Human in the loop

In the previous ConversableAgent example you were able to chat with the agent and this is because another agent was automatically created to represent you, the human in the loop, when using an agent’s run method.

As you build your own workflows, you’ll want to create your own human in the loop agents and decide if and how to use them. To do so, simply use the ConversableAgent and set the human_input_mode to ALWAYS.

Let’s start to build a more useful scenario, a classroom lesson planner, and create our human agent.

from autogen import ConversableAgent

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

planner_system_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>
"""

my_agent = ConversableAgent(
    name="lesson_agent",
    llm_config=llm_config,
    system_message=planner_system_message,
)

# 1. Create our "human" agent
the_human = ConversableAgent(
    name="human",
    human_input_mode="ALWAYS",
)

# 2. Initiate our chat between the agents
the_human.initiate_chat(recipient=my_agent, message="Today, let's introduce our kids to the solar system.")

1. Create our human-in-the-loop agent by setting its human_input_mode, and no llm_config is required.

2. Our the_human agent starts a conversation by sending a message to lesson_planner. An agent’s initiate_chat method is used to start a conversation between two agents.

This creates a conversation between you and the lesson planner agent.