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https://github.com/crewAIInc/crewAI.git
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6ebb6c9b63
* refactor: rely on task completion event to evaluate agents * feat: remove Crew dependency to evaluate agent * feat: drop execution_context in AgentEvaluator * chore: drop experimental Agent Eval feature from stable crew.test * feat: support eval LiteAgent * resolve linter issues
70 lines
2.4 KiB
Python
70 lines
2.4 KiB
Python
from typing import Any, Dict
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from crewai.agent import Agent
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from crewai.task import Task
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from crewai.experimental.evaluation.base_evaluator import BaseEvaluator, EvaluationScore, MetricCategory
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from crewai.experimental.evaluation.json_parser import extract_json_from_llm_response
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class GoalAlignmentEvaluator(BaseEvaluator):
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@property
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def metric_category(self) -> MetricCategory:
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return MetricCategory.GOAL_ALIGNMENT
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def evaluate(
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self,
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agent: Agent,
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execution_trace: Dict[str, Any],
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final_output: Any,
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task: Task | None = None,
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) -> EvaluationScore:
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task_context = ""
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if task is not None:
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task_context = f"Task description: {task.description}\nExpected output: {task.expected_output}\n"
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prompt = [
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{"role": "system", "content": """You are an expert evaluator assessing how well an AI agent's output aligns with its assigned task goal.
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Score the agent's goal alignment on a scale from 0-10 where:
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- 0: Complete misalignment, agent did not understand or attempt the task goal
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- 5: Partial alignment, agent attempted the task but missed key requirements
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- 10: Perfect alignment, agent fully satisfied all task requirements
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Consider:
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1. Did the agent correctly interpret the task goal?
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2. Did the final output directly address the requirements?
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3. Did the agent focus on relevant aspects of the task?
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4. Did the agent provide all requested information or deliverables?
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Return your evaluation as JSON with fields 'score' (number) and 'feedback' (string).
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"""},
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{"role": "user", "content": f"""
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Agent role: {agent.role}
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Agent goal: {agent.goal}
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{task_context}
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Agent's final output:
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{final_output}
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Evaluate how well the agent's output aligns with the assigned task goal.
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"""}
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]
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assert self.llm is not None
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response = self.llm.call(prompt)
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try:
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evaluation_data: dict[str, Any] = extract_json_from_llm_response(response)
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assert evaluation_data is not None
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return EvaluationScore(
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score=evaluation_data.get("score", 0),
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feedback=evaluation_data.get("feedback", response),
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raw_response=response
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)
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except Exception:
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return EvaluationScore(
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score=None,
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feedback=f"Failed to parse evaluation. Raw response: {response}",
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raw_response=response
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)
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