本指南回顾常见的工作流和智能体模式。

• - 工作流具有预定的代码路径，旨在按特定顺序运行。
• - 智能体是动态的，可以定义自己的流程和工具使用方式。

!智能体工作流

LangGraph 在构建智能体和工作流时提供了多项优势，包括 持久化, 流式输出，并支持调试以及 部署.

设置

要构建工作流或智能体，您可以使用 任何支持结构化输出和工具调用的聊天模型 。以下示例使用 Anthropic：

1. 安装依赖项：

pip install langchain_core langchain-anthropic langgraph

1. 初始化 LLM：

from langchain_anthropic import ChatAnthropic

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


_set_env("ANTHROPIC_API_KEY")

llm = ChatAnthropic(model="claude-sonnet-4-6")

LLM 及其增强功能

工作流和智能体系统建立在 LLM 及其各种增强功能之上。 工具调用, 结构化输出和 短期记忆 是定制 LLM 以满足您需求的几种选择。

!LLM 增强功能

# Schema for structured output
from pydantic import BaseModel, Field


class SearchQuery(BaseModel):
    search_query: str = Field(None, description="Query that is optimized web search.")
    justification: str = Field(
        None, description="Why this query is relevant to the user's request."
    )


# Augment the LLM with schema for structured output
structured_llm = llm.with_structured_output(SearchQuery)

# Invoke the augmented LLM
output = structured_llm.invoke("How does Calcium CT score relate to high cholesterol?")

# Define a tool
def multiply(a: int, b: int) -> int:
    return a * b

# Augment the LLM with tools
llm_with_tools = llm.bind_tools([multiply])

# Invoke the LLM with input that triggers the tool call
msg = llm_with_tools.invoke("What is 2 times 3?")

# Get the tool call
msg.tool_calls

提示链

提示链是指每个 LLM 调用都处理前一个调用的输出。它通常用于执行可以分解为更小、可验证步骤的明确定义的任务。一些示例包括：

• - 将文档翻译成不同语言
• - 验证生成内容的一致性

!提示链

from typing_extensions import TypedDict
from langgraph.graph import StateGraph, START, END
from IPython.display import Image, display


# Graph state
class State(TypedDict):
    topic: str
    joke: str
    improved_joke: str
    final_joke: str


# Nodes
def generate_joke(state: State):
    """First LLM call to generate initial joke"""

    msg = llm.invoke(f"Write a short joke about {state['topic']}")
    return {"joke": msg.content}


def check_punchline(state: State):
    """Gate function to check if the joke has a punchline"""

    # Simple check - does the joke contain "?" or "!"
    if "?" in state["joke"] or "!" in state["joke"]:
        return "Pass"
    return "Fail"


def improve_joke(state: State):
    """Second LLM call to improve the joke"""

    msg = llm.invoke(f"Make this joke funnier by adding wordplay: {state['joke']}")
    return {"improved_joke": msg.content}


def polish_joke(state: State):
    """Third LLM call for final polish"""
    msg = llm.invoke(f"Add a surprising twist to this joke: {state['improved_joke']}")
    return {"final_joke": msg.content}


# Build workflow
workflow = StateGraph(State)

# Add nodes
workflow.add_node("generate_joke", generate_joke)
workflow.add_node("improve_joke", improve_joke)
workflow.add_node("polish_joke", polish_joke)

# Add edges to connect nodes
workflow.add_edge(START, "generate_joke")
workflow.add_conditional_edges(
    "generate_joke", check_punchline, {"Fail": "improve_joke", "Pass": END}
)
workflow.add_edge("improve_joke", "polish_joke")
workflow.add_edge("polish_joke", END)

# Compile
chain = workflow.compile()

# Show workflow
display(Image(chain.get_graph().draw_mermaid_png()))

# Invoke
state = chain.invoke({"topic": "cats"})
print("Initial joke:")
print(state["joke"])
print("\n--- --- ---\n")
if "improved_joke" in state:
    print("Improved joke:")
    print(state["improved_joke"])
    print("\n--- --- ---\n")

    print("Final joke:")
    print(state["final_joke"])
else:
    print("Final joke:")
    print(state["joke"])

from langgraph.func import entrypoint, task


# Tasks
@task
def generate_joke(topic: str):
    """First LLM call to generate initial joke"""
    msg = llm.invoke(f"Write a short joke about {topic}")
    return msg.content


def check_punchline(joke: str):
    """Gate function to check if the joke has a punchline"""
    # Simple check - does the joke contain "?" or "!"
    if "?" in joke or "!" in joke:
        return "Fail"

    return "Pass"


