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构建智能体

语言模型本身无法采取行动——它们只输出文本。 LangChain 的一个重要用例是创建智能体（agents）。 智能体是利用大语言模型（LLMs）作为推理引擎的系统，用于确定应采取哪些行动以及执行这些行动所需的输入。 在执行行动后，结果可以反馈给 LLM，以判断是否需要更多行动，或者是否可以结束。这通常通过工具调用来实现。

在本教程中，我们将构建一个可以与搜索引擎交互的智能体。您将能够向该智能体提问，观察它调用搜索工具，并与之进行对话。

端到端智能体​

下面的代码片段代表一个完全可用的智能体，它使用大语言模型来决定使用哪些工具。它配备了一个通用搜索工具。它具有对话记忆功能——意味着它可以用作多轮聊天机器人。

在本指南的其余部分，我们将逐步介绍各个组件及其功能——但如果您只想获取代码并立即开始，欢迎直接使用以下内容！

# Import relevant functionality
from langchain_anthropic import ChatAnthropic
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_core.messages import HumanMessage
from langgraph.checkpoint.memory import MemorySaver
from langgraph.prebuilt import create_react_agent

# Create the agent
memory = MemorySaver()
model = ChatAnthropic(model_name="claude-3-sonnet-20240229")
search = TavilySearchResults(max_results=2)
tools = [search]
agent_executor = create_react_agent(model, tools, checkpointer=memory)
# Use the agent
config = {"configurable": {"thread_id": "abc123"}}
for step in agent_executor.stream(
    {"messages": [HumanMessage(content="hi im bob! and i live in sf")]},
    config,
    stream_mode="values",
):
    step["messages"][-1].pretty_print()
================================ Human Message =================================

hi im bob! and i live in sf
================================== Ai Message ==================================

Hello Bob! Since you didn't ask a specific question, I don't need to use any tools right now. I'm an AI assistant created by Anthropic to be helpful, honest, and harmless. Feel free to ask me anything and I'll do my best to provide a useful response or look up information using my capabilities.
for step in agent_executor.stream(
    {"messages": [HumanMessage(content="whats the weather where I live?")]},
    config,
    stream_mode="values",
):
    step["messages"][-1].pretty_print()
================================ Human Message =================================

whats the weather where I live?
================================== Ai Message ==================================

[{'text': 'To get the current weather for your location in San Francisco, I can use the tavily_search_results_json tool:', 'type': 'text'}, {'id': 'toolu_01AKa2MErG1CU3zRiGsvpBud', 'input': {'query': 'san francisco weather'}, 'name': 'tavily_search_results_json', 'type': 'tool_use'}]
Tool Calls:
  tavily_search_results_json (toolu_01AKa2MErG1CU3zRiGsvpBud)
 Call ID: toolu_01AKa2MErG1CU3zRiGsvpBud
  Args:
    query: san francisco weather
================================= Tool Message =================================
Name: tavily_search_results_json

[{"url": "https://www.weatherapi.com/", "content": "{'location': {'name': 'San Francisco', 'region': 'California', 'country': 'United States of America', 'lat': 37.775, 'lon': -122.4183, 'tz_id': 'America/Los_Angeles', 'localtime_epoch': 1739994486, 'localtime': '2025-02-19 11:48'}, 'current': {'last_updated_epoch': 1739994300, 'last_updated': '2025-02-19 11:45', 'temp_c': 13.3, 'temp_f': 55.9, 'is_day': 1, 'condition': {'text': 'Light rain', 'icon': '//cdn.weatherapi.com/weather/64x64/day/296.png', 'code': 1183}, 'wind_mph': 5.8, 'wind_kph': 9.4, 'wind_degree': 195, 'wind_dir': 'SSW', 'pressure_mb': 1023.0, 'pressure_in': 30.2, 'precip_mm': 0.0, 'precip_in': 0.0, 'humidity': 87, 'cloud': 100, 'feelslike_c': 12.7, 'feelslike_f': 54.8, 'windchill_c': 9.1, 'windchill_f': 48.4, 'heatindex_c': 10.2, 'heatindex_f': 50.3, 'dewpoint_c': 9.8, 'dewpoint_f': 49.7, 'vis_km': 4.0, 'vis_miles': 2.0, 'uv': 1.4, 'gust_mph': 8.9, 'gust_kph': 14.4}}"}, {"url": "https://world-weather.info/forecast/usa/san_francisco/february-2025/", "content": "Weather in San Francisco in February 2025 (California) - Detailed Weather Forecast for a Month Weather World Weather in San Francisco Weather in San Francisco in February 2025 San Francisco Weather Forecast for February 2025, is based on previous years' statistical data. +59°+50° +59°+52° +59°+50° +61°+52° +59°+50° +61°+50° +61°+52° +63°+52° +61°+52° +61°+50° +61°+50° +61°+50° +59°+50° +59°+50° +61°+50° +61°+52° +59°+50° +59°+48° +57°+48° +59°+50° +59°+48° +59°+50° +57°+46° +61°+50° +61°+50° +59°+50° +59°+48° +59°+50° Extended weather forecast in San Francisco HourlyWeek10-Day14-Day30-DayYear Weather in large and nearby cities Weather in Washington, D.C.+41° Sacramento+55° Pleasanton+55° Redwood City+55° San Leandro+55° San Mateo+54° San Rafael+52° San Ramon+52° South San Francisco+54° Vallejo+50° Palo Alto+55° Pacifica+55° Berkeley+54° Castro Valley+55° Concord+52° Daly City+54° Noverd+52° Sign Hill+54° world's temperature today day day Temperature units"}]
================================== Ai Message ==================================

