Update OpenRouter_Agent.py

OpenRouter_Agent.py

@@ -25,6 +25,7 @@ if not OPENROUTER_API_KEY:
 common = dict(
     api_base="https://openrouter.ai/api/v1",
     api_key=OPENROUTER_API_KEY,
+    extra_body={"include": "usage"}
 )
 
 
@@ -39,17 +40,14 @@ class MultiAgentSystem:
     def __init__(self):
         self.deepseek_model = OpenAIServerModel(
             model_id="deepseek/deepseek-r1-0528:free",
-            max_tokens=8096,
             **common,
         )
         self.qwen_model = OpenAIServerModel(
             model_id="qwen/qwen-2.5-coder-32b-instruct:free",
-            max_tokens=8096,
             **common,
         )
         self.gemini_model = OpenAIServerModel(
             model_id="google/gemini-2.0-flash-exp:free",
-            max_tokens=8096,
             **common,
         )
 
@@ -99,35 +97,34 @@ class MultiAgentSystem:
             managed_agents=[self.web_agent, self.info_agent],
             name="manager_agent",
             description=(
-                "You are the manager. Given a {task}, plan which agent to use: "
-                "If web data is needed, delegate to web_agent. If math, parsing, image reasoning, or code is needed, use info_agent. "
-                "After collecting outputs, optionally cross-validate and check correctness, then finalize and submit the best answer using FinalAnswerTool. "
-                "For each task, explicitly explain your planning steps and reasons for choosing which agent, and always prefer the most accurate and complete answer possible."
+                "You are the manager agent. **Respond with a single python code-block only**. "
+                "Inside that block you must call the other agents via `agent(name)(task)` "
+                "and end with `final_answer({...})`. **No natural language outside the block**"
             ),
             additional_authorized_imports=[
                 "json",
                 "pandas",
                 "numpy",
             ],
-            planning_interval=3,
+            planning_interval=6,
             verbosity_level=2,
-            final_answer_checks=[self.check_reasoning],
-            max_steps=8,
+            #final_answer_checks=[self.check_reasoning],
+            max_steps=4,
         )
 
-    def check_reasoning(self, final_answer, agent_memory):
-        model = self.gemini_model
-        verification_prompt = (
-            f"Here is a user-given task and the agent steps: {agent_memory.get_succinct_steps()}. "
-            f"The proposed final answer is: {final_answer}. "
-            "Please check that the reasoning process is correct: do they correctly answer the given task? "
-            "First list reasons why yes/no, then write your final decision: PASS in caps lock if it is satisfactory, FAIL if it is not."
-        )
-        output = model(verification_prompt)
-        print("Feedback: ", output)
-        if "FAIL" in output:
-            raise Exception(output)
-        return True
+    #def check_reasoning(self, final_answer, agent_memory):
+        #model = self.gemini_model
+        #verification_prompt = (
+         #   f"Here is a user-given task and the agent steps: {agent_memory.get_succinct_steps()}. "
+          #  f"The proposed final answer is: {final_answer}. "
+           # "Please check that the reasoning process is correct: do they correctly answer the given task? "
+            #"First list reasons why yes/no, then write your final decision: PASS in caps lock if it is satisfactory, FAIL if it is not."
+        #)
+        #output = model(verification_prompt)
+        #print("Feedback: ", output)
+        #if "FAIL" in output:
+            #raise Exception(output)
+        #return True
 
     def __call__(self, task: str) -> str:
         """