Implementation of sustained memory using a local knowledge graph on claude desktop

Implementation of sustained memory using a local knowledge graph on claude desktop

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A knowledge graph memory server allows Claude Desktop to remember and organize information about a user across multiple chats. It can store things like user preferences, previous conversations and personal details. As the information is stored as a knowledge graph, Claude can understand the relationship between different pieces of information. This leads to more personalized answers and reduces repetition – you don’t have to explain the same things over and over again.

In this tutorial, we implement a simple persistent memory using a local knowledge graph on Claude Desktop, to help it remember user information across chats and give more personalized, consistent answers.

Node.js Installation

We use NPX to run Knowledge Graph -Memory Server, and until Node.js.

  • Download the latest version by node.js from nodejs.org
  • Run the installer.
  • Leave all settings by default and complete the installation

Claude Desktop installation

You can download the latest version of Claude Desktop at https://claude.ai/download. Next, configure Claude to connect to your MCP server. To do this, open claude_desktop_config.json file located in Claude Directory using any text. If the file is not found, go ahead and create it manually.

Enter the following code in the McP.Json file:

{
    "mcpServers": {
      "memory": {
        "command": "npx",
        "args": [
          "-y",
          "@modelcontextprotocol/server-memory"
        ],
        "env": {
            "MEMORY_PATH": "./memory.json"
        }
      }
    }
  }

Now we need to configure Claude so it can use the knowledge graph to create devices, build relationships and download relevant information.

  • Go to File> Settings> Claude Settings> Configure.
  • Add the following text in the Personal Preferencing section:

(This preference applies automatically to all conversations.)

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

When everything is configured, you will see 9 MCP tools available to the Knowledge Graph server. These tools allow you to: create devices, create relationships, add observations, delete devices, delete observations, delete relationships, read the graph, search nodes and open nodes.

In addition, the text we added in the Preferences section provides claude to automatically use these tools during conversations.

Even if we go to a new chat, Claude will remember the information from the previous chat via the Knowledge Graph. The integration of this MCP tool improves Claude’s ability to create, change and use knowledge in real time, making it a strong assistant for tasks such as database management and SQL Question Reminement. With this memory system in place, Claude becomes a more intelligent, responsive and consistent tool for all your future interactions. For more information about the knowledge memory server, visit this link where you will find resources to help you build even more advanced applications.

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I am a candidate for a civil engineer (2022) from Jamia Millia Islamia, New Delhi, and I have a great interest in data sciences, especially neural networks and their use in different areas.

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