Loc 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:

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:

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:

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

• extract_locations

  • Extract and add location entities from text with geographic relationships

  • Input:

    • text (string): Text to extract locations from

    • sourceEntity (string, optional): Entity that mentions these locations

  • Automatically extracts:

    • Cities with states/countries: "New York, NY", "Paris, France"

    • Street addresses: "123 Main Street", "456 Oak Avenue"

    • Landmarks: "Central Park", "Golden Gate Bridge"

    • Geographic features: "Mount Rushmore", "Lake Michigan"

    • Administrative regions: US states, countries

  • Creates:

    • Location entities with entityType: "location"

    • Geographic metadata stored as observations

    • Hierarchical "located_in" relationships (city → state → country)

    • "mentions_location" relations if sourceEntity provided

  • Returns created entities and relations

Usage with Claude Desktop

Setup

Add this to your claude_desktop_config.json:

Docker

NPX

NPX with custom setting

The server can be configured using the following environment variables:

• MEMORY_FILE_PATH: Path to the memory storage JSON file (default: memory.json 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

Docker

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.

Building

Docker:

For Awareness: a prior mcp/memory volume contains an index.js file that could be overwritten by the new container. If you are using a docker volume for storage, delete the old docker volume's index.js file before starting the new container.

License

This MCP server is licensed under the MIT License. This means you are free to use, modify, and distribute the software, subject to the terms and conditions of the MIT License. For more details, please see the LICENSE file in the project repository.