从 langgraph-supervisor 迁移

from langgraph_supervisor import create_supervisor
from langgraph.prebuilt import create_react_agent

research_agent = create_react_agent(
    model=model,
    tools=[web_search],
    name="research_expert",
    prompt="You are a research expert.",
)

math_agent = create_react_agent(
    model=model,
    tools=[add, multiply],
    name="math_expert",
    prompt="You are a math expert.",
)

workflow = create_supervisor(
    [research_agent, math_agent],
    model=model,
    prompt="Route research questions to research_expert and math to math_expert.",
)
app = workflow.compile(checkpointer=checkpointer)

from langchain.agents import create_agent
from langchain.tools import tool
from langgraph.checkpoint.memory import InMemorySaver

research_agent = create_agent(
    model=model,
    tools=[web_search],
    system_prompt="您是一位研究专家。",
)

math_agent = create_agent(
    model=model,
    tools=[add, multiply],
    system_prompt="您是一位数学专家。",
)


@tool("research_expert", description="用于时事和网络查询的研究专家。")
def call_research_agent(query: str) -> str:
    result = research_agent.invoke({"messages": [{"role": "user", "content": query}]})
    return result["messages"][-1].content


@tool("math_expert", description="用于计算的数学专家。")
def call_math_agent(query: str) -> str:
    result = math_agent.invoke({"messages": [{"role": "user", "content": query}]})
    return result["messages"][-1].content


supervisor = create_agent(
    model=model,
    tools=[call_research_agent, call_math_agent],
    system_prompt=(
        "将研究问题路由至研究专家，数学问题路由至数学专家。"
    ),
    checkpointer=InMemorySaver(),
)

# 之前：使用子图节点的 create_supervisor
#
# 监督器 (create_supervisor)
#   └── ResearchAgent (子图节点)
#         └── preview_tool
#               ├── fire_external_api()      # 启动异步作业
#               ├── result = interrupt(...)  # 暂停图，等待回调
#               └── render_results(result)   # 恢复后运行

from langchain.agents import create_agent
from langchain.tools import tool
from langgraph.checkpoint.memory import InMemorySaver
from langgraph.types import interrupt


@tool
def preview_tool(document_id: str) -> str:
    """运行一个异步的增强预览并等待结果。"""
    job_id = fire_external_api(document_id)
    result = interrupt({"job_id": job_id, "status": "pending"})
    return render_results(result)


research_agent = create_agent(
    model=model,
    tools=[preview_tool],
    system_prompt="你是一个研究代理。",
)

@tool("research_agent", description="研究和增强任务。")
def call_research_agent(query: str) -> str:
    result = research_agent.invoke({"messages": [{"role": "user", "content": query}]})
    return result["messages"][-1].content

supervisor = create_agent(
    model=model,
    tools=[call_research_agent],
    system_prompt="将研究任务委派给 research_agent。",
    checkpointer=InMemorySaver(),
)

config = {"configurable": {"thread_id": "1"}}

from langgraph.types import Command

# 调用 — preview_tool 调用 interrupt()，图暂停
response = supervisor.invoke(
    {"messages": [{"role": "user", "content": "Preview enrichment for doc-123"}]},
    config=config,
)
# response 包含 __interrupt__

# 外部服务完成并回调到您的应用
supervisor.invoke(Command(resume=external_result), config=config)

Custom StateGraph (outer, with checkpointer)
  └── prospecting_agent (create_agent, no checkpointer)
        └── call_powerup_agent tool → powerup_agent.invoke(...)
              └── powerup_agent (create_agent, no checkpointer)
                    └── preview_tool → interrupt(...)

from langchain.agents import create_agent
from langchain.tools import tool
from langgraph.checkpoint.memory import InMemorySaver

# 具有自身子代理的中间层代理
billing_team = create_agent(
    model=model,
    tools=[call_refunds_agent, call_invoices_agent],
    system_prompt="协调计费专家。",
)

@tool("billing_team", description="处理计费、退款和发票。")
def call_billing_team(query: str) -> str:
    result = billing_team.invoke({"messages": [{"role": "user", "content": query}]})
    return result["messages"][-1].content

# 顶层监督代理
top_supervisor = create_agent(
    model=model,
    tools=[call_billing_team, call_support_agent],
    system_prompt="将计费问题路由至 billing_team，一般支持问题路由至 support_agent。",
    checkpointer=InMemorySaver(),
)