Persistent memory systems for LLM conversations including short-term, long-term, and entity-based 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 Entity Memory Store and update facts about entities When to use : Need to remember details about people, places, things Memory Aware Prompting Include relevant memories in prompts When to use : Making LLM calls with memory context Sharp Edges Memory store grows unbounded, system slows Severity: HIGH Situation: System slows over time, costs increase Symptoms: Slow memory retrieval High storage costs Increasing latency over time Why this breaks: Every message stored as memory. No cleanup or consolidation. Retrieval over millions of items. Recommended fix: Retrieved memories not relevant to current query Severity: HIGH Situation: Memories included in context but don't help Symptoms: Memories in context seem random User asks about things already in memory Confusion from irrelevant context Why this breaks: Simple keyword matching. No relevance scoring. Including all retrieved memories. Recommended fix: Memories from one user accessible to another Severity: CRITICAL Situation: User sees information from another user's sessions Symptoms: User sees other user's information Privacy complaints Compliance violations Why this breaks: No user isolation in memory store. Shared memory namespace. Cross user retrieval. Recommended fix: Validation Checks No User Isolation in Memory Severity: CRITICAL Message: Memory operations without user isolation. Privacy vulnerability. Fix action: Add userId to all memory operations, filter by user on retrieval No Importance Filtering Severity: WARNING Message: Storing memories without importance filtering. May cause memory explosion. Fix action: Score importance before storing, filter low importance content Memory Storage Without Retrieval Severity: WARNING Message: Storing memories but no retrieval logic. Memories won't be used. Fix action: Implement memory retrieval and include in prompts No Memory Cleanup Severity: INFO Message: No memory cleanup mechanism. Storage will grow unbounded. Fix action: Implement consolidation and cleanup based on age/importance Collaboration Delegation Triggers context window token context window management (Need context optimization) rag retrieval vector rag implementation (Need retrieval system) cache caching prompt caching (Need caching strategies) Complete Memory System Skills: conversation memory, context window management, rag implementation Workflow: Related Skills Works well with: context window management , rag implementation , prompt caching , llm npc dialogue When to Use User mentions or implies: conversation memory User mentions or implies: remember User mentions or implies: memory persistence User mentions or implies: long term memory User mentions or implies: chat history Limitations Use this skill only when the task clearly matches the scope described above. Do not treat the output as a substitute for environment specific validation, testing, or expert review. Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

conversation-memory