Python Integrations

LangSmith allows you to log traces in various ways.

LangChain

To log traces with LangChain, all you need to do is set an environment variable.

export LANGCHAIN_API_KEY=<your-api-key>
export LANGCHAIN_TRACING_V2=true

After that, you can use LangChain as you normally would and all traces will get logged to LangSmith!

OpenAI SDK

We provide a convenient wrapper for the OpenAI SDK.

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

export LANGCHAIN_API_KEY=<your-api-key>

Next, you will need to install the LangSmith SDK:

pip install -U langsmith

After that, you can wrap the OpenAI client:

from openai import OpenAI
from langsmith import wrappers

client = wrappers.wrap_openai(OpenAI())

Now, you can use the OpenAI client as you normally would, but now everything is logged to LangSmith!

client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "Say this is a test"}],
)

Oftentimes, you use the OpenAI client inside of other functions. You can get nested traces by using this wrapped client and decorating those functions with @traceable. See this documentation for more documentation how to use this decorator

from langsmith import traceable

@traceable
def my_function(text: str):
    return client.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "user", "content": f"Say {text}"}],
    )

my_function("hello world")

Instructor

We provide a convenient integration with Instructor.

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

export LANGCHAIN_API_KEY=<your-api-key>

Next, you will need to install the LangSmith SDK:

pip install -U langsmith

After that, you can wrap the OpenAI client:

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-3.5-turbo",
    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. See this documentation for more documentation how to use this 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-3.5-turbo",
        response_model=UserDetail,
        messages=[
            {"role": "user", "content": f"Extract {text}"},
        ]
    )


my_function("Jason is 25 years old")