概述
主管模式是一种多智能体架构,其中中央主管智能体协调专门的工作智能体。这种方法在任务需要不同类型专业知识时表现出色。与其构建一个跨领域管理工具选择的智能体,不如创建由了解整体工作流程的主管协调的专注专家。 在本教程中,您将构建一个个人助理系统,通过一个真实的工作流程来展示这些优势。该系统将协调两个职责根本不同的专家:- 一个日历智能体,负责处理日程安排、可用性检查和事件管理。
- 一个电子邮件智能体,负责管理通信、起草消息和发送通知。
为什么使用主管?
多智能体架构允许您将工具分配给各个工作智能体,每个智能体都有自己的提示或指令。考虑一个直接访问所有日历和电子邮件 API 的智能体:它必须从许多相似的工具中选择,了解每个 API 的确切格式,并同时处理多个领域。如果性能下降,将相关工具和关联提示分离到逻辑组中可能会有所帮助(部分原因是为了管理迭代改进)。概念
我们将涵盖以下概念:设置
安装
本教程需要langchain 包:
pip install langchain
LangSmith
设置 LangSmith 以检查智能体内部发生的情况。然后设置以下环境变量:export LANGSMITH_TRACING="true"
export LANGSMITH_API_KEY="..."
组件
我们需要从 LangChain 的集成套件中选择一个聊天模型:- OpenAI
- Anthropic
- Azure
- Google Gemini
- AWS Bedrock
- HuggingFace
- OpenRouter
👉 阅读 OpenAI 聊天模型集成文档
pip install -U "langchain[openai]"
import os
from langchain.chat_models import init_chat_model
os.environ["OPENAI_API_KEY"] = "sk-..."
model = init_chat_model("gpt-5.2")
👉 阅读 Anthropic 聊天模型集成文档
pip install -U "langchain[anthropic]"
import os
from langchain.chat_models import init_chat_model
os.environ["ANTHROPIC_API_KEY"] = "sk-..."
model = init_chat_model("claude-sonnet-4-6")
👉 阅读 Azure 聊天模型集成文档
pip install -U "langchain[openai]"
import os
from langchain.chat_models import init_chat_model
os.environ["AZURE_OPENAI_API_KEY"] = "..."
os.environ["AZURE_OPENAI_ENDPOINT"] = "..."
os.environ["OPENAI_API_VERSION"] = "2025-03-01-preview"
model = init_chat_model(
"azure_openai:gpt-5.2",
azure_deployment=os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"],
)
👉 阅读 Google GenAI 聊天模型集成文档
pip install -U "langchain[google-genai]"
import os
from langchain.chat_models import init_chat_model
os.environ["GOOGLE_API_KEY"] = "..."
model = init_chat_model("google_genai:gemini-2.5-flash-lite")
👉 阅读 AWS Bedrock 聊天模型集成文档
pip install -U "langchain[aws]"
from langchain.chat_models import init_chat_model
# 按照此处步骤配置您的凭据:
# https://docs.aws.amazon.com/bedrock/latest/userguide/getting-started.html
model = init_chat_model(
"anthropic.claude-3-5-sonnet-20240620-v1:0",
model_provider="bedrock_converse",
)
👉 阅读 HuggingFace 聊天模型集成文档
pip install -U "langchain[huggingface]"
import os
from langchain.chat_models import init_chat_model
os.environ["HUGGINGFACEHUB_API_TOKEN"] = "hf_..."
model = init_chat_model(
"microsoft/Phi-3-mini-4k-instruct",
model_provider="huggingface",
temperature=0.7,
max_tokens=1024,
)
👉 阅读 OpenRouter 聊天模型集成文档
pip install -U "langchain-openrouter"
import os
from langchain.chat_models import init_chat_model
os.environ["OPENROUTER_API_KEY"] = "sk-..."
