diff --git a/lps/common/database/model/models.py b/lps/common/database/model/models.py
index 80f70ea..2be9127 100644
--- a/lps/common/database/model/models.py
+++ b/lps/common/database/model/models.py
@@ -50,3 +50,19 @@ class job(MAIN_BASE):
             postgresql_where=text("status IN (1, 2) AND dedupe_key IS NOT NULL"),
         ),
     )
+
+
+class search_negative(MAIN_BASE):
+    """네거티브 캐시 — '검색해도 없더라'를 TTL 동안 기억해 재검색 낭비를 막는다.
+    until 이 지나면 자동 무효(재도전 허용 — 나중에 입고될 수 있으므로)."""
+
+    @staticmethod
+    def DBType():
+        return DBType.MAIN.value
+
+    __tablename__ = "search_negative"
+
+    key = Column(String(300), primary_key=True)                                                  # 보통 product_code(없으면 query)
+    until = Column(DateTime(timezone=True), nullable=False)                                       # 이 시각까지 not_found 로 간주
+    reason = Column(String(200), nullable=True)                                                   # 종료 사유 메모(관측)
+    created_at = Column(DateTime(timezone=True), nullable=False, server_default=text("now()"))
diff --git a/lps/crud/negative_cache.py b/lps/crud/negative_cache.py
new file mode 100644
index 0000000..f657045
--- /dev/null
+++ b/lps/crud/negative_cache.py
@@ -0,0 +1,47 @@
+"""네거티브 캐시 CRUD — not_found 결론을 TTL 동안 기억.
+
+같은 상품 재요청이 즉시 재검색(브라우저+API+LLM 비용)하는 것을 막는다.
+TTL 만료 후에는 다시 검색 허용(입고 가능성). key 는 보통 product_code."""
+
+from sqlalchemy import text
+
+from common.database.db_session_manager import DB_SESSION_MNG
+from common.enums import DBType, DBWRType
+
+
+class NegativeCache:
+    DB = DBType.MAIN.value
+
+    async def is_negative(self, key: str) -> bool:
+        """key 가 아직 유효한 not_found 로 캐시돼 있으면 True."""
+        if not key:
+            return False
+        s = await DB_SESSION_MNG.start_session(self.DB, DBWRType.DB_READ.value)
+        try:
+            row = (await s.execute(
+                text("SELECT 1 FROM search_negative WHERE key = :k AND until > now()"),
+                {"k": key},
+            )).first()
+            return row is not None
+        finally:
+            await DB_SESSION_MNG.end_session(self.DB, DBWRType.DB_READ.value)
+
+    async def put(self, key: str, ttl_sec: int = 86400, reason: str = "not_found"):
+        """key 를 ttl_sec 동안 not_found 로 기록(upsert)."""
+        if not key:
+            return
+        sql = text("""
+            INSERT INTO search_negative (key, until, reason)
+            VALUES (:k, now() + make_interval(secs => :ttl), :r)
+            ON CONFLICT (key) DO UPDATE
+                SET until = EXCLUDED.until, reason = EXCLUDED.reason, created_at = now()
+        """)
+        s = await DB_SESSION_MNG.start_session(self.DB, DBWRType.DB_WRITE.value)
+        try:
+            await s.execute(sql, {"k": key, "ttl": ttl_sec, "r": reason[:200]})
+            await s.commit()
+        except Exception:
+            await s.rollback()
+            raise
+        finally:
+            await DB_SESSION_MNG.end_session(self.DB, DBWRType.DB_WRITE.value)
diff --git a/lps/services/ai/keyword.py b/lps/services/ai/keyword.py
new file mode 100644
index 0000000..b1e8e35
--- /dev/null
+++ b/lps/services/ai/keyword.py
@@ -0,0 +1,50 @@
+"""LLM 검색어 생성 — 재정제 라운드용 정밀/광역 쿼리 생성.
+
+원본 검색어가 0매칭일 때 사용한다:
+ - precise: 모델명/규격을 반영한 정밀 검색어("스탠리 텀블러" → "스탠리 퀜처 887ml") → 매칭률↑
+ - broad:   핵심 상품 명사만 남긴 광역 검색어 → 정밀도 0건이면 폭넓게 최종 확인
+레퍼런스 keyword_maker 의 규칙(모델명 우선, 일반 카테고리어 제거)을 structured output 으로 이식.
+"""
+
+import os
+
+from openai import AsyncOpenAI
+from pydantic import BaseModel, Field
+
+from common.logger import LOG
+
+_SYSTEM = (
+    "너는 커머스 검색어 생성기다. 상품 정보를 받아 쇼핑몰 검색창에 넣을 한국어 검색어 2개를 만든다.\n"
+    "- precise: 모델명(있으면 최우선)과 핵심 규격(용량/사이즈/개입 등)을 포함한 정밀 검색어. "
+    "동일 상품을 정확히 찾기 위함. 일반 카테고리 단어만 나열하지 말 것.\n"
+    "- broad: 핵심 상품 명사(브랜드+제품군) 위주의 광역 검색어. precise 가 0건일 때 폭넓게 찾기 위함.\n"
+    "검색어에 따옴표/특수문자/불필요한 수식어를 넣지 말 것."
+)
+
+
+class Keywords(BaseModel):
+    precise: str = Field(description="모델/규격 포함 정밀 검색어")
+    broad: str = Field(description="핵심 명사 위주 광역 검색어")
+
+
+class KeywordGenerator:
+    def __init__(self, model: str = "gpt-4o-mini", api_key: str | None = None):
+        self._model = model
+        self._client = AsyncOpenAI(api_key=api_key or os.environ.get("OPENAI_API_KEY"))
+
+    async def generate(self, target: dict) -> Keywords:
+        user = (
+            f"상품명: {target.get('product_name', '')}\n"
+            f"모델: {target.get('model', '')}\n"
+            f"규격: {target.get('specification', '')}\n"
+            f"제조사/브랜드: {target.get('company', '')}"
+        )
+        resp = await self._client.beta.chat.completions.parse(
+            model=self._model,
+            messages=[{"role": "system", "content": _SYSTEM}, {"role": "user", "content": user}],
+            response_format=Keywords,
+            temperature=0,
+        )
+        kw = resp.choices[0].message.parsed or Keywords(precise="", broad="")
+        LOG.d(f"[ai] 검색어 생성 precise={kw.precise!r} broad={kw.broad!r}")
+        return kw
diff --git a/lps/tests/test_negative_cache.py b/lps/tests/test_negative_cache.py
new file mode 100644
index 0000000..7a9d6e6
--- /dev/null
+++ b/lps/tests/test_negative_cache.py
@@ -0,0 +1,36 @@
+"""네거티브 캐시 CRUD 테스트 (실 lps_db)."""
+
+import pytest_asyncio
+from sqlalchemy import text
+
+from crud.negative_cache import NegativeCache
+
+
+@pytest_asyncio.fixture
+async def nc(db_engine):
+    async with db_engine.begin() as conn:
+        await conn.execute(text("TRUNCATE search_negative"))
+    return NegativeCache()
+
+
+async def test_put_then_is_negative(nc):
+    assert await nc.is_negative("P1") is False
+    await nc.put("P1", ttl_sec=3600)
+    assert await nc.is_negative("P1") is True
+
+
+async def test_expired_is_not_negative(nc):
+    await nc.put("P2", ttl_sec=-1)          # 이미 만료
+    assert await nc.is_negative("P2") is False
+
+
+async def test_upsert_refreshes_ttl(nc):
+    await nc.put("P3", ttl_sec=-1)          # 만료 상태
+    assert await nc.is_negative("P3") is False
+    await nc.put("P3", ttl_sec=3600)        # 갱신 → 유효
+    assert await nc.is_negative("P3") is True
+
+
+async def test_empty_key_is_noop(nc):
+    await nc.put("", ttl_sec=3600)
+    assert await nc.is_negative("") is False
diff --git a/lps/tests/test_search_handler.py b/lps/tests/test_search_handler.py
index 26a85ee..9f3da8d 100644
--- a/lps/tests/test_search_handler.py
+++ b/lps/tests/test_search_handler.py
@@ -1,4 +1,4 @@
-"""다중 소스 검색 핸들러 테스트 — 병합·소스별 실패 격리·전체 실패 시 잡 실패 (fake 어댑터)."""
+"""검색 핸들러 테스트 — 병합·실패격리·AI판정·재정제 루프·not_found·네거티브 캐시 (fake 의존성)."""
 