@task
def improve_joke(joke: str):
    """Second LLM call to improve the joke"""
    msg = llm.invoke(f"Make this joke funnier by adding wordplay: {joke}")
    return msg.content


@task
def polish_joke(joke: str):
    """Third LLM call for final polish"""
    msg = llm.invoke(f"Add a surprising twist to this joke: {joke}")
    return msg.content


@entrypoint()
def prompt_chaining_workflow(topic: str):
    original_joke = generate_joke(topic).result()
    if check_punchline(original_joke) == "Pass":
        return original_joke

    improved_joke = improve_joke(original_joke).result()
    return polish_joke(improved_joke).result()

# Invoke
stream = prompt_chaining_workflow.stream_events("cats", version="v3")
for snapshot in stream.values:
    print(snapshot)
    print("\n")

并行化

通过并行化，LLM 可以同时处理任务。这可以通过同时运行多个独立子任务来实现，也可以通过多次运行同一任务来检查不同输出。并行化通常用于：

• - 拆分子任务并并行运行，以提高速度
• - 多次运行任务以检查不同输出，以提高置信度

一些示例包括：

• - 运行一个处理文档关键词的子任务，以及一个检查格式错误的子任务
• - 多次运行一个根据不同标准对文档准确性评分的任务，例如引用数量、使用的来源数量和来源质量

!parallelization.png

# Graph state
class State(TypedDict):
    topic: str
    joke: str
    story: str
    poem: str
    combined_output: str


# Nodes
def call_llm_1(state: State):
    """First LLM call to generate initial joke"""

    msg = llm.invoke(f"Write a joke about {state['topic']}")
    return {"joke": msg.content}


def call_llm_2(state: State):
    """Second LLM call to generate story"""

    msg = llm.invoke(f"Write a story about {state['topic']}")
    return {"story": msg.content}


def call_llm_3(state: State):
    """Third LLM call to generate poem"""

    msg = llm.invoke(f"Write a poem about {state['topic']}")
    return {"poem": msg.content}


def aggregator(state: State):
    """Combine the joke, story and poem into a single output"""

    combined = f"Here's a story, joke, and poem about {state['topic']}!\n\n"
    combined += f"STORY:\n{state['story']}\n\n"
    combined += f"JOKE:\n{state['joke']}\n\n"
    combined += f"POEM:\n{state['poem']}"
    return {"combined_output": combined}


# Build workflow
parallel_builder = StateGraph(State)

# Add nodes
parallel_builder.add_node("call_llm_1", call_llm_1)
parallel_builder.add_node("call_llm_2", call_llm_2)
parallel_builder.add_node("call_llm_3", call_llm_3)
parallel_builder.add_node("aggregator", aggregator)

# Add edges to connect nodes
parallel_builder.add_edge(START, "call_llm_1")
parallel_builder.add_edge(START, "call_llm_2")
parallel_builder.add_edge(START, "call_llm_3")
parallel_builder.add_edge("call_llm_1", "aggregator")
parallel_builder.add_edge("call_llm_2", "aggregator")
parallel_builder.add_edge("call_llm_3", "aggregator")
parallel_builder.add_edge("aggregator", END)
parallel_workflow = parallel_builder.compile()

# Show workflow
display(Image(parallel_workflow.get_graph().draw_mermaid_png()))

# Invoke
state = parallel_workflow.invoke({"topic": "cats"})
print(state["combined_output"])

@task
def call_llm_1(topic: str):
    """First LLM call to generate initial joke"""
    msg = llm.invoke(f"Write a joke about {topic}")
    return msg.content


@task
def call_llm_2(topic: str):
    """Second LLM call to generate story"""
    msg = llm.invoke(f"Write a story about {topic}")
    return msg.content


