Implements persistent tiered memory systems for LLMs to manage short-term, long-term, and entity-specific context.
The Conversation Memory skill provides a comprehensive framework for managing the lifecycle of AI-human interactions beyond simple chat history. It enables developers to implement sophisticated tiered memory architectures—spanning immediate buffers, session-based short-term data, and permanent long-term storage—alongside entity tracking. By offering patterns for memory consolidation, semantic relevance scoring, and secure user isolation, this skill ensures that LLM-driven applications maintain coherent, personalized, and contextually relevant conversations without exceeding context window limits or compromising privacy.
主要功能
01Tiered memory architecture (Buffer, Short-term, Long-term, and Entity)
02Strict user isolation patterns for multi-tenant data security
0339 GitHub stars
04Automated entity-based fact extraction and persistence
05Semantic relevance scoring for intelligent context retrieval
06Memory lifecycle management with importance-based consolidation
使用场景
01Building highly personalized AI assistants that remember user preferences and facts across sessions.
02Developing complex customer support agents that track entity-specific histories like orders or ticket statuses.
03Optimizing token usage in long-running conversations by retrieving only the most relevant historical snippets.