model = init_chat_model(
"auto",
model_provider="openrouter",
)
1. 定义工具
首先定义需要结构化输入的工具。在实际应用中,这些工具会调用实际的 API(Google Calendar、SendGrid 等)。在本教程中,您将使用存根来演示该模式。from langchain.tools import tool
@tool
def create_calendar_event(
title: str,
start_time: str, # ISO 格式: "2024-01-15T14:00:00"
end_time: str, # ISO 格式: "2024-01-15T15:00:00"
attendees: list[str], # 电子邮件地址
location: str = ""
) -> str:
"""创建日历事件。需要精确的 ISO 日期时间格式。"""
# 存根:在实践中,这将调用 Google Calendar API、Outlook API 等。
return f"事件已创建:{title},从 {start_time} 到 {end_time},有 {len(attendees)} 位与会者"
@tool
def send_email(
to: list[str], # 电子邮件地址
subject: str,
body: str,
cc: list[str] = []
) -> str:
"""通过电子邮件 API 发送电子邮件。需要格式正确的地址。"""
# 存根:在实践中,这将调用 SendGrid、Gmail API 等。
return f"电子邮件已发送至 {', '.join(to)} - 主题:{subject}"
@tool
def get_available_time_slots(
attendees: list[str],
date: str, # ISO 格式: "2024-01-15"
duration_minutes: int
) -> list[str]:
"""检查特定日期给定与会者的日历可用性。"""
# 存根:在实践中,这将查询日历 API
return ["09:00", "14:00", "16:00"]
2. 创建专门的子智能体
接下来,我们将创建处理每个领域的专门子智能体。创建日历智能体
日历智能体理解自然语言调度请求,并将其转换为精确的 API 调用。它处理日期解析、可用性检查和事件创建。from langchain.agents import create_agent
CALENDAR_AGENT_PROMPT = (
"您是一个日历调度助手。 "
"将自然语言调度请求(例如,'下周二下午 2 点')解析为正确的 ISO 日期时间格式。 "
"需要时使用 get_available_time_slots 检查可用性。 "
"如果没有合适的时间段,请停止并在您的响应中确认不可用。 "
"使用 create_calendar_event 安排事件。 "
"始终在最终响应中确认已安排的内容。"
)
calendar_agent = create_agent(
model,
tools=[create_calendar_event, get_available_time_slots],
system_prompt=CALENDAR_AGENT_PROMPT,
)
query = "安排一个团队会议,下周二下午 2 点,持续 1 小时"
for step in calendar_agent.stream(
{"messages": [{"role": "user", "content": query}]}
):
for update in step.values():
for message in update.get("messages", []):
message.pretty_print()
================================== Ai Message ==================================
Tool Calls:
get_available_time_slots (call_EIeoeIi1hE2VmwZSfHStGmXp)
Call ID: call_EIeoeIi1hE2VmwZSfHStGmXp
Args:
attendees: []
date: 2024-06-18
duration_minutes: 60
================================= Tool Message =================================
Name: get_available_time_slots
["09:00", "14:00", "16:00"]
================================== Ai Message ==================================
Tool Calls:
create_calendar_event (call_zgx3iJA66Ut0W8S3NpT93kEB)
Call ID: call_zgx3iJA66Ut0W8S3NpT93kEB
Args:
title: Team Meeting
start_time: 2024-06-18T14:00:00
end_time: 2024-06-18T15:00:00
attendees: []
================================= Tool Message =================================
Name: create_calendar_event
Event created: Team Meeting from 2024-06-18T14:00:00 to 2024-06-18T15:00:00 with 0 attendees
================================== Ai Message ==================================
The team meeting has been scheduled for next Tuesday, June 18th, at 2:00 PM and will last for 1 hour. If you need to add attendees or a location, please let me know!
create_calendar_event,并返回自然语言确认。
创建电子邮件智能体
电子邮件智能体处理消息的撰写和发送。它专注于提取收件人信息、撰写适当的主题行和正文文本,以及管理电子邮件通信。EMAIL_AGENT_PROMPT = (
"您是一个电子邮件助手。 "
"根据自然语言请求撰写专业电子邮件。 "
"提取收件人信息并撰写适当的主题行和正文文本。 "
"使用 send_email 发送消息。 "
"始终在最终响应中确认已发送的内容。"
)
email_agent = create_agent(
model,
tools=[send_email],
system_prompt=EMAIL_AGENT_PROMPT,
)
query = "向设计团队发送提醒,要求他们审阅新的模型"
for step in email_agent.stream(
{"messages": [{"role": "user", "content": query}]}
):
for update in step.values():
for message in update.get("messages", []):
message.pretty_print()
================================== Ai Message ==================================
Tool Calls:
send_email (call_OMl51FziTVY6CRZvzYfjYOZr)
Call ID: call_OMl51FziTVY6CRZvzYfjYOZr
Args:
to: ['design-team@example.com']
subject: Reminder: Please Review the New Mockups
body: Hi Design Team,
This is a friendly reminder to review the new mockups at your earliest convenience. Your feedback is important to ensure that we stay on track with our project timeline.
