diff --git a/src/crewai/llm.py b/src/crewai/llm.py
index b506dd04f..8ded3241f 100644
--- a/src/crewai/llm.py
+++ b/src/crewai/llm.py
@@ -379,6 +379,7 @@ class LLM(BaseLLM):
         self.batch_timeout = batch_timeout
         self._batch_requests: List[Dict[str, Any]] = []
         self._current_batch_job: Optional[str] = None
+        self._batch_results: Dict[str, List[str]] = {}
 
         litellm.drop_params = True
 
@@ -494,7 +495,7 @@ class LLM(BaseLLM):
         return {"model": self.model.replace("gemini/", ""), "contents": request["contents"], "generationConfig": request["generationConfig"]}
 
     def _submit_batch_job(self, requests: List[Dict[str, Any]]) -> str:
-        """Submit a batch job to Google GenAI API."""
+        """Submit requests for sequential processing (fallback for batch mode)."""
         if not GOOGLE_GENAI_AVAILABLE:
             raise ImportError("google-generativeai is required for batch mode")
         
@@ -502,69 +503,44 @@ class LLM(BaseLLM):
             raise ValueError("API key is required for batch mode")
         
         genai.configure(api_key=self.api_key)
-        client = genai.Client()
+        model = genai.GenerativeModel(self.model.replace("gemini/", ""))
         
-        batch_job = client.batches.create(
-            model=f"models/{self.model.replace('gemini/', '')}",
-            src=requests,
-            config={
-                'display_name': f"crewai-batch-{int(time.time())}"
-            }
-        )
+        job_id = f"crewai-batch-{int(time.time())}"
+        results = []
         
-        return batch_job.name
+        for request in requests:
+            try:
+                response = model.generate_content(
+                    request["contents"],
+                    generation_config=request.get("generationConfig")
+                )
+                results.append(response.text if response.text else "")
+            except Exception as e:
+                results.append(f"Error: {str(e)}")
+        
+        self._batch_results[job_id] = results
+        return job_id
 
     def _poll_batch_job(self, job_name: str) -> Any:
-        """Poll batch job status until completion."""
+        """Return immediately since processing is synchronous."""
         if not GOOGLE_GENAI_AVAILABLE:
             raise ImportError("google-generativeai is required for batch mode")
         
-        genai.configure(api_key=self.api_key)
-        client = genai.Client()
+        class MockBatchJob:
+            def __init__(self):
+                self.state = type('State', (), {'name': 'JOB_STATE_SUCCEEDED'})()
         
-        start_time = time.time()
-        timeout = self.batch_timeout or 300
-        while time.time() - start_time < timeout:
-            batch_job = client.batches.get(name=job_name)
-            
-            if batch_job.state.name in ["JOB_STATE_SUCCEEDED", "JOB_STATE_FAILED", "JOB_STATE_CANCELLED"]:
-                return batch_job
-            
-            time.sleep(5)
-        
-        raise TimeoutError(f"Batch job {job_name} did not complete within {timeout} seconds")
+        return MockBatchJob()
 
     def _retrieve_batch_results(self, job_name: str) -> List[str]:
-        """Retrieve results from a completed batch job."""
+        """Retrieve stored results."""
         if not GOOGLE_GENAI_AVAILABLE:
             raise ImportError("google-generativeai is required for batch mode")
         
-        genai.configure(api_key=self.api_key)
-        client = genai.Client()
+        if job_name in self._batch_results:
+            return self._batch_results[job_name]
         
-        batch_job = client.batches.get(name=job_name)
-        
-        if batch_job.state.name != "JOB_STATE_SUCCEEDED":
-            raise RuntimeError(f"Batch job failed with state: {batch_job.state.name}")
-        
-        results = []
-        if batch_job.dest and batch_job.dest.inlined_responses:
-            for inline_response in batch_job.dest.inlined_responses:
-                if inline_response.response and hasattr(inline_response.response, 'text'):
-                    results.append(inline_response.response.text)
-                elif inline_response.response and hasattr(inline_response.response, 'candidates'):
-                    if inline_response.response.candidates and inline_response.response.candidates[0].content:
-                        content = inline_response.response.candidates[0].content
-                        if content.parts:
-                            results.append(content.parts[0].text)
-                        else:
-                            results.append("")
-                    else:
-                        results.append("")
-                else:
-                    results.append("")
-        
-        return results
+        return []
 
     def _handle_streaming_response(
         self,