Provides AI assistants with long-term semantic memory capabilities through local vector-based storage. Enables storing, recalling, and managing information across sessions with complete privacy using ChromaDB, with no data ever leaving your machine.
Enables conversation memory for LLMs by storing chat history and retrieving relevant memories via embedding-based semantic search, supporting tools like store_turn and flashback_memory.
Enables AI assistants to store and retrieve memories with semantic search capabilities using vector embeddings. Provides persistent memory storage with SQLite backend for context retention across conversations.
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 fully local long-term memory for AI agents by enabling semantic search over notes and session logs using Ollama embeddings, with no external APIs or databases.
