Nested chat

from autogen import ConversableAgent, GroupChatManager, GroupChat

llm_config = {
    "api_type": "openai",
    "model": "gpt-4o-mini",
}

# Curriculum Standards Agent
curriculum_agent = ConversableAgent(
    name="Curriculum_Agent",
    system_message="""You are a curriculum standards expert for fourth grade education.
    When given a topic, you provide relevant grade-level standards and learning objectives.
    Format every response as:
    STANDARDS:
    - [Standard 1]
    - [Standard 2]
    OBJECTIVES:
    - By the end of this lesson, students will be able to [objective 1]
    - By the end of this lesson, students will be able to [objective 2]""",
    llm_config=llm_config,
    human_input_mode="NEVER",
)

# Lesson Planner Agent
lesson_planner_agent = ConversableAgent(
    name="Lesson_Planner_Agent",
    system_message="""You are a lesson planning specialist.
    Given standards and objectives, you create detailed lesson plans including:
    - Opening/Hook (5-10 minutes)
    - Main Activity (20-30 minutes)
    - Practice Activity (15-20 minutes)
    - Assessment/Closure (5-10 minutes)
    Format as a structured lesson plan with clear timing and materials needed.""",
    llm_config=llm_config,
    human_input_mode="NEVER",
)

# Lesson Reviewer Agent
lesson_reviewer_agent = ConversableAgent(
    name="Lesson_Reviewer_Agent",
    system_message="""You are a lesson plan reviewer who ensures:
    1. Age-appropriate content and activities
    2. Alignment with provided standards
    3. Realistic timing
    4. Clear instructions
    5. Differentiation opportunities
    Provide specific feedback in these areas and suggest improvements if needed.""",
    llm_config=llm_config,
    human_input_mode="NEVER",
)

# Lead Teacher Agent
lead_teacher_agent = ConversableAgent(
    name="Lead_Teacher_Agent",
    system_message="""You are an experienced fourth grade teacher who oversees the lesson planning process.
    Your role is to:
    1. Initiate the planning process with a clear topic
    2. Review and integrate feedback from other agents
    3. Ensure the final lesson plan is practical and engaging
    4. Make final adjustments based on classroom experience""",
    llm_config=llm_config,
    human_input_mode="NEVER",
)

# Create the group chat for collaborative lesson planning
planning_chat = GroupChat(
    agents=[curriculum_agent, lesson_planner_agent, lesson_reviewer_agent],
    messages=[],
    max_round=4,
    send_introductions=True,
)

planning_manager = GroupChatManager(
    groupchat=planning_chat,
    llm_config=llm_config,
)

# Formatter of the final lesson plan to a standard format
formatter_message = """You are a lesson plan formatter. Format the complete plan as follows:
<title>Lesson plan title</title>
<standards>Standards covered</standards>
<learning_objectives>Key learning objectives</learning_objectives>
<materials>Materials required</materials>
<activities>Lesson plan activities</activities>
<assessment>Assessment details</assessment>
"""

lesson_formatter = ConversableAgent(
    name="formatter_agent",
    llm_config=llm_config,
    system_message=formatter_message,
)

# Create nested chats configuration
nested_chats = [
    {
        # The first internal chat determines the standards and objectives
        # A round of revisions is supported with max_turns = 2
        "recipient": curriculum_agent,
        "message": lambda recipient, messages, sender, config: f"Please provide fourth grade standards and objectives for the topic: {messages[-1]['content']}",
        "max_turns": 2,
        "summary_method": "last_msg",
    },
    {
        # A group chat follows, where the lesson plan is created
        "recipient": planning_manager,
        "message": "Based on these standards and objectives, create a detailed lesson plan.",
        "max_turns": 1,
        "summary_method": "last_msg",
    },
    {
        # Finally, a two-agent chat formats the lesson plan
        # The result of this will be the lead_teacher_agent's reply
        "recipient": lesson_formatter,
        "message": "Format the lesson plan.",
        "max_turns": 1,
        "summary_method": "last_msg",
    }
]

# Register nested chats with the lead teacher
lead_teacher_agent.register_nested_chats(
    chat_queue=nested_chats,
    trigger=lambda sender: sender not in [curriculum_agent, planning_manager, lesson_reviewer_agent],
)

# A human-in-the-loop agent
human = ConversableAgent(
    name="human_agent",
    human_input_mode="ALWAYS"
)

# A two-agent chat between our human and the lead_teacher_agent
# to demonstrate the full workflow is within the one agent
result = lead_teacher_agent.initiate_chat(
    recipient=human,
    message="What topic would you like to get a lesson plan for?",
    max_turns=2
)

print("Final Lesson Plan:\n", result.summary)