使用函数式 API - Docs by LangChain中文

函数式 API 让你可以在几乎不改动现有代码的情况下，将 LangGraph 的关键功能（持久化、记忆、人机协作和流式输出）添加到你的应用中。

关于函数式 API 的概念说明，请参阅函数式 API。

创建一个简单工作流

定义 entrypoint 时，输入仅限于函数的第一个参数。如果需要传入多个输入，可以使用字典。

@entrypoint(checkpointer=checkpointer)
def my_workflow(inputs: dict) -> int:
    value = inputs["value"]
    another_value = inputs["another_value"]
    ...

my_workflow.invoke({"value": 1, "another_value": 2})

Extended example: simple workflow

from langchain_core.utils.uuid import uuid7
from langgraph.func import entrypoint, task
from langgraph.checkpoint.memory import InMemorySaver

# Task that checks if a number is even
@task
def is_even(number: int) -> bool:
    return number % 2 == 0

# Task that formats a message
@task
def format_message(is_even: bool) -> str:
    return "The number is even." if is_even else "The number is odd."

# Create a checkpointer for persistence
checkpointer = InMemorySaver()

@entrypoint(checkpointer=checkpointer)
def workflow(inputs: dict) -> str:
    """Simple workflow to classify a number."""
    even = is_even(inputs["number"]).result()
    return format_message(even).result()

# Run the workflow with a unique thread ID
config = {"configurable": {"thread_id": str(uuid7())}}
result = workflow.invoke({"number": 7}, config=config)
print(result)

Extended example: Compose an essay with an LLM

这个示例演示如何从语法上使用 @task 和 @entrypoint 装饰器。由于提供了 checkpointer，工作流结果会被持久化到 checkpointer 中。

import uuid
from langchain.chat_models import init_chat_model
from langgraph.func import entrypoint, task
from langgraph.checkpoint.memory import InMemorySaver

model = init_chat_model('gpt-3.5-turbo')

# Task: generate essay using an LLM
@task
def compose_essay(topic: str) -> str:
    """Generate an essay about the given topic."""
    return model.invoke([
        {"role": "system", "content": "You are a helpful assistant that writes essays."},
        {"role": "user", "content": f"Write an essay about {topic}."}
    ]).content

# Create a checkpointer for persistence
checkpointer = InMemorySaver()

@entrypoint(checkpointer=checkpointer)
def workflow(topic: str) -> str:
    """Simple workflow that generates an essay with an LLM."""
    return compose_essay(topic).result()

# Execute the workflow
config = {"configurable": {"thread_id": str(uuid7())}}
result = workflow.invoke("the history of flight", config=config)
print(result)

并行执行

可以通过并发调用任务并等待结果来并行执行任务。这对于提升 IO 密集型任务的性能很有用（例如，调用 LLM 的 API）。

@task
def add_one(number: int) -> int:
    return number + 1

@entrypoint(checkpointer=checkpointer)
def graph(numbers: list[int]) -> list[str]:
    futures = [add_one(i) for i in numbers]
    return [f.result() for f in futures]

Extended example: parallel LLM calls

这个示例演示如何使用 @task 并行运行多个 LLM 调用。每次调用都会针对不同主题生成一个段落，结果会合并成一个文本输出。

import uuid
from langchain.chat_models import init_chat_model
from langgraph.func import entrypoint, task
from langgraph.checkpoint.memory import InMemorySaver

# Initialize the LLM model
model = init_chat_model("gpt-3.5-turbo")

# Task that generates a paragraph about a given topic
@task
def generate_paragraph(topic: str) -> str:
    response = model.invoke([
        {"role": "system", "content": "You are a helpful assistant that writes educational paragraphs."},
        {"role": "user", "content": f"Write a paragraph about {topic}."}
    ])
    return response.content