Please let me know if you have any questions or need additional information.
Thank you!
Best regards,
================================= Tool Message =================================
Name: send_email
Email sent to design-team@example.com - Subject: Reminder: Please Review the New Mockups
================================== Ai Message ==================================
I've sent a reminder to the design team asking them to review the new mockups. If you need any further communication on this topic, just let me know!
send_email,并返回确认。每个子智能体都有狭窄的关注点,具有特定领域的工具和提示,使其能够在其特定任务中表现出色。
3. 将子智能体包装为工具
现在将每个子智能体包装为一个工具,供主管调用。这是创建分层系统的关键架构步骤。主管将看到高级工具,如 “schedule_event”,而不是低级工具,如 “create_calendar_event”。@tool
def schedule_event(request: str) -> str:
"""使用自然语言安排日历事件。
当用户想要创建、修改或检查日历预约时使用此工具。
处理日期/时间解析、可用性检查和事件创建。
输入:自然语言调度请求(例如,'与设计团队开会,下周二下午 2 点')
"""
result = calendar_agent.invoke({
"messages": [{"role": "user", "content": request}]
})
return result["messages"][-1].text
@tool
def manage_email(request: str) -> str:
"""使用自然语言发送电子邮件。
当用户想要发送通知、提醒或任何电子邮件通信时使用此工具。
处理收件人提取、主题生成和电子邮件撰写。
输入:自然语言电子邮件请求(例如,'向他们发送有关会议的提醒')
"""
result = email_agent.invoke({
"messages": [{"role": "user", "content": request}]
})
return result["messages"][-1].text
4. 创建主管智能体
现在创建协调子智能体的主管。主管只看到高级工具,并在领域级别(而不是单个 API 级别)做出路由决策。SUPERVISOR_PROMPT = (
"您是一个乐于助人的个人助理。 "
"您可以安排日历事件和发送电子邮件。 "
"将用户请求分解为适当的工具调用并协调结果。 "
"当请求涉及多个操作时,按顺序使用多个工具。"
)
supervisor_agent = create_agent(
model,
tools=[schedule_event, manage_email],
system_prompt=SUPERVISOR_PROMPT,
)
5. 使用主管
现在使用需要跨多个领域协调的复杂请求测试您的完整系统:示例 1:简单的单领域请求
query = "安排一个团队站会,明天上午 9 点"
for step in supervisor_agent.stream(
{"messages": [{"role": "user", "content": query}]}
):
for update in step.values():
for message in update.get("messages", []):
message.pretty_print()
================================== Ai Message ==================================
Tool Calls:
schedule_event (call_mXFJJDU8bKZadNUZPaag8Lct)
Call ID: call_mXFJJDU8bKZadNUZPaag8Lct
Args:
request: Schedule a team standup for tomorrow at 9am with Alice and Bob.
================================= Tool Message =================================
Name: schedule_event
The team standup has been scheduled for tomorrow at 9:00 AM with Alice and Bob. If you need to make any changes or add more details, just let me know!
================================== Ai Message ==================================
The team standup with Alice and Bob is scheduled for tomorrow at 9:00 AM. If you need any further arrangements or adjustments, please let me know!
schedule_event,日历智能体处理日期解析和事件创建。
要全面了解信息流,包括每个聊天模型调用的提示和响应,请查看上述运行的 LangSmith 追踪。
示例 2:复杂的多领域请求
query = (
"安排与设计团队的会议,下周二下午 2 点,持续 1 小时,"
"并向他们发送一封关于审阅新模型的电子邮件提醒。"
)
for step in supervisor_agent.stream(
{"messages": [{"role": "user", "content": query}]}
):
for update in step.values():
for message in update.get("messages", []):
message.pretty_print()
================================== Ai Message ==================================
Tool Calls:
schedule_event (call_YA68mqF0koZItCFPx0kGQfZi)
Call ID: call_YA68mqF0koZItCFPx0kGQfZi
Args:
request: meeting with the design team next Tuesday at 2pm for 1 hour
manage_email (call_XxqcJBvVIuKuRK794ZIzlLxx)
Call ID: call_XxqcJBvVIuKuRK794ZIzlLxx
Args:
request: send the design team an email reminder about reviewing the new mockups
================================= Tool Message =================================
Name: schedule_event
Your meeting with the design team is scheduled for next Tuesday, June 18th, from 2:00pm to 3:00pm. Let me know if you need to add more details or make any changes!
