--- name: conversation-memory description: Persistent memory systems for LLM conversations including short-term, long-term, and entity-based memory risk: unknown source: vibeship-spawner-skills (Apache 2.0) date_added: 2026-02-27 --- # 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 { // Always add to buffer this.buffer.add(message); // Extract entities const entities = await extractEntities(message); for (const entity of entities) { await this.entity.upsert(entity); } // Check for memorable content if (await isMemoryWorthy(message)) { await this.shortTerm.add({ content: message.content, timestamp: Date.now(), importance: await scoreImportance(message) }); } } async consolidate(): Promise { // Move important short-term to long-term const memories = await this.shortTerm.getOld(24 * 60 * 60 * 1000); for (const memory of memories) { if (memory.importance > 0.7 || memory.referenced > 2) { await this.longTerm.add(memory); } await this.shortTerm.remove(memory.id); } } async buildContext(query: string): Promise { const parts: string[] = []; // Relevant long-term memories const longTermRelevant = await this.longTerm.search(query, 3); if (longTermRelevant.length) { parts.push('## Relevant Memories\n' + longTermRelevant.map(m => `- ${m.content}`).join('\n')); } // Relevant entities const entities = await this.entity.getRelevant(query); if (entities.length) { parts.push('## Known Entities\n' + entities.map(e => `- ${e.name}: ${e.facts.join(', ')}`).join('\n')); } // Recent conversation const recent = this.buffer.getRecent(10); parts.push('## Recent Conversation\n' + formatMessages(recent)); return parts.join('\n\n'); } } ### Entity Memory Store and update facts about entities **When to use**: Need to remember details about people, places, things interface Entity { id: string; name: string; type: 'person' | 'place' | 'thing' | 'concept'; facts: Fact[]; lastMentioned: number; mentionCount: number; } interface Fact { content: string; confidence: number; source: string; // Which message this came from timestamp: number; } class EntityMemory { async extractAndStore(message: Message): Promise { // Use LLM to extract entities and facts const extraction = await llm.complete(` Extract entities and facts from this message. Return JSON: { "entities": [ { "name": "...", "type": "...", "facts": ["..."] } ]} Message: "${message.content}" `); const { entities } = JSON.parse(extraction); for (const entity of entities) { await this.upsert(entity, message.id); } } async upsert(entity: ExtractedEntity, sourceId: string): Promise { const existing = await this.store.get(entity.name.toLowerCase()); if (existing) { // Merge facts, avoiding duplicates for (const fact of entity.facts) { if (!this.hasSimilarFact(existing.facts, fact)) { existing.facts.push({ content: fact, confidence: 0.9, source: sourceId, timestamp: Date.now() }); } } existing.lastMentioned = Date.now(); existing.mentionCount++; await this.store.set(existing.id, existing); } else { // Create new entity await this.store.set(entity.name.toLowerCase(), { id: generateId(), name: entity.name, type: entity.type, facts: entity.facts.map(f => ({ content: f, confidence: 0.9, source: sourceId, timestamp: Date.now() })), lastMentioned: Date.now(), mentionCount: 1 }); } } } ### Memory-Aware Prompting Include relevant memories in prompts **When to use**: Making LLM calls with memory context async function promptWithMemory( query: string, memory: MemorySystem, systemPrompt: string ): Promise { // Retrieve relevant memories const relevantMemories = await memory.longTerm.search(query, 5); const entities = await memory.entity.getRelevant(query); const recentContext = memory.buffer.getRecent(5); // Build memory-augmented prompt const prompt = ` ${systemPrompt} ## User Context ${entities.length ? `Known about user:\n${entities.map(e => `- ${e.name}: ${e.facts.map(f => f.content).join('; ')}` ).join('\n')}` : ''} ${relevantMemories.length ? `Relevant past interactions:\n${relevantMemories.map(m => `- [${formatDate(m.timestamp)}] ${m.content}` ).join('\n')}` : ''} ## Recent Conversation ${formatMessages(recentContext)} ## Current Query ${query} `.trim(); const response = await llm.complete(prompt); // Extract any new memories from response await memory.addMessage({ role: 'assistant', content: response }); return response; } ## 