@task
def call_llm_3(topic):
    """Third LLM call to generate poem"""
    msg = llm.invoke(f"Write a poem about {topic}")
    return msg.content


@task
def aggregator(topic, joke, story, poem):
    """Combine the joke and story into a single output"""

    combined = f"Here's a story, joke, and poem about {topic}!\n\n"
    combined += f"STORY:\n{story}\n\n"
    combined += f"JOKE:\n{joke}\n\n"
    combined += f"POEM:\n{poem}"
    return combined


# Build workflow
@entrypoint()
def parallel_workflow(topic: str):
    joke_fut = call_llm_1(topic)
    story_fut = call_llm_2(topic)
    poem_fut = call_llm_3(topic)
    return aggregator(
        topic, joke_fut.result(), story_fut.result(), poem_fut.result()
    ).result()

# Invoke
stream = parallel_workflow.stream_events("cats", version="v3")
for snapshot in stream.values:
    print(snapshot)
    print("\n")

路由

路由工作流处理输入，然后将其定向到特定上下文的任务。这允许您为复杂任务定义专门的流程。例如，一个用于回答产品相关问题的工作流可能首先处理问题类型，然后将请求路由到特定流程以处理定价、退款、退货等。

!routing.png

from typing_extensions import Literal
from langchain.messages import HumanMessage, SystemMessage


# Schema for structured output to use as routing logic
class Route(BaseModel):
    step: Literal["poem", "story", "joke"] = Field(
        None, description="The next step in the routing process"
    )


# Augment the LLM with schema for structured output
router = llm.with_structured_output(Route)


# State
class State(TypedDict):
    input: str
    decision: str
    output: str


# Nodes
def llm_call_1(state: State):
    """Write a story"""

    result = llm.invoke(state["input"])
    return {"output": result.content}


def llm_call_2(state: State):
    """Write a joke"""

    result = llm.invoke(state["input"])
    return {"output": result.content}


def llm_call_3(state: State):
    """Write a poem"""

    result = llm.invoke(state["input"])
    return {"output": result.content}


def llm_call_router(state: State):
    """Route the input to the appropriate node"""

    # Run the augmented LLM with structured output to serve as routing logic
    decision = router.invoke(
        [
            SystemMessage(
                content="Route the input to story, joke, or poem based on the user's request."
            ),
            HumanMessage(content=state["input"]),
        ]
    )

    return {"decision": decision.step}


# Conditional edge function to route to the appropriate node
def route_decision(state: State):
    # Return the node name you want to visit next
    if state["decision"] == "story":
        return "llm_call_1"
    elif state["decision"] == "joke":
        return "llm_call_2"
    elif state["decision"] == "poem":
        return "llm_call_3"


# Build workflow
router_builder = StateGraph(State)

# Add nodes
router_builder.add_node("llm_call_1", llm_call_1)
router_builder.add_node("llm_call_2", llm_call_2)
router_builder.add_node("llm_call_3", llm_call_3)
router_builder.add_node("llm_call_router", llm_call_router)

# Add edges to connect nodes
router_builder.add_edge(START, "llm_call_router")
router_builder.add_conditional_edges(
    "llm_call_router",
    route_decision,
    {  # Name returned by route_decision : Name of next node to visit
        "llm_call_1": "llm_call_1",
        "llm_call_2": "llm_call_2",
        "llm_call_3": "llm_call_3",
    },
)
router_builder.add_edge("llm_call_1", END)
router_builder.add_edge("llm_call_2", END)
router_builder.add_edge("llm_call_3", END)

# Compile workflow
router_workflow = router_builder.compile()

# Show the workflow
display(Image(router_workflow.get_graph().draw_mermaid_png()))

# Invoke
state = router_workflow.invoke({"input": "Write me a joke about cats"})
print(state["output"])

from typing_extensions import Literal
from pydantic import BaseModel
from langchain.messages import HumanMessage, SystemMessage


# Schema for structured output to use as routing logic
class Route(BaseModel):
    step: Literal["poem", "story", "joke"] = Field(
        None, description="The next step in the routing process"
    )


# Augment the LLM with schema for structured output
router = llm.with_structured_output(Route)


@task
def llm_call_1(input_: str):
    """Write a story"""
    result = llm.invoke(input_)
    return result.content