# Create a checkpointer for persistence
checkpointer = InMemorySaver()

@entrypoint(checkpointer=checkpointer)
def workflow(topics: list[str]) -> str:
    """Generates multiple paragraphs in parallel and combines them."""
    futures = [generate_paragraph(topic) for topic in topics]
    paragraphs = [f.result() for f in futures]
    return "\n\n".join(paragraphs)

# Run the workflow
config = {"configurable": {"thread_id": str(uuid7())}}
result = workflow.invoke(["quantum computing", "climate change", "history of aviation"], config=config)
print(result)

这个示例使用 LangGraph 的并发模型来缩短执行时间，尤其是在任务涉及 LLM 补全这类 I/O 操作时。

调用图

函数式 API 和 Graph API 可以在同一个应用中一起使用，因为它们共享相同的底层运行时。

from langgraph.func import entrypoint
from langgraph.graph import StateGraph

builder = StateGraph()
...
some_graph = builder.compile()

@entrypoint()
def some_workflow(some_input: dict) -> int:
    # Call a graph defined using the graph API
    result_1 = some_graph.invoke(...)
    # Call another graph defined using the graph API
    result_2 = another_graph.invoke(...)
    return {
        "result_1": result_1,
        "result_2": result_2
    }

Extended example: calling a simple graph from the functional API

import uuid
from typing import TypedDict
from langgraph.func import entrypoint
from langgraph.checkpoint.memory import InMemorySaver
from langgraph.graph import StateGraph

# Define the shared state type
class State(TypedDict):
    foo: int

# Define a simple transformation node
def double(state: State) -> State:
    return {"foo": state["foo"] * 2}

# Build the graph using the Graph API
builder = StateGraph(State)
builder.add_node("double", double)
builder.set_entry_point("double")
graph = builder.compile()

# Define the functional API workflow
checkpointer = InMemorySaver()

@entrypoint(checkpointer=checkpointer)
def workflow(x: int) -> dict:
    result = graph.invoke({"foo": x})
    return {"bar": result["foo"]}

# Execute the workflow
config = {"configurable": {"thread_id": str(uuid7())}}
print(workflow.invoke(5, config=config))  # Output: {'bar': 10}

调用其他 entrypoint

你可以在一个 entrypoint 或 task 内部调用其他 entrypoints。

@entrypoint() # Will automatically use the checkpointer from the parent entrypoint
def some_other_workflow(inputs: dict) -> int:
    return inputs["value"]

@entrypoint(checkpointer=checkpointer)
def my_workflow(inputs: dict) -> int:
    value = some_other_workflow.invoke({"value": 1})
    return value

Extended example: calling another entrypoint

import uuid
from langgraph.func import entrypoint
from langgraph.checkpoint.memory import InMemorySaver

# Initialize a checkpointer
checkpointer = InMemorySaver()

# A reusable sub-workflow that multiplies a number
@entrypoint()
def multiply(inputs: dict) -> int:
    return inputs["a"] * inputs["b"]

# Main workflow that invokes the sub-workflow
@entrypoint(checkpointer=checkpointer)
def main(inputs: dict) -> dict:
    result = multiply.invoke({"a": inputs["x"], "b": inputs["y"]})
    return {"product": result}

# Execute the main workflow
config = {"configurable": {"thread_id": str(uuid7())}}
print(main.invoke({"x": 6, "y": 7}, config=config))  # Output: {'product': 42}

流式输出

函数式 API 使用与 Graph API 相同的流式机制。请阅读 流式输出指南 部分了解更多细节。下面是使用流式 API 同时流式输出更新和自定义数据的示例。

from langgraph.func import entrypoint
from langgraph.checkpoint.memory import InMemorySaver
from langgraph.config import get_stream_writer   

checkpointer = InMemorySaver()

@entrypoint(checkpointer=checkpointer)
def main(inputs: dict) -> int:
    writer = get_stream_writer()
    writer("Started processing")
    result = inputs["x"] * 2
    writer(f"Result is {result}")
    return result

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

for mode, chunk in main.stream(
    {"x": 5},
    stream_mode=["custom", "updates"],
    config=config
):
    print(f"{mode}: {chunk}")