The search results provide the current weather conditions and forecast for San Francisco. According to the data from WeatherAPI, the current temperature in San Francisco is around 55°F (13°C) with light rain and winds around 6 mph. The extended forecast shows temperatures ranging from the upper 40s to low 60s Fahrenheit over the next few weeks.

So in summary, it's a cool, rainy day currently in San Francisco where you live, Bob. Let me know if you need any other details about the weather there!

设置​

Jupyter Notebook​

本指南（以及文档中的大多数其他指南）使用 Jupyter 笔记本，并假设读者也是如此。Jupyter 笔记本是学习如何与大型语言模型系统协作的完美交互式环境，因为事情经常会出错（例如输出意外、API 不可用等），而观察这些情况是更好地理解构建大型语言模型应用的绝佳方式。

此教程及其他教程最方便在 Jupyter notebook 中运行。有关如何安装的说明，请参见 此处。

安装​

安装 LangChain 请运行：

%pip install -U langchain-community langgraph langchain-anthropic tavily-python langgraph-checkpoint-sqlite

有关更多详细信息，请参阅我们的 安装指南。

LangSmith​

您使用 LangChain 构建的许多应用程序将包含多个步骤以及多次 LLM 调用。 随着这些应用程序变得越来越复杂，能够检查您的链或代理内部究竟发生了什么变得至关重要。 实现这一点的最佳方式是使用 LangSmith。

在通过上述链接注册后，请确保设置您的环境变量以开始记录追踪：

export LANGSMITH_TRACING="true"
export LANGSMITH_API_KEY="..."

或者，如果在笔记本中，您可以这样设置：

import getpass
import os

os.environ["LANGSMITH_TRACING"] = "true"
os.environ["LANGSMITH_API_KEY"] = getpass.getpass()

Tavily​

我们将使用 Tavily（一个搜索引擎）作为工具。 要使用它，您需要获取并设置 API 密钥：

export TAVILY_API_KEY="..."

或者，如果在笔记本中，您可以这样设置：

import getpass
import os

os.environ["TAVILY_API_KEY"] = getpass.getpass()

定义工具​

我们首先需要创建要使用的工具。我们的主要工具选择是 Tavily —— 一个搜索引擎。LangChain 内置了一个工具，可以方便地将 Tavily 搜索引擎用作工具。

