Setting up LangSmith tracing in LangChain - Practice Exercises

Challenge - 5 Problems

❓ component_behavior

intermediate

What is the output when LangSmith tracing is enabled with a LangChain LLM?

Consider the following Python code snippet using LangChain with LangSmith tracing enabled. What will be the behavior or output when the LLM is called?

LangChain

from langchain.chat_models import ChatOpenAI
from langchain.schema import HumanMessage
from langchain_experimental.langsmith import LangSmithTracer

tracer = LangSmithTracer()
tracer.start()

llm = ChatOpenAI(model_name="gpt-4", temperature=0)
response = llm.invoke([HumanMessage(content="Hello, LangSmith!")])

tracer.end()

A The code runs but the tracer.start() call disables the LLM from generating any response.

B The code raises a runtime error because LangSmithTracer requires additional configuration before start().

C The LLM response is printed to the console but no tracing data is recorded or sent.

D The LLM generates a response and the interaction is recorded and sent to LangSmith tracing dashboard.

💡 Hint

Think about what enabling tracing with LangSmithTracer does in LangChain.

When LangSmithTracer is started, it hooks into LangChain's LLM calls to record the interactions. The LLM still generates its response normally, but the trace data is sent to LangSmith's dashboard for inspection.

📝 Syntax

intermediate

Which option correctly initializes LangSmithTracer for tracing?

Choose the correct way to create a LangSmithTracer instance for tracing LangChain calls.

A tracer = LangSmithTracer.init()

B tracer = LangSmithTracer()

C tracer = LangSmithTracer.start()

D tracer = LangSmithTracer(enable=True)

💡 Hint

Look for the constructor method to create an instance.

LangSmithTracer is instantiated by calling its constructor LangSmithTracer(). The start() method is called separately to begin tracing.

🔧 Debug

advanced

Why does this LangSmith tracing code fail to record traces?

Given the code below, why are no traces recorded in LangSmith?

LangChain

from langchain_experimental.langsmith import LangSmithTracer
from langchain.chat_models import ChatOpenAI
from langchain.schema import HumanMessage

tracer = LangSmithTracer()

llm = ChatOpenAI(model_name="gpt-4")
response = llm.invoke([HumanMessage(content="Trace this")])

tracer.end()

A Because tracer.start() was never called to activate tracing.

B Because LangSmithTracer requires an API key argument during initialization.

C Because the LLM model_name is invalid and causes silent failure.

D Because tracer.end() must be called before invoking the LLM.

💡 Hint

Check the tracer lifecycle methods needed to enable tracing.

Tracing only starts after calling tracer.start(). Without this, the tracer does not hook into LangChain calls, so no traces are recorded.

❓ state_output

advanced

What is the state of LangSmithTracer after calling start() and end()?

After running the following code, what is the state of the tracer object?

LangChain

tracer = LangSmithTracer()
tracer.start()
# some LLM calls
tracer.end()

A The tracer is destroyed and cannot be restarted.

B The tracer remains active and continues recording traces.

C The tracer is inactive and no longer recording traces.

D The tracer resets and deletes all recorded trace data.

💡 Hint

Think about what end() means in a start/end lifecycle.

Calling end() stops the tracer from recording further traces. It does not destroy the object or delete data, but the tracer becomes inactive.

🧠 Conceptual

expert

Which statement best describes LangSmith tracing integration with LangChain?

Choose the most accurate description of how LangSmith tracing works with LangChain.

A LangSmithTracer hooks into LangChain's internal call stack to capture inputs, outputs, and metadata automatically during LLM calls.

B LangSmithTracer requires manual instrumentation of every LangChain component to record traces.

C LangSmithTracer replaces LangChain's LLM classes with its own versions to enable tracing.

D LangSmithTracer only records errors and exceptions raised by LangChain components.

💡 Hint

Consider how tracing tools usually integrate with libraries to capture data.

LangSmithTracer automatically hooks into LangChain's call lifecycle to capture detailed trace data without manual instrumentation or replacement of classes.