Setting up LangSmith tracing in LangChain - Try It Yourself

Setting up LangSmith tracing

📖 Scenario: You are building a simple Python program using LangChain to run a language model. You want to track and trace the calls your program makes to the language model using LangSmith tracing. This helps you see what inputs and outputs your program produces.

🎯 Goal: Set up LangSmith tracing in your LangChain program to automatically record all language model calls.

📋 What You'll Learn

Create a LangChain OpenAI language model instance

Create a LangSmith tracer instance

Attach the LangSmith tracer to the language model

Run the language model with a prompt and have tracing enabled

💡 Why This Matters

🌍 Real World

Tracing language model calls helps developers debug and understand how their AI programs behave. LangSmith tracing records inputs, outputs, and metadata for each call.

💼 Career

Many AI developer roles require setting up monitoring and tracing for language model applications to ensure reliability and improve performance.

1

Create the OpenAI language model instance

Write code to import OpenAI from langchain.llms and create a variable called llm that is an instance of OpenAI with temperature=0.

LangChain

# Import OpenAI and create llm with temperature=0
# Your code here

Hint

Use from langchain.llms import OpenAI to import the model, then create llm = OpenAI(temperature=0).

2

Create the LangSmith tracer instance

Write code to import LangSmithTracer from langchain_experimental.langsmith and create a variable called tracer that is an instance of LangSmithTracer.

LangChain

from langchain.llms import OpenAI

llm = OpenAI(temperature=0)

# Import LangSmithTracer and create tracer instance
# Your code here

Hint

Import LangSmithTracer and create it with tracer = LangSmithTracer().

3

Attach the tracer to the language model

Write code to set the llm instance's callback_manager to the tracer instance.

LangChain

from langchain.llms import OpenAI
from langchain_experimental.langsmith import LangSmithTracer

llm = OpenAI(temperature=0)

tracer = LangSmithTracer()

# Attach tracer to llm's callback_manager
# Your code here

Hint

Assign llm.callback_manager = CallbackManager([tracer]) to enable tracing.

4

Run the language model with tracing enabled

Write code to call llm with the prompt "Hello, LangSmith!" and assign the result to a variable called response.

LangChain

from langchain.llms import OpenAI
from langchain_experimental.langsmith import LangSmithTracer
from langchain.callbacks.manager import CallbackManager

llm = OpenAI(temperature=0)

tracer = LangSmithTracer()

llm.callback_manager = CallbackManager([tracer])

# Call llm with prompt and save to response
# Your code here

Hint

Call llm("Hello, LangSmith!") and save it to response.

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