npm package: @itseasy21/mcp-knowledge-graph
Description: MCP server enabling persistent memory for Claude through a local knowledge graph
Author: itseasy21
Latest version: 1.0.7

# Knowledge Graph Memory Server

[![smithery badge](https://smithery.ai/badge/@itseasy21/mcp-knowledge-graph)](https://smithery.ai/server/@itseasy21/mcp-knowledge-graph)

An improved implementation of persistent memory using a local knowledge graph with a customizable memory path.

This lets Claude remember information about the user across chats.

> [!NOTE]
> This is a fork of the original [Memory Server](https://github.com/modelcontextprotocol/servers/tree/main/src/memory) and is intended to not use the ephemeral memory npx installation method.

## Server Name

```txt
mcp-knowledge-graph
```

![screen-of-server-name](img/server-name.png)

![read-function](/img/read-function.png)

## 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
- Creation date and version tracking

The version tracking feature helps maintain a historical context of how knowledge evolves over time.

Example:

```json
{
  "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. Each relation includes:

- Source and target entities
- Relationship type
- Creation date and version information

This versioning system helps track how relationships between entities evolve over time.

Example:

```json
{
  "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:

```json
{
  "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 Cursor, Cline or Claude Desktop

### Setup

Add this to your mcp.json or claude_desktop_config.json:

```json
{
    "mcpServers": {
      "memory": {
        "command": "npx",
        "args": [
          "-y",
          "@itseasy21/mcp-knowledge-graph"
        ],
        "env": {
          "MEMORY_FILE_PATH": "/path/to/your/projects.jsonl"
        }
      }
    }
  }
```

### Installing via Smithery

To install Knowledge Graph Memory Server for Claude Desktop automatically via [Smithery](https://smithery.ai/server/@itseasy21/mcp-knowledge-graph):

```bash
npx -y @smithery/cli install @itseasy21/mcp-knowledge-graph --client claude
```

### Custom Memory Path

You can specify a custom path for the memory file in two ways:

1. Using command-line arguments:
```json
{
  "mcpServers": {
    "memory": {
      "command": "npx",
      "args": ["-y", "@itseasy21/mcp-knowledge-graph", "--memory-path", "/path/to/your/memory.jsonl"]
    }
  }
}
```

2. Using environment variables:
```json
{
  "mcpServers": {
    "memory": {
      "command": "npx",
      "args": ["-y", "@itseasy21/mcp-knowledge-graph"],
      "env": {
        "MEMORY_FILE_PATH": "/path/to/your/memory.jsonl"
      }
    }
  }
}
```

If no path is specified, it will default to memory.jsonl in the server's installation 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](https://www.anthropic.com/news/projects).

```txt
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
```

## 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.