📌Overview

Purpose: To enable Claude to remember user-related information across chats through a persistent memory implemented as a local knowledge graph.

Overview: The Knowledge Graph Memory Server provides a structured approach for storing and managing user data using entities, relations, and observations within a knowledge graph. This allows for personalized interactions by enhancing Claude's ability to recall and utilize information about users.

Key Features:

• Entity Management: Create and manage entities with unique identifiers and related observations to represent users, organizations, and events, allowing for a personalized memory.

• Relation Creation: Establish directed relationships between entities to define interactions, enhancing contextual understanding during user interactions.

• Observation Handling: Add, delete, and manage discrete pieces of information associated with entities, allowing the server to maintain up-to-date user profiles.

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Knowledge Graph Memory Server

A basic implementation of persistent memory using a local knowledge graph. This lets Claude remember information about the user across chats.

Core Concepts

Entities

Entities are the primary nodes in the knowledge graph. Each entity has:

• A unique name (identifier)
• An entity type (e.g., "person", "organization", "event")
• A list of observations

Example:

{
  "name": "John_Smith",
  "entityType": "person",
  "observations": ["Speaks fluent Spanish"]
}

Relations

Relations define directed connections between entities. They are always stored in active voice and describe how entities interact or relate to each other.

Example:

{
  "from": "John_Smith",
  "to": "Anthropic",
  "relationType": "works_at"
}

Observations

Observations are discrete pieces of information about an entity. They are:

• Stored as strings
• Attached to specific entities
• Can be added or removed independently
• Should be atomic (one fact per observation)

Example:

{
  "entityName": "John_Smith",
  "observations": [
    "Speaks fluent Spanish",
    "Graduated in 2019",
    "Prefers morning meetings"
  ]
}

API

Tools

• create_entities
  Create multiple new entities in the knowledge graph
  Input: entities (array of objects)
  Each object contains:

  • name (string): Entity identifier
  • entityType (string): Type classification
  • observations (string[]): Associated observations
    Ignores entities with existing names

• create_relations
  Create multiple new relations between entities
  Input: relations (array of objects)
  Each object contains:

  • from (string): Source entity name
  • to (string): Target entity name
  • relationType (string): Relationship type in active voice
    Skips duplicate relations

• add_observations
  Add new observations to existing entities
  Input: observations (array of objects)
  Each object contains:

  • entityName (string): Target entity
  • contents (string[]): New observations to add
    Returns added observations per entity
    Fails if entity doesn't exist

• delete_entities
  Remove entities and their relations
  Input: entityNames (string[])
  Cascading deletion of associated relations
  Silent operation if entity doesn't exist

• delete_observations
  Remove specific observations from entities
  Input: deletions (array of objects)
  Each object contains:

  • entityName (string): Target entity
  • observations (string[]): Observations to remove
    Silent operation if observation doesn't exist

• delete_relations
  Remove specific relations from the graph
  Input: relations (array of objects)
  Each object contains:

  • from (string): Source entity name
  • to (string): Target entity name
  • relationType (string): Relationship type
    Silent operation if relation doesn't exist

• read_graph
  Read the entire knowledge graph
  No input required
  Returns complete graph structure with all entities and relations

• search_nodes
  Search for nodes based on query
  Input: query (string)
  Searches across:

  • Entity names
  • Entity types
  • Observation content
    Returns matching entities and their relations

• open_nodes
  Retrieve specific nodes by name
  Input: names (string[])
  Returns:

  • Requested entities
  • Relations between requested entities
    Silently skips non-existent nodes

Usage with Claude Desktop

Setup

Add this to your claude_desktop_config.json:

Docker

{
  "mcpServers": {
    "memory": {
      "command": "docker",
      "args": ["run", "-i", "-v", "claude-memory:/app/dist", "--rm", "mcp/memory"]
    }
  }
}

NPX

{
  "mcpServers": {
    "memory": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-memory"
      ]
    }
  }
}

NPX with custom setting

The server can be configured using the following environment variables:

{
  "mcpServers": {
    "memory": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-memory"
      ],
      "env": {
        "MEMORY_FILE_PATH": "/path/to/custom/memory.json"
      }
    }
  }
}

• MEMORY_FILE_PATH: Path to the memory storage JSON file (default: memory.json in the server directory)

System Prompt

The prompt for utilizing memory depends on the use case. Changing the prompt will help the model determine the frequency and types of memories created.

Here is an example prompt for chat personalization. You could use this prompt in the "Custom Instructions" field of a Claude.ai Project.

Follow these steps for each interaction:

1. User Identification:
   - You should assume that you are interacting with default_user
   - If you have not identified default_user, proactively try to do so.

2. Memory Retrieval:
   - Always begin your chat by saying only "Remembering..." and retrieve all relevant information from your knowledge graph
   - Always refer to your knowledge graph as your "memory"

3. Memory
   - While conversing with the user, be attentive to any new information that falls into these categories:
     a) Basic Identity (age, gender, location, job title, education level, etc.)
     b) Behaviors (interests, habits, etc.)
     c) Preferences (communication style, preferred language, etc.)
     d) Goals (goals, targets, aspirations, etc.)
     e) Relationships (personal and professional relationships up to 3 degrees of separation)

4. Memory Update:
   - If any new information was gathered during the interaction, update your memory as follows:
     a) Create entities for recurring organizations, people, and significant events
     b) Connect them to the current entities using relations
     b) Store facts about them as observations

Building

Docker:

docker build -t mcp/memory -f src/memory/Dockerfile .

License

This MCP server is licensed under the MIT License. You are free to use, modify, and distribute the software under the terms of the MIT License.