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.

Practice

1. What is the main purpose of setting up LangSmith tracing in a LangChain application?

easy

A. To encrypt data passed between LangChain components

B. To speed up the execution of LangChain components

C. To automatically fix errors in your LangChain code

D. To monitor and visualize the steps of your LangChain workflows

Solution

1. Step 1: Understand LangSmith tracing purpose

   LangSmith tracing is designed to help watch and understand the steps your LangChain app takes.

2. Step 2: Identify the correct purpose

   It does not speed up execution, fix errors automatically, or encrypt data, but helps monitor and visualize workflows.

3. Final Answer:

   To monitor and visualize the steps of your LangChain workflows -> Option D

4. Quick Check:

   Tracing = Monitoring steps [OK]

Hint: Tracing means watching steps clearly in LangChain [OK]

Common Mistakes:

• Thinking tracing speeds up code
• Assuming tracing fixes bugs automatically
• Confusing tracing with data encryption

2. Which of the following is the correct way to create a LangChainTracer for LangSmith tracing?

easy

A. tracer = LangChainTracer()

B. tracer = createTracer()

C. tracer = new LangSmithTracer()

D. tracer = LangSmith.createTracer()

Solution

1. Step 1: Recall LangChainTracer creation syntax

   The LangChainTracer is created by calling its constructor directly: LangChainTracer()

2. Step 2: Check options for correct syntax

   Options B, C, and D use incorrect function or class names or syntax not used in LangChain.

3. Final Answer:

   tracer = LangChainTracer() -> Option A

4. Quick Check:

   Constructor call = LangChainTracer() [OK]

Hint: Use LangChainTracer() constructor to create tracer [OK]

Common Mistakes:

• Using wrong class names like LangSmithTracer
• Calling non-existent functions like createTracer()
• Using 'new' keyword which is not Python syntax

3. Given the code snippet below, what will be the effect of passing the tracer to the LLM?

from langchain.chat_models import ChatOpenAI
from langchain.callbacks import LangChainTracer

tracer = LangChainTracer()
llm = ChatOpenAI(callbacks=[tracer])
response = llm.chat([{'role': 'user', 'content': 'Hello!'}])

medium

A. The LLM will log its steps to LangSmith for tracing

B. The LLM will run without any tracing or logging

C. The code will raise a syntax error due to wrong callback usage

D. The LLM will ignore the tracer and produce no output

Solution

1. Step 1: Understand passing tracer as callback

   Passing LangChainTracer in callbacks enables tracing of LLM steps.

2. Step 2: Analyze code behavior

   The LLM will send its internal steps to LangSmith via the tracer, enabling monitoring.

3. Final Answer:

   The LLM will log its steps to LangSmith for tracing -> Option A

4. Quick Check:

   Callbacks with tracer = tracing enabled [OK]

Hint: Callbacks=[tracer] enables LangSmith tracing [OK]

Common Mistakes:

• Assuming no tracing happens without explicit start call
• Thinking callbacks cause syntax errors here
• Believing tracer disables output

4. Identify the error in this LangSmith tracing setup code:

from langchain.chat_models import ChatOpenAI
from langchain.callbacks import LangChainTracer

tracer = LangChainTracer
llm = ChatOpenAI(callbacks=[tracer])

medium

A. LangChainTracer is not imported correctly

B. LangChainTracer is assigned without parentheses, missing instantiation

C. Callbacks list should be empty for tracing

D. ChatOpenAI does not accept callbacks parameter

Solution

1. Step 1: Check LangChainTracer assignment

   tracer = LangChainTracer misses parentheses, so tracer is a class, not an instance.

2. Step 2: Analyze usage in callbacks

   Passing callbacks=[tracer] passes the class instead of an instance, causing a runtime error when callbacks are used.

3. Final Answer:

   LangChainTracer is assigned without parentheses, missing instantiation -> Option B

4. Quick Check:

   Instantiate with () to create tracer object [OK]

Hint: Always instantiate classes with () before use [OK]

Common Mistakes:

• Assigning class instead of instance
• Calling instance as function
• Ignoring callbacks parameter usage

5. You want to trace both an LLM and a chain in LangChain using LangSmith. Which setup correctly enables tracing for both components?

hard

A. llm = ChatOpenAI()
chain = SomeChain(llm=llm)
tracer = LangChainTracer()
tracer.start()

B. tracer = LangChainTracer
llm = ChatOpenAI(callbacks=tracer)
chain = SomeChain(llm=llm, callbacks=tracer)

C. tracer = LangChainTracer()
llm = ChatOpenAI(callbacks=[tracer])
chain = SomeChain(llm=llm, callbacks=[tracer])

D. tracer = LangChainTracer()
llm = ChatOpenAI()
chain = SomeChain(llm=llm)

Solution

1. Step 1: Instantiate LangChainTracer correctly

   Use tracer = LangChainTracer() to create the tracer instance.

2. Step 2: Pass tracer in callbacks for both LLM and chain

   Both components accept callbacks as a list; passing [tracer] enables tracing on both.

3. Step 3: Evaluate options

   tracer = LangChainTracer() llm = ChatOpenAI(callbacks=[tracer]) chain = SomeChain(llm=llm, callbacks=[tracer]) correctly instantiates tracer and passes it as a list to both components. Others miss instantiation, use wrong types, or omit callbacks.

4. Final Answer:

   tracer = LangChainTracer() llm = ChatOpenAI(callbacks=[tracer]) chain = SomeChain(llm=llm, callbacks=[tracer]) -> Option C

5. Quick Check:

   Instantiate tracer and pass as list to callbacks [OK]

Hint: Instantiate tracer and pass as list to all callbacks [OK]

Common Mistakes:

• Not instantiating tracer with ()
• Passing tracer directly instead of in a list
• Forgetting to add callbacks to chain