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

A persistent, self-organizing memory MCP server for AI assistants, using semantic search, knowledge graphs, and reinforcement learning to automatically manage and retrieve memories.

A persistent, human-like memory system for AI companions in LM Studio, using SQLite and ChromaDB for hybrid storage, enabling seamless recall across sessions and models.

Provides persistent long-term memory for AI agents with semantic search and activation-based decay. Enables AI systems to remember across sessions through layered memory architecture and automatic context-aware retrieval.

Captures user corrections and improvements during interactions, logs them, finds recurring patterns, and prescribes preventive fixes like CLAUDE.md rules, skills, scripts, or MCP tools to avoid repeating mistakes.

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.

Provides local desktop agents with durable project memory and safety gates for writing long novels, enabling continuity management, chapter drafting, and state-aware handoffs.

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

Your coding agent remembers how it solved past tasks and reuses it next time: trigger, steps, outcome, lessons. Hooks recall the right skill before a task; skills strengthen with confirmed use and unused ones decay. Local SQLite, no API key, nothing leaves your machine. 9 MCP tools, works with Claude Code, Codex and Cursor. Agent-written memory stays untrusted until you approve it.

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

Local-first, governable long-term memory for AI agents. Provides SQLite-backed storage, cross-session recall, and traceable memory corrections through a standard MCP interface.

MCP server providing durable Substrate organizational memory tools, including search, read, query, ingest, remember, and sync. It integrates with Codex to automatically capture completed turns and session boundaries for persistent memory.

Enables AI agents to maintain and sync long-term memory and reusable skills across devices using local folder sync, with support for multiple agents and optional semantic recall.

Provides curated long-term memory for Codex CLI via MCP, with Markdown-based storage, deduplication, and decay-based forgetting.

Provides long-term memory for AI agents with per-user scoped recall, ranking by relevance, recency, and importance, consolidation of repeated events, and automatic forgetting.

Long-term memory MCP server for Claude Code with SQLite persistence, encryption, semantic search, and automatic memory linking.

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.

Auto-captured, auto-recalled, path-scoped memory for AI coding agents and teams.

MCP server that provides persistent long-term memory for Claude Code, enabling storage, search, and retrieval of project knowledge across sessions.

Slipstream - Built by Vektor Memory - a persistent multi-layered memory architecture. 4-layer associative graph memory (MAGMA) with autonomous REM cycle, CLI, DXT-MCP Cloak Tools.