@task
def llm_call_2(input_: str):
    """Write a joke"""
    result = llm.invoke(input_)
    return result.content


@task
def llm_call_3(input_: str):
    """Write a poem"""
    result = llm.invoke(input_)
    return result.content


def llm_call_router(input_: str):
    """Route the input to the appropriate node"""
    # Run the augmented LLM with structured output to serve as routing logic
    decision = router.invoke(
        [
            SystemMessage(
                content="Route the input to story, joke, or poem based on the user's request."
            ),
            HumanMessage(content=input_),
        ]
    )
    return decision.step


# Create workflow
@entrypoint()
def router_workflow(input_: str):
    next_step = llm_call_router(input_)
    if next_step == "story":
        llm_call = llm_call_1
    elif next_step == "joke":
        llm_call = llm_call_2
    elif next_step == "poem":
        llm_call = llm_call_3

    return llm_call(input_).result()

# Invoke
stream = router_workflow.stream_events("Write me a joke about cats", version="v3")
for snapshot in stream.values:
    print(snapshot)
    print("\n")

Orchestrator-worker

在编排器-工作器配置中，编排器：

• - 将任务分解为子任务
• - 将子任务委托给工作器
• - 将工作器的输出综合成最终结果

!worker.png

编排器-工作器工作流提供了更大的灵活性，常用于无法预先定义子任务的情况（而使用并行化时可以） 并行化。这在需要跨多个文件编写代码或更新内容的工作流中很常见。例如，一个需要为未知数量的文档更新多个Python库安装说明的工作流可能会使用此模式。

from typing import Annotated, List



# Schema for structured output to use in planning
class Section(BaseModel):
    name: str = Field(
        description="Name for this section of the report.",
    )
    description: str = Field(
        description="Brief overview of the main topics and concepts to be covered in this section.",
    )


class Sections(BaseModel):
    sections: List[Section] = Field(
        description="Sections of the report.",
    )


# Augment the LLM with schema for structured output
planner = llm.with_structured_output(Sections)

from typing import List


# Schema for structured output to use in planning
class Section(BaseModel):
    name: str = Field(
        description="Name for this section of the report.",
    )
    description: str = Field(
        description="Brief overview of the main topics and concepts to be covered in this section.",
    )


class Sections(BaseModel):
    sections: List[Section] = Field(
        description="Sections of the report.",
    )


# Augment the LLM with schema for structured output
planner = llm.with_structured_output(Sections)


@task
def orchestrator(topic: str):
    """Orchestrator that generates a plan for the report"""
    # Generate queries
    report_sections = planner.invoke(
        [
            SystemMessage(content="Generate a plan for the report."),
            HumanMessage(content=f"Here is the report topic: {topic}"),
        ]
    )

    return report_sections.sections


@task
def llm_call(section: Section):
    """Worker writes a section of the report"""

    # Generate section
    result = llm.invoke(
        [
            SystemMessage(content="Write a report section."),
            HumanMessage(
                content=f"Here is the section name: {section.name} and description: {section.description}"
            ),
        ]
    )

    # Write the updated section to completed sections
    return result.content


@task
def synthesizer(completed_sections: list[str]):
    """Synthesize full report from sections"""
    final_report = "\n\n---\n\n".join(completed_sections)
    return final_report


@entrypoint()
def orchestrator_worker(topic: str):
    sections = orchestrator(topic).result()
    section_futures = [llm_call(section) for section in sections]
    final_report = synthesizer(
        [section_fut.result() for section_fut in section_futures]
    ).result()
    return final_report

# Invoke
report = orchestrator_worker.invoke("Create a report on LLM scaling laws")
from IPython.display import Markdown
Markdown(report)

在 LangGraph 中创建工作器

编排器-工作器工作流很常见，LangGraph 对它们有内置支持。 Send API 允许您动态创建工作器节点并向它们发送特定输入。每个工作器都有自己的状态，所有工作器输出都写入共享状态键，编排器图可以访问该键。这使编排器能够访问所有工作器输出，并将其综合成最终输出。下面的示例遍历一个节列表，并使用 Send API 将每个节发送给相应的工作器。

from langgraph.types import Send


# Graph state
class State(TypedDict):
    topic: str  # Report topic
    sections: list[Section]  # List of report sections
    completed_sections: Annotated[
        list, operator.add
    ]  # All workers write to this key in parallel
    final_report: str  # Final report


