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The wrap_anthropic methods in Python allows you to wrap your Anthropic client in order to automatically log traces — no decorator or function wrapping required! Using the wrapper ensures that messages, including tool calls and multimodal content blocks will be rendered nicely in LangSmith. The wrapper works seamlessly with the @traceable decorator or traceable function and you can use both in the same application.
The LANGSMITH_TRACING environment variable must be set to 'true' in order for traces to be logged to LangSmith, even when using wrap_anthropic. 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 this section.
import anthropic
from langsmith import traceable
from langsmith.wrappers import wrap_anthropic

client = wrap_anthropic(anthropic.Anthropic())

# You can also wrap the async client as well
# async_client = wrap_anthropic(anthropic.AsyncAnthropic())

@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}"}
    ]
    messages = client.messages.create(
      model="claude-sonnet-4-20250514",
      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 messages

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