Sequential chat - is_termination_msg with max_turns

[huislaw]

In the Sequential chat pattern example below, if Human replied with Name and Location in first message, the chat does not go to second agent (onboarding_topic_preference_agent ).

It is ok if Human provides Name and Location in two separate turns.

I already set 'is_termination_msg'. Suspect it is because of max_turns=2 ?.

Is there anyway I can set for it to go next Agent, if first Agent already gotten all information in first turn, even though I set max_turns=2 ?

`from autogen import ConversableAgent

onboarding_personal_information_agent = ConversableAgent(
    name="Onboarding Personal Information Agent",
    system_message='''You are a helpful customer onboarding agent,
    you are here to help new customers get started with our product.
    Your job is to gather customer's name and location.
    Do not ask for other information. Return 'TERMINATE' 
    when you have gathered all the information.''',
    llm_config=llm_config,
    code_execution_config=False,
    human_input_mode="NEVER",
    is_termination_msg=lambda msg: "TERMINATE" in msg.get("content"),
)

onboarding_topic_preference_agent = ConversableAgent(
    name="Onboarding Topic preference Agent",
    system_message='''You are a helpful customer onboarding agent,
    you are here to help new customers get started with our product.
    Your job is to gather customer's preferences on news topics.
    Do not ask for other information.
    Return 'TERMINATE' when you have gathered all the information.''',
    llm_config=llm_config,
    code_execution_config=False,
    human_input_mode="NEVER",
    is_termination_msg=lambda msg: "TERMINATE" in msg.get("content"),
)

customer_engagement_agent = ConversableAgent(
    name="Customer Engagement Agent",
    system_message='''You are a helpful customer service agent
    here to provide fun for the customer based on the user's
    personal information and topic preferences.
    This could include fun facts, jokes, or interesting stories.
    Make sure to make it engaging and fun!
    Return 'TERMINATE' when you are done.''',
    llm_config=llm_config,
    code_execution_config=False,
    human_input_mode="NEVER",
    is_termination_msg=lambda msg: "terminate" in msg.get("content").lower(),
)

customer_proxy_agent = ConversableAgent(
    name="customer_proxy_agent",
    llm_config=False,
    code_execution_config=False,
    human_input_mode="ALWAYS",
    is_termination_msg=lambda msg: "terminate" in msg.get("content").lower(),
)

# Now, you can craft a series of tasks to facilitate the onboarding process.

chats = [
    {
        "sender": onboarding_personal_information_agent,
        "recipient": customer_proxy_agent,
        "message": 
            "Hello, I'm here to help you get started with our product."
            "Could you tell me your name and location?",
        "summary_method": "reflection_with_llm",
        "summary_args": {
            "summary_prompt" : "Return the customer information. Do not make up information. Leave empty if user didn't provide "
                             "into as JSON object only: "
                             "{'name': '', 'location': ''}",
        },
        "max_turns": 2,
        "clear_history" : True
    },
    {
        "sender": onboarding_topic_preference_agent,
        "recipient": customer_proxy_agent,
        "message": 
                "Great! Could you tell me what topics you are "
                "interested in reading about?",
        "summary_method": "reflection_with_llm",
        "max_turns": 1,
        "clear_history" : False
    },
    {
        "sender": customer_proxy_agent,
        "recipient": customer_engagement_agent,
        "message": "Let's find something fun to read.",
        "max_turns": 1,
        "summary_method": "reflection_with_llm",
    },
]


from autogen import initiate_chats

chat_results = initiate_chats(chats)`

[ekzhu]

Do you have output?