# Worker state
class WorkerState(TypedDict):
    section: Section
    completed_sections: Annotated[list, operator.add]


# Nodes
def orchestrator(state: State):
    """Orchestrator that generates a plan for the report"""

    # Generate queries
    report_sections = planner.invoke(
        [
            SystemMessage(content="Generate a plan for the report."),
            HumanMessage(content=f"Here is the report topic: {state['topic']}"),
        ]
    )

    return {"sections": report_sections.sections}


def llm_call(state: WorkerState):
    """Worker writes a section of the report"""

    # Generate section
    section = llm.invoke(
        [
            SystemMessage(
                content="Write a report section following the provided name and description. Include no preamble for each section. Use markdown formatting."
            ),
            HumanMessage(
                content=f"Here is the section name: {state['section'].name} and description: {state['section'].description}"
            ),
        ]
    )

    # Write the updated section to completed sections
    return {"completed_sections": [section.content]}


def synthesizer(state: State):
    """Synthesize full report from sections"""

    # List of completed sections
    completed_sections = state["completed_sections"]

    # Format completed section to str to use as context for final sections
    completed_report_sections = "\n\n---\n\n".join(completed_sections)

    return {"final_report": completed_report_sections}


# Conditional edge function to create llm_call workers that each write a section of the report
def assign_workers(state: State):
    """Assign a worker to each section in the plan"""

    # Kick off section writing in parallel via Send() API
    return [Send("llm_call", {"section": s}) for s in state["sections"]]


# Build workflow
orchestrator_worker_builder = StateGraph(State)

# Add the nodes
orchestrator_worker_builder.add_node("orchestrator", orchestrator)
orchestrator_worker_builder.add_node("llm_call", llm_call)
orchestrator_worker_builder.add_node("synthesizer", synthesizer)

# Add edges to connect nodes
orchestrator_worker_builder.add_edge(START, "orchestrator")
orchestrator_worker_builder.add_conditional_edges(
    "orchestrator", assign_workers, ["llm_call"]
)
orchestrator_worker_builder.add_edge("llm_call", "synthesizer")
orchestrator_worker_builder.add_edge("synthesizer", END)

# Compile the workflow
orchestrator_worker = orchestrator_worker_builder.compile()

# Show the workflow
display(Image(orchestrator_worker.get_graph().draw_mermaid_png()))

# Invoke
state = orchestrator_worker.invoke({"topic": "Create a report on LLM scaling laws"})

from IPython.display import Markdown
Markdown(state["final_report"])

Evaluator-optimizer

在评估器-优化器工作流中，一个 LLM 调用创建响应，另一个评估该响应。如果评估器或 human-in-the-loop 确定响应需要改进，则提供反馈并重新生成响应。此循环持续进行，直到生成可接受的响应。

评估器-优化器工作流通常用于任务有特定的成功标准但需要迭代才能达到该标准的情况。例如，在两种语言之间翻译文本时并不总是能完美匹配。可能需要几次迭代才能生成在两种语言中具有相同含义的翻译。

!评估器_optimizer.png

# Graph state
class State(TypedDict):
    joke: str
    topic: str
    feedback: str
    funny_or_not: str


# Schema for structured output to use in evaluation
class Feedback(BaseModel):
    grade: Literal["funny", "not funny"] = Field(
        description="Decide if the joke is funny or not.",
    )
    feedback: str = Field(
        description="If the joke is not funny, provide feedback on how to improve it.",
    )


# Augment the LLM with schema for structured output
evaluator = llm.with_structured_output(Feedback)


# Nodes
def llm_call_generator(state: State):
    """LLM generates a joke"""

    if state.get("feedback"):
        msg = llm.invoke(
            f"Write a joke about {state['topic']} but take into account the feedback: {state['feedback']}"
        )
    else:
        msg = llm.invoke(f"Write a joke about {state['topic']}")
    return {"joke": msg.content}


def llm_call_evaluator(state: State):
    """LLM evaluates the joke"""

    grade = evaluator.invoke(f"Grade the joke {state['joke']}")
    return {"funny_or_not": grade.grade, "feedback": grade.feedback}


