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qualityFEGIS is a Model Context Protocol server that gives LLMs structured, persistent and portable memory through customizable cognitive tools defined in schema.
Last updated -
16
Python
MIT License
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Why this server?
Provides structured, persistent and portable memory through customizable cognitive tools defined in schema, which can incorporate graph-based storage.
Why this server?
Offers comprehensive memory management for AI assistants, supporting information management across conversations.
Why this server?
Serves as a bridge to the mem0 cloud service, specializing in project management by storing, retrieving, and searching project information in a structured format.
Why this server?
Implements a basic persistent memory using a local knowledge graph, allowing Claude to remember information about the user across chats.
Why this server?
A memory manager for AI apps and Agents using various graph and vector stores and allowing ingestion from 30+ data sources
Why this server?
Allows AI models to query and interact with FalkorDB graph databases through the Model Context Protocol.
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?
Provides vector database capabilities through Chroma, enabling semantic document search, metadata filtering, and document management with persistent storage which is relevant to RAG.
Why this server?
An enhanced Model Context Protocol server that enables LLMs to inspect database schemas with rich metadata and execute read-only SQL queries with safety checks, facilitating RAG with relational data.
Why this server?
A local vector database system that provides LLM coding agents with fast, efficient semantic search capabilities for software projects via the Message Control Protocol.