Improve kickoff_for_each_parallel based on PR feedback

Co-Authored-By: Joe Moura <joao@crewai.com>

src/crewai/crew.py

@@ -7,7 +7,7 @@ import warnings
 from concurrent.futures import Future
 from copy import copy as shallow_copy
 from hashlib import md5
-from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Union
+from typing import Any, Callable, Dict, List, Optional, Sequence, Set, Tuple, Union
 
 from pydantic import (
     UUID4,
@@ -709,22 +709,21 @@ class Crew(BaseModel):
         self._task_output_handler.reset()
         return results
         
-    def kickoff_for_each_parallel(self, inputs: List[Dict[str, Any]], max_workers: Optional[int] = None) -> List[CrewOutput]:
+    def kickoff_for_each_parallel(self, inputs: Sequence[Dict[str, Any]], max_workers: Optional[int] = None) -> List[CrewOutput]:
         """Executes the Crew's workflow for each input in the list in parallel using ThreadPoolExecutor.
         
         Args:
-            inputs: List of input dictionaries to be passed to each crew execution.
+            inputs: Sequence of input dictionaries to be passed to each crew execution.
             max_workers: Maximum number of worker threads to use. If None, uses the default
                         ThreadPoolExecutor behavior (typically min(32, os.cpu_count() + 4)).
         
         Returns:
             List of CrewOutput objects, one for each input.
         """
-        import concurrent.futures
-        from concurrent.futures import ThreadPoolExecutor
+        from concurrent.futures import ThreadPoolExecutor, as_completed
         
-        if not isinstance(inputs, list):
-            raise TypeError("Inputs must be a list of dictionaries.")
+        if not isinstance(inputs, (list, tuple)):
+            raise TypeError(f"Inputs must be a list of dictionaries. Received {type(inputs).__name__} instead.")
         
         if not inputs:
             return []
@@ -738,26 +737,30 @@ class Crew(BaseModel):
         crew_copies = [self.copy() for _ in inputs]
         
         # Execute each crew in parallel
-        with ThreadPoolExecutor(max_workers=max_workers) as executor:
-            # Submit all tasks to the executor
-            future_to_crew = {
-                executor.submit(crew_copies[i].kickoff, inputs[i]): i 
-                for i in range(len(inputs))
-            }
-            
-            # Process results as they complete
-            for future in concurrent.futures.as_completed(future_to_crew):
-                crew_index = future_to_crew[future]
-                try:
-                    output = future.result()
-                    results.append(output)
-                    
-                    # Aggregate usage metrics
-                    if crew_copies[crew_index].usage_metrics:
-                        total_usage_metrics.add_usage_metrics(crew_copies[crew_index].usage_metrics)
-                except Exception as exc:
-                    # Re-raise the exception to maintain consistent behavior with kickoff_for_each
-                    raise exc
+        try:
+            with ThreadPoolExecutor(max_workers=max_workers) as executor:
+                # Submit all tasks to the executor
+                future_to_crew = {
+                    executor.submit(crew_copies[i].kickoff, inputs[i]): i 
+                    for i in range(len(inputs))
+                }
+                
+                # Process results as they complete
+                for future in as_completed(future_to_crew):
+                    crew_index = future_to_crew[future]
+                    try:
+                        output = future.result()
+                        results.append(output)
+                        
+                        # Aggregate usage metrics
+                        if crew_copies[crew_index].usage_metrics:
+                            total_usage_metrics.add_usage_metrics(crew_copies[crew_index].usage_metrics)
+                    except Exception as exc:
+                        # Re-raise the exception to maintain consistent behavior with kickoff_for_each
+                        raise exc
+        finally:
+            # Clean up to assist garbage collection
+            crew_copies.clear()
         
         # Set the aggregated metrics on the parent crew
         self.usage_metrics = total_usage_metrics