from langchain_community.tools.tavily_search import TavilySearchResults

search = TavilySearchResults(max_results=2)
search_results = search.invoke("what is the weather in SF")
print(search_results)
# If we want, we can create other tools.
# Once we have all the tools we want, we can put them in a list that we will reference later.
tools = [search]
[{'url': 'https://www.weatherapi.com/', 'content': "{'location': {'name': 'San Francisco', 'region': 'California', 'country': 'United States of America', 'lat': 37.775, 'lon': -122.4183, 'tz_id': 'America/Los_Angeles', 'localtime_epoch': 1739993250, 'localtime': '2025-02-19 11:27'}, 'current': {'last_updated_epoch': 1739992500, 'last_updated': '2025-02-19 11:15', 'temp_c': 13.3, 'temp_f': 55.9, 'is_day': 1, 'condition': {'text': 'Light rain', 'icon': '//cdn.weatherapi.com/weather/64x64/day/296.png', 'code': 1183}, 'wind_mph': 5.8, 'wind_kph': 9.4, 'wind_degree': 195, 'wind_dir': 'SSW', 'pressure_mb': 1023.0, 'pressure_in': 30.2, 'precip_mm': 0.0, 'precip_in': 0.0, 'humidity': 87, 'cloud': 100, 'feelslike_c': 12.7, 'feelslike_f': 54.8, 'windchill_c': 9.1, 'windchill_f': 48.4, 'heatindex_c': 10.2, 'heatindex_f': 50.3, 'dewpoint_c': 9.8, 'dewpoint_f': 49.7, 'vis_km': 4.0, 'vis_miles': 2.0, 'uv': 1.4, 'gust_mph': 8.9, 'gust_kph': 14.4}}"}, {'url': 'https://weathershogun.com/weather/usa/ca/san-francisco/480/february/2025-02-19', 'content': 'San Francisco, California Weather: Wednesday, February 19, 2025. Cloudy weather, overcast skies with clouds. Day 61°. Night 43°.'}]

使用语言模型​

接下来，让我们学习如何使用语言模型来调用工具。LangChain 支持多种不同的语言模型，您可以互换使用它们——请在下方选择您想要使用的模型！

pip install -qU "langchain[openai]"
import getpass
import os

if not os.environ.get("OPENAI_API_KEY"):
  os.environ["OPENAI_API_KEY"] = getpass.getpass("Enter API key for OpenAI: ")

from langchain.chat_models import init_chat_model

model = init_chat_model("gpt-4", model_provider="openai")

您可以通过传递消息列表来调用语言模型。默认情况下，响应是一个content字符串。

from langchain_core.messages import HumanMessage

response = model.invoke([HumanMessage(content="hi!")])
response.content
API 参考:人类消息
'Hi there!'

我们现在可以看到如何使该模型具备工具调用能力。为此，我们使用.bind_tools来让语言模型了解这些工具

model_with_tools = model.bind_tools(tools)

我们现在可以调用模型了。让我们先用一条普通消息调用它，看看它的响应。我们可以同时查看content字段和tool_calls字段。

response = model_with_tools.invoke([HumanMessage(content="Hi!")])

print(f"ContentString: {response.content}")
print(f"ToolCalls: {response.tool_calls}")
ContentString: Hello!
ToolCalls: []

现在，让我们尝试用一些期望调用工具的输入来调用它。

response = model_with_tools.invoke([HumanMessage(content="What's the weather in SF?")])

print(f"ContentString: {response.content}")
print(f"ToolCalls: {response.tool_calls}")
ContentString: 
ToolCalls: [{'name': 'tavily_search_results_json', 'args': {'query': 'weather san francisco'}, 'id': 'toolu_01VTP7DUvSfgtYxsq9x4EwMp'}]

我们可以看到现在没有文本内容，但有一个工具调用！它希望我们调用 Tavily Search 工具。

这还没有调用该工具——它只是在告诉我们要这样做。为了实际调用它，我们将需要创建我们的代理。

创建智能体​

现在我们已经定义了工具和大型语言模型（LLM），就可以创建智能体了。我们将使用 LangGraph 来构建该智能体。 目前，我们使用的是高层接口来构建智能体，但 LangGraph 的妙处在于，该高层接口底层由一个低层、高度可控制的 API 支持，以便您在需要修改智能体逻辑时能够进行自定义。

现在，我们可以使用 LLM 和工具来初始化智能体。

请注意，我们传入的是 model，而不是 model_with_tools。这是因为 create_react_agent 会在底层为我们调用 .bind_tools。

from langgraph.prebuilt import create_react_agent

agent_executor = create_react_agent(model, tools)

运行智能体​

我们现在可以使用一些查询来运行代理！请注意，目前这些查询都是无状态的（它不会记住之前的交互）。请注意，代理将在交互结束时返回最终状态（其中包括任何输入，我们稍后将看到如何仅获取输出）。

首先，让我们看看在没有需要调用工具时它如何响应：

response = agent_executor.invoke({"messages": [HumanMessage(content="hi!")]})

response["messages"]
[HumanMessage(content='hi!', id='a820fcc5-9b87-457a-9af0-f21768143ee3'),
 AIMessage(content='Hello!', response_metadata={'id': 'msg_01VbC493X1VEDyusgttiEr1z', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 264, 'output_tokens': 5}}, id='run-0e0ddae8-a85b-4bd6-947c-c36c857a4698-0', usage_metadata={'input_tokens': 264, 'output_tokens': 5, 'total_tokens': 269})]