从 langgraph.config 导入 get_stream_writer。
在 entrypoint 内获取 stream writer 实例。
在计算开始前发出自定义数据。
在计算结果后发出另一条自定义消息。
使用 .stream() 处理流式输出。
指定要使用哪些流式模式。

('updates', {'add_one': 2})
('updates', {'add_two': 3})
('custom', 'hello')
('custom', 'world')
('updates', {'main': 5})

Python < 3.11 的异步用法 如果使用 Python < 3.11 并编写异步代码，使用 get_stream_writer 将无法正常工作。请改为直接使用 StreamWriter 类。更多细节请参阅 Python < 3.11 的异步用法。

from langgraph.types import StreamWriter

@entrypoint(checkpointer=checkpointer)
async def main(inputs: dict, writer: StreamWriter) -> int:
...

重试策略

from langgraph.checkpoint.memory import InMemorySaver
from langgraph.func import entrypoint, task
from langgraph.types import RetryPolicy

# This variable is just used for demonstration purposes to simulate a network failure.
# It's not something you will have in your actual code.
attempts = 0

# Let's configure the RetryPolicy to retry on ValueError.
# The default RetryPolicy is optimized for retrying specific network errors.
retry_policy = RetryPolicy(retry_on=ValueError)

@task(retry_policy=retry_policy)
def get_info():
    global attempts
    attempts += 1

    if attempts < 2:
        raise ValueError('Failure')
    return "OK"

checkpointer = InMemorySaver()

@entrypoint(checkpointer=checkpointer)
def main(inputs, writer):
    return get_info().result()

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

main.invoke({'any_input': 'foobar'}, config=config)

'OK'

设置 task 和 entrypoint 超时

在 @task 或 @entrypoint 中使用 timeout 参数，可以限制单次异步尝试的运行时间。超时时间可以用秒表示，也可以使用 datetime.timedelta。

import asyncio

from langgraph.errors import NodeTimeoutError
from langgraph.func import entrypoint, task
from langgraph.types import RetryPolicy


@task(
    timeout=1.0,
    retry_policy=RetryPolicy(retry_on=NodeTimeoutError),
)
async def call_api(url: str) -> str:
    await asyncio.sleep(2)
    return f"result from {url}"


@entrypoint(timeout=5.0)
async def workflow(inputs: dict) -> str:
    return await call_api(inputs["url"])


try:
    await workflow.ainvoke({"url": "https://example.com"})
except NodeTimeoutError:
    print("Task timed out")

超时仅支持异步 task 和 entrypoint。如果你在同步函数上设置 timeout，LangGraph 会在声明该 task 或 entrypoint 时抛出错误。当 task 或 entrypoint 超过其超时时间时，LangGraph 会抛出 NodeTimeoutError，它是 Python 内置 TimeoutError 的子类。如果某个重试策略会重试 TimeoutError 或 NodeTimeoutError，那么超时的尝试会被重试。超时时间会独立应用于每次尝试，因此每次重试都会重置计时器。

缓存任务

import time
from langgraph.cache.memory import InMemoryCache
from langgraph.func import entrypoint, task
from langgraph.types import CachePolicy


@task(cache_policy=CachePolicy(ttl=120))
def slow_add(x: int) -> int:
    time.sleep(1)
    return x * 2


@entrypoint(cache=InMemoryCache())
def main(inputs: dict) -> dict[str, int]:
    result1 = slow_add(inputs["x"]).result()
    result2 = slow_add(inputs["x"]).result()
    return {"result1": result1, "result2": result2}


for chunk in main.stream({"x": 5}, stream_mode="updates"):
    print(chunk)

#> {'slow_add': 10}
#> {'slow_add': 10, '__metadata__': {'cached': True}}
#> {'main': {'result1': 10, 'result2': 10}}

ttl 以秒为单位指定。缓存会在此时间之后失效。

出错后恢复

import time
from langgraph.checkpoint.memory import InMemorySaver
from langgraph.func import entrypoint, task
from langgraph.types import StreamWriter

