"Memory" matching MCP servers:

Enables MCP-capable coding agents to semantically search, store, and inspect a single local, cross-tool index of session histories and project docs. Agents can retrieve what other tools and prior sessions already discovered.

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.

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

A long-horizon memory architecture for AI agents, providing a scalable, graph-based memory with causal typing and an MCP interface.

A portable, self-hosted semantic memory layer for LLM agents, enabling them to search and add memories by meaning over the Model Context Protocol.

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.

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.

MCP Memory is a MCP Server that gives clients like Cursor and Claude the ability to remember user preferences and behaviors across conversations using vector search.

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 fully-local AI memory system that saves notes, code, and terminal output to a vector database via Ollama and LanceDB, enabling context retrieval from terminal, API calls, or inside Claude Code.

An MCP server that gives AI assistants the ability to remember user information (preferences, behaviors) across conversations using vector search technology.

Enables AI assistants to remember user information and preferences across conversations using vector search technology. Built on Cloudflare infrastructure with isolated user namespaces for secure, persistent memory storage.

Enables MCP clients to remember user preferences and behaviors across conversations using vector search technology.

Structured agent memory system with hash-chain provenance, temporal decay, drift detection, provenance archaeology, vector embeddings, and git integration for OpenCode agents.

A local-first MCP server for persistent memory with vector search, metadata filtering, fact tracking, and graceful degradation when dependencies fail.

Provides a shared local memory MCP server so multiple coding agents can store, retrieve, and attribute memories across sessions. It enables agents to recall project knowledge and past fixes with provenance.

Enables AI agents to maintain persistent, multi-layer cognitive memory with vector search, deterministic verification, experiment tracking, and cost-aware recall, all through 87 MCP tools and a local Flask UI.

An MCP server that gives AI assistants persistent memory across conversations, storing and retrieving user preferences and behaviors using vector search.

A Model Context Protocol server that provides AI assistants with persistent semantic memory and knowledge graph capabilities using PostgreSQL and vector embeddings. It enables cross-session storage, hybrid search, and complex relationship tracking for enhanced contextual awareness.

An MCP server that enables clients like Cursor, Claude, and Windsurf to remember user information and preferences across conversations using vector search technology.

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.

Gives AI agents a shared, encrypted memory store accessible over MCP, with per-agent keys, per-namespace permissions, history and revert, attachments, and semantic search that handles Czech as well as English. Memories, agents, network access, and HTTPS can be managed through a self-hosted admin web on the same port, with backups being simply copies of the encrypted data file.

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

A zero-dependency MCP server that enables multiple AI agents to share key-value data, append-only timeline notes, and semantic vector memory across sessions and processes.

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).

This MCP server lets multiple AI agents connect to one self-hosted long-term memory vault, so what one agent learns can be recalled by another. It provides tools for recalling, remembering, proposing, importing, listing, and forgetting memories, with user-controlled approval, backup, restore, and deletion.

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