[Python] Triggering next node in langgraph

I am making a chatbot using langgraph each node allocated to teach a topic e.g AI, Prompts and LLM etc. i am using this Docuemnt: https://langchain-ai.github.io/langgraph/how-tos/multi-agent-network/ here is the code

from typing_extensions import Literal
from langchain_openai import ChatOpenAI
from langchain_core.messages import ToolMessage
from langchain_core.tools import tool
from langgraph.graph import MessagesState, StateGraph, START
from langgraph.checkpoint.memory import MemorySaver
from langgraph.types import Command


model = ChatOpenAI(model="gpt-4o", temperature=0)

# Define a helper for each of the agent nodes to call


@tool
def transfer_to_chat_handler():
"""Ask travel advisor for help."""
# This tool is not returning anything: we're just using it
# as a way for LLM to signal that it needs to hand off to another agent
# (See the paragraph above)
return


@tool
def transfer_to_teach_ai():
"""Ask chat handler for help."""
return
@tool
def transfer_to_teach_prompt():
"""Ask chat handler for help."""
return

def chathandler(
state: MessagesState,
) -> Command[Literal["teach_ai", "__end__"]]:
system_prompt = (
"Your name is arti, you are a friendly teaching chatbot, teaching students about different topics"
"you only job is get user's name and age and direct user to next lesson"
# "here is the list of teachers: 'AI Teacher', 'Prompt Teacher"
"trigger and transition to 'teach_ai' when you recieve user's name and age"
# "trigger and transition to 'teach_prompt' when user is cleared about AI"
)
messages = [{"role": "system", "content": system_prompt}] + state["messages"]
ai_msg = model.bind_tools([transfer_to_teach_ai]).invoke(messages)
# If there are tool calls, the LLM needs to hand off to another agent
if len(ai_msg.tool_calls) > 0:
if "tool_calls" in ai_msg.additional_kwargs:
tool_name = ai_msg.additional_kwargs["tool_calls"][0]["function"]["name"]

if tool_name == "transfer_to_teach_ai":
tool_call_id = ai_msg.tool_calls[-1]["id"]
tool_msg = {
"role": "tool",
"content": "Successfully transferred to teach_ai",
"tool_call_id": tool_call_id,
}
return Command(goto="teach _ai", update={"messages": [ai_msg, tool_msg]})
# elif tool_name == "transfer_to_teach_prompt":
# tool_call_id = ai_msg.tool_calls[-1]["id"]
# tool_msg = {
# "role": "tool",
# "content": "Successfully transferred to teach_prompt",
# "tool_call_id": tool_call_id,
# }
# return Command(goto="teach_prompt", update={"messages": [ai_msg, tool_msg]})

return {"messages": [ai_msg]}


def teach_ai(
state: MessagesState,
) -> Command[Literal[ "chathandler", "__end__"]]:
system_prompt = (
"introduce yourself that you will teach about AI in a fun and clear way.\n"
"give a proper definition and examples"
"ask user if they have any follow up questions"
"If the user has completely understand the lesson, trigger a transition to 'chathandler'"
)

messages = [{"role": "system", "content": system_prompt}] + state["messages"]
ai_msg = model.bind_tools([transfer_to_chat_handler]).invoke(messages)
# If there are tool calls, the LLM needs to hand off to another agent
if "tool_calls" in ai_msg.additional_kwargs:
tool_name = ai_msg.additional_kwargs["tool_calls"][0]["function"]["name"]

if tool_name == "transfer_to_chat_handler":
tool_call_id = ai_msg.tool_calls[-1]["id"]
tool_msg = {
"role": "tool",
"content": "Successfully transferred to chat_handler",
"tool_call_id": tool_call_id,
}
return Command(goto="chathandler", update={"messages": [ai_msg, tool_msg]})

# If the expert has an answer, return it directly to the user
return {"messages": [ai_msg]}


builder = StateGraph(MessagesState)
builder.add_node("chathandler", chathandler)
builder.add_node("teach_ai", teach_ai)
# we'll always start with a general travel advisor
builder.add_edge(START, "chathandler")
memory = MemorySaver()
graph = builder.compile(checkpointer=memory)


i have added "chathandler" to initiate conversation. as soon as you get the user's name and age trigger the next node you can see a func: transfer_to_teach_prompt that was being used to trigger teach_prompt node after completing teach_ai node i added if tool_name == "transfer_to_teach_ai": so that i can add more nodes later on however i have problem traversing node. as soon as the teach_ai is triggered i do not see anything on the response side here is a screenshot

here is a code for running the bot for your reference

config = {"configurable": {"user_id": "3", "thread_id": "1"}}
while True:
user_input = input("user:")
if user_input == 'q':
break
for chunk in graph.stream(
{"messages": [("user", user_input)]}, config=config):
pretty_print_messages(chunk)


i am sure i am making a mistake in implementation, maybe i am missing some information, however, later i want to implement more nodes, and each nodes should be triggered after user completes a lesson being taught by the node.

Continue reading...