================================= Tool Message =================================
Name: manage_email
I've sent an email reminder to the design team requesting them to review the new mockups. If you need to include more information or recipients, just let me know!
================================== Ai Message ==================================
Your meeting with the design team is scheduled for next Tuesday, June 18th, from 2:00pm to 3:00pm.
I've also sent an email reminder to the design team, asking them to review the new mockups.
Let me know if you'd like to add more details to the meeting or include additional information in the email!
schedule_event,然后为提醒调用 manage_email。每个子智能体完成其任务,主管将两个结果综合成一个连贯的响应。
请参阅 LangSmith 追踪 以查看上述运行的详细信息流,包括各个聊天模型的提示和响应。
完整的工作示例
以下是所有内容组合在一起的可运行脚本:Show 查看完整代码
Show 查看完整代码
"""
个人助理主管示例
此示例演示了多智能体系统的工具调用模式。
一个主管智能体协调专门的子智能体(日历和电子邮件),
这些子智能体被包装为工具。
"""
from langchain.tools import tool
from langchain.agents import create_agent
from langchain.chat_models import init_chat_model
# ============================================================================
# 步骤 1:定义低级 API 工具(存根)
# ============================================================================
@tool
def create_calendar_event(
title: str,
start_time: str, # ISO 格式: "2024-01-15T14:00:00"
end_time: str, # ISO 格式: "2024-01-15T15:00:00"
attendees: list[str], # 电子邮件地址
location: str = ""
) -> str:
"""创建日历事件。需要精确的 ISO 日期时间格式。"""
return f"事件已创建:{title},从 {start_time} 到 {end_time},有 {len(attendees)} 位与会者"
@tool
def send_email(
to: list[str], # 电子邮件地址
subject: str,
body: str,
cc: list[str] = []
) -> str:
"""通过电子邮件 API 发送电子邮件。需要格式正确的地址。"""
return f"电子邮件已发送至 {', '.join(to)} - 主题:{subject}"
@tool
def get_available_time_slots(
attendees: list[str],
date: str, # ISO 格式: "2024-01-15"
duration_minutes: int
) -> list[str]:
"""检查特定日期给定与会者的日历可用性。"""
return ["09:00", "14:00", "16:00"]
# ============================================================================
# 步骤 2:创建专门的子智能体
# ============================================================================
model = init_chat_model("gpt-5.4") # 示例
calendar_agent = create_agent(
model,
tools=[create_calendar_event, get_available_time_slots],
system_prompt=(
"您是一个日历调度助手。 "
"将自然语言调度请求(例如,'下周二下午 2 点')解析为正确的 ISO 日期时间格式。 "
"需要时使用 get_available_time_slots 检查可用性。 "
"如果没有合适的时间段,请停止并在您的响应中确认不可用。 "
"使用 create_calendar_event 安排事件。 "
"始终在最终响应中确认已安排的内容。"
)
)
email_agent = create_agent(
model,
tools=[send_email],
system_prompt=(
"您是一个电子邮件助手。 "
"根据自然语言请求撰写专业电子邮件。 "
"提取收件人信息并撰写适当的主题行和正文文本。 "
"使用 send_email 发送消息。 "
"始终在最终响应中确认已发送的内容。"
)
)
# ============================================================================
# 步骤 3:将子智能体包装为供主管使用的工具
# ============================================================================
@tool
def schedule_event(request: str) -> str:
"""使用自然语言安排日历事件。
当用户想要创建、修改或检查日历预约时使用此工具。
处理日期/时间解析、可用性检查和事件创建。
输入:自然语言调度请求(例如,'与设计团队开会,下周二下午 2 点')
"""
result = calendar_agent.invoke({
"messages": [{"role": "user", "content": request}]
})
return result["messages"][-1].text
@tool
def manage_email(request: str) -> str:
"""使用自然语言发送电子邮件。
当用户想要发送通知、提醒或任何电子邮件通信时使用此工具。
处理收件人提取、主题生成和电子邮件撰写。