# Conditional edge function to route back to joke generator or end based upon feedback from the evaluator
def route_joke(state: State):
    """Route back to joke generator or end based upon feedback from the evaluator"""

    if state["funny_or_not"] == "funny":
        return "Accepted"
    elif state["funny_or_not"] == "not funny":
        return "Rejected + Feedback"


# Build workflow
optimizer_builder = StateGraph(State)

# Add the nodes
optimizer_builder.add_node("llm_call_generator", llm_call_generator)
optimizer_builder.add_node("llm_call_evaluator", llm_call_evaluator)

# Add edges to connect nodes
optimizer_builder.add_edge(START, "llm_call_generator")
optimizer_builder.add_edge("llm_call_generator", "llm_call_evaluator")
optimizer_builder.add_conditional_edges(
    "llm_call_evaluator",
    route_joke,
    {  # Name returned by route_joke : Name of next node to visit
        "Accepted": END,
        "Rejected + Feedback": "llm_call_generator",
    },
)

# Compile the workflow
optimizer_workflow = optimizer_builder.compile()

# Show the workflow
display(Image(optimizer_workflow.get_graph().draw_mermaid_png()))

# Invoke
state = optimizer_workflow.invoke({"topic": "Cats"})
print(state["joke"])

# Schema for structured output to use in evaluation
class Feedback(BaseModel):
    grade: Literal["funny", "not funny"] = Field(
        description="Decide if the joke is funny or not.",
    )
    feedback: str = Field(
        description="If the joke is not funny, provide feedback on how to improve it.",
    )


# Augment the LLM with schema for structured output
evaluator = llm.with_structured_output(Feedback)


# Nodes
@task
def llm_call_generator(topic: str, feedback: Feedback):
    """LLM generates a joke"""
    if feedback:
        msg = llm.invoke(
            f"Write a joke about {topic} but take into account the feedback: {feedback}"
        )
    else:
        msg = llm.invoke(f"Write a joke about {topic}")
    return msg.content


@task
def llm_call_evaluator(joke: str):
    """LLM evaluates the joke"""
    feedback = evaluator.invoke(f"Grade the joke {joke}")
    return feedback


@entrypoint()
def optimizer_workflow(topic: str):
    feedback = None
    while True:
        joke = llm_call_generator(topic, feedback).result()
        feedback = llm_call_evaluator(joke).result()
        if feedback.grade == "funny":
            break

    return joke

# Invoke
stream = optimizer_workflow.stream_events("Cats", version="v3")
for snapshot in stream.values:
    print(snapshot)
    print("\n")

智能体

智能体通常实现为使用 工具执行操作的 LLM。它们在连续的反馈循环中运行，用于问题和解决方案不可预测的情况。智能体比工作流有更多自主权，可以决定使用哪些工具以及如何解决问题。您仍然可以定义可用工具集和行为指南。

!agent.png

from langchain.tools import tool


# Define tools
@tool
def multiply(a: int, b: int) -> int:
    """Multiply `a` and `b`.

    Args:
        a: First int
        b: Second int
    """
    return a * b


@tool
def add(a: int, b: int) -> int:
    """Adds `a` and `b`.

    Args:
        a: First int
        b: Second int
    """
    return a + b


@tool
def divide(a: int, b: int) -> float:
    """Divide `a` and `b`.

    Args:
        a: First int
        b: Second int
    """
    return a / b


# Augment the LLM with tools
tools = [add, multiply, divide]
tools_by_name = {tool.name: tool for tool in tools}
llm_with_tools = llm.bind_tools(tools)

from langgraph.graph import MessagesState
from langchain.messages import SystemMessage, HumanMessage, ToolMessage


