diff --git a/packages/gen/gen_ai_hub/proxy/langchain/init_models.py b/packages/gen/gen_ai_hub/proxy/langchain/init_models.py
index 0f3794a8..a5be61dc 100644
--- a/packages/gen/gen_ai_hub/proxy/langchain/init_models.py
+++ b/packages/gen/gen_ai_hub/proxy/langchain/init_models.py
@@ -93,6 +93,7 @@ def _init_model(proxy_client: Optional[BaseProxyClient],
                 kwargs: Dict[str, Any],
                 init_func: Optional[Callable] = None,
                 model_kwargs: Optional[Dict[str, Any]] = None):
+    proxy_client = proxy_client or get_proxy_client()
     model_kwargs = model_kwargs or {}
     if init_func:
         return _init_custom_model(proxy_client=proxy_client, init_func=init_func, args=args, kwargs=kwargs,
diff --git a/packages/gen/tests/proxy/langchain_/test_init_models.py b/packages/gen/tests/proxy/langchain_/test_init_models.py
index bf78687d..ee57664b 100644
--- a/packages/gen/tests/proxy/langchain_/test_init_models.py
+++ b/packages/gen/tests/proxy/langchain_/test_init_models.py
@@ -1,4 +1,5 @@
 import unittest
+import unittest.mock
 from datetime import datetime
 from unittest.mock import MagicMock
 
@@ -75,6 +76,24 @@ def test_init_embedding_model(self):
             model = init_embedding_model(model_name, proxy_client=self.proxy_client)
             self.assertIsInstance(model, model_class)
 
+    def test_init_llm_without_proxy_client(self):
+        """init_llm should fall back to get_proxy_client() when proxy_client is not passed."""
+        with unittest.mock.patch(
+            'gen_ai_hub.proxy.langchain.init_models.get_proxy_client',
+            return_value=self.proxy_client,
+        ):
+            model = init_llm('gpt-5')
+        self.assertIsInstance(model, openai.ChatOpenAI)
+
+    def test_init_embedding_model_without_proxy_client(self):
+        """init_embedding_model should fall back to get_proxy_client() when proxy_client is not passed."""
+        with unittest.mock.patch(
+            'gen_ai_hub.proxy.langchain.init_models.get_proxy_client',
+            return_value=self.proxy_client,
+        ):
+            model = init_embedding_model('text-embedding-3-small')
+        self.assertIsInstance(model, openai.OpenAIEmbeddings)
+
     def test_init_llm(self):
         model_kwargs = {'top_k': 3}
         for model_name, model_class in self.llm.items():