Provides persistent memory with semantic search for MCP-based AI agents, enabling them to store and recall information across sessions using vector embeddings.
Provides persistent semantic memory for AI agents via MCP, enabling them to remember, recall, list, update, and forget memories with vector-based similarity search.
MCP server that provides AI assistants persistent context memory across sessions, using Supabase PostgreSQL and pgvector to save, search, list, and delete memories via natural language.
Provides persistent memory for AI coding agents via MCP, enabling agents to store and semantically recall facts, events, and lessons across sessions, all running locally without cloud dependencies.
A self-organizing, persistent semantic memory layer that enables AI agents to store, categorize, and retrieve information using hybrid vector and keyword search. It features autonomous chunking, deduplication, and hierarchical taxonomy management through a PostgreSQL-backed MCP server.