how to pass output from agent1 to agent2 as input?

[IamExperimenting]

@vbarda @hwchase17

I'm creating multi-agent tool calling. I have 4 functions(tools) and I want LLM to take action or call right function/tool to finish the process.

can you please help me, I'm new to LangGraph, not sure where i'm making mistake.

from langchain.chat_models import init_chat_model
from langgraph.prebuilt import ToolNode
from langgraph.graph import StateGraph, MessagesState, START, END

def read_file(file_location: str):
    """This function is to read csv file."""
    df_ = pd.read_csv(file_location)
    return df_.to_json()

def process_data(input_data: pd.DataFrame):
    """This function process the dataframe."""
    first_output_data = input_data[['col1','col2']]
    second_output_data = input_data[['col3', 'col4']]
    return first_output_data , second_output_data 



tool_node = ToolNode([read_file, process_data])

model = init_chat_model(model="claude-3-5-haiku-latest")
model_with_tools = model.bind_tools([read_file, process_data])

def should_continue(state: MessagesState):
    messages = state["messages"]
    last_message = messages[-1]
    if last_message.tool_calls:
        return "tools"
    return END

def call_model(state: MessagesState):
    messages = state["messages"]
    response = model_with_tools.invoke(messages)
    return {"messages": [response]}

builder = StateGraph(MessagesState)

# Define the two nodes we will cycle between
builder.add_node("call_model", call_model)
builder.add_node("tools", tool_node)

builder.add_edge(START, "call_model")
builder.add_conditional_edges("call_model", should_continue, ["tools", END])
builder.add_edge("tools", "call_model")

graph = builder.compile()

graph.invoke({"messages": [{"role": "user", "content": "read file ./home/csv_data.csv and process it?"}]})

[Tik1993]

Any error message?
If you want to keep processing until finish the tool call, you can use tools_condition from Langgraph.prebuilt
from langgraph.prebuilt import tools_condition

and then adjust the builder.add_conditional_edges to builder.add_conditional_edges("call_model",tools_condition)