Make the models work in parallel 5

crew.py

@@ -146,6 +146,8 @@ class GAIACrew():
 
 import concurrent.futures
 
+import concurrent.futures
+
 def run_parallel_crew(question: str, file_path: str):
     """
     1) Prepares the prompt (including file data if any).
@@ -164,31 +166,30 @@ def run_parallel_crew(question: str, file_path: str):
         else:
             final_question = f"{question} File path: {file_path}."
 
-    # 2) Instantiate your crew and split manager vs workers by zipping names → agents
+    # 2) Instantiate your crew and split manager vs workers
     crew_instance = GAIACrew()
     names  = list(crew_instance.agents_config.keys())
     agents = crew_instance.agents
+    pairs  = list(zip(names, agents))
 
-    # Build (name, agent) pairs
-    pairs   = list(zip(names, agents))
-    workers = [agent for name, agent in pairs if name != "manager_agent"]
-    manager = next(agent for name, agent in pairs if name == "manager_agent")
+    workers = [(n, a) for n, a in pairs if n != "manager_agent"]
+    manager_name, manager = next((n, a) for n, a in pairs if n == "manager_agent")
 
-    # 3) Run workers in parallel
+    # 3) Run workers in parallel, giving each the plain-string prompt
     results = {}
     with concurrent.futures.ThreadPoolExecutor(max_workers=len(workers)) as pool:
-        futures = {
-            pool.submit(lambda ag: ag.kickoff({"question": final_question}), ag): name
-            for name, ag in pairs if name != "manager_agent"
+        future_to_name = {
+            pool.submit(agent.kickoff, final_question): name
+            for name, agent in workers
         }
-        for fut in concurrent.futures.as_completed(futures):
-            name = futures[fut]
+        for fut in concurrent.futures.as_completed(future_to_name):
+            name = future_to_name[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
+    # 4) Compose a manager prompt with all the raw outputs
     combined = "\n\n".join(f"--- {n} output ---\n{out}"
                            for n, out in results.items())
     manager_prompt = (
@@ -198,8 +199,10 @@ def run_parallel_crew(question: str, file_path: str):
         f"Original question: {question}"
     )
 
-    # 5) Run the manager for the final answer
-    final = manager.kickoff({"question": manager_prompt})
+    # 5) Run the manager agent on the combined prompt
+    final = manager.kickoff(manager_prompt)
+
+    # 6) Post-process via your final-answer model
     return get_final_answer(FINAL_ANSWER_MODEL, question, str(final))