 import pytest
 
@@ -8,59 +8,23 @@ from worker.handlers import build_search_handler
 
 
 class FakeAdapter:
-    def __init__(self, source, products=None, fail=False):
+    def __init__(self, source, by_query=None, products=None, fail=False):
         self.source = source
+        self._by_query = by_query      # {query: [products]}
         self._products = products or []
         self._fail = fail
+        self.calls = []
 
     async def search(self, query, limit=40):
+        self.calls.append(query)
         if self._fail:
             raise AdapterError("boom", source=self.source, blocked=True)
+        if self._by_query is not None:
+            return self._by_query.get(query, [])
         return self._products
 
 
-def _np(source, price):
-    return NormalizedProduct(source=source, name=f"{source}-{price}", price=price)
-
-
-def _job():
-    return {"job_type": JobType.SEARCH.value, "attempts": 1, "payload": {"product_name": "x"}}
-
-
-async def test_merges_and_ranks_across_sources():
-    adapters = {
-        "coupang": FakeAdapter("coupang", [_np("coupang", 3000), _np("coupang", 1000)]),
-        "naver": FakeAdapter("naver", [_np("naver", 2000), _np("naver", 500)]),
-    }
-    r = await build_search_handler(adapters, top_n=3)(_job())
-    assert r["lowest"]["price"] == 500 and r["lowest"]["source"] == "naver"
-    assert [p["price"] for p in r["top"]] == [500, 1000, 2000]
-    assert r["sources"]["coupang"]["count"] == 2 and r["sources"]["naver"]["count"] == 2
-
-
-async def test_isolates_single_source_failure():
-    adapters = {
-        "coupang": FakeAdapter("coupang", fail=True),
-        "naver": FakeAdapter("naver", [_np("naver", 900)]),
-    }
-    r = await build_search_handler(adapters)(_job())
-    assert "error" in r["sources"]["coupang"]       # 실패 격리
-    assert r["sources"]["naver"]["count"] == 1
-    assert r["lowest"]["price"] == 900              # 성공 소스로 결과 산출
-
-
-async def test_all_sources_fail_raises():
-    adapters = {
-        "coupang": FakeAdapter("coupang", fail=True),
-        "naver": FakeAdapter("naver", fail=True),
-    }
-    with pytest.raises(RuntimeError):
-        await build_search_handler(adapters)(_job())
-
-
 class FakeJudge:
-    """가격 조건으로 매칭을 흉내내는 판정기(실제 OpenAI 호출 없음)."""
-
     def __init__(self, predicate):
         self._pred = predicate
 
@@ -70,10 +34,89 @@ class FakeJudge:
                 for i, c in enumerate(candidates)]
 
