Setting up LangSmith tracing in LangChain - Performance Optimization Steps

Performance: Setting up LangSmith tracing

MEDIUM IMPACT

This affects the runtime overhead and network latency during LangChain operations by adding tracing calls.

Adding tracing to monitor LangChain calls

LangChain

import { LangChainTracer } from "@langchain/core/tracers/langchain";
import { Client } from "langsmith";
const client = new Client();
const tracer = new LangChainTracer({
  client,
  projectName: "important",
  samplingRate: 0.1
});
result = await model.invoke("some input", { callbacks: [tracer] });

Selective tracer with sampling reduces traces sent asynchronously, minimizing overhead.

📈 Performance GainLowers network/processing load, improving interaction responsiveness (INP)

Adding tracing to monitor LangChain calls

LangChain

(window as any).LANGCHAIN_TRACING_V2 = "true";
(window as any).LANGCHAIN_API_KEY = "lsv2_...";
// Every LangChain call (e.g., model.invoke()) will send full trace data
result = await model.invoke("some input");

Global tracing enabled without sampling or filtering, causing every call to queue and send full trace data.

📉 Performance CostAdds processing and network latency on every call, increasing JS execution time and INP

Performance Comparison

Pattern	DOM Operations	Reflows	Paint Cost	Verdict
Global tracing enabled for all events	Minimal	0	Minimal	[X] Bad
Selective async tracing with sampling	Minimal	0	Minimal	[OK] Good

Rendering Pipeline

Tracing calls add extra JavaScript execution and network requests during LangChain operations, affecting the interaction responsiveness stage.

→JavaScript Execution

→Network

→Idle

⚠️ BottleneckUnfiltered tracing creates frequent network requests and JS overhead during interactions.

Core Web Vital Affected

INP

This affects the runtime overhead and network latency during LangChain operations by adding tracing calls.

Optimization Tips

1Avoid global tracing for all events; use selective callbacks.

2Set samplingRate < 1.0 to reduce trace volume.

3Monitor interaction responsiveness (INP) when enabling tracing.

Performance Quiz - 3 Questions

What is the main performance impact of enabling LangSmith tracing globally on every LangChain call?

A Reduces network requests by batching traces

B Improves page load speed by caching traces

C Increases interaction latency by adding JS and network overhead

D Has no impact on performance

DevTools: Performance

How to check: Record a performance profile while running LangChain operations with tracing enabled. Look for long tasks and network requests during interaction.

What to look for: Check for blocking network calls and long JavaScript execution times that increase INP.

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