Argument Correctness

LLM-as-a-judge

Single-turn

Referenceless

Agent

The argument correctness metric is an agentic LLM metric that assesses your LLM agent's ability to generate the correct arguments for the tools it calls. It is calculated by determining whether the arguments for each tool call is correct based on the input.

info

The ArgumentCorrectnessMetric uses an LLM to determine argument correctness, and is also referenceless. If you're looking to determistically evaluate argument correctness, refer to the tool correctness metric instead.

Required Arguments

To use the ArgumentCorrectnessMetric, you'll have to provide the following arguments when creating an LLMTestCase:

• input
• actual_output
• tools_called

Read the How Is It Calculated section below to learn how test case parameters are used for metric calculation.

Usage

The ArgumentCorrectnessMetric() can be used for end-to-end evaluation:

from deepeval import evaluate
from deepeval.metrics import ArgumentCorrectnessMetric
from deepeval.test_case import LLMTestCase, ToolCall

metric = ArgumentCorrectnessMetric(
    threshold=0.7,
    model="gpt-4",
    include_reason=True
)
test_case = LLMTestCase(
    input="When did Trump first raise tariffs?",
    actual_output="Trump first raised tariffs in 2018 during the U.S.-China trade war.",
    tools_called=[
        ToolCall(
            name="WebSearch Tool",
            description="Tool to search for information on the web.",
            input={"search_query": "Trump first raised tariffs year"}
        ),
        ToolCall(
            name="History FunFact Tool",
            description="Tool to provide a fun fact about the topic.",
            input={"topic": "Trump tariffs"}
        )
    ]
)

# To run metric as a standalone
# metric.measure(test_case)
# print(metric.score, metric.reason)

evaluate(test_cases=[test_case], metrics=[metric])

There are SIX optional parameters when creating an ArgumentCorrectnessMetric:

• [Optional] threshold: a float representing the minimum passing threshold, defaulted to 0.5.
• [Optional] model: a string specifying which of OpenAI's GPT models to use, OR any custom LLM model of type DeepEvalBaseLLM. Defaulted to 'gpt-4.1'.
• [Optional] include_reason: a boolean which when set to True, will include a reason for its evaluation score. Defaulted to True.
• [Optional] strict_mode: a boolean which when set to True, enforces a binary metric score: 1 for perfection, 0 otherwise. It also overrides the current threshold and sets it to 1. Defaulted to False.
• [Optional] async_mode: a boolean which when set to True, enables concurrent execution within the measure() method. Defaulted to True.
• [Optional] verbose_mode: a boolean which when set to True, prints the intermediate steps used to calculate said metric to the console, as outlined in the How Is It Calculated section. Defaulted to False.

Within components

You can also run the ArgumentCorrectnessMetric within nested components for component-level evaluation.

from deepeval.dataset import Golden
from deepeval.tracing import observe, update_current_span
...

@observe(metrics=[metric])
def inner_component():
    # Set test case at runtime
    test_case = LLMTestCase(input="...", actual_output="...", tools_called=[...])
    update_current_span(test_case=test_case)
    return

@observe
def llm_app(input: str):
    # Component can be anything from an LLM call, retrieval, agent, tool use, etc.
    inner_component()
    return

evaluate(observed_callback=llm_app, goldens=[Golden(input="Hi!")])

As a standalone

You can also run the ArgumentCorrectnessMetric on a single test case as a standalone, one-off execution.

...

metric.measure(test_case)
print(metric.score, metric.reason)

caution

This is great for debugging or if you wish to build your own evaluation pipeline, but you will NOT get the benefits (testing reports, Confident AI platform) and all the optimizations (speed, caching, computation) the evaluate() function or deepeval test run offers.

How Is It Calculated?

The ArgumentCorrectnessMetric score is calculated according to the following equation:

$$\text{Argument Correctness} = \frac{\text{Number of Correctly Generated Input Parameters}}{\text{Total Number of Tool Calls}}$$

The ArgumentCorrectnessMetric assesses the correctness of the arguments (input parameters) for each tool call, based on the task outlined in the input.

note

You can set the verbose_mode of ANY deepeval metric to True to debug the measure() method:

...

metric = ArgumentCorrectnessMetric(verbose_mode=True)
metric.measure(test_case)