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Neo4j Agent Memory Server

Neo4j Agent Memory Server

by knowall-ai

GitHub 60 4 uses
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About

Neo4j Agent Memory gives AI assistants persistent, graph-based memory using Neo4j. Store information as interconnected nodes — people, places, organizations, projects, events — and create meaningful relationships between them with semantic predicates like KNOWS, WORKS_AT, CREATED, or MANAGES. Key features: - Store facts as labeled nodes with flexible schemas and custom properties - Build directional relationships with optional metadata (e.g., "since: 2023") - Search memory content with word-tokenized matching (querying "John Smith" finds "John" OR "Smith") - Traverse connections up to 3 levels deep to surface related information - Filter and retrieve memories by creation date with automatic timestamp tracking - Connect to any Neo4j instance including Neo4j Enterprise Edition with multi-database support - 10 specialized tools for CRUD operations and intelligent graph exploration without embedding complex logic in the tools themselves

README

[](https://smithery.ai/server/@knowall-ai/mcp-neo4j-agent-memory)

Neo4j Agent Memory MCP Server

A specialized MCP server that bridges Neo4j graph database with AI agents, providing memory-focused tools for storing, recalling, and connecting information in a knowledge graph.

Quick Start 🚀

You can run this MCP server directly using npx:

npx @knowall-ai/mcp-neo4j-agent-memory

Or add it to your Claude Desktop configuration:

{
  "mcpServers": {
    "neo4j-memory": {
      "command": "npx",
      "args": ["@knowall-ai/mcp-neo4j-agent-memory"],
      "env": {
        "NEO4J_URI": "bolt://localhost:7687",
        "NEO4J_USERNAME": "neo4j",
        "NEO4J_PASSWORD": "your-password",
        "NEO4J_DATABASE": "neo4j"
      }
    }
  }
}

Features

  • 🧠 Persistent Memory Storage - Store and retrieve memories across conversations
  • 🔗 Semantic Relationships - Create meaningful connections between memories (KNOWS, WORKS_AT, CREATED, etc.)
  • 🔍 Intelligent Search - Natural language search across all memory properties and relationships
  • 🏷️ Flexible Labeling - Use any label for memories (person, place, project, idea, etc.)
  • Temporal Tracking - Automatic timestamps and date-based queries
  • 🌐 Graph Exploration - Traverse relationships to discover connected information
  • 🎯 Context-Aware - Search with depth to include related memories
  • 🔧 LLM-Optimized - Simple tools that let the AI handle the complexity
  • 🏢 Enterprise Ready - Supports multiple Neo4j databases
  • 📚 Built-in Guidance - Get help on best practices and usage patterns
  • Philosophy: LLM-Driven Intelligence

    Unlike traditional approaches that embed complex logic in tools, this server provides simple, atomic operations and lets the LLM handle all the intelligence:

  • No hidden logic: Tools do exactly what they say - no auto-disambiguation or smart matching
  • LLM decides everything: Entity recognition, relationship inference, and conflict resolution
  • Transparent operations: Every action is explicit and predictable
  • Maximum flexibility: The LLM can implement any strategy without tool limitations
  • Search Behavior

    The search_memories tool uses word tokenization:
  • Query "John Smith" finds memories containing "John" OR "Smith"
  • This returns more results, letting the LLM pick the most relevant
  • Better than exact substring matching for names and multi-word queries
  • This approach makes the system more powerful and adaptable, as improvements in LLM capabilities directly translate to better memory management.

    Neo4j Enterprise Support

    This server now supports connecting to specific databases in Neo4j Enterprise Edition. By default, it connects to the "neo4j" database, but you can specify a different database using the NEO4J_DATABASE environment variable.

    Memory Tools

  • search_memories: Search and retrieve memories from the knowledge graph
  • - Word-based search: Searches for ANY word in your query (e.g., "Ben Weeks" finds memories containing "Ben" OR "Weeks") - Natural language search across all memory properties (or leave empty to get all) - Filter by memory type (person, place, project, etc.) - Filter by date with since_date parameter (ISO format) - Control relationship depth and result limits - Sort by any field (created_at, name, etc.)

  • create_memory: Create a new memory in the knowledge graph
  • - Flexible type system - use any label in lowercase (person, place, project, skill, etc.) - Store any properties as key-value pairs - Automatic timestamps for temporal tracking

  • create_connection: Create relationships between memories
  • - Link memories using semantic relationship types (KNOWS, WORKS_AT, LIVES_IN, etc.) - Add properties to relationships (since, role, status, etc.) - Build complex knowledge networks

  • update_memory: Update properties of existing memories
  • - Add or modify any property - Set properties to null to remove them

  • update_connection: Update relationship properties
  • - Modify relationship metadata - Track changes over time

  • delete_memory: Remove memories and all their connections
  • - Use with caution - permanent deletion - Automatically removes all relationships

  • delete_connection: Remove specific relationships
  • - Precise relationship removal - Keeps the memories intact

  • list_memory_labels: List all unique memory labels in use
  • - Shows all labels with counts - Helps maintain consistency - Prevents duplicate label variations

  • get_guidance: Get help on using the memory tools effectively
  • - Topics: labels, relationships, best-practices, examples - Returns comprehensive guidance for LLMs - Use when uncertain about label/relationship naming

    Prerequisites

    1. Neo4j Database (v4.4+ or v5.x) - Install Neo4j Community or En

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