[Bug Fix] Gemini-CLI - The Gemini Custom API request has an incorrect authorization format (#13098)

* fix GoogleGenAIConfig

* fix validate_environment

* test_agenerate_content_x_goog_api_key_header

litellm/llms/gemini/google_genai/transformation.py

@@ -30,6 +30,12 @@ class GoogleGenAIConfig(BaseGoogleGenAIGenerateContentConfig, VertexLLM):
     """
     Configuration for calling Google models in their native format.
     """
+    ##############################
+    # Constants
+    ##############################
+    XGOOGLE_API_KEY = "x-goog-api-key"
+    ##############################
+    
     @property
     def custom_llm_provider(self) -> Literal["gemini", "vertex_ai"]:
         return "gemini"
@@ -113,8 +119,9 @@ class GoogleGenAIConfig(BaseGoogleGenAIGenerateContentConfig, VertexLLM):
         default_headers = {
             "Content-Type": "application/json",
         }
-        if api_key is not None:
-            default_headers["Authorization"] = f"Bearer {api_key}"
+        gemini_api_key = self._get_google_ai_studio_api_key(dict(litellm_params or {}))
+        if gemini_api_key is not None:
+            default_headers[self.XGOOGLE_API_KEY] = gemini_api_key
         if headers is not None:
             default_headers.update(headers)
 

litellm/proxy/proxy_config.yaml

@@ -1,19 +1,5 @@
 model_list:
-  - model_name: anthropic/*
+  - model_name: vertex_ai/*
     litellm_params:
-      model: anthropic/*
-  - model_name: openai/*
-    litellm_params:
-      model: openai/*
-
-  - model_name: vertex_ai/gemini-2.5-pro # Vertex AI Gemini
-    litellm_params:
-      model: vertex_ai/gemini-2.5-pro
-      thinking: {"type": "enabled", "budget_tokens": 1024}
-      merge_reasoning_content_in_choices: true
-
-
-litellm_settings:
-  callbacks: ["datadog_llm_observability"]
-  cache: true
+      model: gemini/*
 

tests/test_litellm/google_genai/test_google_genai_adapter.py

@@ -1125,4 +1125,79 @@ async def test_google_generate_content_with_openai():
         passed_fields = set(call_kwargs.keys())
         # remove any GenericLiteLLMParams fields
         passed_fields = passed_fields - set(GenericLiteLLMParams.model_fields.keys())
-        assert passed_fields == set(["model", "messages"]), f"Expected only model, contents, systemInstruction, and safetySettings to be passed through, got {passed_fields}"
+        assert passed_fields == set(["model", "messages"]), f"Expected only model, contents, systemInstruction, and safetySettings to be passed through, got {passed_fields}"
+
+@pytest.mark.asyncio
+async def test_agenerate_content_x_goog_api_key_header():
+    """
+    Test that agenerate_content passes x-goog-api-key header correctly.
+    
+    This test verifies that when calling agenerate_content with a Google GenAI model,
+    the HTTP request includes the x-goog-api-key header with the correct API key value.
+    """
+    import os
+    import unittest.mock
+
+    import httpx
+    
+    test_api_key = "test-gemini-api-key-123"
+    
+    # Mock environment to ensure we use our test API key
+    with unittest.mock.patch.dict(os.environ, {"GEMINI_API_KEY": test_api_key}, clear=False):
+        # Mock the AsyncHTTPHandler's post method to capture headers
+        with unittest.mock.patch("litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post", new_callable=unittest.mock.AsyncMock) as mock_post:
+            # Mock a successful response
+            mock_response = unittest.mock.MagicMock()
+            mock_response.json.return_value = {
+                "candidates": [
+                    {
+                        "content": {
+                            "parts": [{"text": "Hello! How can I help you today?"}],
+                            "role": "model"
+                        },
+                        "finishReason": "STOP",
+                        "index": 0
+                    }
+                ],
+                "usageMetadata": {
+                    "promptTokenCount": 5,
+                    "candidatesTokenCount": 10,
+                    "totalTokenCount": 15
+                }
+            }
+            mock_response.status_code = 200
+            mock_response.headers = {}
+            mock_post.return_value = mock_response
+            
+            # Call agenerate_content with Google AI Studio model
+            try:
+                response = await agenerate_content(
+                    model="gemini/gemini-1.5-flash",
+                    contents=[
+                        {"role": "user", "parts": [{"text": "Hello, world!"}]}
+                    ],
+                    api_key=test_api_key
+                )
+            except Exception:
+                # Ignore any response processing errors, we just want to check the headers
+                pass
+            
+            # Verify that AsyncHTTPHandler.post was called
+            mock_post.assert_called_once()
+            
+            # Get the arguments passed to the post call
+            call_args, call_kwargs = mock_post.call_args
+            
+            # Verify that headers contain x-goog-api-key
+            headers = call_kwargs.get("headers", {})
+            assert "x-goog-api-key" in headers, f"x-goog-api-key header not found in headers: {list(headers.keys())}"
+            
+            # Verify the API key is set (could be our test key or from api_key parameter)
+            api_key_value = headers["x-goog-api-key"]
+            assert api_key_value == test_api_key, f"Expected x-goog-api-key to be {test_api_key}, got {api_key_value}"
+            
+            # Verify other expected headers
+            assert headers.get("Content-Type") == "application/json", f"Expected Content-Type application/json, got {headers.get('Content-Type')}"
+            
+            print(f"✓ Test passed: x-goog-api-key header correctly set to {api_key_value}")
+            print(f"✓ All headers: {list(headers.keys())}")