diff --git a/src/neo4j_graphrag/llm/vertexai_llm.py b/src/neo4j_graphrag/llm/vertexai_llm.py
index 0b492697..38aa72d4 100644
--- a/src/neo4j_graphrag/llm/vertexai_llm.py
+++ b/src/neo4j_graphrag/llm/vertexai_llm.py
@@ -34,17 +34,17 @@
 from neo4j_graphrag.message_history import MessageHistory
 from neo4j_graphrag.tool import Tool
 from neo4j_graphrag.types import LLMMessage
+from google import genai
+from google.genai import types
 
 try:
     from vertexai.generative_models import (
         Content,
         FunctionCall,
-        FunctionDeclaration,
         GenerationResponse,
         GenerativeModel,
         Part,
         ResponseValidationError,
-        Tool as VertexAITool,
         ToolConfig,
     )
 except ImportError:
@@ -189,8 +189,8 @@ async def ainvoke(
         except ResponseValidationError as e:
             raise LLMGenerationError("Error calling VertexAILLM") from e
 
-    def _to_vertexai_function_declaration(self, tool: Tool) -> FunctionDeclaration:
-        return FunctionDeclaration(
+    def _to_vertexai_function_declaration(self, tool: Tool) -> types.FunctionDeclaration:
+        return types.FunctionDeclaration(
             name=tool.get_name(),
             description=tool.get_description(),
             parameters=tool.get_parameters(exclude=["additional_properties"]),
@@ -198,11 +198,11 @@ def _to_vertexai_function_declaration(self, tool: Tool) -> FunctionDeclaration:
 
     def _get_llm_tools(
         self, tools: Optional[Sequence[Tool]]
-    ) -> Optional[list[VertexAITool]]:
+    ) -> Optional[list[types.Tool]]:
         if not tools:
             return None
         return [
-            VertexAITool(
+            types.Tool(
                 function_declarations=[
                     self._to_vertexai_function_declaration(tool) for tool in tools
                 ]
@@ -254,7 +254,20 @@ async def _acall_llm(
     ) -> GenerationResponse:
         model = self._get_model(system_instruction=system_instruction)
         options = self._get_call_params(input, message_history, tools)
-        response = await model.generate_content_async(**options)
+
+        client = genai.Client()
+        response = await client.aio.models.generate_content(
+            model=self.model_name,
+            contents=types.Content(
+                role="user",
+                parts=[types.Part.from_text(text=input)],
+            ),
+            config=types.GenerateContentConfig(
+                tools=options["tools"],
+                system_instruction=system_instruction,
+                temperature=0.0,
+            ),
+        )
         return response  # type: ignore[no-any-return]
 
     def _call_llm(
@@ -276,7 +289,7 @@ def _to_tool_call(self, function_call: FunctionCall) -> ToolCall:
         )
 
     def _parse_tool_response(self, response: GenerationResponse) -> ToolCallResponse:
-        function_calls = response.candidates[0].function_calls
+        function_calls = response.function_calls
         return ToolCallResponse(
             tool_calls=[self._to_tool_call(f) for f in function_calls],
             content=None,