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: // Implement memory lifecycle management class ManagedMemory { // Limits private readonly SHORT_TERM_MAX = 100; private readonly LONG_TERM_MAX = 10000; private readonly CONSOLIDATION_INTERVAL = 24 * 60 * 60 * 1000; async add(memory: Memory): Promise { // Score importance before storing const score = await this.scoreImportance(memory); if (score < 0.3) return; // Don't store low-importance memory.importance = score; await this.shortTerm.add(memory); // Check limits await this.enforceShortTermLimit(); } async enforceShortTermLimit(): Promise { const count = await this.shortTerm.count(); if (count > this.SHORT_TERM_MAX) { // Consolidate: move important to long-term, delete rest const memories = await this.shortTerm.getAll(); memories.sort((a, b) => b.importance - a.importance); const toKeep = memories.slice(0, this.SHORT_TERM_MAX * 0.7); const toConsolidate = memories.slice(this.SHORT_TERM_MAX * 0.7); for (const m of toConsolidate) { if (m.importance > 0.7) { await this.longTerm.add(m); } await this.shortTerm.remove(m.id); } } } async scoreImportance(memory: Memory): Promise { const factors = { hasUserPreference: /prefer|like|don't like|hate|love/i.test(memory.content) ? 0.3 : 0, hasDecision: /decided|chose|will do|won't do/i.test(memory.content) ? 0.3 : 0, hasFactAboutUser: /my|I am|I have|I work/i.test(memory.content) ? 0.2 : 0, length: memory.content.length > 100 ? 0.1 : 0, userMessage: memory.role === 'user' ? 0.1 : 0, }; return Object.values(factors).reduce((a, b) => a + b, 0); } } ### 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: // Intelligent memory retrieval async function retrieveRelevant( query: string, memories: MemoryStore, maxResults: number = 5 ): Promise { // 1. Semantic search const candidates = await memories.semanticSearch(query, maxResults * 3); // 2. Score relevance with context const scored = await Promise.all(candidates.map(async (m) => { const relevanceScore = await llm.complete(` Rate 0-1 how relevant this memory is to the query. Query: "${query}" Memory: "${m.content}" Return just the number. `); return { ...m, relevance: parseFloat(relevanceScore) }; })); // 3. Filter low relevance const relevant = scored.filter(m => m.relevance > 0.5); // 4. Sort and limit return relevant .sort((a, b) => b.relevance - a.relevance) .slice(0, maxResults); } ### Memories from one user accessible to another Severity: CRITICAL Situation: User sees information from another user's sessions Symptoms: - User sees other user's information - Privacy complaints - Compliance violations Why this breaks: No user isolation in memory store. Shared memory namespace. Cross-user retrieval. Recommended fix: // Strict user isolation in memory class IsolatedMemory { private getKey(userId: string, memoryId: string): string { // Namespace all keys by user return `user:${userId}:memory:${memoryId}`; } async add(userId: string, memory: Memory): Promise { // Validate userId is authenticated if (!isValidUserId(userId)) { throw new Error('Invalid user ID'); } const key = this.getKey(userId, memory.id); memory.userId = userId; // Tag with user await this.store.set(key, memory); } async search(userId: string, query: string): Promise { // CRITICAL: Filter by user in query return await this.store.search({ query, filter: { userId: userId }, // Mandatory filter limit: 10 }); } async delete(userId: string, memoryId: string): Promise { const memory = await this.get(userId, memoryId); // Verify ownership before delete if (memory.userId !== userId) { throw new Error('Access denied'); } await this.store.delete(this.getKey(userId, memoryId)); } // User data export (GDPR compliance) async exportUserData(userId: string): Promise { return await this.store.getAll({ userId }); } // User data deletion (GDPR compliance) async deleteUserData(userId: string): Promise { const memories = await this.exportUserData(userId); for (const m of memories) { await this.store.delete(this.getKey(userId, m.id)); } } } ## 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: ``` 1. Design memory tiers 2. Implement storage and retrieval 3. Integrate with context management 4. Add consolidation and cleanup ``` ## 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

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Conversation Memory

Install