"Information and resources about memory" matching MCP servers:

An MCP (Model Context Protocol) server that gives AI agents live, structured ad intelligence across Facebook, Google, and Instagram — data that no base model can produce from training alone. Powered by Apify actors. Works with any MCP-compatible client: Cursor, Claude, etc.

MCP server that demonstrates the Resources feature by exposing static and dynamic resources, including contact data and personalized greetings, through MCP.

Relays a result screenshot to Slack so you can review a coding agent's output without opening the app.

Enables building and evaluating LangGraph agents over a protocol-level MCP server to measure failure rates across four agent topologies.

A different approach from typical persistent-memory MCPs. Instead of a local SQLite + embeddings store, the memory lives as plain files in a .ai-memory/ directory you commit to your repo (facts.jsonl, decisions/\*.md, gotchas.md). Git is the sync layer — what one Claude/Cursor/Cline learns about a repo, the next session (or a teammate's agent) picks up automatically. 5 MCP tools: get_rep

Provides persistent memory for Claude with hierarchical categorization, cross-corpus recall, session journals, and customizable persona, enabling memory continuity across sessions.

Provides persistent memory with semantic search for MCP-based AI agents, enabling them to store and recall information across sessions using vector embeddings.

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.

An MCP server that provides real-time weather alerts, forecasts, stock market data, and news information through various API integrations.

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 with local vector search for your codebase. Smart indexing, semantic search, Git history — all offline.

Lets AI clients store, retrieve, update, and forget long-term facts in a local SQLite database with deterministic lexical search, sharing the same memory across multiple clients without relying on model APIs or embedding services.

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.

Local persistent memory for AI agents with recall, semantic search, forgetting, and encrypted storage.

MCP server providing persistent memory for AI applications, enabling Claude to store and recall memories with hybrid search and knowledge graph.

Hosted memory for AI agents that learns from outcomes, with shared rooms. One key across Claude, Cursor & ChatGPT.

A local-first MCP server that exposes personal notes and files as unified semantic context for AI agents via vector search and file monitoring.

Enables AI assistants to maintain a persistent, local memory using SQLite with ranked full-text search, German morphological matching, and two-axis filing, all without external services.

Enables LLM agents to use associative memory with key/value graph-based retrieval, supporting multi-hop traversal and human-like associative leaps beyond embedding similarity.

An MCP server that gives agents persistent memory with namespaced facts, tags, full-text search, and TTL expiry, all running locally on SQLite with zero external dependencies.