# This variable is just used for demonstration purposes to simulate a network failure.
# It's not something you will have in your actual code.
attempts = 0

@task()
def get_info():
    """
    Simulates a task that fails once before succeeding.
    Raises an exception on the first attempt, then returns "OK" on subsequent tries.
    """
    global attempts
    attempts += 1

    if attempts < 2:
        raise ValueError("Failure")  # Simulate a failure on the first attempt
    return "OK"

# Initialize an in-memory checkpointer for persistence
checkpointer = InMemorySaver()

@task
def slow_task():
    """
    Simulates a slow-running task by introducing a 1-second delay.
    """
    time.sleep(1)
    return "Ran slow task."

@entrypoint(checkpointer=checkpointer)
def main(inputs, writer: StreamWriter):
    """
    Main workflow function that runs the slow_task and get_info tasks sequentially.

    Parameters:
    - inputs: Dictionary containing workflow input values.
    - writer: StreamWriter for streaming custom data.

    The workflow first executes `slow_task` and then attempts to execute `get_info`,
    which will fail on the first invocation.
    """
    slow_task_result = slow_task().result()  # Blocking call to slow_task
    get_info().result()  # Exception will be raised here on the first attempt
    return slow_task_result

# Workflow execution configuration with a unique thread identifier
config = {
    "configurable": {
        "thread_id": "1"  # Unique identifier to track workflow execution
    }
}

# This invocation will take ~1 second due to the slow_task execution
try:
    # First invocation will raise an exception due to the `get_info` task failing
    main.invoke({'any_input': 'foobar'}, config=config)
except ValueError:
    pass  # Handle the failure gracefully

当我们恢复执行时，不需要重新运行 slow_task，因为它的结果已经保存在 checkpoint 中。

main.invoke(None, config=config)

'Ran slow task.'

人机协作

函数式 API 支持使用 interrupt 函数和 Command 原语构建人机协作工作流。

基本人机协作工作流

我们将创建三个 tasks：

追加 "bar"。
暂停以等待人工输入。恢复时，追加人工输入。
追加 "qux"。

from langgraph.func import entrypoint, task
from langgraph.types import Command, interrupt


@task
def step_1(input_query):
    """Append bar."""
    return f"{input_query} bar"


@task
def human_feedback(input_query):
    """Append user input."""
    feedback = interrupt(f"Please provide feedback: {input_query}")
    return f"{input_query} {feedback}"


@task
def step_3(input_query):
    """Append qux."""
    return f"{input_query} qux"

现在我们可以在 entrypoint 中组合这些任务：

from langgraph.checkpoint.memory import InMemorySaver

checkpointer = InMemorySaver()


@entrypoint(checkpointer=checkpointer)
def graph(input_query):
    result_1 = step_1(input_query).result()
    result_2 = human_feedback(result_1).result()
    result_3 = step_3(result_2).result()

    return result_3

interrupt() 会在任务内部被调用，使人工能够查看并编辑上一个任务的输出。之前任务的结果——在本例中是 step_1——会被持久化，因此在 interrupt 之后不会再次运行它们。我们发送一个查询字符串：

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

for event in graph.stream("foo", config):
    print(event)
    print("\n")

请注意，我们在 step_1 之后通过 interrupt 暂停了。该 interrupt 会提供恢复运行的说明。要恢复，我们发出一个包含 human_feedback 任务所需数据的 Command。

# Continue execution
for event in graph.stream(Command(resume="baz"), config):
    print(event)
    print("\n")

恢复后，运行会继续执行剩余步骤，并按预期结束。

审核工具调用

要在执行前审核工具调用，我们添加一个 review_tool_call 函数，它会调用 interrupt。调用此函数时，执行会暂停，直到我们发出命令来恢复它。给定一个工具调用，我们的函数会通过 interrupt 请求人工审核。此时我们可以：

