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Goal Accuracy

LLM-as-a-judge
Multi-turn
Referenceless
Agent

The Goal Accuracy metric is a multi-turn agentic metric that evaluates your LLM agent's abilities on planning and executing the plan to finish a task or reach a goal. It is a self-explaining eval, which means it outputs a reason for its metric score.

Required Arguments​

To use the GoalAccuracyMetric, you'll have to provide the following arguments when creating a ConversationalTestCase:

  • turns

You can learn more about how it is calculated here.

Usage​

The GoalAccuracyMetric() can be used for end-to-end multi-turn evaluations of agents.

from deepeval import evaluate
from deepeval.metrics import GoalAccuracyMetric
from deepeval.test_case import Turn, ConversationalTestCase, ToolCall

convo_test_case = ConversationalTestCase(
    turns=[
        Turn(role="...", content="..."), 
        Turn(role="...", content="...", tools_called=[...])
    ],
)
metric = GoalAccuracyMetric(threshold=0.5)

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

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

There are SIX optional parameters when creating a GoalAccuracyMetric:

  • [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-4o'.
  • [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.

As a standalone​

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

...

metric.measure(convo_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) and all the optimizations (speed, caching, computation) the evaluate() function or deepeval test run offers.

How Is It Calculated​

The GoalAccuracyMetric score is calculated using the following steps:

  • Find individual goals and steps taken by your LLM agent for each user-assistat interactions.
  • Find goal accuracy scores for each of the goal-steps pairs using the evaluation model.
  • Find plan quality and plan adherence scores for each of the goal-step pairs using the evaluation model.
Goal Accuracy Score=Goal Accuracy Score + Plan Evaluation Score2\text{Goal Accuracy Score} = \frac{\text{Goal Accuracy Score + Plan Evaluation Score}}{\text{2}}
info

The GoalAccuracyMetric extracts the task from user's messages in each interaction and evalutes the steps taken by the LLM agent to find it's plan and how accurately it has finished the task or reached the goal in that interaction.