Memory Custom
hybrid server
The server is able to function both locally and remotely, depending on the configuration or use case.
Memory Custom
This project adds new features to the Memory server offered by the MCP team. It allows for the creation and management of a knowledge graph that captures interactions via a language model (LLM).
New Features
1. Custom Memory Paths
- Users can now specify different memory file paths for various projects.
- Why?: This feature enhances organization and management of memory data, allowing for project-specific memory storage.
2. Timestamping
- The server now generates timestamps for interactions.
- Why?: Timestamps enable tracking of when each memory was created or modified, providing better context and history for the stored data.
Getting Started
Prerequisites
- Node.js (version 16 or higher)
Installing via Smithery
To install Knowledge Graph Memory Server for Claude Desktop automatically via Smithery:
Installation
- Clone the repository:Copy
- Install the dependencies:Copy
Configuration
Before running the server, you can set the MEMORY_FILE_PATH
environment variable to specify the path for the memory file. If not set, the server will default to using memory.json
in the same directory as the script.
Running the Server
Updating the mcp server json file
Add this to your claude_desktop_config.json
/ .cursor/mcp.json
file:
System Prompt changes:
Running the Server Locally
To start the Knowledge Graph Memory Server, run:
The server will listen for requests via standard input/output.
API Endpoints
The server exposes several tools that can be called with specific parameters:
- Get Current Time
- Set Memory File Path
- Create Entities
- Create Relations
- Add Observations
- Delete Entities
- Delete Observations
- Delete Relations
- Read Graph
- Search Nodes
- Open Nodes
Acknowledgments
- Inspired by the Memory server from Anthropic.
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A customized MCP memory server that enables creation and management of a knowledge graph with features like custom memory paths and timestamping for capturing interactions via language models.