```python
from autogen import ConversableAgent, GroupChat, GroupChatManager, LLMConfig
from dotenv import load_dotenv
import os
load_dotenv()

# Put your key in the OPENAI_API_KEY environment variable
llm_config = LLMConfig(config_list={"api_type": "openai", "model": "gpt-5-nano","api_key":os.getenv("OPENAI_API_KEY")})

planner_message = """You are a classroom lesson agent.
Given a topic, write a lesson plan for a fourth grade class.
Use the following format:
<title>Lesson plan title</title>
<learning_objectives>Key learning objectives</learning_objectives>
<script>How to introduce the topic to the kids</script>
"""

reviewer_message = """You are a classroom lesson reviewer.
You compare the lesson plan to the fourth grade curriculum and provide a maximum of 3 recommended changes.
Provide only one round of reviews to a lesson plan.
"""

# 1. Add a separate 'description' for our planner and reviewer agents
planner_description = "Creates or revises lesson plans."

reviewer_description = """Provides one round of reviews to a lesson plan
for the lesson_planner to revise."""

lesson_planner = ConversableAgent(
    name="planner_agent",
    system_message=planner_message,
    description=planner_description,
    llm_config=llm_config,
)

lesson_reviewer = ConversableAgent(
    name="reviewer_agent",
    system_message=reviewer_message,
    description=reviewer_description,
    llm_config=llm_config,
)

# 2. The teacher's system message can also be used as a description, so we don't define it
teacher_message = """You are a classroom teacher.
You decide topics for lessons and work with a lesson planner.
and reviewer to create and finalise lesson plans.
When you are happy with a lesson plan, output "DONE!".
"""

teacher = ConversableAgent(
    name="teacher_agent",
    system_message=teacher_message,
    # 3. Our teacher can end the conversation by saying DONE!
    is_termination_msg=lambda x: "DONE!" in (x.get("content", "") or "").upper(),
    llm_config=llm_config,
)

# 4. Create the GroupChat with agents and selection method
groupchat = GroupChat(
    agents=[teacher, lesson_planner, lesson_reviewer],
    speaker_selection_method="auto",
    messages=[],
)

# 5. Our GroupChatManager will manage the conversation and uses an LLM to select the next agent
manager = GroupChatManager(
    name="group_manager",
    groupchat=groupchat,
    llm_config=llm_config,
)

# 6. Initiate the chat with the GroupChatManager as the recipient
teacher.initiate_chat(
    recipient=manager,
    message="Today, let's introduce our kids to the solar system."
)
```
