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

# Trace Anthropic applications

The Anthropic wrapper methods in Python ([`wrap_anthropic`](https://reference.langchain.com/python/langsmith/wrappers/_anthropic/wrap_anthropic)) and Typescript ([`wrapAnthropic`](https://reference.langchain.com/javascript/functions/langsmith.wrappers_anthropic.wrapAnthropic.html)) allow you to wrap your Anthropic client in order to log traces automatically. Using the wrapper ensures that messages, including tool calls and multimodal content blocks will be rendered nicely in LangSmith. The wrapper works seamlessly alongside the `@traceable` decorator (Python) or `traceable` function (TypeScript), so you can trace your Anthropic calls with the wrapper and trace other parts of your application with the decorator or function.

<Note>
  The `LANGSMITH_TRACING` environment variable must be set to `'true'` in order for traces to be logged to LangSmith, even when using `wrap_anthropic` or `wrapAnthropic`. This allows you to toggle tracing on and off without changing your code.

  Additionally, you will need to set the `LANGSMITH_API_KEY` environment variable to your API key (see [Setup](/) for more information).

  If your LangSmith API key is linked to multiple workspaces, set the `LANGSMITH_WORKSPACE_ID` environment variable to specify which workspace to use.

  By default, the traces will be logged to a project named `default`. To log traces to a different project, see [Log traces to a specific project](/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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