如何实现 Agent 之间的移交¶

先决条件

本指南假设您熟悉以下内容

在多 Agent 架构中，Agent 可以表示为图节点。每个 Agent 节点执行其步骤并决定是完成执行还是路由到另一个 Agent，包括可能路由到自身（例如，在循环中运行）。多 Agent 交互中的一种自然模式是移交，其中一个 Agent 将控制权移交给另一个 Agent。移交允许您指定

目标：要导航到的目标 Agent - LangGraph 中的节点名称
载荷：要传递给该 Agent 的信息 - LangGraph 中的状态更新

为了在 LangGraph 中实现移交，Agent 节点可以返回 Command 对象，该对象允许您组合控制流和状态更新

def agent(state) -> Command[Literal["agent", "another_agent"]]:
    # the condition for routing/halting can be anything, e.g. LLM tool call / structured output, etc.
    goto = get_next_agent(...)  # 'agent' / 'another_agent'
    return Command(
        # Specify which agent to call next
        goto=goto,
        # Update the graph state
        update={"my_state_key": "my_state_value"}
    )

最常见的 Agent 类型之一是工具调用 Agent。对于这些类型的 Agent，一种模式是将移交包装在工具调用中，例如：

@tool
def transfer_to_bob(state):
    """Transfer to bob."""
    return Command(
        goto="bob",
        update={"my_state_key": "my_state_value"},
        # Each tool-calling agent is implemented as a subgraph.
        # As a result, to navigate to another agent (a sibling sub-graph), 
        # we need to specify that navigation is w/ respect to the parent graph.
        graph=Command.PARENT,
    )

本指南展示了如何

使用 Command 实现移交：Agent 节点决定移交给谁（通常基于 LLM），并通过 Command 显式返回移交。当您需要精细控制 Agent 如何路由到另一个 Agent 时，这些非常有用。它可能非常适合在监督者架构中实现监督者 Agent。
使用工具实现移交：工具调用 Agent 可以访问可以通过 Command 返回移交的工具。Agent 中的工具执行节点识别工具返回的 Command 对象并相应地路由。移交工具是一种通用的原语，在任何包含工具调用 Agent 的多 Agent 系统中都很有用。

设置¶

%%capture --no-stderr
%pip install -U langgraph langchain-anthropic

import getpass
import os


def _set_env(var: str):
    if not os.environ.get(var):
        os.environ[var] = getpass.getpass(f"{var}: ")


_set_env("ANTHROPIC_API_KEY")

设置 LangSmith 以进行 LangGraph 开发

注册 LangSmith 以快速发现问题并提高 LangGraph 项目的性能。LangSmith 允许您使用跟踪数据来调试、测试和监控使用 LangGraph 构建的 LLM 应用程序 — 在此处阅读更多关于如何开始的信息。

使用 `Command` 实现移交¶

让我们实现一个包含两个 Agent 的系统

一个加法专家（只能加数字）
一个乘法专家（只能乘数字）。

在本示例中，Agent 将依赖 LLM 进行数学运算。在一个更真实的后续示例中，我们将为 Agent 提供进行数学运算的工具。

当加法专家需要乘法方面的帮助时，它会移交给乘法专家，反之亦然。这是一个简单的多 Agent 网络的示例。

每个 Agent 都将有一个对应的节点函数，该函数可以有条件地返回一个 Command 对象（例如，我们的移交）。节点函数将使用带有系统提示和工具的 LLM，该工具使其可以在需要移交给另一个 Agent 时发出信号。如果 LLM 使用工具调用响应，我们将返回 Command(goto=<other_agent>)。

注意：虽然我们使用工具让 LLM 发出需要移交的信号，但移交的条件可以是任何内容：来自 LLM 的特定响应文本、来自 LLM 的结构化输出、任何其他自定义逻辑等。

from typing_extensions import Literal
from langchain_core.messages import ToolMessage
from langchain_core.tools import tool
from langchain_anthropic import ChatAnthropic
from langgraph.graph import MessagesState, StateGraph, START
from langgraph.types import Command

model = ChatAnthropic(model="claude-3-5-sonnet-latest")


