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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.
Last updated -
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14,700
52,147
Python
MIT License
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Why this server?
This server provides basic persistent memory using a local knowledge graph, which would help Claude remember information across chats without having to repaste files.
Why this server?
This server offers neural memory sequence learning, which could be used for code understanding and generation, with state management and persistence capabilities.
Why this server?
A TypeScript-based server that provides a memory system for LLMs, allowing users to interact with multiple LLM providers while maintaining conversation history.
Why this server?
Provides semantic memory and persistent storage for Claude, leveraging ChromaDB and sentence transformers for enhanced search and retrieval.
Why this server?
A line-oriented text file editor, optimized for LLM tools with efficient partial file access to minimize token usage. Allows you to work with files without sending the entire content in each request.
Why this server?
This server enables direct interaction with on-disk documents through agentic RAG and hybrid search in LanceDB, allowing you to ask questions about the dataset as a whole or specific documents.
Why this server?
A Python-based server that implements the Model Context Protocol with efficient memory management, useful for handling larger files without exceeding context limits.
Why this server?
A high-performance server utilizing libSQL for persistent memory and vector search, which could enhance the memory functionality.
Why this server?
An improved implementation of persistent memory using a local knowledge graph with a customizable memory path, letting Claude remember information across chats.