 
+class FakeKeywordGen:
+    def __init__(self, precise="", broad=""):
+        self._p, self._b = precise, broad
+
+    async def generate(self, target):
+        from services.ai.keyword import Keywords
+        return Keywords(precise=self._p, broad=self._b)
+
+
+class FakeNegCache:
+    def __init__(self, negative=False):
+        self._neg = negative
+        self.puts = []
+
+    async def is_negative(self, key):
+        return self._neg
+
+    async def put(self, key, ttl_sec=86400, reason="x"):
+        self.puts.append(key)
+
+
+def _np(source, price):
+    return NormalizedProduct(source=source, name=f"{source}-{price}", price=price)
+
+
+def _job(**payload):
+    payload.setdefault("product_name", "x")
+    return {"job_type": JobType.SEARCH.value, "attempts": 1, "payload": payload}
+
+
+# ── 병합 / 실패격리 / AI 판정 (round 0) ─────────────────────────────
+async def test_merges_and_ranks_across_sources():
+    adapters = {
+        "coupang": FakeAdapter("coupang", products=[_np("coupang", 3000), _np("coupang", 1000)]),
+        "naver": FakeAdapter("naver", products=[_np("naver", 2000), _np("naver", 500)]),
+    }
+    r = await build_search_handler(adapters, top_n=3)(_job())
+    assert r["outcome"] == "found" and r["lowest"]["price"] == 500
+    assert [p["price"] for p in r["top"]] == [500, 1000, 2000]
+
+
+async def test_isolates_single_source_failure_but_still_found():
+    adapters = {"coupang": FakeAdapter("coupang", fail=True), "naver": FakeAdapter("naver", products=[_np("naver", 900)])}
+    r = await build_search_handler(adapters)(_job())
+    assert r["outcome"] == "found" and r["lowest"]["price"] == 900
+    assert "error" in r["sources"]["coupang"]
+
+
 async def test_ai_judge_filters_non_matches():
-    adapters = {"naver": FakeAdapter("naver", [_np("naver", 1000), _np("naver", 2000), _np("naver", 3000)])}
-    judge = FakeJudge(lambda c: c.price == 2000)  # 2000 만 '같은 상품'
-    r = await build_search_handler(adapters, judge=judge)(_job())
-    assert [p["price"] for p in r["top"]] == [2000]        # 비매칭 제거됨
-    ai_stage = next(s for s in r["stages"] if s["stage"] == "ai_match")
-    assert ai_stage["in"] == 3 and ai_stage["out"] == 1
+    adapters = {"naver": FakeAdapter("naver", products=[_np("naver", 1000), _np("naver", 2000), _np("naver", 3000)])}
+    r = await build_search_handler(adapters, judge=FakeJudge(lambda c: c.price == 2000))(_job())
+    assert [p["price"] for p in r["top"]] == [2000]
+    assert next(s for s in r["stages"] if s["stage"] == "ai_match")["out"] == 1
+
+
+# ── 재정제 루프 ────────────────────────────────────────────────────
+async def test_refines_to_precise_query_when_original_empty():
+    adapters = {"naver": FakeAdapter("naver", by_query={"스탠리 퀜처 887ml": [_np("naver", 40000)]})}  # 원본은 0건
+    kw = FakeKeywordGen(precise="스탠리 퀜처 887ml", broad="스탠리 텀블러")
+    r = await build_search_handler(adapters, keyword_gen=kw)(_job(product_name="스탠리 텀블러"))
+    assert r["outcome"] == "found" and r["round"] == "precise" and r["rounds_tried"] == 2
+    assert r["lowest"]["price"] == 40000
+
+
+async def test_not_found_after_all_rounds_and_caches():
+    adapters = {"naver": FakeAdapter("naver", by_query={})}  # 어떤 쿼리든 0건
+    kw = FakeKeywordGen(precise="P", broad="B")
+    neg = FakeNegCache()
+    r = await build_search_handler(adapters, keyword_gen=kw, neg_cache=neg)(_job(product_code="PC1", product_name="없는상품"))
+    assert r["outcome"] == "not_found" and r["rounds_tried"] == 3
+    assert r["lowest"] is None and r["top"] == []
+    assert neg.puts == ["PC1"]  # 네거티브 캐시에 기록
+
+
+async def test_negative_cache_short_circuits():
+    adapters = {"naver": FakeAdapter("naver", products=[_np("naver", 100)])}
+    neg = FakeNegCache(negative=True)
+    r = await build_search_handler(adapters, neg_cache=neg)(_job(product_code="PC9"))
+    assert r["outcome"] == "not_found" and r["cached"] is True
+    assert adapters["naver"].calls == []  # 재검색 안 함
+
+
+async def test_technical_failure_with_zero_match_raises():
+    adapters = {"coupang": FakeAdapter("coupang", fail=True), "naver": FakeAdapter("naver", by_query={})}
+    with pytest.raises(RuntimeError):
+        await build_search_handler(adapters)(_job())  # 0매칭 + 차단 → 기술 재시도
diff --git a/lps/worker/handlers.py b/lps/worker/handlers.py
index 4936ed9..bc8ccb3 100644
--- a/lps/worker/handlers.py
+++ b/lps/worker/handlers.py
@@ -1,8 +1,16 @@
 """잡 핸들러 — job_type 별 처리. 현재는 SEARCH(검색)만.
 
-검색 핸들러: 여러 소스 어댑터를 동시 검색 → 병합 → 코어 파이프라인(필터·이상치·top-N 최저가).
-소스별 실패는 격리한다(한 소스가 죽어도 나머지로 결과 산출). 모든 소스 실패 시에만 잡 실패(재시도).
-AI 유사도 판정은 파이프라인 슬롯에 키 준비 시 결합한다.
+검색 핸들러 = 한정된 재정제 루프 + 명시적 outcome:
+  0. 네거티브 캐시 확인(최근 not_found면 즉시 반환)
+  각 라운드(원본 → 정밀(LLM) → 광역, 최대 max_rounds):
+     소스 동시 검색 → 필터 → 이상치 → AI 같은상품 판정
+     ├ 매칭 있음 → DONE(outcome=found), 조기 종료
+     ├ 0매칭 + 기술적 실패(차단/예외) 있음 → raise → 큐가 잡 전체 백오프 재시도(→소진 시 DEAD)
+     └ 0매칭 + 소스 정상 → 다음 라운드
+  라운드 소진 → DONE(outcome=not_found) + 네거티브 캐시 기록
+
+두 재시도 축을 분리한다: 기술적(큐 attempts/백오프) ≠ 검색어(refine 라운드, 유한).
+'못 찾음'은 정상 종료(DONE)지 dead-letter 가 아니다.
 """
 