@tool
def transfer_to_multiplication_expert():
    """Ask multiplication agent 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_addition_expert():
    """Ask addition agent for help."""
    return


def addition_expert(
    state: MessagesState,
) -> Command[Literal["multiplication_expert", "__end__"]]:
    system_prompt = (
        "You are an addition expert, you can ask the multiplication expert for help with multiplication. "
        "Always do your portion of calculation before the handoff."
    )
    messages = [{"role": "system", "content": system_prompt}] + state["messages"]
    ai_msg = model.bind_tools([transfer_to_multiplication_expert]).invoke(messages)
    # If there are tool calls, the LLM needs to hand off to another agent
    if len(ai_msg.tool_calls) > 0:
        tool_call_id = ai_msg.tool_calls[-1]["id"]
        # NOTE: it's important to insert a tool message here because LLM providers are expecting
        # all AI messages to be followed by a corresponding tool result message
        tool_msg = {
            "role": "tool",
            "content": "Successfully transferred",
            "tool_call_id": tool_call_id,
        }
        return Command(
            goto="multiplication_expert", update={"messages": [ai_msg, tool_msg]}
        )

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


def multiplication_expert(
    state: MessagesState,
) -> Command[Literal["addition_expert", "__end__"]]:
    system_prompt = (
        "You are a multiplication expert, you can ask an addition expert for help with addition. "
        "Always do your portion of calculation before the handoff."
    )
    messages = [{"role": "system", "content": system_prompt}] + state["messages"]
    ai_msg = model.bind_tools([transfer_to_addition_expert]).invoke(messages)
    if len(ai_msg.tool_calls) > 0:
        tool_call_id = ai_msg.tool_calls[-1]["id"]
        tool_msg = {
            "role": "tool",
            "content": "Successfully transferred",
            "tool_call_id": tool_call_id,
        }
        return Command(goto="addition_expert", update={"messages": [ai_msg, tool_msg]})

    return {"messages": [ai_msg]}

现在让我们将这两个节点组合成一个图。请注意，Agent 之间没有边！如果专家有答案，它将直接返回给用户，否则它将路由到另一位专家寻求帮助。

builder = StateGraph(MessagesState)
builder.add_node("addition_expert", addition_expert)
builder.add_node("multiplication_expert", multiplication_expert)
# we'll always start with the addition expert
builder.add_edge(START, "addition_expert")
graph = builder.compile()

最后，让我们定义一个辅助函数来漂亮地呈现流式输出

from langchain_core.messages import convert_to_messages


def pretty_print_messages(update):
    if isinstance(update, tuple):
        ns, update = update
        # skip parent graph updates in the printouts
        if len(ns) == 0:
            return

        graph_id = ns[-1].split(":")[0]
        print(f"Update from subgraph {graph_id}:")
        print("\n")

    for node_name, node_update in update.items():
        print(f"Update from node {node_name}:")
        print("\n")

        for m in convert_to_messages(node_update["messages"]):
            m.pretty_print()
        print("\n")

API 参考：convert_to_messages

让我们使用一个需要加法和乘法的表达式来运行该图

for chunk in graph.stream(
    {"messages": [("user", "what's (3 + 5) * 12")]},
):
    pretty_print_messages(chunk)

Update from node addition_expert:


==================================[1m Ai Message [0m==================================

[{'text': "Let me help break this down:\n\nFirst, I'll handle the addition part since I'm the addition expert:\n3 + 5 = 8\n\nNow, for the multiplication of 8 * 12, I'll need to ask the multiplication expert for help.", 'type': 'text'}, {'id': 'toolu_015LCrsomHbeoQPtCzuff78Y', 'input': {}, 'name': 'transfer_to_multiplication_expert', 'type': 'tool_use'}]
Tool Calls:
  transfer_to_multiplication_expert (toolu_015LCrsomHbeoQPtCzuff78Y)
 Call ID: toolu_015LCrsomHbeoQPtCzuff78Y
  Args:
=================================[1m Tool Message [0m=================================

Successfully transferred


Update from node multiplication_expert:


==================================[1m Ai Message [0m==================================

[{'text': 'I see there was an error in my approach. I am actually the multiplication expert, and I need to ask the addition expert for help with (3 + 5) first.', 'type': 'text'}, {'id': 'toolu_01HFcB8WesPfDyrdgxoXApZk', 'input': {}, 'name': 'transfer_to_addition_expert', 'type': 'tool_use'}]
Tool Calls:
  transfer_to_addition_expert (toolu_01HFcB8WesPfDyrdgxoXApZk)
 Call ID: toolu_01HFcB8WesPfDyrdgxoXApZk
  Args:
=================================[1m Tool Message [0m=================================