为了确切地了解底层发生了什么（并确保它没有调用工具），我们可以查看 LangSmith 追踪

现在让我们在一个应该调用工具的示例中尝试一下

response = agent_executor.invoke(
    {"messages": [HumanMessage(content="whats the weather in sf?")]}
)
response["messages"]
[HumanMessage(content='whats the weather in sf?', id='1d6c96bb-4ddb-415c-a579-a07d5264de0d'),
 AIMessage(content=[{'id': 'toolu_01Y5EK4bw2LqsQXeaUv8iueF', 'input': {'query': 'weather in san francisco'}, 'name': 'tavily_search_results_json', 'type': 'tool_use'}], response_metadata={'id': 'msg_0132wQUcEduJ8UKVVVqwJzM4', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'input_tokens': 269, 'output_tokens': 61}}, id='run-26d5e5e8-d4fd-46d2-a197-87b95b10e823-0', tool_calls=[{'name': 'tavily_search_results_json', 'args': {'query': 'weather in san francisco'}, 'id': 'toolu_01Y5EK4bw2LqsQXeaUv8iueF'}], usage_metadata={'input_tokens': 269, 'output_tokens': 61, 'total_tokens': 330}),
 ToolMessage(content='[{"url": "https://www.weatherapi.com/", "content": "{\'location\': {\'name\': \'San Francisco\', \'region\': \'California\', \'country\': \'United States of America\', \'lat\': 37.78, \'lon\': -122.42, \'tz_id\': \'America/Los_Angeles\', \'localtime_epoch\': 1717238703, \'localtime\': \'2024-06-01 3:45\'}, \'current\': {\'last_updated_epoch\': 1717237800, \'last_updated\': \'2024-06-01 03:30\', \'temp_c\': 12.0, \'temp_f\': 53.6, \'is_day\': 0, \'condition\': {\'text\': \'Mist\', \'icon\': \'//cdn.weatherapi.com/weather/64x64/night/143.png\', \'code\': 1030}, \'wind_mph\': 5.6, \'wind_kph\': 9.0, \'wind_degree\': 310, \'wind_dir\': \'NW\', \'pressure_mb\': 1013.0, \'pressure_in\': 29.92, \'precip_mm\': 0.0, \'precip_in\': 0.0, \'humidity\': 88, \'cloud\': 100, \'feelslike_c\': 10.5, \'feelslike_f\': 50.8, \'windchill_c\': 9.3, \'windchill_f\': 48.7, \'heatindex_c\': 11.1, \'heatindex_f\': 51.9, \'dewpoint_c\': 8.8, \'dewpoint_f\': 47.8, \'vis_km\': 6.4, \'vis_miles\': 3.0, \'uv\': 1.0, \'gust_mph\': 12.5, \'gust_kph\': 20.1}}"}, {"url": "https://www.timeanddate.com/weather/usa/san-francisco/hourly", "content": "Sun & Moon. Weather Today Weather Hourly 14 Day Forecast Yesterday/Past Weather Climate (Averages) Currently: 59 \\u00b0F. Passing clouds. (Weather station: San Francisco International Airport, USA). See more current weather."}]', name='tavily_search_results_json', id='37aa1fd9-b232-4a02-bd22-bc5b9b44a22c', tool_call_id='toolu_01Y5EK4bw2LqsQXeaUv8iueF'),
 AIMessage(content='Based on the search results, here is a summary of the current weather in San Francisco:\n\nThe weather in San Francisco is currently misty with a temperature of around 53°F (12°C). There is complete cloud cover and moderate winds from the northwest around 5-9 mph (9-14 km/h). Humidity is high at 88%. Visibility is around 3 miles (6.4 km). \n\nThe results provide an hourly forecast as well as current conditions from a couple different weather sources. Let me know if you need any additional details about the San Francisco weather!', response_metadata={'id': 'msg_01BRX9mrT19nBDdHYtR7wJ92', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 920, 'output_tokens': 132}}, id='run-d0325583-3ddc-4432-b2b2-d023eb97660f-0', usage_metadata={'input_tokens': 920, 'output_tokens': 132, 'total_tokens': 1052})]

