MemoryMesh is a knowledge graph server designed for AI models, particularly tailored for text-based RPGs and interactive storytelling. It helps AI maintain consistent, structured memory across conversations, enabling richer and more dynamic interactions.
MemoryMesh empowers users to build and manage structured information for AI models, making it ideal for applications like text-based RPGs, social network simulations, organizational planning, or any scenario involving structured data. Its key features include dynamic schema-based tools, relationship handling, metadata guidance, and event tracking.
Developers, game designers, and AI enthusiasts working on projects involving structured data, such as text-based RPGs, interactive storytelling, simulations, or organizational planning, can benefit from MemoryMesh.
MemoryMesh can be installed locally on your machine using Node.js (version 18 or higher) and npm. It integrates with Claude Desktop via the MCP server configuration and can be accessed through its dynamic tools and Memory Viewer interface.
You should consider using MemoryMesh when you need structured data management for AI-driven projects, especially those requiring persistent memory and dynamic interaction, such as text-based RPGs, simulations, or other complex storytelling systems.
Nodes represent entities or concepts within the knowledge graph, while edges represent relationships between these nodes. Nodes have attributes like 'name', 'nodeType', 'metadata', and 'weight', while edges define connections with properties like 'from', 'to', and 'edgeType'.
Schemas define the structure of data in MemoryMesh. They guide the creation of nodes and edges, providing required fields, enumerated types, and relationship definitions. Schemas are stored in the 'dist/data/schemas' directory and must follow the naming convention '[add_entity].schema.json'.
The Memory Viewer is a standalone web application that allows users to visualize and inspect the contents of the knowledge graph managed by MemoryMesh. It provides features like graph visualization, node inspection, edge exploration, search/filtering, table view, raw JSON view, and stats panel.
To integrate MemoryMesh with Claude Desktop, configure the 'claude_desktop_config.json' file by adding an entry for 'memorymesh' in the 'mcpServers' section. Specify the absolute path to the compiled 'index.js' file in your project directory and restart Claude Desktop.
One limitation is that the AI may hesitate to delete nodes from the knowledge graph unless explicitly encouraged through prompts. Additionally, proper setup requires careful attention to paths and configurations to ensure smooth operation.
MCP (Model Context Protocol) is an open protocol designed to standardize how applications provide context information to large language models (LLMs). Like a 'USB-C port' for AI applications, MCP ensures AI models can seamlessly connect with various data sources and tools.
An MCP Server is a server that supports the MCP protocol, enabling the exchange of contextual information between applications and AI models in a standardized way. It provides developers with an easy way to integrate AI models with databases, APIs, or other data sources.
An MCP Server eliminates the complexity of developing custom adapters by unifying the connection between AI models and various data sources. Whether you're a developer, data scientist, or AI app builder, an MCP Server simplifies the integration process, saving time and resources.
An MCP Server acts as an intermediary bridge, converting contextual information from various data sources into a format that AI models can understand. By adhering to the MCP protocol, it ensures data is transmitted between applications and AI models in a standardized manner.
At mcpserver.shop, you can browse our MCP Server Directory. The directory is categorized by industry (e.g., finance, healthcare, education), and each server comes with detailed descriptions and tags to help you quickly find the option that suits your needs.
The MCP Server Directory on mcpserver.shop is free to browse. However, some servers are hosted by third-party providers and may involve usage fees. Check the detailed page of each server for specific information.
MCP Servers support a wide range of data sources, including databases, APIs, cloud services, and custom tools. The flexibility of the MCP protocol allows it to connect almost any type of data source to AI models.
MCP Servers are primarily designed for developers, data scientists, and AI app builders. However, mcpserver.shop provides detailed documentation and guides to help users of varying technical levels get started easily.
Yes, MCP is an open-source protocol that encourages community participation and collaboration. For more details or to contribute, visit the official MCP documentation.
On mcpserver.shop, each MCP Server’s detailed page includes the provider’s contact information or a link. You can directly reach out to the provider for more details or technical support.