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

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.

Enables AI agents to maintain long-term, cross-session memory by extracting facts, reconciling state conflicts, and retrieving relevant memories via vector search.

Local-first MCP server for long-term memory of dev sessions. It indexes session metadata with embeddings in Postgres/pgvector, enables semantic search over past sessions, and resolves only relevant session files for context.

Implements long-term memory capabilities for AI assistants using PostgreSQL with pgvector for efficient vector similarity search, enabling semantic retrieval of stored information.

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 AI-assisted long-term memory storage and retrieval with exact-phrase and semantic hybrid search, enabling save, read, delete, and admin recovery via MCP tools.

Personal memory service using semantic vector retrieval and knowledge graphs to persist long-term context, supporting memory evolution and conflict labeling for the same entity.

Enables AI assistants to store and retrieve long-term memories with semantic search, supporting various memory types and tags via PostgreSQL and pgvector.

Enables AI assistants to store and retrieve long-term memories using PostgreSQL with vector similarity search. Supports semantic memory operations, tagging, and real-time updates for persistent learning across conversations.

A self-hosted MCP memory and state system for long-running AI relationships, storing source text locally with explicit memory tools and a web manager for humans.

Embedded, local-first agent memory: facts extracted into a per-namespace SQLite file (vec0 + FTS5) with hybrid retrieval and point-in-time (time-travel) queries. ADD-only history over stdio — no server process, no cloud dependency.

Provides persistent memory for AI agents using hybrid search (vector embeddings + BM25) with neural reranking, enabling storage and retrieval of insights, debugging solutions, and patterns across coding sessions.

Provides AI agents with persistent, searchable memory using semantic search, auto-linking, and categorization, with zero-config local setup or production-ready external providers.

Enhanced MCP knowledge graph memory server with cloud persistence and semantic search, acting as a drop-in replacement for the standard memory server.

Enables Claude Desktop and compatible agents to search, read, add, update, and delete a locally stored, user-curated memory using hybrid retrieval with reranking and Qdrant. All processing runs locally without cloud services or API keys.

Enables AI agents to store and recall persistent long-term memories across sessions using LanceDB, with semantic search, automatic linking, conflict detection, and maintenance tools.

Enables persistent memory management with semantic vector recall, BM25 keyword search, and 3-way fused retrieval combining lexical, semantic, and tag-based signals.

An MCP server that provides AI assistants with persistent, semantic memory using Turso for storage and OpenAI for vector search. It enables natural language operations to store, retrieve, and refine information with automatic duplicate detection and quality validation.

A high-performance MCP server utilizing libSQL for persistent memory and vector search capabilities, enabling efficient entity management and semantic knowledge storage.

An MCP server for neuroanatomically-inspired memory consolidation using Neo4j and semantic search, enabling episodic memory encoding, semantic retrieval, and graph operations.