Make the models work in parallel

crew.py

@@ -144,19 +144,16 @@ class GAIACrew():
             verbose=True
         )
 
+import concurrent.futures
 
-def run_crew(question, file_path):
+def run_parallel_crew(question: str, file_path: str):
     """
-    Orchestrates the GAIA crew to answer a question, optionally with a file.
-
-    Args:
-        question (str): The user's question.
-        file_path (str): Optional path to a data file to include in the prompt.
-
-    Returns:
-        str: The final answer from the manager agent.
+    1) Prepares the prompt (including file data if any).
+    2) Runs every non-manager agent in parallel on that prompt.
+    3) Gathers their raw outputs.
+    4) Sends a combined prompt to the manager_agent for the final answer.
     """
-    # Build the final prompt, including file JSON if needed
+    # 1) Build the final prompt
     final_question = question
     if file_path:
         if is_ext(file_path, ".csv") or is_ext(file_path, ".xls") \
@@ -166,22 +163,41 @@ def run_crew(question, file_path):
             final_question = f"{question} JSON data:\n{json_data}."
         else:
             final_question = f"{question} File path: {file_path}."
-    
-    # Instantiate the crew and kick off the workflow
+
+    # 2) Instantiate your crew and split manager vs workers
     crew_instance = GAIACrew()
-    crew = crew_instance.get_crew()
-    answer = crew.kickoff(inputs={"question": final_question})
-    
-    # Post-process through the final-answer model
-    final_answer = get_final_answer(FINAL_ANSWER_MODEL, question, str(answer))
-
-    # Debug logging
-    print(f"=> Initial question: {question}")
-    print(f"=> Final question: {final_question}")
-    print(f"=> Initial answer: {answer}")
-    print(f"=> Final answer: {final_answer}")
-    
-    return final_answer
+    all_agents    = crew_instance.agents
+    workers       = [a for a in all_agents if a.config.get("name") != "manager_agent"]
+    manager       = next(a for a in all_agents if a.config.get("name") == "manager_agent")
+
+    # 3) Run workers in parallel
+    inputs = {"question": final_question}
+    results = {}
+    with concurrent.futures.ThreadPoolExecutor(max_workers=len(workers)) as pool:
+        futures = {
+            pool.submit(lambda ag: ag.kickoff(inputs), ag): ag.config["name"]
+            for ag in workers
+        }
+        for fut in concurrent.futures.as_completed(futures):
+            name = futures[fut]
+            try:
+                results[name] = fut.result()
+            except Exception as e:
+                results[name] = f"<error: {e}>"
+
+    # 4) Compose a manager prompt that includes all worker outputs
+    combined = "\n\n".join(f"--- {name} output ---\n{out}"
+                           for name, out in results.items())
+    manager_prompt = (
+        f"You have received these reports from your coworkers:\n\n"
+        f"{combined}\n\n"
+        f"Now, based on the original question, provide the final answer.\n"
+        f"Original question: {question}"
+    )
+
+    # 5) Run the manager agent for the final answer
+    final = manager.kickoff(inputs={"question": manager_prompt})
+    return get_final_answer(FINAL_ANSWER_MODEL, question, str(final))
 
 
 def get_final_answer(model, question, answer):