使用函数式 API¶

函数式 API 允许您以最少的代码改动，将 LangGraph 的核心功能——持久化、记忆、人在环和流式处理——添加到您的应用程序中。

提示

关于函数式 API 的概念信息，请参阅函数式 API。

创建一个简单的工作流¶

在定义 entrypoint 时，输入仅限于函数的第一个参数。要传递多个输入，您可以使用字典。

@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})

扩展示例：简单工作流

import uuid
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(uuid.uuid4())}}
result = workflow.invoke({"number": 7}, config=config)
print(result)

扩展示例：使用 LLM 撰写一篇文章

本示例演示了如何从语法上使用 @task 和 @entrypoint 装饰器。如果提供了检查点，工作流的结果将持久化到检查点中。

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

llm = init_chat_model('openai: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 llm.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(uuid.uuid4())}}
result = workflow.invoke("the history of flight", config=config)
print(result)

并行执行¶

通过并发调用任务并等待结果，可以并行执行任务。这对于提高 IO 密集型任务（例如，调用 LLM 的 API）的性能非常有用。

@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]

扩展示例：并行 LLM 调用

本示例演示了如何使用 @task 并行运行多个 LLM 调用。每个调用都会针对不同主题生成一个段落，然后将结果合并为单个文本输出。

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
llm = init_chat_model("openai:gpt-3.5-turbo")

# Task that generates a paragraph about a given topic
@task
def generate_paragraph(topic: str) -> str:
    response = llm.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(uuid.uuid4())}}
result = workflow.invoke(["quantum computing", "climate change", "history of aviation"], config=config)
print(result)

本示例利用 LangGraph 的并发模型来缩短执行时间，尤其是在任务涉及 I/O（如 LLM 补全）时。

调用图¶

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

^{API 参考：entrypoint | StateGraph}

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
    }

扩展示例：从函数式 API 调用一个简单的图

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(uuid.uuid4())}}
print(workflow.invoke(5, config=config))  # Output: {'bar': 10}

调用其他入口点¶

您可以在一个入口点或任务中调用其他入口点。

@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

扩展示例：调用另一个入口点

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(uuid.uuid4())}}
print(main.invoke({"x": 6, "y": 7}, config=config))  # Output: {'product': 42}

流式传输¶

函数式 API 使用与图 API 相同的流式处理机制。请阅读流式处理指南部分以获取更多详细信息。

使用流式 API 流式传输更新和自定义数据的示例。

^{API 参考：entrypoint | InMemorySaver | get_stream_writer}

from langgraph.func import entrypoint
from langgraph.checkpoint.memory import InMemorySaver
from langgraph.config import get_stream_writer # (1)!

checkpointer = InMemorySaver()

@entrypoint(checkpointer=checkpointer)
def main(inputs: dict) -> int:
    writer = get_stream_writer() # (2)!
    writer("Started processing") # (3)!
    result = inputs["x"] * 2
    writer(f"Result is {result}") # (4)!
    return result

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

for mode, chunk in main.stream( # (5)!
    {"x": 5},
    stream_mode=["custom", "updates"], # (6)!
    config=config
):
    print(f"{mode}: {chunk}")

从 langgraph.config 导入 get_stream_writer。
在入口点内获取一个流写入器实例。
在计算开始前发出自定义数据。
计算结果后发出另一条自定义消息。
使用 .stream() 处理流式输出。
指定要使用的流式处理模式。

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

Python < 3.11 的异步

如果您使用的 Python 版本低于 3.11 并且正在编写异步代码，使用 get_stream_writer() 将无法工作。请直接使用 StreamWriter 类。更多详情请参阅使用 Python < 3.11 的异步编程。

from langgraph.types import StreamWriter

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

重试策略¶

^{API 参考：InMemorySaver | entrypoint | task | RetryPolicy}

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'

缓存任务¶

^{API 参考：entrypoint | task}

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))  # (1)!
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。缓存将在此时间后失效。

从错误中恢复¶

^{API 参考：InMemorySaver | entrypoint | task | StreamWriter}

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，我们将不需要重新运行 slow_task，因为它的结果已经保存在检查点中。

main.invoke(None, config=config)

'Ran slow task.'

