[G1-Sync] Manual knowledge update
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Antigravity Agent
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---
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id: ai-memory-persistence-deep
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title: AI Memory Persistence — long-term / vector / graph
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category: Coding
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status: draft
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source_trust_level: B
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verification_status: conceptual
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created_at: 2026-05-09
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updated_at: 2026-05-09
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tags: [ai, memory, agents, vibe-coding]
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tech_stack: { language: "TS / Python", applicable_to: ["AI"] }
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applied_in: []
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aliases: [agent memory, long-term memory, episodic, semantic, mem0, Letta, memgpt]
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---
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# AI Memory Persistence
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> Agent 가 session 끝 = 잃음 = bad UX. **Short-term (context) + long-term (vector / graph DB) + episodic (timeline)**. mem0 / Letta / MemGPT.
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## 📖 핵심 개념
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- Short-term: context window (LLM input).
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- Long-term: persistent (DB).
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- Episodic: timeline (when X happened).
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- Semantic: 일반 fact ("user is vegan").
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## 💻 코드 패턴
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### Naive (no memory)
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```ts
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async function chat(message: string) {
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return llm.complete({ prompt: message });
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}
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// 매 호출 = 새 — 이전 conversation 잃음.
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```
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### Conversation history (단순)
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```ts
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const messages: Message[] = [];
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async function chat(input: string) {
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messages.push({ role: 'user', content: input });
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const r = await llm.complete({ messages });
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messages.push({ role: 'assistant', content: r.text });
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return r.text;
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}
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```
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→ Context window 한계. 100 message+ = 잃음.
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### Sliding window
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```ts
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function recent(messages: Message[], n: number = 20) {
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return messages.slice(-n);
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}
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await llm.complete({ messages: recent(allMessages) });
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```
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→ Old 잃음. Simple.
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### Summary cascade
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```ts
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async function getContext() {
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const all = await db.messages.find({});
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if (all.length < 20) return all;
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const old = all.slice(0, -10);
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const recent = all.slice(-10);
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const summary = await llm.complete({
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system: 'Summarize this conversation in 200 tokens.',
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messages: old,
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});
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return [
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{ role: 'system', content: `Previous: ${summary}` },
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...recent,
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];
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}
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```
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→ Old → summary. Recent intact.
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### Vector memory
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```ts
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// 매 message → embed → store
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async function remember(message: Message) {
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const emb = await embed(message.content);
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await db.memories.insert({
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content: message.content,
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embedding: emb,
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timestamp: Date.now(),
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type: 'message',
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});
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}
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// Search
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async function recall(query: string) {
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const emb = await embed(query);
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return db.memories.find({
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$vectorSearch: { embedding: emb, k: 5 },
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});
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}
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```
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→ Topic 별 retrieval. Long history OK.
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### mem0 (managed)
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```python
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from mem0 import Memory
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m = Memory()
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# Add
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m.add("My favorite color is blue", user_id="alice")
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m.add("I prefer dark mode", user_id="alice")
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# Search
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results = m.search(query="What does Alice like?", user_id="alice")
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# → ['favorite color is blue', 'prefer dark mode']
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```
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→ 자동 extract + dedupe.
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### Letta (formerly MemGPT)
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```python
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from letta import create_client
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client = create_client()
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agent = client.create_agent(
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name='Alice',
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memory={
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'human': 'User name: Bob.',
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'persona': 'I am a helpful assistant.',
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},
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)
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response = client.user_message(agent_id=agent.id, message='Hi')
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```
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→ "Memory editing" — agent 가 자기 memory 도 편집.
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### Memory hierarchy (Letta 의 idea)
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```
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Core memory: 항상 context (작은).
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Recall memory: search 가능 (RAG).
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Archival: 영구 cold storage.
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→ Hot / cold tier.
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```
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### Episodic memory (timeline)
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```ts
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interface Episode {
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id: string;
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timestamp: Date;
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summary: string;
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actors: string[];
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tags: string[];
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embedding: number[];
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}
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await db.episodes.insert({
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timestamp: new Date(),
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summary: 'User asked about pricing',
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actors: ['user', 'agent'],
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tags: ['pricing', 'sales'],
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});
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// "지난 주 price 토의 가 있었나?"
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const r = await db.episodes.find({
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timestamp: { $gte: new Date(Date.now() - 7 * 86400000) },
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tags: 'pricing',
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});
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```
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### Semantic (fact)
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```ts
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interface Fact {
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subject: string;
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predicate: string;
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object: string;
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}
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const facts: Fact[] = [
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{ subject: 'Alice', predicate: 'likes', object: 'blue' },
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{ subject: 'Alice', predicate: 'works_at', object: 'Acme Corp' },
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];
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// Simple lookup
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const aliceFacts = facts.filter(f => f.subject === 'Alice');
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```
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→ Knowledge graph 식.
