os.environ["LANGCHAIN_TRACING_V2"] = "true"
os.environ["LANGCHAIN_API_KEY"] = "lsv2_pt_d17ed5xxxxxxxxxxxxx xxxxxx   xxxx_cf08898efc"
os.environ["LANGCHAIN_PROJECT"] = "chatbot_test"

4、代码运行

代码实现

from langchain_openai import ChatOpenAI
from langgraph.graph import StateGraph, START, END
from langgraph.graph.message import MessagesState
import os

llm = ChatOpenAI(
    base_url="https://lxxxxx.enovo.com/v1/", 
    api_key="sxxxxxxxwW",
    model_name="qwen2.5-instruct"
    )


os.environ["LANGCHAIN_TRACING_V2"] = "true"
os.environ["LANGCHAIN_API_KEY"] = "lsv2_pt_d17ed5xxxxxxxxxxxxx xxxxxx   xxxx_cf08898efc"
os.environ["LANGCHAIN_PROJECT"] = "chatbot_test"

graph_builder = StateGraph(MessagesState)

def chatbot(state: MessagesState):
    return {"messages": [llm.invoke(state["messages"])]}

graph_builder.add_node("chatbot", chatbot)
graph_builder.add_edge(START, "chatbot")
graph_builder.add_edge("chatbot", END)
graph = graph_builder.compile()

for event in graph.stream({"messages": [{"role": "user", "content": "中国最好的大学是哪所？"}]}):
    for value in event.values():
        print("Assistant:", value["messages"][-1].content)

这是上一章的代码，直接执行即可

5、网站测试

打开刚才的网址，点击Tracing Projects，如下图：

可以查看相关的详细信息

当然，还有其他功能，此处不在赘述

四、LangGraph Platform

1、简介

部署应用的工具，可以在本地或云端部署，基于Fast API框架，有以下几种方式：

自托管轻量版: 一个免费（每年最多执行100万个节点）的LangGraph平台的受限版本，你可以在本地或自托管的方式下运行

云SaaS: 作为LangSmith的一部分托管。

自带云: 我们管理基础设施，因此你不必自己管理，但所有基础设施都在你的云环境中运行。

自托管企业版: 完全由你管理。

本文仅介绍本地启动一个 LangGraph 服务器，并使用 REST API 和 LangGraph Studio Web UI 与其进行交互

2、使用方法

（1）安装LangGraph CLI

pip install --upgrade "langgraph-cli[inmem]"

（2）创建LangGraph应用

langgraph new /usr/local/test --template react-agent-python

使用react-agent模板，创建应用在/usr/local/test下

也可以使用 langgraph new，其方法如下

(venv) jack@Laptop-L14-gen4:~/langchain_test/langchain_learn/langgraph_tt/integration_lang$ langgraph new
📂 Please specify the path to create the application [.]: chat_bot
🌟 Please select a template:
1. New LangGraph Project - A simple, minimal chatbot with memory.
2. ReAct Agent - A simple agent that can be flexibly extended to many tools.
3. Memory Agent - A ReAct-style agent with an additional tool to store memories for use across conversational threads.
4. Retrieval Agent - An agent that includes a retrieval-based question-answering system.
5. Data-enrichment Agent - An agent that performs web searches and organizes its findings into a structured format.
Enter the number of your template choice (default is 1): 1

You selected: New LangGraph Project - A simple, minimal chatbot with memory.
Choose language (1 for Python 🐍, 2 for JS/TS 🌐): 1
📥 Attempting to download repository as a ZIP archive...
URL: https://github.com/langchain-ai/new-langgraph-project/archive/refs/heads/main.zip
✅ Downloaded and extracted repository to /home/jack/langchain_test/langchain_learn/langgraph_tt/integration_lang/chat_bot
🎉 New project created at /home/jack/langchain_test/langchain_learn/langgraph_tt/integration_lang/chat_bot

需要先指定应用名称，再选择模板，最后选择language（python或js）

（3）修改graph

按照自己的需求添加节点、边、状态信息

修改文件路径：chat_bot/src/agent/graph.py

将内容修改如下：

"""Define a simple chatbot agent.

This agent returns a predefined response without using an actual LLM.
"""

from langgraph.graph import StateGraph


from langchain_openai import ChatOpenAI
from langgraph.graph import StateGraph, START, END
from langgraph.graph.message import MessagesState


llm = ChatOpenAI(
    base_url="https://lxxxxx.enovo.com/v1/", 
    api_key="sxxxxxxxwW",
    model_name="qwen2.5-instruct"
    )

graph_builder = StateGraph(MessagesState)

def chatbot(state: MessagesState):
    return {"messages": [llm.invoke(state["messages"])]}

graph_builder.add_node("chatbot", chatbot)
graph_builder.add_edge(START, "chatbot")
graph_builder.add_edge("chatbot", END)
graph = graph_builder.compile()
graph.name = "New Graph"  # This defines the custom name in LangSmith

此处，我偷懒了，并未完全按照LangGraph的推荐用法。仅仅为了最简化实现Platform的功能实现

（4）运行应用

langgraph dev 命令启动 LangGraph 服务器时使用的是内存模式。这种模式适合于开发和测试目的。对于生产环境，你应该部署 LangGraph 服务器并提供持久存储后端的访问权限。

如果你想使用持久存储后端测试你的应用程序，可以使用 langgraph up 命令代替 langgraph dev。你需要在你的机器上安装 docker 才能使用此命令。

效果如下：

(venv) jack@Laptop-L14-gen4:~/langchain_test/langchain...chat_bot$ langgraph dev
INFO:langgraph_api.cli:

        Welcome to

╦  ┌─┐┌┐┌┌─┐╔═╗┬─┐┌─┐┌─┐┬ ┬
║  ├─┤││││ ┬║ ╦├┬┘├─┤├─┘├─┤
╩═╝┴ ┴┘└┘└─┘╚═╝┴└─┴ ┴┴  ┴ ┴

- 🚀 API: http://127.0.0.1:2024
- 🎨 Studio UI: https://smith.langchain.com/studio/?baseUrl=http://127.0.0.1:2024
- 📚 API Docs: http://127.0.0.1:2024/docs

This in-memory server is designed for development and testing.
For production use, please use LangGraph Cloud.

成功运行

三个url，分别是：API 、API Docs、Studio UI，点击链接可直接使用，关于API相关的url大家自行研究，此处不再赘述。