How to Use LangSmith for Tracing in LangChain

To use LangSmithTracer for tracing in LangChain, import and initialize it, then pass it as the tracing parameter when creating your chains or agents. This enables automatic logging of inputs, outputs, and intermediate steps to LangSmith's UI for easy debugging and analysis.

Syntax

The main steps to use LangSmith tracing in LangChain are:

• Import the LangSmithTracer from langchain_experimental.
• Initialize the tracer with optional parameters like project_name or session_name.
• Pass the tracer instance to your chain or agent using the tracing argument.

This setup automatically tracks the execution flow and sends trace data to LangSmith's dashboard.

python

from langchain_experimental import LangSmithTracer
from langchain.llms import OpenAI
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate

# Initialize tracer
tracer = LangSmithTracer(project_name="my-project", session_name="session-1")

# Create your LLM and prompt
llm = OpenAI(temperature=0)
prompt = PromptTemplate(template="Translate this to French: {text}", input_variables=["text"])

# Create chain with tracing enabled
chain = LLMChain(llm=llm, prompt=prompt, tracing=tracer)

# Run chain
result = chain.run(text="Hello, world!")
print(result)

Output

Bonjour, le monde!

Example

This example shows how to enable LangSmith tracing for a simple translation chain. It logs the input, output, and intermediate steps to LangSmith's UI for easy inspection.

python

from langchain_experimental import LangSmithTracer
from langchain.llms import OpenAI
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate

# Initialize LangSmith tracer
tracer = LangSmithTracer(project_name="translation-demo", session_name="test-session")

# Setup LLM and prompt
llm = OpenAI(temperature=0)
prompt = PromptTemplate(template="Translate this to Spanish: {text}", input_variables=["text"])

# Create chain with tracer
chain = LLMChain(llm=llm, prompt=prompt, tracing=tracer)

# Run the chain
output = chain.run(text="Good morning")
print(output)

Output

Buenos días

Common Pitfalls

Some common mistakes when using LangSmith tracing in LangChain include:

• Not initializing LangSmithTracer before passing it to chains or agents.
• Forgetting to pass the tracing parameter, so no trace data is collected.
• Using incompatible versions of LangChain or LangSmith packages that lack tracing support.
• Not setting environment variables or API keys required by LangSmith, causing silent failures.

Always ensure your environment is configured correctly and the tracer is properly passed.

python

from langchain_experimental import LangSmithTracer
from langchain.chains import LLMChain

# Wrong: tracer not initialized
# chain = LLMChain(llm=llm, prompt=prompt, tracing=None)  # No tracing enabled

# Right: initialize and pass tracer
tracer = LangSmithTracer()
chain = LLMChain(llm=llm, prompt=prompt, tracing=tracer)

Quick Reference

Summary tips for using LangSmith tracing in LangChain:

• Import LangSmithTracer from langchain_experimental.
• Initialize the tracer with optional project/session names.
• Pass the tracer instance to chains or agents via the tracing argument.
• Ensure your LangSmith API key is set in environment variables.
• Use LangSmith's UI to view detailed trace logs and debug your workflows.

Key Takeaways

Initialize LangSmithTracer and pass it to your LangChain chains or agents using the tracing parameter.

LangSmith tracing automatically logs inputs, outputs, and intermediate steps for easy debugging.

Ensure your environment has the necessary LangSmith API keys configured to enable tracing.

Common mistakes include forgetting to pass the tracer or using incompatible package versions.

Use LangSmith's dashboard to visualize and analyze your language model workflows.