Cost tracking across runs in LangChain

Introduction

Cost tracking helps you see how much you spend using LangChain over time. It keeps your spending clear and easy to manage.

You want to know how much each LangChain run costs to avoid surprises.

You need to compare costs between different runs or projects.

You want to track spending to stay within a budget.

You want to analyze which parts of your app use the most resources.

You want to log costs for reporting or billing purposes.

Syntax

LangChain

from langchain.callbacks import get_openai_callback

with get_openai_callback() as cb:
    # your LangChain code here
    result = chain.run(input)

print(f"Total Tokens: {cb.total_tokens}")
print(f"Prompt Tokens: {cb.prompt_tokens}")
print(f"Completion Tokens: {cb.completion_tokens}")
print(f"Total Cost (USD): ${cb.total_cost:.6f}")

The get_openai_callback() function tracks tokens and cost during the code inside the with block.

After the block, you can access token counts and cost from the callback object.

Examples

Tracks cost for a single chain run with input "Hello".

LangChain

from langchain.callbacks import get_openai_callback

with get_openai_callback() as cb:
    response = chain.run("Hello")

print(f"Cost: ${cb.total_cost:.6f}")

Tracks total cost for multiple runs inside one callback context.

LangChain

from langchain.callbacks import get_openai_callback

with get_openai_callback() as cb:
    for prompt in ["Hi", "How are you?"]:
        chain.run(prompt)

print(f"Total cost for all runs: ${cb.total_cost:.6f}")

Sample Program

This example runs the chain twice with different names. It tracks and prints the total tokens and cost used across both runs.

LangChain

from langchain.llms import OpenAI
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain.callbacks import get_openai_callback

# Setup prompt and chain
prompt = PromptTemplate(template="Say hello to {name}", input_variables=["name"])
llm = OpenAI(temperature=0)
chain = LLMChain(llm=llm, prompt=prompt)

# Track cost across multiple runs
with get_openai_callback() as cb:
    print(chain.run(name="Alice"))
    print(chain.run(name="Bob"))

print(f"Total Tokens Used: {cb.total_tokens}")
print(f"Prompt Tokens: {cb.prompt_tokens}")
print(f"Completion Tokens: {cb.completion_tokens}")
print(f"Total Cost (USD): ${cb.total_cost:.6f}")

Important Notes

Costs depend on the model and token usage; check your OpenAI pricing for details.

Use get_openai_callback() to wrap all runs you want to track together.

Tracking cost helps you optimize your app and avoid unexpected charges.

Summary

Use get_openai_callback() to track tokens and cost during LangChain runs.

Wrap your code inside a with block to measure usage across multiple calls.

Access token counts and cost after the block to monitor spending.

Practice

1. What is the main purpose of using get_openai_callback() in LangChain?

easy

A. To connect LangChain with external databases

B. To speed up the execution of LangChain chains

C. To store the output of LangChain chains permanently

D. To track token usage and cost during LangChain runs

Solution

1. Step 1: Understand the role of get_openai_callback()

   This function is designed to monitor token usage and cost when running LangChain chains.

2. Step 2: Compare with other options

   The other options describe unrelated functionalities like speeding up execution, storing outputs, or database connections, which get_openai_callback() does not do.

3. Final Answer:

   To track token usage and cost during LangChain runs -> Option D

4. Quick Check:

   Cost tracking = A [OK]

Hint: Remember: callback tracks usage and cost only [OK]

Common Mistakes:

• Thinking it speeds up chain execution
• Confusing it with output storage
• Assuming it manages database connections

2. Which of the following is the correct way to use get_openai_callback() to track cost across multiple LangChain calls?

easy

A. with get_openai_callback() as cb:
chain.run(input1)
chain.run(input2)
print(cb.total_cost)

B. cb = get_openai_callback()
chain.run(input1)
chain.run(input2)
print(cb.total_cost)

C. chain.run(input1)
chain.run(input2)
cb = get_openai_callback()
print(cb.total_cost)

D. print(get_openai_callback().total_cost)
chain.run(input1)
chain.run(input2)

Solution

1. Step 1: Identify correct usage pattern

   The get_openai_callback() must be used as a context manager with a with block to track usage across multiple calls.

2. Step 2: Analyze options

   with get_openai_callback() as cb: chain.run(input1) chain.run(input2) print(cb.total_cost) correctly wraps calls inside the with block and accesses cb.total_cost after. The other options misuse the callback or access cost before running chains.

3. Final Answer:

   with get_openai_callback() as cb: chain.run(input1) chain.run(input2) print(cb.total_cost) -> Option A

4. Quick Check:

   Use with block for callback [OK]

Hint: Always use with to track cost across runs [OK]

Common Mistakes:

• Not using with block for callback
• Accessing cost before running chains
• Creating callback after chain runs

3. Given the code below, what will be printed?

with get_openai_callback() as cb:
    chain.run("Hello")
    chain.run("World")
print(cb.total_tokens)

medium

A. Zero, because tokens are not counted automatically

B. The total number of tokens used in both runs combined

C. The number of tokens used only in the last run

D. An error because total_tokens is not a valid attribute

Solution

1. Step 1: Understand token counting in callback

   Inside the with block, all calls to chain.run() accumulate token usage tracked by cb.

2. Step 2: Check what cb.total_tokens represents

   This attribute holds the total tokens used during the entire with block, so it sums tokens from both runs.

3. Final Answer:

   The total number of tokens used in both runs combined -> Option B

4. Quick Check:

   Total tokens = sum of all runs [OK]

Hint: Tokens add up inside the with block [OK]

Common Mistakes:

• Thinking it counts only last run tokens
• Assuming tokens are not tracked automatically
• Believing total_tokens is invalid

4. Identify the error in the following code snippet for tracking cost:

cb = get_openai_callback()
chain.run("Test")
print(cb.total_cost)

medium

A. The chain.run() method must be called before creating callback

B. The total_cost attribute does not exist on callback

C. Callback is not used as a context manager, so cost is not tracked

D. The print statement should be inside the callback block

Solution

1. Step 1: Check callback usage

   The callback must be used inside a with block to track usage properly. Here, it is created but not used as a context manager.

2. Step 2: Understand consequences

   Without the with block, the callback does not track token or cost usage, so total_cost will not reflect actual usage.

3. Final Answer:

   Callback is not used as a context manager, so cost is not tracked -> Option C

4. Quick Check:

   Use with for tracking [OK]

Hint: Always use with to activate callback tracking [OK]

Common Mistakes:

• Creating callback without with
• Assuming attributes exist without context
• Placing print outside tracking scope

5. You want to track token usage and cost for multiple LangChain runs, but also reset tracking between different user sessions. Which approach correctly achieves this?

hard

A. Use separate with get_openai_callback() as cb: blocks for each session

B. Create one callback outside all sessions and reuse it without resetting

C. Call get_openai_callback() once and manually reset cb.total_cost to zero

D. Track cost by printing cb.total_cost after each run without context manager

Solution

1. Step 1: Understand session-based tracking needs

   Each user session should have isolated cost tracking to avoid mixing token counts and costs.

2. Step 2: Evaluate approaches

   Using separate with blocks creates fresh callbacks per session, resetting counts automatically. Other options either reuse callbacks incorrectly or try manual resets which are unsupported.

3. Final Answer:

   Use separate with get_openai_callback() as cb: blocks for each session -> Option A

4. Quick Check:

   Separate with blocks reset tracking [OK]

Hint: Use new with block per session to reset cost [OK]

Common Mistakes:

• Reusing one callback for all sessions
• Trying to manually reset callback attributes
• Not using with blocks for tracking