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

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.

A local MCP server that gives AI assistants persistent long-term memory using a biological neural architecture with cortex, synapses, and hippocampus.

Provides AI agents with persistent, long-term memory via OKF-formatted markdown and SQLite indexing, enabling stateful storage, retrieval, and search across sessions.

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.

A persistent memory MCP server for Claude Code that enables long-term recall across sessions via hybrid search, code intelligence, and tools for reading/writing memory.

Minimal long-term memory MCP server for AI assistants backed by SQLite FTS5 full-text search, optional hybrid vector retrieval, scoped memory graphs, and VSCode extension.

Enables AI agents to store, search, and retrieve long-term memories with BM25 full-text search, auto-tagging, and importance scoring.

Enables searching and retrieving documentation from crawled documentation sites as an MCP server, allowing coding agents to query real docs instead of relying on training data.

Nexus Memory gives every MCP-compatible agent one persistent, self-hosted shared memory with hybrid retrieval, drift detection, and anti-poisoning features.

Enables LLMs to query documents using semantic search, supporting PDFs, Word, Excel, and more. Organizes documents by topics from folder structure and provides advanced search features like phrase matching and date filtering.

Shared, live team memory for Claude — a remote MCP server on Cloudflare Workers + D1.

A generic MCP memory service that provides AI clients with long-term memory capabilities, including semantic search, categorization, and priority management. Supports both local JSON and cloud PostgreSQL storage.

A general-purpose MCP memory server that gives AI clients long-term memory with semantic search, memory classification, priority management, and progressive injection, supporting local JSON or cloud PostgreSQL storage.

Provides persistent long-term memory for LLMs via local SQLite storage and semantic search, enabling recall across sessions without external APIs.

Provides any AI CLI tool with long-term memory using a three-tier architecture (sensory, short-term, long-term) for cross-session persistence, fully local and private.

Enables querying Rick and Morty characters, locations, and episodes using the Rick and Morty API with no authentication required.

Long-term memory MCP server for Claude Code with SQLite FTS5 full-text search, BM25 ranking, access tracking, and upsert deduplication. Zero config via npx.

A lightweight MCP memory server built on SQLite + FTS5, providing cross-session long-term memory for Claude Code.

A Cloudflare Worker that transforms Cloudflare AI Search (AutoRAG) instances into an MCP server for querying documentation. It enables AI models to search and retrieve relevant information from custom document sets stored in R2 buckets.

A local-first MCP server that stores meeting transcripts and maintains a searchable markdown wiki of decisions, commitments, and contradictions, enabling Claude to recall context across meetings.