# Nodes
def llm_call(state: MessagesState):
    """LLM decides whether to call a tool or not"""

    return {
        "messages": [
            llm_with_tools.invoke(
                [
                    SystemMessage(
                        content="You are a helpful assistant tasked with performing arithmetic on a set of inputs."
                    )
                ]
                + state["messages"]
            )
        ]
    }


def tool_node(state: MessagesState):
    """Performs the tool call"""

    result = []
    for tool_call in state["messages"][-1].tool_calls:
        tool = tools_by_name[tool_call["name"]]
        observation = tool.invoke(tool_call["args"])
        result.append(ToolMessage(content=observation, tool_call_id=tool_call["id"]))
    return {"messages": result}


# Conditional edge function to route to the tool node or end based upon whether the LLM made a tool call
def should_continue(state: MessagesState) -> Literal["tool_node", END]:
    """Decide if we should continue the loop or stop based upon whether the LLM made a tool call"""

    messages = state["messages"]
    last_message = messages[-1]

    # If the LLM makes a tool call, then perform an action
    if last_message.tool_calls:
        return "tool_node"

    # Otherwise, we stop (reply to the user)
    return END


# Build workflow
agent_builder = StateGraph(MessagesState)

# Add nodes
agent_builder.add_node("llm_call", llm_call)
agent_builder.add_node("tool_node", tool_node)

# Add edges to connect nodes
agent_builder.add_edge(START, "llm_call")
agent_builder.add_conditional_edges(
    "llm_call",
    should_continue,
    ["tool_node", END]
)
agent_builder.add_edge("tool_node", "llm_call")

# Compile the agent
agent = agent_builder.compile()

# Show the agent
display(Image(agent.get_graph(xray=True).draw_mermaid_png()))

# Invoke
messages = [HumanMessage(content="Add 3 and 4.")]
messages = agent.invoke({"messages": messages})
for m in messages["messages"]:
    m.pretty_print()

from langgraph.graph import add_messages
from langchain.messages import (
    SystemMessage,
    HumanMessage,
    ToolCall,
)
from langchain_core.messages import BaseMessage


@task
def call_llm(messages: list[BaseMessage]):
    """LLM decides whether to call a tool or not"""
    return llm_with_tools.invoke(
        [
            SystemMessage(
                content="You are a helpful assistant tasked with performing arithmetic on a set of inputs."
            )
        ]
        + messages
    )


@task
def call_tool(tool_call: ToolCall):
    """Performs the tool call"""
    tool = tools_by_name[tool_call["name"]]
    return tool.invoke(tool_call)


@entrypoint()
def agent(messages: list[BaseMessage]):
    llm_response = call_llm(messages).result()

    while True:
        if not llm_response.tool_calls:
            break

        # Execute tools
        tool_result_futures = [
            call_tool(tool_call) for tool_call in llm_response.tool_calls
        ]
        tool_results = [fut.result() for fut in tool_result_futures]
        messages = add_messages(messages, [llm_response, *tool_results])
        llm_response = call_llm(messages).result()

    messages = add_messages(messages, llm_response)
    return messages

# Invoke
messages = [HumanMessage(content="Add 3 and 4.")]
stream = agent.stream_events(messages, version="v3")
for snapshot in stream.values:
    print(snapshot)
    print("\n")

ToolNode

@[ToolNode是一个预构建节点，用于在 LangGraph 工作流中执行工具。它自动处理并行工具执行、错误处理和状态注入。

当您需要对图的工具执行方式进行细粒度控制时使用 ToolNode。这是许多 LangGraph 智能体模式中工具执行的基石。

from langchain.tools import tool
from langgraph.prebuilt import ToolNode
from langgraph.graph import MessagesState, StateGraph

@tool
def search(query: str) -> str:
    """Search for information."""
    return f"Results for: {query}"

@tool
def calculator(expression: str) -> str:
    """Evaluate a math expression."""
    return str(eval(expression))

builder = StateGraph(MessagesState)
builder.add_node("tools", ToolNode([search, calculator]))
# ... add other nodes and edges
graph = builder.compile()

从工具访问图状态和上下文

执行的工具 ToolNode 接收模型生成的参数作为 它们的第一个参数。要读取不是由 模型生成的图侧数据，请使用以下选项之一：

- 在 Python 中，从注入的 ToolRuntime 参数。 - 在 JavaScript 中，从工具的第二个 参数中读取状态和运行作用域上下文，类型为 ToolRuntime.