Successfully transferred


Update from node addition_expert:


==================================[1m Ai Message [0m==================================

Now that I have the result of 3 + 5 = 8 from the addition expert, I can multiply 8 * 12:

8 * 12 = 96

So, (3 + 5) * 12 = 96

您可以看到加法专家首先处理了括号中的表达式，然后移交给乘法专家以完成计算。

现在让我们看看如何使用特殊的移交工具来实现相同的系统，并为我们的 Agent 提供实际的数学工具。

使用工具实现移交¶

实现一个移交工具¶

在前面的示例中，我们在每个 Agent 节点中显式定义了自定义移交。另一种模式是创建特殊的 移交工具，它们直接返回 Command 对象。当 Agent 调用这样的工具时，它会将控制权移交给不同的 Agent。具体来说，Agent 中的工具执行节点识别工具返回的 Command 对象并相应地路由控制流。注意：与前面的示例不同，工具调用 Agent 不是单个节点，而是另一个可以作为子图节点添加到多 Agent 图中的图。

在实现移交工具时，有几个重要的注意事项

由于每个 Agent 都是另一个图中的子图节点，并且工具将在 Agent 子图节点之一（例如，工具执行器）中调用，因此我们需要在 Command 中指定 graph=Command.PARENT，以便 LangGraph 知道导航到 Agent 子图之外
我们可以选择性地指定一个状态更新，该更新将在调用下一个 Agent 之前应用于父图状态
- 这些状态更新可用于控制目标 Agent 可以看到多少聊天消息历史记录。例如，您可以选择仅共享当前 Agent 的最后 AI 消息，或其完整的内部聊天历史记录等。在下面的示例中，我们将共享完整的内部聊天历史记录。
我们可以选择性地向工具提供以下内容（在工具函数签名中）
- 图状态（使用 InjectedState）
- 图长期记忆（使用 InjectedStore）
- 当前的工具调用 ID（使用 InjectedToolCallId）
这些不是必需的，但对于创建传递给下一个 Agent 的状态更新很有用。

from typing import Annotated

from langchain_core.tools import tool
from langchain_core.tools.base import InjectedToolCallId
from langgraph.prebuilt import InjectedState


def make_handoff_tool(*, agent_name: str):
    """Create a tool that can return handoff via a Command"""
    tool_name = f"transfer_to_{agent_name}"

    @tool(tool_name)
    def handoff_to_agent(
        # # optionally pass current graph state to the tool (will be ignored by the LLM)
        state: Annotated[dict, InjectedState],
        # optionally pass the current tool call ID (will be ignored by the LLM)
        tool_call_id: Annotated[str, InjectedToolCallId],
    ):
        """Ask another agent for help."""
        tool_message = {
            "role": "tool",
            "content": f"Successfully transferred to {agent_name}",
            "name": tool_name,
            "tool_call_id": tool_call_id,
        }
        return Command(
            # navigate to another agent node in the PARENT graph
            goto=agent_name,
            graph=Command.PARENT,
            # This is the state update that the agent `agent_name` will see when it is invoked.
            # We're passing agent's FULL internal message history AND adding a tool message to make sure
            # the resulting chat history is valid. See the paragraph above for more information.
            update={"messages": state["messages"] + [tool_message]},
        )

    return handoff_to_agent

API 参考：tool | InjectedToolCallId | InjectedState

与自定义 Agent 一起使用¶

为了演示如何使用移交工具，让我们首先实现预构建的 create_react_agent 的简单版本。如果您想拥有自定义的工具调用 Agent 实现并想利用移交工具，这将非常有用。

from typing_extensions import Literal
from langchain_core.messages import ToolMessage
from langchain_core.tools import tool
from langgraph.graph import MessagesState, StateGraph, START
from langgraph.types import Command


def make_agent(model, tools, system_prompt=None):
    model_with_tools = model.bind_tools(tools)
    tools_by_name = {tool.name: tool for tool in tools}

    def call_model(state: MessagesState) -> Command[Literal["call_tools", "__end__"]]:
        messages = state["messages"]
        if system_prompt:
            messages = [{"role": "system", "content": system_prompt}] + messages

        response = model_with_tools.invoke(messages)
        if len(response.tool_calls) > 0:
            return Command(goto="call_tools", update={"messages": [response]})

        return {"messages": [response]}

    # NOTE: this is a simplified version of the prebuilt ToolNode
    # If you want to have a tool node that has full feature parity, please refer to the source code
    def call_tools(state: MessagesState) -> Command[Literal["call_model"]]:
        tool_calls = state["messages"][-1].tool_calls
        results = []
        for tool_call in tool_calls:
            tool_ = tools_by_name[tool_call["name"]]
            tool_input_fields = tool_.get_input_schema().model_json_schema()[
                "properties"
            ]