我们可以查看 LangSmith 追踪，以确认其是否有效地调用了搜索工具。

流式消息​

我们已经看到如何通过调用 .invoke 获取最终响应。如果代理执行多个步骤，这可能需要一些时间。为了显示中间进度，我们可以实时流式返回发生的消息。

for step in agent_executor.stream(
    {"messages": [HumanMessage(content="whats the weather in sf?")]},
    stream_mode="values",
):
    step["messages"][-1].pretty_print()
================================ Human Message =================================

whats the weather in sf?
================================== Ai Message ==================================

[{'text': 'Okay, let me look up the current weather for San Francisco using a search engine:', 'type': 'text'}, {'id': 'toolu_01H1brh5EZpZqtqHBxkosPtN', 'input': {'query': 'san francisco weather'}, 'name': 'tavily_search_results_json', 'type': 'tool_use'}]
Tool Calls:
  tavily_search_results_json (toolu_01H1brh5EZpZqtqHBxkosPtN)
 Call ID: toolu_01H1brh5EZpZqtqHBxkosPtN
  Args:
    query: san francisco weather
================================= Tool Message =================================
Name: tavily_search_results_json

[{"url": "https://www.weatherapi.com/", "content": "{'location': {'name': 'San Francisco', 'region': 'California', 'country': 'United States of America', 'lat': 37.775, 'lon': -122.4183, 'tz_id': 'America/Los_Angeles', 'localtime_epoch': 1739994486, 'localtime': '2025-02-19 11:48'}, 'current': {'last_updated_epoch': 1739994300, 'last_updated': '2025-02-19 11:45', 'temp_c': 13.3, 'temp_f': 55.9, 'is_day': 1, 'condition': {'text': 'Light rain', 'icon': '//cdn.weatherapi.com/weather/64x64/day/296.png', 'code': 1183}, 'wind_mph': 5.8, 'wind_kph': 9.4, 'wind_degree': 195, 'wind_dir': 'SSW', 'pressure_mb': 1023.0, 'pressure_in': 30.2, 'precip_mm': 0.0, 'precip_in': 0.0, 'humidity': 87, 'cloud': 100, 'feelslike_c': 12.7, 'feelslike_f': 54.8, 'windchill_c': 9.1, 'windchill_f': 48.4, 'heatindex_c': 10.2, 'heatindex_f': 50.3, 'dewpoint_c': 9.8, 'dewpoint_f': 49.7, 'vis_km': 4.0, 'vis_miles': 2.0, 'uv': 1.4, 'gust_mph': 8.9, 'gust_kph': 14.4}}"}, {"url": "https://world-weather.info/forecast/usa/san_francisco/february-2025/", "content": "Weather in San Francisco in February 2025 (California) - Detailed Weather Forecast for a Month Weather World Weather in San Francisco Weather in San Francisco in February 2025 San Francisco Weather Forecast for February 2025, is based on previous years' statistical data. +59°+50° +59°+52° +59°+50° +61°+52° +59°+50° +61°+50° +61°+52° +63°+52° +61°+52° +61°+50° +61°+50° +61°+50° +59°+50° +59°+50° +61°+50° +61°+52° +59°+50° +59°+48° +57°+48° +59°+50° +59°+48° +59°+50° +57°+46° +61°+50° +61°+50° +59°+50° +59°+48° +59°+50° Extended weather forecast in San Francisco HourlyWeek10-Day14-Day30-DayYear Weather in large and nearby cities Weather in Washington, D.C.+41° Sacramento+55° Pleasanton+55° Redwood City+55° San Leandro+55° San Mateo+54° San Rafael+52° San Ramon+52° South San Francisco+54° Vallejo+50° Palo Alto+55° Pacifica+55° Berkeley+54° Castro Valley+55° Concord+52° Daly City+54° Noverd+52° Sign Hill+54° world's temperature today day day Temperature units"}]
================================== Ai Message ==================================

The search results provide details on the current weather conditions and forecast for San Francisco. Some key details:

- It is lightly raining in San Francisco right now, with a temperature around 55°F/13°C. 
- The forecast for the rest of February 2025 shows daytime highs mostly in the upper 50s to low 60s F, with night lows in the upper 40s to low 50s F. 
- Typical weather includes some rain, clouds, cool temperatures and breezy conditions.

So in summary, as is common for San Francisco in late winter, it is currently cool with light rain showers, and similar mild, unsettled weather is expected over the next couple weeks. Layers and a light jacket would be advisable for being outdoors. Let me know if you need any other details!

