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"Techniques and Strategies to Improve Long-Term Memory" matching MCP servers:

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    A persistent, self-organizing memory MCP server for AI assistants, using semantic search, knowledge graphs, and reinforcement learning to automatically manage and retrieve memories.
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    MIT
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    Provides access to Brian Eno and Peter Schmidt's Oblique Strategies card deck to help users overcome creative blocks through lateral thinking. It enables searching and retrieving random prompts from various editions, including collections adapted specifically for programmers.
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    MCP Long Context Reader is a Python-based toolkit designed to overcome the context window limitations and high costs associated with Large Language Models (LLMs) processing extensive documents. It provides a FastMCP server with multiple, powerful strategies for an LLM agent to 'read' and query long documents without needing to load the entire text into its context window.
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    MIT
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    An MCP server implementing Recursive Language Models (RLM) to process arbitrarily large contexts through a programmatic probe, recurse, and synthesize loop. It enables LLMs to perform multi-step investigations and evidence-backed extraction across massive file sets without being limited by standard context windows.
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    Standalone memory microservice that converts completed agent sessions into long-term memory and exposes retrieval through an MCP tool interface, enabling agents to search their past interactions.
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    MIT
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    MCP server for zero-config, traceable long-term memory using SQLite, enabling agents to store, search, trace, and monitor memory with tools like memory_store, memory_search, and memory_health.
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    MIT
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    Provides persistent AI agent memory using a local vector database for long-term semantic storage and short-term session scratchpads. It enables low-latency memory operations including search, storage, and bulk management without external cloud dependencies.
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    An MCP server providing long-term memory for LLMs via hybrid search (FTS5 + vector) stored in SQLite, with time-decay scoring and support for multiple AI assistants.
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    A production-grade long-term memory MCP server that enables AI agents to persist and recall memories across sessions with importance weighting, confidence calibration, and efficient context window management.
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    MIT
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    An MCP server that gives Claude-based agents persistent long-term memory with semantic search, BM25 hybrid reranking, associative recall, episode grouping, and sleep consolidation.
    Last updated
    Apache 2.0