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Fetch the complete documentation index at: https://docs.langchain.com/llms.txt

Use this file to discover all available pages before exploring further.

LangSmith provides a convenient integration with Instructor, a popular open-source library for generating structured output with LLMs. In order to use, you first need to set your LangSmith API key. 
export LANGSMITH_API_KEY=<your-api-key>
# For LangSmith API keys linked to multiple workspaces, set the LANGSMITH_WORKSPACE_ID environment variable to specify which workspace to use.
export LANGSMITH_WORKSPACE_ID=<your-workspace-id>
Next, you will need to install the LangSmith SDK: 
pip install -U langsmith
Wrap your OpenAI client with langsmith.wrappers.wrap_openai 
from openai import OpenAI
from langsmith import wrappers

client = wrappers.wrap_openai(OpenAI())
After this, you can patch the wrapped OpenAI client using instructor: 
import instructor

client = instructor.patch(client)
Now, you can use instructor as you normally would, but now everything is logged to LangSmith! 
from pydantic import BaseModel


class UserDetail(BaseModel):
    name: str
    age: int


user = client.chat.completions.create(
    model="gpt-5.4-mini",
    response_model=UserDetail,
    messages=[
        {"role": "user", "content": "Extract Jason is 25 years old"},
    ]
)
Oftentimes, you use instructor inside of other functions. You can get nested traces by using this wrapped client and decorating those functions with @traceable. Please see Custom instrumentation for more information on how to annotate your code for tracing with the @traceable decorator. 
# You can customize the run name with the `name` keyword argument
@traceable(name="Extract User Details")
def my_function(text: str) -> UserDetail:
    return client.chat.completions.create(
        model="gpt-5.4-mini",
        response_model=UserDetail,
        messages=[
            {"role": "user", "content": f"Extract {text}"},
        ]
    )

my_function("Jason is 25 years old")
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