 import asyncio
@@ -20,52 +28,87 @@ def build_search_handler(
     limit: int = 40,
     top_n: int = 5,
     judge=None,
+    keyword_gen=None,
+    max_rounds: int = 3,
+    neg_cache=None,
 ):
-    """검색 핸들러 생성. adapters = {source: SearchAdapter}. sources 미지정 시 전체 사용.
-    judge(SimilarityJudge) 주입 시 필터 뒤·top-N 앞에 '같은 상품' AI 판정을 끼운다(없으면 생략)."""
+    """검색 핸들러 생성.
+    judge: SimilarityJudge(같은 상품 판정) / keyword_gen: KeywordGenerator(정밀·광역 재검색어) /
+    neg_cache: NegativeCache(TTL not_found 캐시). 모두 선택 — 없으면 해당 단계 생략."""
     use = list(sources) if sources else list(adapters.keys())
 
+    async def _search_round(query: str):
+        """한 라운드: 모든 소스 동시 검색 → (products, per_source, tech_failed)."""
+        results = await asyncio.gather(*[adapters[s].search(query, limit=limit) for s in use], return_exceptions=True)
+        products, per_source, tech_failed = [], {}, False
+        for src, res in zip(use, results):
+            if isinstance(res, Exception):
+                tech_failed = True
+                per_source[src] = {"error": f"{type(res).__name__}: {res}"}
+                LOG.w(f"[{src}] 검색 실패: {type(res).__name__}: {res}")
+            else:
+                products.extend(res)
+                per_source[src] = {"count": len(res)}
+        return products, per_source, tech_failed
+
+    async def _round_queries(base_query: str, target: dict):
+        """라운드 쿼리 지연 생성: 원본 → (0매칭 시에만 LLM 호출로) 정밀 → 광역."""
+        yield ("original", base_query)
+        if keyword_gen is not None:
+            kw = await keyword_gen.generate(target)  # 원본이 실패해 여기까지 온 경우에만 호출됨
+            seen = {base_query}
+            for label, q in (("precise", kw.precise), ("broad", kw.broad)):
+                q = (q or "").strip()
+                if q and q not in seen:
+                    seen.add(q)
+                    yield (label, q)
+
     async def handler(job: dict) -> dict:
         if job["job_type"] != JobType.SEARCH.value:
             raise ValueError(f"unsupported job_type: {job['job_type']}")
 
         payload = job.get("payload") or {}
-        query = (payload.get("product_name") or "").strip()
-        if not query:
+        base_query = (payload.get("product_name") or "").strip()
+        if not base_query:
             raise ValueError("empty product_name")
+        target = {k: payload.get(k, "") for k in ("product_name", "model", "specification", "company")}
         base_price = parse_price(payload.get("price"))
+        cache_key = payload.get("product_code") or base_query
 
-        # 소스 동시 검색 (실패는 예외로 수거해 격리)
-        results = await asyncio.gather(
-            *[adapters[s].search(query, limit=limit) for s in use],
-            return_exceptions=True,
-        )
+        # 0) 네거티브 캐시 — 최근 not_found면 재검색 생략
+        if neg_cache is not None and await neg_cache.is_negative(cache_key):
+            return {"outcome": "not_found", "cached": True, "query": base_query,
+                    "rounds_tried": 0, "lowest": None, "top": [], "stages": [], "sources": {}}
 
-        products = []
-        per_source: dict[str, dict] = {}
-        for src, res in zip(use, results):
-            if isinstance(res, Exception):
-                LOG.w(f"[{src}] 검색 실패: {type(res).__name__}: {res}")
-                per_source[src] = {"error": f"{type(res).__name__}: {res}"}
-            else:
-                products.extend(res)
-                per_source[src] = {"count": len(res)}
+        rounds_done = 0
+        last_stages, last_sources = [], {}
+        async for label, query in _round_queries(base_query, target):
+            if rounds_done >= max_rounds:
+                break
+            rounds_done += 1
 
