supervisor agent can not work in output_mode="last_message" with return_direct tools

[sjy3]

my env :
'''
Python 3.13.3
langchain 0.3.25
langgraph 0.4.3
'''

I get the following issues when supervisor agent work in output_mode="last_message" with sub agent bind return direct tools.

ValueError: {'code': None, 'param': 'messages.[4].role', 'message': "Invalid parameter: messages with role 'tool' must be a response to a preceeding message with 'tool_calls'.", 'type': 'invalid_request_error'}

And the code example as follows:

from langchain.tools import tool
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI
from langgraph_supervisor import create_supervisor


@tool("multiply-tool", return_direct=True)
def multiply(a: float, b: float) -> float:
    """Multiply two numbers"""
    print(f"{a}*{b} input add")
    return a * b

product_cost_agent = create_react_agent(
    model=ChatOpenAI(
        model="gpt-4o",
        temperature=0.0,
    ),
    tools=[multiply],
    prompt=(
        "You are a price calculation agent.\n\n"
        "Responsible for providing the prices required to purchase multiple products."
    ),
    name="product_cost_agent",
)


@tool("multiply-tool", return_direct=True)
def sku_search(product_id: str) -> dict:
    """Get a product info by sku_id"""
    return {
        "product_id": product_id,
        "product_name": "test_product",
        "price": 12.8,
    }


product_info_agent = create_react_agent(
    model=ChatOpenAI(
        model="gpt-4o",
        temperature=0.0,
    ),
    tools=[sku_search],
    prompt=(
        "You are a product information query agent.\n\n"
        "responsible for providing product information based on the product ID input."
    ),
    name="product_info_agent",
)

supervisor_agent = create_supervisor(
    model=ChatOpenAI(
        model="gpt-4o",
        temperature=0.0,
    ),
    agents=[product_info_agent, product_cost_agent],
    prompt=(
        "You are a supervisory agent managing two agents:\n"
        "- product_info_agent. Please assign the task of querying product information to it\n"
        "- product_cost_agent. Please assign the task of calculating product costs to it\n"
        "You don't need to personally handle any work, just summarize the final results and return them."
    ),
    add_handoff_back_messages=True,
    # output_mode="full_history",
    output_mode="last_message",
).compile(
    name="supervisor_agent",
    # checkpointer=checkpointer,
    # store=store
)

if __name__ == '__main__':
    for chunk in supervisor_agent.stream(
            {"messages": [("human", "How much does it cost to purchase 10 pieces of a product with product_i 11111?")]},
            stream_mode="values",
            debug=True,
    ):
        chunk["messages"][-1].pretty_print()