            # this is simplified for demonstration purposes and
            # is different from the ToolNode implementation
            if "state" in tool_input_fields:
                # inject state
                tool_call = {**tool_call, "args": {**tool_call["args"], "state": state}}

            tool_response = tool_.invoke(tool_call)
            if isinstance(tool_response, ToolMessage):
                results.append(Command(update={"messages": [tool_response]}))

            # handle tools that return Command directly
            elif isinstance(tool_response, Command):
                results.append(tool_response)

        # NOTE: nodes in LangGraph allow you to return list of updates, including Command objects
        return results

    graph = StateGraph(MessagesState)
    graph.add_node(call_model)
    graph.add_node(call_tools)
    graph.add_edge(START, "call_model")
    graph.add_edge("call_tools", "call_model")

    return graph.compile()

API 参考：ToolMessage | tool | StateGraph | START | Command

让我们也定义一些数学工具，我们将提供给我们的 Agent

@tool
def add(a: int, b: int) -> int:
    """Adds two numbers."""
    return a + b


@tool
def multiply(a: int, b: int) -> int:
    """Multiplies two numbers."""
    return a * b

让我们测试一下 Agent 的实现，以确保它按预期工作

agent = make_agent(model, [add, multiply])

for chunk in agent.stream({"messages": [("user", "what's (3 + 5) * 12")]}):
    pretty_print_messages(chunk)

Update from node call_model:


==================================[1m Ai Message [0m==================================

[{'text': "I'll help break this down into two steps:\n1. First calculate 3 + 5\n2. Then multiply that result by 12\n\nLet me make these calculations:\n\n1. Adding 3 and 5:", 'type': 'text'}, {'id': 'toolu_01DUAzgWFqq6XZtj1hzHTka9', 'input': {'a': 3, 'b': 5}, 'name': 'add', 'type': 'tool_use'}]
Tool Calls:
  add (toolu_01DUAzgWFqq6XZtj1hzHTka9)
 Call ID: toolu_01DUAzgWFqq6XZtj1hzHTka9
  Args:
    a: 3
    b: 5


Update from node call_tools:


=================================[1m Tool Message [0m=================================
Name: add

8


Update from node call_model:


==================================[1m Ai Message [0m==================================

[{'text': '2. Multiplying the result (8) by 12:', 'type': 'text'}, {'id': 'toolu_01QXi1prSN4etgJ1QCuFJsgN', 'input': {'a': 8, 'b': 12}, 'name': 'multiply', 'type': 'tool_use'}]
Tool Calls:
  multiply (toolu_01QXi1prSN4etgJ1QCuFJsgN)
 Call ID: toolu_01QXi1prSN4etgJ1QCuFJsgN
  Args:
    a: 8
    b: 12


Update from node call_tools:


=================================[1m Tool Message [0m=================================
Name: multiply

96


Update from node call_model:


==================================[1m Ai Message [0m==================================

The result of (3 + 5) * 12 = 96

现在，我们可以使用乘法和加法专家 Agent 来实现我们的多 Agent 系统。这次我们将为他们提供进行数学运算的工具，以及我们特殊的移交工具

addition_expert = make_agent(
    model,
    [add, make_handoff_tool(agent_name="multiplication_expert")],
    system_prompt="You are an addition expert, you can ask the multiplication expert for help with multiplication.",
)
multiplication_expert = make_agent(
    model,
    [multiply, make_handoff_tool(agent_name="addition_expert")],
    system_prompt="You are a multiplication expert, you can ask an addition expert for help with addition.",
)

builder = StateGraph(MessagesState)
builder.add_node("addition_expert", addition_expert)
builder.add_node("multiplication_expert", multiplication_expert)
builder.add_edge(START, "addition_expert")
graph = builder.compile()