接受该工具调用
修改该工具调用并继续
生成一条自定义工具消息（例如，指示模型重新格式化其工具调用）

from typing import Union

def review_tool_call(tool_call: ToolCall) -> Union[ToolCall, ToolMessage]:
    """Review a tool call, returning a validated version."""
    human_review = interrupt(
        {
            "question": "Is this correct?",
            "tool_call": tool_call,
        }
    )
    review_action = human_review["action"]
    review_data = human_review.get("data")
    if review_action == "continue":
        return tool_call
    elif review_action == "update":
        updated_tool_call = {**tool_call, **{"args": review_data}}
        return updated_tool_call
    elif review_action == "feedback":
        return ToolMessage(
            content=review_data, name=tool_call["name"], tool_call_id=tool_call["id"]
        )

现在可以更新我们的 entrypoint，用来审核生成的工具调用。如果某个工具调用被接受或修改，我们就像之前一样执行它。否则，我们只追加人工提供的 ToolMessage。之前任务的结果——在本例中是初始模型调用——会被持久化，因此在 interrupt 之后不会再次运行它们。

from langgraph.checkpoint.memory import InMemorySaver
from langgraph.graph.message import add_messages
from langgraph.types import Command, interrupt


checkpointer = InMemorySaver()


@entrypoint(checkpointer=checkpointer)
def agent(messages, previous):
    if previous is not None:
        messages = add_messages(previous, messages)

    model_response = call_model(messages).result()
    while True:
        if not model_response.tool_calls:
            break

        # Review tool calls
        tool_results = []
        tool_calls = []
        for i, tool_call in enumerate(model_response.tool_calls):
            review = review_tool_call(tool_call)
            if isinstance(review, ToolMessage):
                tool_results.append(review)
            else:  # is a validated tool call
                tool_calls.append(review)
                if review != tool_call:
                    model_response.tool_calls[i] = review  # update message

        # Execute remaining tool calls
        tool_result_futures = [call_tool(tool_call) for tool_call in tool_calls]
        remaining_tool_results = [fut.result() for fut in tool_result_futures]

        # Append to message list
        messages = add_messages(
            messages,
            [model_response, *tool_results, *remaining_tool_results],
        )

        # Call model again
        model_response = call_model(messages).result()

    # Generate final response
    messages = add_messages(messages, model_response)
    return entrypoint.final(value=model_response, save=messages)

短期记忆

短期记忆允许在同一个 thread id 的不同调用之间存储信息。更多细节请参阅短期记忆。

管理 checkpoint

你可以查看和删除 checkpointer 存储的信息。

查看线程状态

config = {
    "configurable": {
        "thread_id": "1",
        # optionally provide an ID for a specific checkpoint,
        # otherwise the latest checkpoint is shown
        # "checkpoint_id": "1f029ca3-1f5b-6704-8004-820c16b69a5a"  #

    }
}
graph.get_state(config)

StateSnapshot(
    values={'messages': [HumanMessage(content="hi! I'm bob"), AIMessage(content='Hi Bob! How are you doing today?), HumanMessage(content="what's my name?"), AIMessage(content='Your name is Bob.')]}, next=(),
    config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1f029ca3-1f5b-6704-8004-820c16b69a5a'}},
    metadata={
        'source': 'loop',
        'writes': {'call_model': {'messages': AIMessage(content='Your name is Bob.')}},
        'step': 4,
        'parents': {},
        'thread_id': '1'
    },
    created_at='2025-05-05T16:01:24.680462+00:00',
    parent_config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1f029ca3-1790-6b0a-8003-baf965b6a38f'}},
    tasks=(),
    interrupts=()
)

查看线程历史

config = {
    "configurable": {
        "thread_id": "1"
    }
}
list(graph.get_state_history(config))

