Update multi_agent.py

multi_agent.py

@@ -1,78 +1,42 @@
-import autogen
+from autogen import ConversableAgent, AssistantAgent
+from autogen.coding import LocalCommandLineCodeExecutor
+
+#import os
+#from IPython.display import Image
 
 def run_multi_agent(llm, task):
     llm_config = {"model": llm}
     
-    user_proxy = autogen.ConversableAgent(
-        name="Admin",
-        system_message="Give the task, and send "
-        "instructions to writer to refine the blog post.",
-        code_execution_config=False,
-        llm_config=llm_config,
-        human_input_mode="NEVER",
+    executor = LocalCommandLineCodeExecutor(
+        timeout=60,
+        work_dir="coding",
     )
     
-    planner = autogen.ConversableAgent(
-        name="Planner",
-        system_message="Given a task, please determine "
-        "what information is needed to complete the task. "
-        "Please note that the information will all be retrieved using"
-        " Python code. Please only suggest information that can be "
-        "retrieved using Python code. "
-        "After each step is done by others, check the progress and "
-        "instruct the remaining steps. If a step fails, try to "
-        "workaround",
-        description="Planner. Given a task, determine what "
-        "information is needed to complete the task. "
-        "After each step is done by others, check the progress and "
-        "instruct the remaining steps",
-        llm_config=llm_config,
+    code_executor_agent = ConversableAgent(
+        name="code_executor_agent",
+        llm_config=False,
+        code_execution_config={"executor": executor},
+        human_input_mode="NEVER",
+        default_auto_reply=
+        "Please continue. If everything is done, reply 'TERMINATE'.",
     )
     
-    engineer = autogen.AssistantAgent(
-        name="Engineer",
+    code_writer_agent = AssistantAgent(
+        name="code_writer_agent",
         llm_config=llm_config,
-        description="An engineer that writes code based on the plan "
-        "provided by the planner.",
-    )
-    
-    executor = autogen.ConversableAgent(
-        name="Executor",
-        system_message="Execute the code written by the "
-        "engineer and report the result.",
+        code_execution_config=False,
         human_input_mode="NEVER",
-        code_execution_config={
-            "last_n_messages": 3,
-            "work_dir": "coding",
-            "use_docker": False,
-        },
     )
     
-    writer = autogen.ConversableAgent(
-        name="Writer",
-        llm_config=llm_config,
-        system_message="Writer."
-        "Please write blogs in markdown format (with relevant titles)"
-        " and put the content in pseudo ```md``` code block. "
-        "You take feedback from the admin and refine your blog.",
-        description="Writer."
-        "Write blogs based on the code execution results and take "
-        "feedback from the admin to refine the blog."
-    )
+    code_writer_agent_system_message = code_writer_agent.system_message
     
-    groupchat = autogen.GroupChat(
-        agents=[user_proxy, engineer, writer, executor, planner],
-        messages=[],
-        max_round=25,
-    )
+    print(code_writer_agent_system_message)
     
-    manager = autogen.GroupChatManager(
-        groupchat=groupchat, llm_config=llm_config
+    chat_result = code_executor_agent.initiate_chat(
+        code_writer_agent,
+        message=message,
     )
     
-    groupchat_result = user_proxy.initiate_chat(
-        manager,
-        message=task,
-    )
+    #Image(os.path.join("coding", "ytd_stock_gains.png"))
     
-    return groupchat_result
+    return chat_result