MCP servers for long-term memory and vector search: store notes, embeddings and facts so an AI assistant remembers across sessions. 799 servers in this category.
Paste its GitHub repo and we list it after a quick review.
Stores and retrieves semantic memories in a Qdrant vector database for AI clients to recall context.
Provides AI agents with persistent memory, semantic search, causal debugging, and tamper-evident audit logs.
$claude mcp add zizkadb-mcp -- uvx zizkadb-mcp
Lets AI agents log conversation exchanges and recall persistent memory context through MCP tools.
$claude mcp add --transport http synap https://synap-mcp.maximem.ai/mcp
Lets AI clients store, manage, and search data in Redis using natural language, including hashes, lists, sets, streams, JSON, and vector search.
Allows AI agents to store and retrieve verbatim Markdown and text notes locally to preserve original context across sessions.
$claude mcp add fidelis -- uvx fidelis-memory
Provides a token-light local memory layer for AI agents using SQLite with hybrid BM25 and dense retrieval.
$claude mcp add tinycontext -- docker run -i --rm docker.io/marcellm01/tinycontext:v0.5.4
Lets AI coding assistants run semantic code search, symbol graph navigation, and persistent memory queries over an indexed codebase.
Screens an AI agent's memory writes and reads through detectors and a declarative policy to block poisoning attacks.
Lets AI clients query Memgraph graphs and store agent memory, knowledge graphs, and document extractions.
Lets an AI client query Neo4j graph databases with Cypher, store knowledge graph memory, and manage Aura cloud instances.