让我们使用与之前相同的多步骤计算输入来运行该图

for chunk in graph.stream(
    {"messages": [("user", "what's (3 + 5) * 12")]}, subgraphs=True
):
    pretty_print_messages(chunk)

Update from subgraph addition_expert:


Update from node call_model:


==================================[1m Ai Message [0m==================================

[{'text': "I can help with the addition part (3 + 5), but I'll need to ask the multiplication expert for help with multiplying the result by 12. Let me break this down:\n\n1. First, let me calculate 3 + 5:", 'type': 'text'}, {'id': 'toolu_01McaW4XWczLGKaetg88fxQ5', 'input': {'a': 3, 'b': 5}, 'name': 'add', 'type': 'tool_use'}]
Tool Calls:
  add (toolu_01McaW4XWczLGKaetg88fxQ5)
 Call ID: toolu_01McaW4XWczLGKaetg88fxQ5
  Args:
    a: 3
    b: 5


Update from subgraph addition_expert:


Update from node call_tools:


=================================[1m Tool Message [0m=================================
Name: add

8


Update from subgraph addition_expert:


Update from node call_model:


==================================[1m Ai Message [0m==================================

[{'text': "Now that we have 8, we need to multiply it by 12. I'll ask the multiplication expert for help with this:", 'type': 'text'}, {'id': 'toolu_01KpdUhHuyrmha62z5SduKRc', 'input': {}, 'name': 'transfer_to_multiplication_expert', 'type': 'tool_use'}]
Tool Calls:
  transfer_to_multiplication_expert (toolu_01KpdUhHuyrmha62z5SduKRc)
 Call ID: toolu_01KpdUhHuyrmha62z5SduKRc
  Args:


Update from subgraph multiplication_expert:


Update from node call_model:


==================================[1m Ai Message [0m==================================

[{'text': 'Now that we have 8 as the result of the addition, I can help with the multiplication by 12:', 'type': 'text'}, {'id': 'toolu_01Vnp4k3TE87siad3BNJgRKb', 'input': {'a': 8, 'b': 12}, 'name': 'multiply', 'type': 'tool_use'}]
Tool Calls:
  multiply (toolu_01Vnp4k3TE87siad3BNJgRKb)
 Call ID: toolu_01Vnp4k3TE87siad3BNJgRKb
  Args:
    a: 8
    b: 12


Update from subgraph multiplication_expert:


Update from node call_tools:


=================================[1m Tool Message [0m=================================
Name: multiply

96


Update from subgraph multiplication_expert:


Update from node call_model:


==================================[1m Ai Message [0m==================================

The final result is 96.

To break down the steps:
1. 3 + 5 = 8
2. 8 * 12 = 96

我们可以看到，在加法专家完成计算的第一部分（在调用 add 工具之后）后，它决定移交给乘法专家，后者计算最终结果。

与预构建的 ReAct Agent 一起使用¶

如果您不需要额外的自定义，可以使用预构建的 create_react_agent，它通过 ToolNode 内置了对移交工具的支持。

from langgraph.prebuilt import create_react_agent

addition_expert = create_react_agent(
    model,
    [add, make_handoff_tool(agent_name="multiplication_expert")],
    prompt="You are an addition expert, you can ask the multiplication expert for help with multiplication.",
)

multiplication_expert = create_react_agent(
    model,
    [multiply, make_handoff_tool(agent_name="addition_expert")],
    prompt="You are a multiplication expert, you can ask an addition expert for help with addition.",
)

builder = StateGraph(MessagesState)
builder.add_node("addition_expert", addition_expert)
builder.add_node("multiplication_expert", multiplication_expert)
builder.add_edge(START, "addition_expert")
graph = builder.compile()

API 参考：create_react_agent

我们现在可以验证预构建的 ReAct Agent 与上面的自定义 Agent 工作方式完全相同

for chunk in graph.stream(
    {"messages": [("user", "what's (3 + 5) * 12")]}, subgraphs=True
):
    pretty_print_messages(chunk)

Update from subgraph addition_expert:


Update from node agent:


==================================[1m Ai Message [0m==================================

