SKILL.md
---
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<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({