"A server for finding information about memory banks" 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.

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.

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.

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.

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

Provides a centralized MCP-based system for managing and accessing multi-project memory banks remotely, with features like project isolation, file structure validation, and type-safe operations.

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 a structured documentation system for context preservation in AI assistant environments, helping users create and manage memory banks for their projects.

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.

Lets any MCP-capable agent look up and extend a shared local store of entities, relations and facts about the user — people, projects, places, channels — resolving free phrases to cards and searching past conversations by words and by meaning.

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.

Memory Bank Server provides a set of tools and resources for AI assistants to interact with Memory Banks. Memory Banks are structured repositories of information that help maintain context and track progress across multiple sessions.

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

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

A lightweight MCP server that gives Claude Code Channels on Telegram persistent memory by storing facts, conversation logs, and session summaries in Supabase.

Enables AI agents to keep and query long-term memory of past conversations, with hybrid keyword-plus-semantic search, session, day, week, month and year summaries, saved notes, and a running set of current facts about the user. Exposes this over MCP streamable HTTP through five tools so any client can retrieve context that would otherwise be lost between 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.

Enables AI assistants to have persistent, self-hosted memory across sessions and devices, storing context in a SQLite database you control.

Provides persistent cognitive memory for AI agents and robots with no LLM in the loop, using algorithms for storage, recall, and forgetting; supports MCP, HTTP, and ROS2.

A persistent, trust-scored project memory for AI coding agents, backed by PostgreSQL + pgvector, providing durable memory of architecture decisions, bug patterns, and coding conventions.

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.

Provides persistent memory for MCP clients, enabling them to remember user preferences and behaviors across conversations using vector search and Cloudflare's infrastructure.

Durable, inspectable memory for MCP agents. Preserves decisions, preferences, and project knowledge across sessions with full provenance and version history.

A production-ready MCP server for persistent AI memory across LLMs like Claude and ChatGPT. Provides automatic conversation backup, multi-user support, and multi-storage (PostgreSQL, Redis, Qdrant).