-        if not products and all("error" in v for v in per_source.values()):
-            raise RuntimeError(f"모든 소스 검색 실패: {per_source}")  # 잡 실패 → 재시도
+            products, per_source, tech_failed = await _search_round(query)
+            candidates, stages = apply_filters(products, base_price=base_price)
+            if judge is not None and candidates:
+                verdicts = await judge.judge(target, candidates)
+                matched = [c for c, v in zip(candidates, verdicts) if v.is_match]
+                stages.append({"stage": "ai_match", "in": len(candidates), "out": len(matched)})
+                candidates = matched
+            last_stages, last_sources = stages, per_source
 
-        # 필터(mall·밴드·이상치) → [AI 유사도 판정] → top-N 최저가
-        candidates, stages = apply_filters(products, base_price=base_price)
-        if judge is not None and candidates:
-            target = {k: payload.get(k, "") for k in ("product_name", "model", "specification", "company")}
-            verdicts = await judge.judge(target, candidates)
-            matched = [c for c, v in zip(candidates, verdicts) if v.is_match]
-            stages.append({"stage": "ai_match", "in": len(candidates), "out": len(matched)})
-            candidates = matched
+            if candidates:  # 찾음 → 조기 종료
+                result = rank_result(candidates, len(products), stages, top_n)
+                result.update(outcome="found", query=query, round=label, rounds_tried=rounds_done, sources=per_source)
+                return result
 
-        result = rank_result(candidates, len(products), stages, top_n)
-        result["query"] = query
-        result["sources"] = per_source  # 소스별 건수/에러 (관측)
+            if tech_failed:  # 0매칭인데 소스가 죽어 있었음 → '없음'이라 단정 불가 → 기술 재시도
+                raise RuntimeError(f"기술적 실패로 0매칭(round={label}) — 잡 재시도: {per_source}")
+
+        # 모든 라운드 클린 0매칭 → 정상 not_found 종료
+        if neg_cache is not None:
+            await neg_cache.put(cache_key, reason=f"not_found after {rounds_done} rounds")
+        result = rank_result([], 0, last_stages, top_n)
+        result.update(outcome="not_found", query=base_query, rounds_tried=rounds_done, sources=last_sources)
         return result
 
     return handler
diff --git a/lps/worker_main.py b/lps/worker_main.py
index 793b700..702fdb8 100644
--- a/lps/worker_main.py
+++ b/lps/worker_main.py
@@ -11,9 +11,11 @@ import os
 from common.logger import LOG
 from config.server_configs import web_server_config
 from crud.job_crud import JobQueue
+from crud.negative_cache import NegativeCache
 from services.search.coupang.adapter import CoupangAdapter
 from services.search.naver.adapter import NaverAdapter
 from services.ai.similarity import SimilarityJudge
+from services.ai.keyword import KeywordGenerator
 from worker.handlers import build_search_handler
 from worker.notify import JobListener
 from worker.runner import Worker, run_reaper
@@ -25,10 +27,12 @@ async def main(concurrency: int = 1):
     queue = JobQueue()
     # 쿠팡(브라우저, 무거움) + 네이버(오픈API, 가벼움) 동시 검색 → 병합 최저가
     adapters = {"coupang": CoupangAdapter(headless=False), "naver": NaverAdapter()}
-    # OPENAI_API_KEY 있으면 '같은 상품' AI 판정 활성화(없으면 기계적 최저가만)
-    judge = SimilarityJudge() if os.environ.get("OPENAI_API_KEY") else None
-    LOG.i(f"AI 유사도 판정: {'ON' if judge else 'OFF(키 없음)'}")
-    handler = build_search_handler(adapters, judge=judge)
+    # OPENAI_API_KEY 있으면 '같은 상품' AI 판정 + 재검색어 생성 활성화
+    has_openai = bool(os.environ.get("OPENAI_API_KEY"))
+    judge = SimilarityJudge() if has_openai else None
+    keyword_gen = KeywordGenerator() if has_openai else None
+    LOG.i(f"AI(판정+검색어생성): {'ON' if has_openai else 'OFF(키 없음)'}")
+    handler = build_search_handler(adapters, judge=judge, keyword_gen=keyword_gen, neg_cache=NegativeCache())
 
     stop = asyncio.Event()
     listeners: list[JobListener] = []