conversation-memory

# Conversation Memory Persistent memory systems for LLM conversations including short-term, long-term, and entity-based memory ## Capabilities - short-term-memory - long-term-memory - entity-memory - memory-persistence - memory-retrieval - memory-consolidation ## Prerequisites - Knowledge: LLM conversation patterns, Database basics, Key-value stores - Skills_recommended: context-window-management, rag-implementation ## Scope - Does_not_cover: Knowledge graph construction, Semantic search implementation, Database administration - Boundaries: Focus is memory patterns for LLMs, Covers storage and retrieval strategies ## Ecosystem ### Primary_tools - Mem0 - Memory layer for AI applications - LangChain Memory - Memory utilities in LangChain - Redis - In-memory data store for session memory ## Patterns ### Tiered Memory System Different memory tiers for different purposes **When to use**: Building any conversational AI interface MemorySystem { // Buffer: Current conversation (in context) buffer: ConversationBuffer; // Short-term: Recent interactions (session) shortTerm: ShortTermMemory; // Long-term: Persistent across sessions longTerm: LongTermMemory; // Entity: Facts about people, places, things entity: EntityMemory; } class TieredMemory implements MemorySystem { async addMessage(message: Message): Promise<void> { // Always add to buffer this.buffer.add(message); // Extract entities const enti...