Description

# LangSmith Client SDK [](https://github.com/langchain-ai/langsmith-sdk/releases) [](https://pypi.org/project/langsmith/) This package contains the Python client for interacting with the [LangSmith platform](https://smith.langchain.com/). To install: ```bash pip install -U langsmith export LANGSMITH_TRACING=true export LANGSMITH_API_KEY=ls_... ``` Then trace: ```python import openai from langsmith.wrappers import wrap_openai from langsmith import traceable # Auto-trace LLM calls in-context client = wrap_openai(openai.Client()) @traceable # Auto-trace this function def pipeline(user_input: str): result = client.chat.completions.create( messages=[{"role": "user", "content": user_input}], model="gpt-3.5-turbo" ) return result.choices[0].message.content pipeline("Hello, world!") ``` See the resulting nested trace [🌐 here](https://smith.langchain.com/public/b37ca9b1-60cd-4a2a-817e-3c4e4443fdc0/r). LangSmith helps you and your team develop and evaluate language models and intelligent agents. It is compatible with any LLM application. > **Cookbook:** For tutorials on how to get more value out of LangSmith, check out the [Langsmith Cookbook](https://github.com/langchain-ai/langsmith-cookbook/tree/main) repo. A typical workflow looks like: 1. Set up an account with LangSmith. 2. Log traces while debugging and prototyping. 3. Run benchmark evaluations and continuously improve with the collected data. We'll walk through these steps in more detail below. ## 1. Connect to LangSmith Sign up for [LangSmith](https://smith.langchain.com/) using your GitHub, Discord accounts, or an email address and password. If you sign up with an email, make sure to verify your email address before logging in. Then, create a unique API key on the [Settings Page](https://smith.langchain.com/settings), which is found in the menu at the top right corner of the page. > [!NOTE] > Save the API Key in a secure location. It will not be shown again. ## 2. Log Traces You can log traces natively using the LangSmith SDK or within your LangChain application. ### Logging Traces with LangChain LangSmith seamlessly integrates with the Python LangChain library to record traces from your LLM applications. 1. **Copy the environment variables from the Settings Page and add them to your application.** Tracing can be activated by setting the following environment variables or by manually specifying the LangChainTracer. ```python import os os.environ["LANGSMITH_TRACING"] = "true" os.environ["LANGSMITH_ENDPOINT"] = "https://api.smith.langchain.com" # os.environ["LANGSMITH_ENDPOINT"] = "https://eu.api.smith.langchain.com" # If signed up in the EU region os.environ["LANGSMITH_API_KEY"] = "<YOUR-LANGSMITH-API-KEY>" # os.environ["LANGSMITH_PROJECT"] = "My Project Name" # Optional: "default" is used if not set # os.environ["LANGSMITH_WORKSPACE_ID"] = "<YOUR-WORKSPACE-ID>" # Required for org-scoped API keys ``` > **Tip:** Projects are groups of traces. All runs are logged to a project. If not specified, the project is set to `default`. 2. **Run an Agent, Chain, or Language Model in LangChain** If the environment variables are correctly set, your application will automatically connect to the LangSmith platform. ```python from langchain_core.runnables import chain @chain def add_val(x: dict) -> dict: return {"val": x["val"] + 1} add_val({"val": 1}) ``` ### Logging Traces Outside LangChain You can still use the LangSmith development platform without depending on any LangChain code. 1. **Copy the environment variables from the Settings Page and add them to your application.** ```python import os os.environ["LANGSMITH_ENDPOINT"] = "https://api.smith.langchain.com" os.environ["LANGSMITH_API_KEY"] = "<YOUR-LANGSMITH-API-KEY>" # os.environ["LANGSMITH_PROJECT"] = "My Project Name" # Optional: "default" is used if not set ``` 2. **Log traces** The easiest way to log traces using the SDK is via the `@traceable` decorator. Below is an example. ```python from datetime import datetime from typing import List, Optional, Tuple import openai from langsmith import traceable from langsmith.wrappers import wrap_openai client = wrap_openai(openai.Client()) @traceable def argument_generator(query: str, additional_description: str = "") -> str: return client.chat.completions.create( [ {"role": "system", "content": "You are a debater making an argument on a topic." f"{additional_description}" f" The current time is {datetime.now()}"}, {"role": "user", "content": f"The discussion topic is {query}"} ] ).choices[0].message.content @traceable def argument_chain(query: str, additional_description: str = "") -> str: argument = argument_generator(query, additional_description) # ... Do othe