Search for:
Why this server?
Provides structured memory management across chat sessions, allowing Claude to maintain context and build a knowledge base within project directories. This directly addresses the need for a memory bank.
Why this server?
Enables AI assistants to enhance their responses with relevant documentation through a semantic vector search, offering tools for managing and processing documentation efficiently. Addresses the need for pulling from a knowledge base.
Why this server?
A custom Memory MCP Server that acts as a cache for Infrastructure-as-Code information, allowing users to store, summarize, and manage notes with a custom URI scheme and simple resource handling. It directly relates to the memory bank functionality requested.
Why this server?
A basic implementation of persistent memory using a local knowledge graph. This lets Claude remember information about the user across chats, providing the memory aspect requested.
Why this server?
Memory manager for AI apps and Agents using various graph and vector stores and allowing ingestion from 30+ data sources
Why this server?
Provides a centralized MCP-based system for managing and accessing multi-project memory banks remotely, with features like project isolation, file structure validation, and type-safe operations.
Why this server?
A Model Context Protocol server providing vector database capabilities through Chroma, enabling semantic document search, metadata filtering, and document management with persistent storage.
Why this server?
Provides knowledge graph functionality for managing entities, relations, and observations in memory with strict validation rules to maintain data consistency.
Why this server?
A high-performance MCP server utilizing libSQL for persistent memory and vector search capabilities, enabling efficient entity management and semantic knowledge storage.
Why this server?
A Model Context Protocol server that enables semantic search and retrieval of documentation using a vector database (Qdrant).