Provides semantic memory capabilities using Qdrant vector database with configurable embedding providers, allowing storage and retrieval of information using vector similarity.
Enables storing and retrieving information using semantic search with Qdrant vector database. Acts as a memory layer for LLMs to persistently store and semantically search through information and metadata.
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 AI agent memory using a local vector database for long-term semantic storage and short-term session scratchpads. It enables low-latency memory operations including search, storage, and bulk management without external cloud dependencies.
Provides a shared long-term memory across multiple AI clients, enabling persistent storage and retrieval of facts, preferences, decisions, and snippets with semantic search.
Provides persistent semantic memory for AI agents via MCP, enabling them to remember, recall, list, update, and forget memories with vector-based similarity search.