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qualityMaintains consistent LLM interaction styles across conversations by storing emoji-based context keys (emojikeys) that can be used across different devices and applications.
Last updated -
4
30
3
TypeScript
MIT License
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Why this server?
Maintains consistent LLM interaction styles across conversations by storing emoji-based context keys (emojikeys) that can be used across different devices and applications. This addresses the need to maintain context across conversations.
Why this server?
Enables LLMs to search, retrieve, and manage documents through Rememberizer's knowledge management API, which helps in providing past conversation background.
Why this server?
Memory manager for AI apps and Agents using various graph and vector stores and allowing ingestion from 30+ data sources, this can help the AI remember past interactions.
Why this server?
A Model Context Protocol server for Claude Desktop that provides structured memory management across chat sessions, allowing Claude to maintain context and build a knowledge base within project directories.
Why this server?
A persistent memory implementation using a local knowledge graph that lets Claude remember information about users across conversations, effectively retaining past interactions.
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, supporting persistent memory.
Why this server?
A server for managing project documentation and context across Claude AI sessions through global and branch-specific memory banks, enabling consistent knowledge management with structured JSON document storage.
Why this server?
A TypeScript-based server that provides a memory system for Large Language Models (LLMs), allowing users to interact with multiple LLM providers while maintaining conversation history.
Why this server?
This server enables users to store, manage, and summarize notes using a custom URI scheme, with functionality to add new notes and generate summaries with varying levels of detail, which can act as memory.
Why this server?
A flexible memory system for AI applications that supports multiple LLM providers and can be used either as an MCP server or as a direct library integration, enabling autonomous memory management without explicit commands.