> ## 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 Instructor applications

LangSmith provides a convenient integration with [Instructor](https://python.useinstructor.com/), a popular open-source library for generating structured output with LLMs.

In order to use, you first need to set your LangSmith API key.

```shell theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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:

<CodeGroup>
  ```bash pip theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
  pip install -U langsmith
  ```

  ```bash uv theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
  uv add langsmith
  ```
</CodeGroup>

Wrap your OpenAI client with `langsmith.wrappers.wrap_openai`

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from openai import OpenAI
from langsmith import wrappers

client = wrappers.wrap_openai(OpenAI())
```

After this, you can patch the wrapped OpenAI client using `instructor`:

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import instructor

client = instructor.patch(client)
```

Now, you can use `instructor` as you normally would, but now everything is logged to LangSmith!

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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](/langsmith/annotate-code) for more information on how to annotate your code for tracing with the `@traceable` decorator.

```python {highlight={2}} theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
# 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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