reflexion agent also

agent.py

@@ -207,26 +207,42 @@ agent_excel = create_react_agent(
 
     tools=[open_xlsx_doc, create_agent_and_answer, ],
     prompt=(
-        "You are an agent psecialized with Excel files.\n\n"
+        "You are an agent specialized with Excel files.\n\n"
         "INSTRUCTIONS:\n"
         "- Assist ONLY when an Excel file is mentionned \n"
         "- First, when you receive an Excel file path, you have to use the 'open_xlsx_doc' tool. Then you use the 'create_agent_and_answer'\n"
-        "- The output of the first tool (the pd.Dataframe) is a part of the input of the first solution\n"
+        "- The output of the first tool  is a part of the input of the second tool\n"
 
         "- Once you have got a response from the 'create_agent_and_answer_tool', transmit it to your supervisor \n"
     ),
     name="agent_excel",
 )
 
+reflexion_agent = create_react_agent(
+    model=llm_reasoning,
+
+    tools=[],
+    prompt=(
+        "You are an intelligent agent\n\n"
+        "INSTRUCTIONS:\n"
+        "- Assist ONLY when you are called \n"
+        "- You are the most intelligent agent"
+        "- Your job is to solve hard problems when your supervisor ask you to do so"
+        "- Give him a precise answer. "
+    ),
+    name="reflexion_agent",
+)
+
 supervisor = create_supervisor(
-    model=init_chat_model("openai:gpt-4o", api_key = api_open_ai_agent_key),
-    agents=[research_agent, math_agent, web_search_openai_agent, agent_excel],
+    model=init_chat_model("openai:gpt-4.1", api_key = api_open_ai_agent_key),
+    agents=[research_agent, math_agent, web_search_openai_agent, agent_excel, reflexion_agent],
     prompt=(
-        "You are a supervisor managing four agents:\n"
+        "You are a supervisor managing five agents:\n"
         "- research_agent: Specialised in ArXiv and Wikipedia. Assign research-related tasks to this agent.\n"
         "- math_agent: Handles math-related tasks such as solving equations or performing calculations.\n"
         "- web_search_openai_agent: Can browse the web to find up-to-date and relevant information. Assign web-related tasks to this agent.\n"
         "- agent_excel: Can exploit Excel file. You have to give him an Excel path file with a question, and wait for his response.\n"
+        "- reflexion_agent: This agent uses a powerful model. It is your most intelligent agent. Useful for reflexion tasks\n"
         "Assign work to one agent at a time. Do not call agents in parallel.\n"
         "When a new question arises, always first consult the research_agent — it may provide useful information.\n"
         "If research_agent yields no results, then delegate the task to web_search_openai_agent.\n"
@@ -248,6 +264,7 @@ def response_from_agent(question):
     for chunk in supervisor.stream(
             {"messages": [{"role": "user", "content": question}]}
     ):
+
         response = chunk
 
 

app.py

@@ -63,7 +63,11 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
     results_log = []
     answers_payload = []
     print(f"Running agent on {len(questions_data)} questions...")
+    i = 0
     for item in questions_data:
+        i+=1
+        print(f'on est la {i} ieme question')
+
         task_id = item.get("task_id")
         question_text = item.get("question")
         filename = item.get('file_name')
@@ -71,7 +75,11 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
             print(f"Skipping item with missing task_id or question: {item}")
             continue
         try:
+            print(f'la question est {question_text}')
+            if filename :
+                print('fichier attaché : ', filename)
             submitted_answer = response_from_agent(question_text + 'The file_name (path) is : ' + filename)
+            print('submitted_answer:', submitted_answer)
             answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
             results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer})
         except Exception as e: