diff --git a/backend/src/agent/_llm_health.py b/backend/src/agent/_llm_health.py
index 4b285c3a..fd5dc94a 100644
--- a/backend/src/agent/_llm_health.py
+++ b/backend/src/agent/_llm_health.py
@@ -17,11 +17,7 @@ import time
 from typing import Any
 
 import httpx
-from langchain_core.messages import HumanMessage
-from langchain_openai import ChatOpenAI
-from openai import APIConnectionError, APITimeoutError, AuthenticationError, RateLimitError
 
-from src.agent._llm_params import chat_openai_kwargs
 from src.core.logger import logger
 
 # ── LLM provider health cache ─────────────────────────────────────────
@@ -41,12 +37,35 @@ _LLM_LAST_ERROR_TS_KEY = "last_llm_error_ts"
 # @POST Returns status string: 'ok' | 'unavailable' | 'timeout' | 'auth_error'.
 # @SIDE_EFFECT Makes a probe request to the LLM provider; caches result in module memory.
 # @RATIONALE Prevents sending every user request into a dead LLM backend.
+# @REJECTED Module-level imports of langchain_core/openai were rejected — these
+#   packages are only installed in the agent container, not the backend container.
+#   The /api/agent/llm-status endpoint runs in the backend container and must not
+#   fail with ModuleNotFoundError. All langchain/openai imports are lazy (inside
+#   the function body) with ImportError handled gracefully.
 async def _check_llm_provider_health() -> str:
     """Check LLM provider connectivity. Cached for _LLM_CHECK_CACHE_TTL seconds."""
     now = time.time()
     if now - _llm_status["last_check_ts"] < _LLM_CHECK_CACHE_TTL:
         return _llm_status["status"]
 
+    # Lazy imports: langchain_core, langchain_openai, and openai are only
+    # installed in the agent container, not the backend container.
+    try:
+        from langchain_core.messages import HumanMessage
+        from langchain_openai import ChatOpenAI
+        from openai import (
+            APIConnectionError,
+            APITimeoutError,
+            AuthenticationError,
+            RateLimitError,
+        )
+        from src.agent._llm_params import chat_openai_kwargs
+    except ImportError as exc:
+        _llm_status["status"] = "unavailable"
+        _llm_status["last_error"] = f"LLM dependencies not installed: {exc}"
+        _llm_status["last_check_ts"] = time.time()
+        return "unavailable"
+
     try:
         from src.agent.langgraph_setup import _fetch_llm_config as _get_config