Knowledge Graph Memory Server

optimized-memory-mcp-server

This is to test and demonstrate Claude AI's coding abilities, as well as good AI workflows and prompt design. This is a fork of a Python Memory MCP Server (I believe the official one is in Java) which uses SQLite for a backend.

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", "--rm", "mcp/memory"]
    }
  }
}

NPX

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

Knowledge Graph Memory Server

Integrations

optimized-memory-mcp-server