人机协作 (Human-in-the-loop)¶

函数式 API 使用 interrupt 函数和 Command 原语支持人在环工作流。

基本的人在环工作流¶

我们将创建三个任务

追加 "bar"。
暂停等待人类输入。恢复时，追加人类输入。
追加 "qux"。

^{API 参考：entrypoint | task | Command | interrupt}

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"

我们现在可以在一个入口点中组合这些任务

^{API 参考：InMemorySaver}

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() 在任务内部被调用，使得人类可以审查和编辑前一个任务的输出。先前任务的结果——在本例中是 step_1——被持久化，因此在 interrupt 之后不会再次运行。

让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，在 step_1 之后我们使用 interrupt 暂停了。中断提供了恢复运行的指令。要恢复，我们发出一个Command，其中包含 human_feedback 任务所期望的数据。

# 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的步骤并按预期终止。

审查工具调用¶

为了在执行前审查工具调用，我们添加了一个调用interrupt的 review_tool_call 函数。当此函数被调用时，执行将被暂停，直到我们发出命令来恢复它。

给定一个工具调用，我们的函数将 interrupt 以进行人工审查。此时我们可以

接受工具调用
修改工具调用并继续
生成一个自定义工具消息（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"]
        )

我们现在可以更新我们的入口点来审查生成的工具调用。如果工具调用被接受或修改，我们像以前一样执行。否则，我们只追加由人类提供的 ToolMessage。先前任务的结果——在本例中是初始模型调用——被持久化，因此在 interrupt 之后不会再次运行。

^{API 参考：InMemorySaver | add_messages | Command | interrupt}

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)

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

        # Review tool calls
        tool_results = []
        tool_calls = []
        for i, tool_call in enumerate(llm_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:
                    llm_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,
            [llm_response, *tool_results, *remaining_tool_results],
        )

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

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

短期记忆¶

短期记忆允许在同一线程 ID 的不同调用之间存储信息。更多详情请参阅短期记忆。

管理检查点¶

您可以查看和删除由检查点存储的信息。

查看线程状态（检查点）¶

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=()
)

查看线程的历史记录（检查点）¶

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 将返回给调用者的内容与持久化在检查点中的内容解耦。这在以下情况下很有用

您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供下次调用使用。
您需要控制在下一次运行时传递给上一个参数的内容。

^{API 参考：entrypoint | InMemorySaver}

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

checkpointer = InMemorySaver()

@entrypoint(checkpointer=checkpointer)
def accumulate(n: int, *, previous: Optional[int]) -> 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¶

一个使用函数式 API 和 InMemorySaver 检查点的简单聊天机器人示例。该机器人能够记住之前的对话并从中断处继续。

^{API 参考：BaseMessage | add_messages | entrypoint | task | InMemorySaver | ChatAnthropic}

from langchain_core.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-3-5-sonnet-latest")

@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()

扩展示例：构建一个简单的聊天机器人

如何添加线程级持久化（函数式 API）：展示了如何向函数式 API 工作流添加线程级持久化，并实现了一个简单的聊天机器人。

长期记忆¶

长期记忆允许在不同的线程 ID 之间存储信息。这对于在一个对话中学习有关特定用户的信息并在另一个对话中使用它很有用。

扩展示例：添加长期记忆

如何添加跨线程持久化（函数式 API）：展示了如何向函数式 API 工作流添加跨线程持久化，并实现了一个简单的聊天机器人。

工作流¶

工作流和代理指南，了解更多关于如何使用函数式 API 构建工作流的示例。

代理¶

如何从零开始创建一个代理（函数式 API）：展示了如何使用函数式 API 从零开始创建一个简单的代理。
如何构建一个多代理网络：展示了如何使用函数式 API 构建一个多代理网络。
如何在多代理应用中添加多轮对话（函数式 API）：允许最终用户与一个或多个代理进行多轮对话。

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 LangGraph 的功能（如持久化、记忆和流式处理）添加到其他本身不提供这些功能的代理框架中。