流式传输令牌​

除了流式返回消息外，流式返回令牌也很有用。 我们可以通过指定stream_mode="messages"来实现这一点。

::: note

下面我们使用 message.text()，这需要 langchain-core>=0.3.37。

:::

for step, metadata in agent_executor.stream(
    {"messages": [HumanMessage(content="whats the weather in sf?")]},
    stream_mode="messages",
):
    if metadata["langgraph_node"] == "agent" and (text := step.text()):
        print(text, end="|")


Base|d on the weather| search| results, here| are the key details| about the weather in| San Francisco:|

- The current temperature| in| San Francisco is aroun|d 55|-|56|°F (13|°|C).| Light| rain is occurring with| |100|% clou|d cover. |

-| Winds| are aroun|d 5-9| mph from| the south|-southwest.|

- The| forecast| for| the rest| of February| 2025 |shows da|ytime highs mostly| in the upper| 50s to| low| 60s°|F,| with overnight lows| in| the upper| 40s to| low| 50s°|F.|

-| Overall|, typical| cool| an|d show|ery late| winter weather is| expected in San Francisco| for the remainder| of February,| with a| mix| of rain| and dry| periods|.| Temperatures will be| season|able| for| this| time of year.|

So| in summary, San| Francisco is| experiencing light| rain an|d cool| temperatures currently, but| the late| winter forecast| shows typical mil|d and show|ery conditions| pers|isting through the en|d of the| month.| Let| me know if you| need any other| details about| the weather in the| city!|

在内存中添加​

如前所述，该代理是无状态的。这意味着它不会记住之前的交互。为了赋予它记忆功能，我们需要传入一个检查点器（checkpointer）。在传入检查点器时，调用代理时还必须传入 thread_id（以便它知道从哪个线程/对话中恢复）。

from langgraph.checkpoint.memory import MemorySaver

memory = MemorySaver()
API 参考:内存保存器
agent_executor = create_react_agent(model, tools, checkpointer=memory)

config = {"configurable": {"thread_id": "abc123"}}
for chunk in agent_executor.stream(
    {"messages": [HumanMessage(content="hi im bob!")]}, config
):
    print(chunk)
    print("----")
{'agent': {'messages': [AIMessage(content="Hello Bob! It's nice to meet you again.", response_metadata={'id': 'msg_013C1z2ZySagEFwmU1EsysR2', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 1162, 'output_tokens': 14}}, id='run-f878acfd-d195-44e8-9166-e2796317e3f8-0', usage_metadata={'input_tokens': 1162, 'output_tokens': 14, 'total_tokens': 1176})]}}
----
for chunk in agent_executor.stream(
    {"messages": [HumanMessage(content="whats my name?")]}, config
):
    print(chunk)
    print("----")
{'agent': {'messages': [AIMessage(content='You mentioned your name is Bob when you introduced yourself earlier. So your name is Bob.', response_metadata={'id': 'msg_01WNwnRNGwGDRw6vRdivt6i1', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 1184, 'output_tokens': 21}}, id='run-f5c0b957-8878-405a-9d4b-a7cd38efe81f-0', usage_metadata={'input_tokens': 1184, 'output_tokens': 21, 'total_tokens': 1205})]}}
----

示例 LangSmith 追踪

如果您想开始新的对话，只需更改所使用的 thread_id 即可

config = {"configurable": {"thread_id": "xyz123"}}
for chunk in agent_executor.stream(
    {"messages": [HumanMessage(content="whats my name?")]}, config
):
    print(chunk)
    print("----")
{'agent': {'messages': [AIMessage(content="I'm afraid I don't actually know your name. As an AI assistant without personal information about you, I don't have a specific name associated with our conversation.", response_metadata={'id': 'msg_01NoaXNNYZKSoBncPcLkdcbo', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 267, 'output_tokens': 36}}, id='run-c9f7df3d-525a-4d8f-bbcf-a5b4a5d2e4b0-0', usage_metadata={'input_tokens': 267, 'output_tokens': 36, 'total_tokens': 303})]}}
----

结论​

这就结束了！在本快速入门中，我们介绍了如何创建一个简单的智能体。 随后，我们展示了如何流式返回响应——不仅包含中间步骤，还包括 token！ 我们还添加了内存功能，使您能够与它们进行对话。 智能体是一个复杂的主题，有许多需要学习的内容！

有关智能体的更多信息，请查看 LangGraph 文档。它拥有自己的一套概念、教程和实操指南。