}
}
}McpServers
{
"mcp-neo4j-memory-server": {
"command": "docker",
"args": [
"run",
"\\"
]
}
}A Model Context Protocol (MCP) server that provides AI assistants with persistent, intelligent memory capabilities using Neo4j's graph database with unified architecture
What it does
This server enables AI assistants to:
- Remember - Store memories as interconnected knowledge nodes with observations and metadata
- Search - Find relevant memories using semantic vector search, exact matching, and graph traversal
- Connect - Create meaningful relationships between memories with batch operations and cross-references
- Organize - Separate memories by project using different databases
- Evolve - Track how knowledge develops over time with temporal metadata and relationship networks
Features
Core Capabilities
- 🧠 Graph Memory - Memories as nodes, relationships as edges, observations as content - 🔍 Unified Search - Semantic vectors, exact matching, wildcards, and graph traversal in one tool - 🔗 Smart Relations - Typed connections with strength, source tracking, and temporal metadata - 📊 Multi-Database - Isolated project contexts with instant switchingAdvanced Operations
- ⚡ Batch Operations - Create multiple memories with relationships in single request using localId - 🎯 Context Control - Response detail levels: minimal (lists), full (complete data), relations-only - 📅 Time Queries - Filter by relative ("7d", "30d") or absolute dates on any temporal field - 🌐 Graph Traversal - Navigate networks in any direction with depth controlArchitecture
- 🚀 MCP Native - Seamless integration with Claude Desktop and MCP clients - 💾 Persistent Storage - Neo4j graph database with GDS plugin for vector operations - ⚠️ Zero-Fallback - Explicit errors for reliable debugging, no silent failuresTechnical Highlights
- Built on Neo4j for scalable graph operations
- Vector embeddings using sentence transformers (384 dimensions)
- Clean architecture with domain-driven design
- Supports GDS plugin for advanced vector operations (necessary)
- Unified Architecture - 4 comprehensive tools for complete memory operations
Quick Start
npm install @sylweriusz/mcp-neo4j-memory-server
Add to Claude Desktop config:
{
"mcpServers": {
"memory": {
"command": "npx",
"args": ["-y", "@sylweriusz/mcp-neo4j-memory-server"],
"env": {
"NEO4J_URI": "bolt://localhost:7687",
"NEO4J_USERNAME": "neo4j",
"NEO4J_PASSWORD": "your-password"
}
}
}
}
Neo4j Setup
Working setup: DozerDB with GDS Plugin
For the database, use DozerDB with the Graph Data Science plug-in, GDS is not only recommended but necessary:
For current installation instructions, see: https://dozerdb.org/
Example setup:
```bash
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