Memory

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Enhance AI assistant capabilities with a robust server that prevents 'memory loss' between conversations. This tool offers cross-session memory for user preferences and historical dialogues, semantic retrieval that understands context beyond keywords, and a dynamic knowledge graph for complex reasoning. By integrating with AI assistants via the Model Context Protocol, it ensures AI can remember, learn, and reason more effectively over time.

Características Principales

• Persistent semantic memory system with vector embeddings and HNSW indexing for high-performance retrieval.
• Dynamic memory decay algorithm to manage information relevance based on confidence, time, and access.
• Comprehensive knowledge graph engine for creating entities, tracking relationships, and supporting transitive inference.
• Integrated LRU cache for embedding vectors, significantly reducing external API calls.
• Hybrid search combining vector similarity and keyword matching for improved recall.
• 1 GitHub stars

Casos de Uso

• Enable AI assistants to remember user preferences and past interactions across multiple sessions.
• Allow AI to perform context-aware searches, understanding the 'meaning' of queries rather than just keywords.
• Build and leverage a structured knowledge base for AI assistants to perform complex reasoning and information synthesis.