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### Knowledge graph + LLM
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```ts
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// Neo4j / Memgraph
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await graph.run(`
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MERGE (a:Person {name: 'Alice'})
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MERGE (b:Color {name: 'blue'})
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MERGE (a)-[:LIKES]->(b)
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`);
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await graph.run(`
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MATCH (a:Person {name: 'Alice'})-[:LIKES]->(c)
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RETURN c.name
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`);
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// ['blue']
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```
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### Auto-extraction (LLM)
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```ts
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async function extractFacts(message: string) {
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const r = await llm.complete({
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system: 'Extract facts as {subject, predicate, object}. Only solid facts.',
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prompt: message,
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});
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return JSON.parse(r.text);
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}
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// "I'm Alice and I love coffee"
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// → [{subject: 'user', predicate: 'is', object: 'Alice'}, {predicate: 'loves', object: 'coffee'}]
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```
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### Forgetting (TTL / decay)
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```ts
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// 30 days 이전 = remove
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await db.memories.deleteMany({ timestamp: { $lt: Date.now() - 30 * 86400000 } });
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// 또는 importance score
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interface Memory {
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importance: number; // 1-10
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lastAccessed: Date;
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}
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// Recently accessed = 유지. 아니면 decay.
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```
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### Memory consolidation (잠 식)
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```ts
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// 주기 — 매일 / 매 N message
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async function consolidate(userId: string) {
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const recent = await db.memories.find({ userId, since: yesterday });
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// Cluster
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const clusters = cluster(recent);
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// Each cluster → summary
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for (const c of clusters) {
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const summary = await llm.complete({
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prompt: `Summarize these related memories: ${JSON.stringify(c)}`,
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});
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await db.consolidated.insert({ userId, summary, count: c.length });
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await db.memories.deleteMany({ id: { $in: c.map(x => x.id) } });
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}
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}
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```
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→ 인간 의 sleep 식 — episode → semantic.
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### Working memory (current task)
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```ts
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class WorkingMemory {
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private items: any[] = [];
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add(item: any) {
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this.items.push(item);
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if (this.items.length > 7) this.items.shift();
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}
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context(): string {
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return this.items.map(i => JSON.stringify(i)).join('\n');
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}
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}
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```
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→ 7 ± 2 인간 식.
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### Thread / session memory
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```ts
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// 매 thread 가 separate
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const threadMemory = new Map<string, Message[]>();
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async function chat(threadId: string, input: string) {
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const messages = threadMemory.get(threadId) ?? [];
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messages.push({ role: 'user', content: input });
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const r = await llm.complete({ messages });
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messages.push({ role: 'assistant', content: r.text });
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threadMemory.set(threadId, messages);
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return r.text;
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}
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```
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### Multi-user memory
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```ts
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// User 별 memory.
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await db.memories.insert({
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userId: 'alice',
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content: '...',
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embedding: ...,
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});
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// Search 시 filter
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const r = await db.memories.find({ userId: 'alice', $vectorSearch: ... });
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```
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→ Privacy: user A 의 memory 가 user B 에 leak X.
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### Privacy / GDPR
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```ts
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// 사용자 가 forget
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async function forgetUser(userId: string) {
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await db.memories.deleteMany({ userId });
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await db.episodes.deleteMany({ userId });
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}
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```
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### Observability
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```ts
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@trace
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async function recall(query: string) {
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const r = await db.memories.search(...);
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log({ query, retrieved: r.map(x => x.id), count: r.length });
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return r;
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}
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```
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→ "Why agent forget?" debug.
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### Cost
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```
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Vector embed: $0.02 / 1M token.
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Storage: $ / GB month.
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Search: $ / 1k query.
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작은 system: Postgres pgvector (cheap).
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큰: Pinecone / Qdrant.
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→ User 별 N memory × M user = 빠르게 큰.
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```
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### 함정
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```
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- Memory 너무 많음: noise / cost.
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- Forgetting 없음: stale 정보.
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- Auto-extract 의 hallucination.
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- Memory 누설 (cross-user).
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- Embed cost 무시.
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- Long-term 가 context 불일치 (옛 fact).
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```
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### Real-world
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- **ChatGPT memory**: 사용자 별 explicit memory.
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- **Character.AI**: persistent agent.
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- **Letta**: full memory framework.
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- **mem0**: memory-as-a-service.
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- **Pi (Inflection)**: long memory companion.
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## 🤔 의사결정 기준
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| 상황 | 추천 |
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|---|---|
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| 단순 chat | Sliding window |
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| 긴 history | Summary cascade |
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| Topic recall | Vector memory |
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| Personal facts | Semantic / KG |
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| Timeline query | Episodic |
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| Production ready | mem0 / Letta |
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| Multi-user | User-scoped + privacy |
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## ❌ 안티패턴
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- **모든 history sent**: cost / context.
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- **No forget**: stale 정보.
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- **Cross-user leak**: privacy.
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- **Auto-extract 신뢰 100%**: hallucinate.
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- **No deduplication**: same fact 여러 번.
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- **No tracing**: debug 불가.
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- **Vector 만**: 정확 fact 안 됨.
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## 🤖 LLM 활용 힌트
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- Memory = short + long + episodic + semantic.
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- mem0 / Letta 가 production-ready.
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- Forgetting (TTL / consolidation) 가 인간 식.
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- Privacy (user-scoped) 필수.
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## 🔗 관련 문서
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- [[AI_Memory_Systems]]
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- [[AI_RAG_Advanced]]
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- [[AI_Token_Budget_Patterns]]
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