输入:自然语言电子邮件请求(例如,'向他们发送有关会议的提醒')
"""
result = email_agent.invoke({
"messages": [{"role": "user", "content": request}]
})
return result["messages"][-1].text
# ============================================================================
# 步骤 4:创建主管智能体
# ============================================================================
supervisor_agent = create_agent(
model,
tools=[schedule_event, manage_email],
system_prompt=(
"您是一个乐于助人的个人助理。 "
"您可以安排日历事件和发送电子邮件。 "
"将用户请求分解为适当的工具调用并协调结果。 "
"当请求涉及多个操作时,按顺序使用多个工具。"
)
)
# ============================================================================
# 步骤 5:使用主管
# ============================================================================
if __name__ == "__main__":
# 示例:需要日历和电子邮件协调的用户请求
user_request = (
"安排与设计团队的会议,下周二下午 2 点,持续 1 小时,"
"并向他们发送一封关于审阅新模型的电子邮件提醒。"
)
print("用户请求:", user_request)
print("\n" + "="*80 + "\n")
for step in supervisor_agent.stream(
{"messages": [{"role": "user", "content": user_request}]}
):
for update in step.values():
for message in update.get("messages", []):
message.pretty_print()
理解架构
您的系统有三层。底层包含需要精确格式的刚性 API 工具。中间层包含接受自然语言、将其转换为结构化 API 调用并返回自然语言确认的子智能体。顶层包含主管,它路由到高级功能并综合结果。 这种关注点分离提供了几个好处:每个层都有专注的职责,您可以添加新领域而不影响现有领域,并且可以独立测试和迭代每个层。6. 添加人机回环审查
对于敏感操作,结合人机回环审查是谨慎的做法。LangChain 包含内置中间件来审查工具调用,在本例中是由子智能体调用的工具。 让我们为人机回环审查添加到两个子智能体:- 我们将
create_calendar_event和send_email工具配置为中断,允许所有响应类型(approve、edit、reject) - 我们仅在顶级智能体添加一个检查点。这是暂停和恢复执行所必需的。
from langchain.agents import create_agent
from langchain.agents.middleware import HumanInTheLoopMiddleware
from langgraph.checkpoint.memory import InMemorySaver
calendar_agent = create_agent(
model,
tools=[create_calendar_event, get_available_time_slots],
system_prompt=CALENDAR_AGENT_PROMPT,
middleware=[
HumanInTheLoopMiddleware(
interrupt_on={"create_calendar_event": True},
description_prefix="Calendar event pending approval",
),
],
)
email_agent = create_agent(
model,
tools=[send_email],
system_prompt=EMAIL_AGENT_PROMPT,
middleware=[
HumanInTheLoopMiddleware(
interrupt_on={"send_email": True},
description_prefix="Outbound email pending approval",
),
],
)
supervisor_agent = create_agent(
model,
tools=[schedule_event, manage_email],
system_prompt=SUPERVISOR_PROMPT,
checkpointer=InMemorySaver(),
)
query = (
"安排与设计团队的会议,下周二下午 2 点,持续 1 小时,"
"并向他们发送一封关于审阅新模型的电子邮件提醒。"
)
config = {"configurable": {"thread_id": "6"}}
interrupts = []
for step in supervisor_agent.stream(
{"messages": [{"role": "user", "content": query}]},
config,
):
for update in step.values():
if isinstance(update, dict):
for message in update.get("messages", []):
message.pretty_print()
else:
interrupt_ = update[0]
interrupts.append(interrupt_)
print(f"\nINTERRUPTED: {interrupt_.id}")
================================== Ai Message ==================================
Tool Calls:
schedule_event (call_t4Wyn32ohaShpEZKuzZbl83z)
Call ID: call_t4Wyn32ohaShpEZKuzZbl83z
Args:
request: Schedule a meeting with the design team next Tuesday at 2pm for 1 hour.
manage_email (call_JWj4vDJ5VMnvkySymhCBm4IR)
Call ID: call_JWj4vDJ5VMnvkySymhCBm4IR
Args:
request: Send an email reminder to the design team about reviewing the new mockups before our meeting next Tuesday at 2pm.