[
    StateSnapshot(
        values={'messages': [HumanMessage(content="hi! I'm bob"), AIMessage(content='Hi Bob! How are you doing today? Is there anything I can help you with?'), HumanMessage(content="what's my name?"), AIMessage(content='Your name is Bob.')]},
        next=(),
        config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1f029ca3-1f5b-6704-8004-820c16b69a5a'}},
        metadata={'source': 'loop', 'writes': {'call_model': {'messages': AIMessage(content='Your name is Bob.')}}, 'step': 4, 'parents': {}, 'thread_id': '1'},
        created_at='2025-05-05T16:01:24.680462+00:00',
        parent_config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1f029ca3-1790-6b0a-8003-baf965b6a38f'}},
        tasks=(),
        interrupts=()
    ),
    StateSnapshot(
        values={'messages': [HumanMessage(content="hi! I'm bob"), AIMessage(content='Hi Bob! How are you doing today? Is there anything I can help you with?'), HumanMessage(content="what's my name?")]},
        next=('call_model',),
        config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1f029ca3-1790-6b0a-8003-baf965b6a38f'}},
        metadata={'source': 'loop', 'writes': None, 'step': 3, 'parents': {}, 'thread_id': '1'},
        created_at='2025-05-05T16:01:23.863421+00:00',
        parent_config={...}
        tasks=(PregelTask(id='8ab4155e-6b15-b885-9ce5-bed69a2c305c', name='call_model', path=('__pregel_pull', 'call_model'), error=None, interrupts=(), state=None, result={'messages': AIMessage(content='Your name is Bob.')}),),
        interrupts=()
    ),
    StateSnapshot(
        values={'messages': [HumanMessage(content="hi! I'm bob"), AIMessage(content='Hi Bob! How are you doing today? Is there anything I can help you with?')]},
        next=('__start__',),
        config={...},
        metadata={'source': 'input', 'writes': {'__start__': {'messages': [{'role': 'user', 'content': "what's my name?"}]}}, 'step': 2, 'parents': {}, 'thread_id': '1'},
        created_at='2025-05-05T16:01:23.863173+00:00',
        parent_config={...}
        tasks=(PregelTask(id='24ba39d6-6db1-4c9b-f4c5-682aeaf38dcd', name='__start__', path=('__pregel_pull', '__start__'), error=None, interrupts=(), state=None, result={'messages': [{'role': 'user', 'content': "what's my name?"}]}),),
        interrupts=()
    ),
    StateSnapshot(
        values={'messages': [HumanMessage(content="hi! I'm bob"), AIMessage(content='Hi Bob! How are you doing today? Is there anything I can help you with?')]},
        next=(),
        config={...},
        metadata={'source': 'loop', 'writes': {'call_model': {'messages': AIMessage(content='Hi Bob! How are you doing today? Is there anything I can help you with?')}}, 'step': 1, 'parents': {}, 'thread_id': '1'},
        created_at='2025-05-05T16:01:23.862295+00:00',
        parent_config={...}
        tasks=(),
        interrupts=()
    ),
    StateSnapshot(
        values={'messages': [HumanMessage(content="hi! I'm bob")]},
        next=('call_model',),
        config={...},
        metadata={'source': 'loop', 'writes': None, 'step': 0, 'parents': {}, 'thread_id': '1'},
        created_at='2025-05-05T16:01:22.278960+00:00',
        parent_config={...}
        tasks=(PregelTask(id='8cbd75e0-3720-b056-04f7-71ac805140a0', name='call_model', path=('__pregel_pull', 'call_model'), error=None, interrupts=(), state=None, result={'messages': AIMessage(content='Hi Bob! How are you doing today? Is there anything I can help you with?')}),),
        interrupts=()
    ),
    StateSnapshot(
        values={'messages': []},
        next=('__start__',),
        config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1f029ca3-0870-6ce2-bfff-1f3f14c3e565'}},
        metadata={'source': 'input', 'writes': {'__start__': {'messages': [{'role': 'user', 'content': "hi! I'm bob"}]}}, 'step': -1, 'parents': {}, 'thread_id': '1'},
        created_at='2025-05-05T16:01:22.277497+00:00',
        parent_config=None,
        tasks=(PregelTask(id='d458367b-8265-812c-18e2-33001d199ce6', name='__start__', path=('__pregel_pull', '__start__'), error=None, interrupts=(), state=None, result={'messages': [{'role': 'user', 'content': "hi! I'm bob"}]}),),
        interrupts=()
    )
]

