A Knowledge Graph's knowledge memory server allows Claude Desktop to remember and organize information about a user on several cats. It can store things such as user preferences, past conversations and personal details. Because information is recorded as a graph of knowledge, Claude can understand the relationships between different information. This leads to more personalized answers and reduces rehearsal – you will not have to explain the same things again and again.
In this tutorial, we will implement a simple persistent memory using a local knowledge graphic in Claude Desktop, to help him remember the user information through cats and provide more personalized and consistent answers.
Installation of node.js
We will use NPX to execute the memory server of the knowledge graphic, and for that, Node.js is required.
- Download the latest version of node.js of nodejs.org
- Run the installation program.
- Leave all the default settings and complete the installation
Installation of Claude Desktop
You can download the latest version of Claude Desktop to https://claude.ai/download. Then you will need to configure Claude to connect with your MCP server. To do this, open the Claude_Desktop_Config.json file located in the Claude directory using any text editor. If the file does not exist, go for it and create it manually.
In the MCP.Json file, enter the following code:
{
"mcpServers": {
"memory": {
"command": "npx",
"args": (
"-y",
"@modelcontextprotocol/server-memory"
),
"env": {
"MEMORY_PATH": "./memory.json"
}
}
}
}Now we must configure Claude so that he can use the knowledge graphic to create entities, establish relationships and recover relevant information.
- Go to file> Settings> Claude settings> Configure.
- In the personal preference section, add the following text:
(This preference will automatically apply 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 observationsOnce everything is configured, you will see 9 MCP tools available for the knowledge graphics server. These tools allow you to: Create entities, create relationships, add observations, delete entities, delete observations, delete relationships, read the graphic, research nodes and open nodes.
In addition, the text we added in the Preferences section allows Claude to automatically use these tools during conversations.
Even if we go to a new cat, Claude will remember the information from previous cats via the knowledge graphic. The integration of this MCP tool improves Claude's ability to create, modify and use knowledge in real time, making it a powerful assistant for tasks such as database management and the generation of SQL queries. With this memory system in place, Claude becomes a more intelligent, reactive and coherent tool for all your future interactions. For more details on the knowledge memory server, you can visit This linkWhere you will find resources to help you create even more advanced applications.
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I graduated in Civil Engineering (2022) by Jamia Millia Islamia, New Delhi, and I have a great interest in data science, in particular neural networks and their application in various fields.