INTERRUPTED: 4f994c9721682a292af303ec1a46abb7
INTERRUPTED: 2b56f299be313ad8bc689eff02973f16
for interrupt_ in interrupts:
for request in interrupt_.value["action_requests"]:
print(f"INTERRUPTED: {interrupt_.id}")
print(f"{request['description']}\n")
INTERRUPTED: 4f994c9721682a292af303ec1a46abb7
Calendar event pending approval
Tool: create_calendar_event
Args: {'title': 'Meeting with the Design Team', 'start_time': '2024-06-18T14:00:00', 'end_time': '2024-06-18T15:00:00', 'attendees': ['design team']}
INTERRUPTED: 2b56f299be313ad8bc689eff02973f16
Outbound email pending approval
Tool: send_email
Args: {'to': ['designteam@example.com'], 'subject': 'Reminder: Review New Mockups Before Meeting Next Tuesday at 2pm', 'body': "Hello Team,\n\nThis is a reminder to review the new mockups ahead of our meeting scheduled for next Tuesday at 2pm. Your feedback and insights will be valuable for our discussion and next steps.\n\nPlease ensure you've gone through the designs and are ready to share your thoughts during the meeting.\n\nThank you!\n\nBest regards,\n[Your Name]"}
Command 通过其 ID 为每个中断指定决策。有关更多详细信息,请参阅人机回环指南。出于演示目的,这里我们将接受日历事件,但编辑外发电子邮件的主题:
from langgraph.types import Command
resume = {}
for interrupt_ in interrupts:
if interrupt_.id == "2b56f299be313ad8bc689eff02973f16":
# 编辑电子邮件
edited_action = interrupt_.value["action_requests"][0].copy()
edited_action["args"]["subject"] = "Mockups reminder"
resume[interrupt_.id] = {
"decisions": [{"type": "edit", "edited_action": edited_action}]
}
else:
resume[interrupt_.id] = {"decisions": [{"type": "approve"}]}
interrupts = []
for step in supervisor_agent.stream(
Command(resume=resume),
config,
):
for update in step.values():
if isinstance(update, dict):
for message in update.get("messages", []):
message.pretty_print()
else:
interrupt_ = update[0]
interrupts.append(interrupt_)
print(f"\nINTERRUPTED: {interrupt_.id}")
================================= Tool Message =================================
Name: schedule_event
Your meeting with the design team has been scheduled for next Tuesday, June 18th, from 2:00 pm to 3:00 pm.
================================= Tool Message =================================
Name: manage_email
Your email reminder to the design team has been sent. Here’s what was sent:
- Recipient: designteam@example.com
- Subject: Mockups reminder
- Body: A reminder to review the new mockups before the meeting next Tuesday at 2pm, with a request for feedback and readiness for discussion.
Let me know if you need any further assistance!
================================== Ai Message ==================================
- Your meeting with the design team has been scheduled for next Tuesday, June 18th, from 2:00 pm to 3:00 pm.
- An email reminder has been sent to the design team about reviewing the new mockups before the meeting.
Let me know if you need any further assistance!
7. 高级:控制信息流
默认情况下,子智能体只接收来自主管的请求字符串。您可能希望传递额外的上下文,例如对话历史或用户偏好。向子智能体传递额外的对话上下文
from langchain.tools import tool, ToolRuntime
@tool
def schedule_event(
request: str,
runtime: ToolRuntime
) -> str:
"""使用自然语言安排日历事件。"""
# 自定义子智能体接收的上下文
original_user_message = next(
message for message in runtime.state["messages"]
if message.type == "human"
)
prompt = (
"您正在协助处理以下用户查询:\n\n"
f"{original_user_message.text}\n\n"
"您的任务是处理以下子请求:\n\n"
f"{request}"
)
result = calendar_agent.invoke({
"messages": [{"role": "user", "content": prompt}],
})
return result["messages"][-1].text
您可以在 LangSmith 追踪的聊天模型调用 中查看子智能体接收的完整上下文。
控制主管接收的内容
您还可以自定义流回主管的信息:import json
@tool
def schedule_event(request: str) -> str:
"""使用自然语言安排日历事件。"""
result = calendar_agent.invoke({
"messages": [{"role": "user", "content": request}]
})
# 选项 1:仅返回确认消息
return result["messages"][-1].text
# 选项 2:返回结构化数据
# return json.dumps({
# "status": "success",
# "event_id": "evt_123",
# "summary": result["messages"][-1].text
# })
8. 关键要点
主管模式创建了抽象层,其中每个层都有明确的职责。在设计主管系统时,从清晰的领域边界开始,并为每个子智能体提供专注的工具和提示。为主管编写清晰的工具描述,在集成之前独立测试每个层,并根据您的特定需求控制信息流。何时使用主管模式当您有多个不同的领域(日历、电子邮件、CRM、数据库),每个领域有多个工具或复杂逻辑,您想要集中式工作流控制,并且子智能体不需要直接与用户对话时,使用主管模式。对于只有几个工具的简单情况,请使用单个智能体。当智能体需要与用户进行对话时,请使用交接。对于智能体之间的点对点协作,请考虑其他多智能体模式。