将返回值与保存值解耦

使用 entrypoint.final 可以将返回给调用方的内容与持久化到 checkpoint 中的内容解耦。这在以下场景中很有用：

你想返回一个计算结果（例如摘要或状态），但保存一个不同的内部值，以便下次调用时使用。
你需要控制下一次运行时传递给 previous 参数的内容。

from langgraph.func import entrypoint
from langgraph.checkpoint.memory import InMemorySaver

checkpointer = InMemorySaver()

@entrypoint(checkpointer=checkpointer)
def accumulate(n: int, *, previous: int | None) -> entrypoint.final[int, int]:
    previous = previous or 0
    total = previous + n
    # Return the *previous* value to the caller but save the *new* total to the checkpoint.
    return entrypoint.final(value=previous, save=total)

config = {"configurable": {"thread_id": "my-thread"}}

print(accumulate.invoke(1, config=config))  # 0
print(accumulate.invoke(2, config=config))  # 1
print(accumulate.invoke(3, config=config))  # 3

聊天机器人示例

下面是一个使用函数式 API 和 InMemorySaver checkpointer 的简单聊天机器人示例。这个机器人能够记住之前的对话，并从上次中断的地方继续。

from langchain.messages import BaseMessage
from langgraph.graph import add_messages
from langgraph.func import entrypoint, task
from langgraph.checkpoint.memory import InMemorySaver
from langchain_anthropic import ChatAnthropic

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

@task
def call_model(messages: list[BaseMessage]):
    response = model.invoke(messages)
    return response

checkpointer = InMemorySaver()

@entrypoint(checkpointer=checkpointer)
def workflow(inputs: list[BaseMessage], *, previous: list[BaseMessage]):
    if previous:
        inputs = add_messages(previous, inputs)

    response = call_model(inputs).result()
    return entrypoint.final(value=response, save=add_messages(inputs, response))

config = {"configurable": {"thread_id": "1"}}
input_message = {"role": "user", "content": "hi! I'm bob"}
for chunk in workflow.stream([input_message], config, stream_mode="values"):
    chunk.pretty_print()

input_message = {"role": "user", "content": "what's my name?"}
for chunk in workflow.stream([input_message], config, stream_mode="values"):
    chunk.pretty_print()

长期记忆

长期记忆允许跨不同 thread ids 存储信息。这对于在一次对话中学习某个用户的信息，并在另一次对话中使用这些信息很有用。

工作流

工作流和 agent 指南提供了更多使用函数式 API 构建工作流的示例。

与其他库集成

使用函数式 API 将 LangGraph 的功能添加到其他框架：将持久化、记忆和流式输出等 LangGraph 功能添加到那些默认不提供这些能力的其他 agent 框架中。

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Edit this page on GitHub or file an issue.

​创建一个简单工作流

​并行执行

​调用图

​调用其他 entrypoint

​流式输出

​重试策略

​设置 task 和 entrypoint 超时

​缓存任务

​出错后恢复

​人机协作

​基本人机协作工作流

​审核工具调用

​短期记忆

​管理 checkpoint

​查看线程状态

​查看线程历史

​将返回值与保存值解耦

​聊天机器人示例

​长期记忆

​工作流

​与其他库集成

创建一个简单工作流

并行执行

调用图

调用其他 entrypoint

流式输出

重试策略

设置 task 和 entrypoint 超时

缓存任务

出错后恢复

人机协作

基本人机协作工作流

审核工具调用

短期记忆

管理 checkpoint

查看线程状态

查看线程历史

将返回值与保存值解耦

聊天机器人示例

长期记忆

工作流

与其他库集成