"A search for information about memory" matching MCP servers:

Provides an intelligent, graph-based memory system for LLM agents using the Zettelkasten principle, enabling automatic note construction, semantic linking, memory evolution, and autonomous graph maintenance with background optimization processes.

Provides Claude Co-work with MCP tools to search and record organizational memory, including search, read, query, ingest, remember, sync, and status operations.

MCP Memory is a MCP Server that gives MCP Clients the ability to remember information about users across conversations using vector search.

Multi-user, file-backed MCP memory server enabling isolated namespaces and persistent memory across sessions and AI clients.

An MCP server that enables AI agents to pause and request human approval or information via Slack, Telegram, or macOS dialogs before proceeding with actions.

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.

Two-layer memory for AI agents. Episodes compress into identity. The only MCP memory server with an immune system. Patterns earn permanence through evidence, false knowledge gets caught and demoted, and stale information fades — so your agent's memory gets smarter over time, not just bigger. Zero dependencies. 5 tools. Works with any MCP client.

Provides persistent memory for AI assistants via MCP, enabling them to store and recall facts, preferences, and tasks across conversations using either local file storage or a cloud backend with semantic search.

Enables AI agents to record and rank learnings, facts, and methods through a collaborative voting framework. It provides tools for agents to surface the most useful information across sessions using persistent memory storage.

Cognitive memory engine for AI agents with 5,100+ knowledge modules, circadian rhythm awareness, emotional state tracking (PAD model), and hybrid semantic search. Supports persistent per-user memory, project-scoped contexts, and multi-protocol access.

Enables Claude to maintain persistent memory across conversations using a local knowledge graph with fuzzy search capabilities, allowing it to remember and recall information about users, relationships, and context.

Provides AI agents with associative long-term memory using Sparse Distributed Memory, enabling recall from vague cues and interference detection.

A Model Context Protocol server that provides knowledge graph-based persistent memory for LLMs, allowing them to store, retrieve, and reason about information across multiple conversations and sessions.

Provides over 1,000 creative ways to decline requests across four categories (polite, humorous, professional, and creative). The MCP server wraps a REST API to help users craft professional rejections through natural language interactions.

MCP server providing 29 A-share analysis skills including real-time data, capital flow, limit-up tracking, technical/fundamental analysis, backtesting, risk control, and Xueqiu portfolio tracking, enabling AI agents to execute market research and strategy tasks.

MCP Memory is an MCP Server that gives MCP Clients the ability to remember information about users across conversations. It uses vector search technology to find relevant memories based on meaning, not just keywords.

A persistent, bitemporal, injection-safe memory MCP server for coding agents, offering unified retrieval across six memory layers, non-destructive belief revision, and multi-tenant namespace isolation.

Enables MCP clients like Cursor and Claude to remember user preferences and information across conversations using vector search.

A shared memory and coordination server for multiple AI coding agents, built on the Model Context Protocol (MCP).