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.

Published on npm as @modelcontextprotocol/server-memory.

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
• Fails if either the source or target entity doesn't exist

• 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
• No error if an entity doesn't exist; the response reports which names were not found

• delete_observations
• Remove specific observations from entities
• Input: deletions (array of objects)
• Each object contains:
• entityName (string): Target entity
• observations (string[]): Observations to remove
• No error if an observation doesn't exist; the response reports how many were deleted

• 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
• No error if a relation doesn't exist; the response reports how many were deleted

• 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

Resources

• knowledge-graph (memory://knowledge-graph)
• The full knowledge graph as a readable MCP Resource
• MIME type: application/json
• Returns the same shape as read_graph (entities and relations)
• Mutation tools (create_entities, create_relations, add_observations, delete_entities, delete_observations, delete_relations) emit notifications/resources/updated for this URI, so subscribed clients see live changes

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"
      ]
    }
  }
}

On Windows, use cmd /c to launch npx:

{
  "mcpServers": {
    "memory": {
      "command": "cmd",
      "args": [
        "/c",
        "npx",
        "-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.jsonl"
      }
    }
  }
}

On Windows, use:

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

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

VS Code Installation Instructions

For quick installation, use one of the one-click installation buttons below:

For manual installation, you can configure the MCP server using one of these methods:

Method 1: User Configuration (Recommended) Add the configuration to your user-level MCP configuration file. Open the Command Palette (Ctrl + Shift + P) and run MCP: Open User Configuration. This will open your user mcp.json file where you can add the server configuration.

Method 2: Workspace Configuration Alternatively, you can add the configuration to a file called .vscode/mcp.json in your workspace. This will allow you to share the configuration with others.

For more details about MCP configuration in VS Code, see the official VS Code MCP documentation.

#### NPX

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

On Windows, use:

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

#### Docker

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

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