If you’ve decided you no longer want to trace your runs, you can remove the LANGSMITH_TRACING environment variable. Note that this does not affect the RunTree objects or API users, as these are meant to be low-level and not affected by the tracing toggle.
There are several ways to log traces to LangSmith.
If you are using LangChain (either Python or JS/TS), you can skip this section and go directly to the LangChain-specific instructions.

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Use @traceable / traceable

LangSmith makes it easy to log traces with minimal changes to your existing code with the @traceable decorator in Python and traceable function in TypeScript.
The LANGSMITH_TRACING environment variable must be set to 'true' in order for traces to be logged to LangSmith, even when using @traceable or traceable. 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).By default, the traces will be logged to a project named default. To log traces to a different project, see this section.
The @traceable decorator is a simple way to log traces from the LangSmith Python SDK. Simply decorate any function with @traceable.
from langsmith import traceable
from openai import Client

openai = Client()

@traceable
def format_prompt(subject):
  return [
      {
          "role": "system",
          "content": "You are a helpful assistant.",
      },
      {
          "role": "user",
          "content": f"What's a good name for a store that sells {subject}?"
      }
  ]

@traceable(run_type="llm")
def invoke_llm(messages):
  return openai.chat.completions.create(
      messages=messages, model="gpt-4o-mini", temperature=0
  )

@traceable
def parse_output(response):
  return response.choices[0].message.content

@traceable
def run_pipeline():
  messages = format_prompt("colorful socks")
  response = invoke_llm(messages)
  return parse_output(response)

run_pipeline()

​
Use the trace context manager (Python only)

In Python, you can use the trace context manager to log traces to LangSmith. This is useful in situations where:
  1. You want to log traces for a specific block of code.
  2. You want control over the inputs, outputs, and other attributes of the trace.
  3. It is not feasible to use a decorator or wrapper.
  4. Any or all of the above.
The context manager integrates seamlessly with the traceable decorator and wrap_openai wrapper, so you can use them together in the same application. 
import openai
import langsmith as ls
from langsmith.wrappers import wrap_openai

client = wrap_openai(openai.Client())

@ls.traceable(run_type="tool", name="Retrieve Context")
def my_tool(question: str) -> str:
    return "During this morning's meeting, we solved all world conflict."

def chat_pipeline(question: str):
    context = my_tool(question)
    messages = [
        { "role": "system", "content": "You are a helpful assistant. Please respond to the user's request only based on the given context." },
        { "role": "user", "content": f"Question: {question}\nContext: {context}"}
    ]
    chat_completion = client.chat.completions.create(
        model="gpt-4o-mini", messages=messages
    )
    return chat_completion.choices[0].message.content

app_inputs = {"input": "Can you summarize this morning's meetings?"}

with ls.trace("Chat Pipeline", "chain", project_name="my_test", inputs=app_inputs) as rt:
    output = chat_pipeline("Can you summarize this morning's meetings?")
    rt.end(outputs={"output": output})

​
Wrap the OpenAI client

The wrap_openai/wrapOpenAI methods in Python/TypeScript allow you to wrap your OpenAI 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. Also note that 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_openai or wrapOpenAI. 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).By default, the traces will be logged to a project named default. To log traces to a different project, see this section.
import openai
from langsmith import traceable
from langsmith.wrappers import wrap_openai

client = wrap_openai(openai.Client())

@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": "system", "content": "You are a helpful assistant. Please respond to the user's request only based on the given context." },
      { "role": "user", "content": f"Question: {question}\nContext: {context}"}
  ]
  chat_completion = client.chat.completions.create(
      model="gpt-4o-mini", messages=messages
  )
  return chat_completion.choices[0].message.content

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

​
Wrap the Anthropic client (Python only)

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).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}"}
    ]
    chat_completion = client.messages.create(
      model="claude-sonnet-4-20250514",
      messages=messages,
      max_tokens=3,
      system="You are a helpful assistant. Please respond to the user's request only based on the given context."
    )
    return chat_completion.choices[0].message.content

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

​
Use the RunTree API

Another, more explicit way to log traces to LangSmith is via the RunTree API. This API allows you more control over your tracing - you can manually create runs and children runs to assemble your trace. You still need to set your LANGSMITH_API_KEY, but LANGSMITH_TRACING is not necessary for this method. This method is not recommended, as it’s easier to make mistakes in propagating trace context. 
import openai
from langsmith.run_trees import RunTree

# This can be a user input to your app
question = "Can you summarize this morning's meetings?"

# Create a top-level run
pipeline = RunTree(
  name="Chat Pipeline",
  run_type="chain",
  inputs={"question": question}
)
pipeline.post()

# This can be retrieved in a retrieval step
context = "During this morning's meeting, we solved all world conflict."
messages = [
  { "role": "system", "content": "You are a helpful assistant. Please respond to the user's request only based on the given context." },
  { "role": "user", "content": f"Question: {question}\nContext: {context}"}
]

# Create a child run
child_llm_run = pipeline.create_child(
  name="OpenAI Call",
  run_type="llm",
  inputs={"messages": messages},
)
child_llm_run.post()

# Generate a completion
client = openai.Client()
chat_completion = client.chat.completions.create(
  model="gpt-4o-mini", messages=messages
)

# End the runs and log them
child_llm_run.end(outputs=chat_completion)
child_llm_run.patch()
pipeline.end(outputs={"answer": chat_completion.choices[0].message.content})
pipeline.patch()

​
Example usage

You can extend the utilities above to conveniently trace any code. Below are some example extensions: Trace any public method in a class: 
from typing import Any, Callable, Type, TypeVar

T = TypeVar("T")

def traceable_cls(cls: Type[T]) -> Type[T]:
    """Instrument all public methods in a class."""
    def wrap_method(name: str, method: Any) -> Any:
        if callable(method) and not name.startswith("__"):
            return traceable(name=f"{cls.__name__}.{name}")(method)
        return method
    
    # Handle __dict__ case
    for name in dir(cls):
        if not name.startswith("_"):
            try:
                method = getattr(cls, name)
                setattr(cls, name, wrap_method(name, method))
            except AttributeError:
                # Skip attributes that can't be set (e.g., some descriptors)
                pass
    
    # Handle __slots__ case
    if hasattr(cls, "__slots__"):
        for slot in cls.__slots__:  # type: ignore[attr-defined]
            if not slot.startswith("__"):
                try:
                    method = getattr(cls, slot)
                    setattr(cls, slot, wrap_method(slot, method))
                except AttributeError:
                    # Skip slots that don't have a value yet
                    pass
    
    return cls

@traceable_cls
class MyClass:
    def __init__(self, some_val: int):
        self.some_val = some_val
    
    def combine(self, other_val: int):
        return self.some_val + other_val

# See trace: https://smith.langchain.com/public/882f9ecf-5057-426a-ae98-0edf84fdcaf9/r
MyClass(13).combine(29)

​
Ensure all traces are submitted before exiting

LangSmith’s tracing is done in a background thread to avoid obstructing your production application. This means that your process may end before all traces are successfully posted to LangSmith. Here are some options for ensuring all traces are submitted before exiting your application.

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Using the LangSmith SDK

If you are using the LangSmith SDK standalone, you can use the flush method before exit: 
from langsmith import Client

client = Client()

@traceable(client=client)
async def my_traced_func():
  # Your code here...
  pass

try:
  await my_traced_func()
finally:
  await client.flush()

​
Using LangChain

If you are using LangChain, please refer to our LangChain tracing guide. If you prefer a video tutorial, check out the Tracing Basics video from the Introduction to LangSmith Course.