> ## Documentation Index
> Fetch the complete documentation index at: https://cndoc-langchain.site/llms.txt
> Use this file to discover all available pages before exploring further.

# 追踪 Anthropic 应用

Python 中的 Anthropic 包装器方法（[`wrap_anthropic`](https://reference.langchain.com/python/langsmith/wrappers/_anthropic/wrap_anthropic)）和 Typescript 中的（[`wrapAnthropic`](https://reference.langchain.com/javascript/functions/langsmith.wrappers_anthropic.wrapAnthropic.html)）允许您包装您的 Anthropic 客户端，以便自动记录跟踪信息。使用该包装器可确保消息（包括工具调用和多模态内容块）在 LangSmith 中得到良好呈现。该包装器可与 `@traceable` 装饰器（Python）或 `traceable` 函数（TypeScript）无缝协作，因此您可以使用包装器跟踪 Anthropic 调用，并使用装饰器或函数跟踪应用程序的其他部分。

<Note>
  即使使用 `wrap_anthropic` 或 `wrapAnthropic`，也必须将 `LANGSMITH_TRACING` 环境变量设置为 `'true'`，跟踪信息才会被记录到 LangSmith。这允许您在不更改代码的情况下切换跟踪的开启和关闭。

  此外，您需要将 `LANGSMITH_API_KEY` 环境变量设置为您的 API 密钥（更多信息请参阅[设置](/)）。

  如果您的 LangSmith API 密钥链接到多个工作区，请设置 `LANGSMITH_WORKSPACE_ID` 环境变量以指定要使用的工作区。

  默认情况下，跟踪信息将记录到名为 `default` 的项目中。要将跟踪记录到其他项目，请参阅[将跟踪记录到特定项目](/langsmith/log-traces-to-project)。
</Note>

<CodeGroup>
  ```python Python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
  import anthropic
  from langsmith import traceable
  from langsmith.wrappers import wrap_anthropic

  client = wrap_anthropic(anthropic.Anthropic())

  @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": "user", "content": f"Question: {question}\nContext: {context}"}
    ]
    message = client.messages.create(
        model="claude-sonnet-4-6",
        messages=messages,
        max_tokens=1024,
        system="You are a helpful assistant. Please respond to the user's request only based on the given context."
    )
    return message

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

  ```typescript TypeScript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
  import Anthropic from "@anthropic-ai/sdk";
  import { traceable } from "langsmith/traceable";
  import { wrapAnthropic } from "langsmith/wrappers/anthropic";

  const client = wrapAnthropic(new Anthropic());

  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: "user", content: `Question: ${question}\nContext: ${context}` }
    ];
    const message = await client.messages.create({
        model: "claude-sonnet-4-6",
        messages: messages,
        max_tokens: 1024,
        system: "You are a helpful assistant. Please respond to the user's request only based on the given context."
    });
    return message;
  }, { name: "Chat Pipeline" });

  await chatPipeline("Can you summarize this morning's meetings?");
  ```
</CodeGroup>

***

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