追踪 OpenAI 应用

Python/TypeScript 中的 wrap_openai / wrapOpenAI 方法允许你包装 OpenAI 客户端，从而自动记录追踪 —— 无需装饰器或函数包装！使用该包装器可确保消息（包括工具调用与多模态内容块）在 LangSmith 中良好呈现。另请注意，该包装器可与 @traceable 装饰器或 traceable 函数无缝配合，你可以在同一个应用中同时使用两者。

NOTE

即使使用 wrap_openai 或 wrapOpenAI，也必须将 LANGSMITH_TRACING 环境变量设为 'true'，追踪才会记录到 LangSmith。这样你无需修改代码即可开启或关闭追踪。

此外，你还需要将 LANGSMITH_API_KEY 环境变量设置为你的 API key（更多信息见设置。）

如果你的 LangSmith API key 关联了多个工作区，请设置 LANGSMITH_WORKSPACE_ID 环境变量以指定使用哪个工作区。

默认情况下，追踪会记录到名为 default 的项目。要将追踪记录到其他项目，请参阅将追踪记录到指定项目。

Python

import openai
from langsmith import traceable
from langsmith.wrappers import wrap_openai

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(name="Chat Pipeline")
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-5.5", messages=messages
  )
  return chat_completion.choices[0].message.content

chat_pipeline("Can you summarize this morning's meetings?")

TypeScript

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

const client = wrapOpenAI(new OpenAI());

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

const chatPipeline = traceable(async (question: string) => {
  const context = await myTool(question);
  const 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: `Question: ${question} Context: ${context}` },
  ];
  const chatCompletion = await client.chat.completions.create({
      model: "gpt-5.5",
      messages: messages,
  });
  return chatCompletion.choices[0].message.content;
}, { name: "Chat Pipeline" });

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

要追踪提供 OpenAI 兼容 API 的提供商，请参阅追踪 OpenAI 兼容的提供商。

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