Conversation memory

Skill ranbot-ai/awesome-skills/skills/conversation-memory

Awesome Claude Skills, Tools for Customizing Claude AI workflows

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npx -y skills add ranbot-ai/awesome-skills --skill conversation-memory

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Persistent memory systems for LLM conversations including short-term, long-term, and entity-based memory

SKILL.md

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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 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<void> {
    // 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<string> {
    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<void> { // 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<void> {
    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