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构建 Agent

语言模型本身无法执行操作 - 它们只是输出文本。 LangChain 的一个重要用例是创建 agents。 Agents 是使用 LLM 作为推理引擎的系统，以确定要采取哪些操作以及执行操作所需的输入。执行操作后，可以将结果反馈回 LLM，以确定是否需要更多操作，或者是否可以完成。这通常通过 工具调用 实现。

在本教程中，我们将构建一个可以与搜索引擎交互的 Agent。您将能够向此 Agent 提问，观看它调用搜索工具，并与之进行对话。

端到端 Agent​

下面的代码片段表示一个功能齐全的 Agent，它使用 LLM 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 - 意味着它可以作为多轮聊天机器人使用。

在本指南的其余部分，我们将逐步介绍各个组件以及每个部分的作用 - 但如果您只想获取一些代码并开始使用，请随时使用此代码！

# 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 notebooks，并假定读者也是如此。 Jupyter notebooks 是学习如何使用 LLM 系统的完美交互式环境，因为通常情况下事情可能会出错（意外输出、API 宕机等），而观察这些情况是更好地理解使用 LLM 构建的绝佳方式。

本教程和其他教程可能最方便在 Jupyter notebook 中运行。 有关如何安装的说明，请参阅 此处。

安装​

要安装 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，其中包含对 LLM 调用的多次调用。 随着这些应用程序变得越来越复杂，能够检查链或 Agent 内部究竟发生了什么变得至关重要。 最好的方法是使用 LangSmith。

在上面的链接注册后，请确保设置您的环境变量以开始记录跟踪

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

或者，如果在 notebook 中，您可以使用以下命令设置它们

import getpass
import os

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

Tavily​

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

export TAVILY_API_KEY="..."

或者，如果在 notebook 中，您可以使用以下命令设置它

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。 我们主要选择的工具是 Tavily - 一个搜索引擎。 我们在 LangChain 中内置了一个工具，可以轻松地将 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]
API 参考：TavilySearchResults
[{'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 支持许多不同的语言模型，您可以互换使用 - 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")

您可以通过传入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：HumanMessage
'Hi there!'

我们现在可以看到启用此模型进行工具调用是什么样的。 为了启用它，我们使用 .bind_tools 为语言模型提供这些工具的知识

model_with_tools = model.bind_tools(tools)

我们现在可以调用模型了。 让我们首先用一条普通消息调用它，看看它如何响应。 我们可以查看 content 字段以及 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 搜索工具。

这还没有调用该工具 - 它只是告诉我们这样做。 为了实际调用它，我们将需要创建我们的 Agent。

创建 Agent​

现在我们已经定义了工具和 LLM，我们可以创建 Agent 了。 我们将使用 LangGraph 来构建 Agent。 目前，我们正在使用高级接口来构建 Agent，但 LangGraph 的好处在于，这个高级接口由一个低级、高度可控的 API 支持，以防您想要修改 Agent 逻辑。

现在，我们可以使用 LLM 和工具初始化 Agent。

请注意，我们正在传入 model，而不是 model_with_tools。 这是因为 create_react_agent 会在后台为我们调用 .bind_tools。

from langgraph.prebuilt import create_react_agent

agent_executor = create_react_agent(model, tools)
API 参考：create_react_agent

运行 Agent​

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

首先，让我们看看在不需要调用工具时它是如何响应的

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})]

为了准确地了解幕后发生了什么（并确保它没有调用工具），我们可以查看 LangSmith 跟踪

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 跟踪，以确保它有效地调用了搜索工具。

流式传输消息​

我们已经了解了如何使用 .invoke 调用 Agent 以获得最终响应。 如果 Agent 执行多个步骤，这可能需要一段时间。 为了显示中间进度，我们可以流式传输消息，因为它们会发生。

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​

除了流式传输消息之外，流式传输令牌也很有用。 我们可以通过指定 stream_mode="messages" 来做到这一点。

::: 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!|

添加内存​

如前所述，此 Agent 是无状态的。 这意味着它不记得之前的交互。 为了给它记忆，我们需要传入一个检查点。 当传入检查点时，我们还必须在调用 Agent 时传入一个 thread_id（以便它知道要从哪个线程/对话恢复）。

from langgraph.checkpoint.memory import MemorySaver

memory = MemorySaver()
API 参考：MemorySaver
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 跟踪

如果您想开始新的对话，您所要做的就是更改使用的 thread_id

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​

总结一下！ 在这个快速入门中，我们介绍了如何创建一个简单的 Agent。 然后，我们展示了如何流式传输响应 - 不仅包括中间步骤，还包括令牌！ 我们还添加了内存，以便您可以与他们进行对话。 Agent 是一个复杂的主题，有很多东西要学习！

有关 Agent 的更多信息，请查看 LangGraph 文档。 这有自己的一套概念、教程和操作指南。

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