conversation-memory
Persistent memory systems for LLM conversations with tiered storage and intelligent retrieval.
- Implements three memory types: short-term (immediate context), long-term (historical facts), and entity-based (facts about specific entities)
- Provides memory retrieval and consolidation capabilities to surface relevant memories without overwhelming context windows
- Addresses critical concerns including unbounded memory growth, retrieval relevance, and strict user isolation to prevent cross-user data leaks
- Works alongside context management, RAG, and prompt caching skills for cohesive conversation state handling
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
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