[{'text': "I can help with the addition part of this calculation (3 + 5), and then I'll need to ask the multiplication expert for help with multiplying the result by 12.\n\nLet me first calculate 3 + 5:", 'type': 'text'}, {'id': 'toolu_01GUasumGGJVXDV7TJEqEfmY', 'input': {'a': 3, 'b': 5}, 'name': 'add', 'type': 'tool_use'}]
Tool Calls:
  add (toolu_01GUasumGGJVXDV7TJEqEfmY)
 Call ID: toolu_01GUasumGGJVXDV7TJEqEfmY
  Args:
    a: 3
    b: 5


Update from subgraph addition_expert:


Update from node tools:


=================================[1m Tool Message [0m=================================
Name: add

8


Update from subgraph addition_expert:


Update from node agent:


==================================[1m Ai Message [0m==================================

[{'text': "Now that we have 8, we need to multiply it by 12. Since I'm an addition expert, I'll transfer this to the multiplication expert to complete the calculation:", 'type': 'text'}, {'id': 'toolu_014HEbwiH2jVno8r1Pc6t9Qh', 'input': {}, 'name': 'transfer_to_multiplication_expert', 'type': 'tool_use'}]
Tool Calls:
  transfer_to_multiplication_expert (toolu_014HEbwiH2jVno8r1Pc6t9Qh)
 Call ID: toolu_014HEbwiH2jVno8r1Pc6t9Qh
  Args:


Update from subgraph multiplication_expert:


Update from node agent:


==================================[1m Ai Message [0m==================================

[{'text': 'I notice I made a mistake - I actually don\'t have access to the "add" function or "transfer_to_multiplication_expert". Instead, I am the multiplication expert and I should ask the addition expert for help with the first part. Let me correct this:', 'type': 'text'}, {'id': 'toolu_01VAGpmr4ysHjvvuZp3q5Dzj', 'input': {}, 'name': 'transfer_to_addition_expert', 'type': 'tool_use'}]
Tool Calls:
  transfer_to_addition_expert (toolu_01VAGpmr4ysHjvvuZp3q5Dzj)
 Call ID: toolu_01VAGpmr4ysHjvvuZp3q5Dzj
  Args:


Update from subgraph addition_expert:


Update from node agent:


==================================[1m Ai Message [0m==================================

[{'text': "I'll help you with the addition part of (3 + 5) * 12. First, let me calculate 3 + 5:", 'type': 'text'}, {'id': 'toolu_01RE16cRGVo4CC4wwHFB6gaE', 'input': {'a': 3, 'b': 5}, 'name': 'add', 'type': 'tool_use'}]
Tool Calls:
  add (toolu_01RE16cRGVo4CC4wwHFB6gaE)
 Call ID: toolu_01RE16cRGVo4CC4wwHFB6gaE
  Args:
    a: 3
    b: 5


Update from subgraph addition_expert:


Update from node tools:


=================================[1m Tool Message [0m=================================
Name: add

8


Update from subgraph addition_expert:


Update from node agent:


==================================[1m Ai Message [0m==================================

[{'text': "Now that we have 8, we need to multiply it by 12. Since I'm an addition expert, I'll need to transfer this to the multiplication expert to complete the calculation:", 'type': 'text'}, {'id': 'toolu_01HBDRh64SzGcCp7EX1u3MFa', 'input': {}, 'name': 'transfer_to_multiplication_expert', 'type': 'tool_use'}]
Tool Calls:
  transfer_to_multiplication_expert (toolu_01HBDRh64SzGcCp7EX1u3MFa)
 Call ID: toolu_01HBDRh64SzGcCp7EX1u3MFa
  Args:


Update from subgraph multiplication_expert:


Update from node agent:


==================================[1m Ai Message [0m==================================

[{'text': 'Now that I have the result of 3 + 5 = 8, I can help with multiplying by 12:', 'type': 'text'}, {'id': 'toolu_014Ay95rsKvvbWWJV4CcZSPY', 'input': {'a': 8, 'b': 12}, 'name': 'multiply', 'type': 'tool_use'}]
Tool Calls:
  multiply (toolu_014Ay95rsKvvbWWJV4CcZSPY)
 Call ID: toolu_014Ay95rsKvvbWWJV4CcZSPY
  Args:
    a: 8
    b: 12


Update from subgraph multiplication_expert:


Update from node tools:


=================================[1m Tool Message [0m=================================
Name: multiply

96


Update from subgraph multiplication_expert:


Update from node agent:


==================================[1m Ai Message [0m==================================

The final result is 96. Here's the complete calculation:
(3 + 5) * 12 = 8 * 12 = 96