conversation-memory

Persistent memory systems for LLM conversations with tiered storage and intelligent retrieval. Persistent memory systems for LLM conversations including short-term, long-term, and entity-based memory Different memory tiers for different purposes When to use : Building any conversational AI interface MemorySystem { // Buffer: Current conversation (in context) buffer: ConversationBuffer; } class TieredMemory implements MemorySystem { async addMessage(message: Message): Promise { // Always add to buffer this.buffer.add(message); } 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": ["..."] } ]} } Include relevant memories in prompts When to use : Making LLM calls with memory context async function promptWithMemory( query: string, memory: MemorySystem, systemPrompt: string ): Promise { //...