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modelcontextprotocol

Knowledge Graph Memory Server

README.md
# 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`](https://www.npmjs.com/package/@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:
```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.

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
  - 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

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

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

On Windows, use `cmd /c` to launch `npx`:

```json
{
  "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:

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

On Windows, use:

```json
{
  "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:

[![Install with NPX in VS Code](https://img.shields.io/badge/VS_Code-NPM-0098FF?style=flat-square&logo=visualstudiocode&logoColor=white)](https://insiders.vscode.dev/redirect/mcp/install?name=memory&config=%7B%22command%22%3A%22npx%22%2C%22args%22%3A%5B%22-y%22%2C%22%40modelcontextprotocol%2Fserver-memory%22%5D%7D) [![Install with NPX in VS Code Insiders](https://img.shields.io/badge/VS_Code_Insiders-NPM-24bfa5?style=flat-square&logo=visualstudiocode&logoColor=white)](https://insiders.vscode.dev/redirect/mcp/install?name=memory&config=%7B%22command%22%3A%22npx%22%2C%22args%22%3A%5B%22-y%22%2C%22%40modelcontextprotocol%2Fserver-memory%22%5D%7D&quality=insiders)

[![Install with Docker in VS Code](https://img.shields.io/badge/VS_Code-Docker-0098FF?style=flat-square&logo=visualstudiocode&logoColor=white)](https://insiders.vscode.dev/redirect/mcp/install?name=memory&config=%7B%22command%22%3A%22docker%22%2C%22args%22%3A%5B%22run%22%2C%22-i%22%2C%22-v%22%2C%22claude-memory%3A%2Fapp%2Fdist%22%2C%22--rm%22%2C%22mcp%2Fmemory%22%5D%7D) [![Install with Docker in VS Code Insiders](https://img.shields.io/badge/VS_Code_Insiders-Docker-24bfa5?style=flat-square&logo=visualstudiocode&logoColor=white)](https://insiders.vscode.dev/redirect/mcp/install?name=memory&config=%7B%22command%22%3A%22docker%22%2C%22args%22%3A%5B%22run%22%2C%22-i%22%2C%22-v%22%2C%22claude-memory%3A%2Fapp%2Fdist%22%2C%22--rm%22%2C%22mcp%2Fmemory%22%5D%7D&quality=insiders)

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](https://code.visualstudio.com/docs/copilot/customization/mcp-servers).

#### NPX

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

On Windows, use:

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

#### Docker

```json
{
  "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](https://www.anthropic.com/news/projects). 

```
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
     c) Store facts about them as observations
```

## Building

Docker:

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

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.

TDQS

B3.3/5.0

Scored across 9 tools

Disambiguation4/5

Most tools have distinct purposes targeting specific operations like adding/deleting observations, entities, or relations, and reading/searching the graph. However, 'open_nodes' and 'search_nodes' could be confused as both involve finding nodes, though 'open_nodes' seems to retrieve specific nodes by name while 'search_nodes' queries based on content.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern in snake_case, such as 'add_observations', 'create_entities', and 'delete_relations'. This uniformity makes the tool set predictable and easy to navigate for an agent.

Tool Count5/5

With 9 tools, the server is well-scoped for managing a knowledge graph, covering core operations like CRUD for entities, relations, and observations, as well as reading and searching. This count is appropriate and each tool appears to earn its place without being overwhelming.

Completeness4/5

The tool set provides comprehensive coverage for basic knowledge graph operations, including creation, deletion, reading, and searching. A minor gap is the lack of update tools for entities, relations, or observations, which might require workarounds like delete-and-recreate, but core workflows are well-supported.

Maintenance

ActivityActive
ResponsivenessSlow