无需设置环境变量进行追踪

import openai
from langsmith import Client, tracing_context, traceable
from langsmith.wrappers import wrap_openai

langsmith_client = Client(
  api_key="YOUR_LANGSMITH_API_KEY",  # 可以从密钥管理器中获取
  api_url="https://api.smith.langchain.com",  # 对于自托管安装或欧盟区域，请相应更新
  workspace_id="YOUR_WORKSPACE_ID", # 对于限定在多个工作区的API密钥必须指定
)

client = wrap_openai(openai.Client())

@traceable(run_type="tool", name="Retrieve Context")
def my_tool(question: str) -> str:
  return "During this morning's meeting, we solved all world conflict."

@traceable
def chat_pipeline(question: str):
  context = my_tool(question)
  messages = [
      { "role": "system", "content": "You are a helpful assistant. Please respond to the user's request only based on the given context." },
      { "role": "user", "content": f"Question: {question}\nContext: {context}"}
  ]
  chat_completion = client.chat.completions.create(
      model="gpt-5.4-mini", messages=messages
  )
  return chat_completion.choices[0].message.content

# 可以设置为False以在不改变代码结构的情况下禁用追踪
with tracing_context(enabled=True):
  # 使用 langsmith_extra 传递自定义客户端
  chat_pipeline("Can you summarize this morning's meetings?", langsmith_extra={"client": langsmith_client})

import { Client } from "langsmith";
import { traceable } from "langsmith/traceable";
import { wrapOpenAI } from "langsmith/wrappers";
import { OpenAI } from "openai";

const client = new Client({
    apiKey: "YOUR_API_KEY",  // 可以从密钥管理器中获取
    apiUrl: "https://api.smith.langchain.com",  // 对于自托管安装或欧盟区域，请相应更新
});

const openai = wrapOpenAI(new OpenAI());

const tool = traceable((question: string) => {
    return "During this morning's meeting, we solved all world conflict.";
}, { name: "Retrieve Context", runType: "tool" });

const pipeline = traceable(
    async (question: string) => {
        const context = await tool(question);

        const completion = await openai.chat.completions.create({
            model: "gpt-5.4-mini",
            messages: [
                { role: "system" as const, content: "You are a helpful assistant. Please respond to the user's request only based on the given context." },
                { role: "user" as const, content: `Question: ${question}\nContext: ${context}`}
            ]
        });

        return completion.choices[0].message.content;
    },
    { name: "Chat", client, tracingEnabled: true }
);

await pipeline("Can you summarize this morning's meetings?");