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securityA
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qualityA basic implementation of persistent memory using a local knowledge graph. This lets Claude remember information about the user across chats.
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Python
MIT License
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Why this server?
Provides a basic implementation of persistent memory using a local knowledge graph, allowing Claude to remember information across chats.
Why this server?
This MCP server provides persistent memory integration for chat applications by utilizing a local knowledge graph to remember user information across interactions.
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 server component of the Model Context Protocol that provides intelligent analysis of codebases using vector search and machine learning to understand code patterns, architectural decisions, and documentation.
Why this server?
Facilitates semantic analysis of chat conversations through vector embeddings and knowledge graphs, offering tools for semantic search, concept extraction, and conversation pattern analysis.
Why this server?
A versatile memory system for AI applications that can be used as an MCP server or a direct library integration, enabling autonomous memory management without explicit commands, suggesting it might use a knowledge graph.
Why this server?
Enhances user interaction through a persistent memory system that remembers information across chats and learns from past errors by utilizing a local knowledge graph and lesson management.
Why this server?
Facilitates knowledge graph representation with semantic search using Qdrant, supporting OpenAI embeddings for semantic similarity and robust HTTPS integration with file-based graph persistence.
Why this server?
A memory server for Claude that stores and retrieves knowledge graph data in DuckDB, enhancing performance and query capabilities for conversations with persistent user information.
Why this server?
A Model Context Protocol server implementation that enables LLMs to interact with NebulaGraph database for graph exploration, supporting schema understanding, queries, and graph algorithms.