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10_Wiki/Topics/Topic_Programming/Backend/S-component (State Store).md

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+---
+id: wiki-2026-0508-s-component-state-store
+title: S-component (State Store)
+category: 10_Wiki/Topics
+status: verified
+canonical_id: self
+aliases: [State Store, Agent State, S-component]
+duplicate_of: none
+source_trust_level: A
+confidence_score: 0.85
+verification_status: applied
+tags: [agent, state, architecture, llm]
+raw_sources: []
+last_reinforced: 2026-05-10
+github_commit: pending
+tech_stack:
+  language: typescript
+  framework: langgraph/anthropic-sdk
+---
+
+# S-component (State Store)
+
+## 매 한 줄
+> **"매 LLM agent 의 working memory + 영속 store"**. 매 LATS / LangGraph / Anthropic Memory Tool 2025 의 backbone 컴포넌트. 매 ephemeral conversation context 의 limit (200K-1M token) 을 넘어 agent 의 task-state, scratchpad, learned facts 를 영속화.
+
+## 매 핵심
+
+### 매 Layer
+- **Working memory**: current turn 의 scratchpad (tool result, observations).
+- **Episodic memory**: 매 session log + summarization.
+- **Semantic memory**: extracted facts + embeddings (vector DB).
+- **Procedural**: skill / playbook (file system).
+
+### 매 vs Context Window
+- Context = read-only attention. State = read-write.
+- Context = volatile. State = persistent across sessions.
+- Context = expensive (caching). State = cheap (KV / vector).
+
+### 매 응용
+1. Multi-session task agent (e.g., research assistant).
+2. CRM-style customer history.
+3. Coding agent 의 codebase index + recent edits.
+4. Game NPC memory.
+
+## 💻 패턴
+
+### LangGraph state schema
+```python
+from typing import TypedDict, Annotated
+from langgraph.graph import StateGraph
+import operator
+
+class AgentState(TypedDict):
+    messages: Annotated[list, operator.add]
+    scratchpad: dict
+    tool_calls: Annotated[list, operator.add]
+    final_answer: str | None
+
+graph = StateGraph(AgentState)
+graph.add_node("plan", planner)
+graph.add_node("act", actor)
+graph.add_edge("plan", "act")
+```
+
+### Anthropic Memory Tool 2025
+```python
+import anthropic
+client = anthropic.Anthropic()
+
+resp = client.messages.create(
+    model="claude-opus-4-7",
+    max_tokens=4096,
+    tools=[{"type": "memory_20250604", "name": "memory"}],
+    messages=[{"role": "user", "content": "Remember: I prefer Python type hints."}],
+    extra_headers={"anthropic-beta": "context-management-2025-06-04"},
+)
+# Memory persisted across calls; agent reads/writes via tool
+```
+
+### Redis-backed working state
+```ts
+import { createClient } from 'redis';
+const redis = createClient({ url: process.env.REDIS_URL });
+
+class AgentStateStore {
+  async get(sessionId: string): Promise<AgentState> {
+    const data = await redis.get(`agent:${sessionId}`);
+    return data ? JSON.parse(data) : { scratchpad: {}, messages: [] };
+  }
+  async set(sessionId: string, state: AgentState) {
+    await redis.set(`agent:${sessionId}`, JSON.stringify(state), { EX: 3600 });
+  }
+  async append(sessionId: string, msg: Message) {
+    await redis.rPush(`agent:${sessionId}:msgs`, JSON.stringify(msg));
+  }
+}
+```
+
+### Vector store for semantic
+```ts
+import { Pinecone } from '@pinecone-database/pinecone';
+const pc = new Pinecone();
+const idx = pc.index('agent-memory');
+
+async function remember(fact: string, embed: (s: string) => Promise<number[]>) {
+  const vec = await embed(fact);
+  await idx.upsert([{ id: crypto.randomUUID(), values: vec, metadata: { fact, ts: Date.now() }}]);
+}
+async function recall(query: string, embed: (s: string) => Promise<number[]>) {
+  const vec = await embed(query);
+  const r = await idx.query({ vector: vec, topK: 5, includeMetadata: true });
+  return r.matches.map(m => m.metadata?.fact);
+}
+```
+
+### Summarization compaction
+```ts
+async function compactState(s: AgentState): Promise<AgentState> {
+  if (s.messages.length < 50) return s;
+  const summary = await llm.summarize(s.messages.slice(0, 30));
+  return { ...s, messages: [{role: 'system', content: summary}, ...s.messages.slice(30)] };
+}
+```
+
+### S-component in 6-component pattern (S/T/L/I/M/E)
+```ts
+// S = State, T = Tools, L = LLM, I = Input, M = Memory, E = Effect
+type Agent = {
+  S: StateStore;
+  T: ToolRegistry;
+  L: LLMClient;
+  I: InputParser;
+  M: MemoryRetriever;
+  E: SideEffectExecutor;
+};
+```
+
+## 매 결정 기준
+| 상황 | Approach |
+|---|---|
+| Single-turn, stateless | No S-component |
+| Multi-turn, single session | In-memory map |
+| Cross-session, single user | Redis / SQLite |
+| Production multi-user | DB + vector store + memory tool |
+| Coding agent | File system + git as state |
+
+**기본값**: Redis + Postgres + Anthropic Memory Tool — 2026 의 production pattern.
+
+## 🔗 Graph
+- 부모: [[Agent Architecture]]
+- 변형: [[Working Memory]]
+- 응용: [[LangGraph]]
+- Adjacent: [[T-component (Tool Registry)]]
+
+## 🤖 LLM 활용
+**언제**: state schema design, summarization prompt, recall query.
+**언제 X**: high-throughput stateless API (overhead ↑).
+
+## ❌ 안티패턴
+- **Unbounded growth**: state 의 GC 부재 → cost explosion.
+- **Stale state**: TTL 없는 cache 의 contradiction.
+- **Tight coupling**: S 와 L 의 직접 dep → testing 어려움.
+- **No versioning**: schema migration 깨짐.
+
+## 🧪 검증 / 중복
+- Verified (LangGraph docs, Anthropic Memory Tool 2025 release notes).
+- 신뢰도 A.
+
+## 🕓 Changelog
+| 날짜 | 변경 |
+|---|---|
+| 2026-05-08 | Phase 1 |
+| 2026-05-10 | Manual cleanup — S-component full coverage |