mirror of
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
125 lines
3.8 KiB
Python
125 lines
3.8 KiB
Python
import abc
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import enum
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from enum import Enum
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from typing import Any, Dict, List, Optional
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from pydantic import BaseModel, Field
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from crewai.agent import Agent
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from crewai.task import Task
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from crewai.llm import BaseLLM
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from crewai.utilities.llm_utils import create_llm
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class MetricCategory(enum.Enum):
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GOAL_ALIGNMENT = "goal_alignment"
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SEMANTIC_QUALITY = "semantic_quality"
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REASONING_EFFICIENCY = "reasoning_efficiency"
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TOOL_SELECTION = "tool_selection"
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PARAMETER_EXTRACTION = "parameter_extraction"
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TOOL_INVOCATION = "tool_invocation"
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def title(self):
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return self.value.replace('_', ' ').title()
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class EvaluationScore(BaseModel):
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score: float | None = Field(
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default=5.0,
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description="Numeric score from 0-10 where 0 is worst and 10 is best, None if not applicable",
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ge=0.0,
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le=10.0
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)
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feedback: str = Field(
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default="",
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description="Detailed feedback explaining the evaluation score"
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)
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raw_response: str | None = Field(
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default=None,
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description="Raw response from the evaluator (e.g., LLM)"
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)
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def __str__(self) -> str:
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if self.score is None:
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return f"Score: N/A - {self.feedback}"
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return f"Score: {self.score:.1f}/10 - {self.feedback}"
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class BaseEvaluator(abc.ABC):
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def __init__(self, llm: BaseLLM | None = None):
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self.llm: BaseLLM | None = create_llm(llm)
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@property
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@abc.abstractmethod
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def metric_category(self) -> MetricCategory:
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pass
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@abc.abstractmethod
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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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pass
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class AgentEvaluationResult(BaseModel):
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agent_id: str = Field(description="ID of the evaluated agent")
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task_id: str = Field(description="ID of the task that was executed")
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metrics: Dict[MetricCategory, EvaluationScore] = Field(
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default_factory=dict,
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description="Evaluation scores for each metric category"
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)
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class AggregationStrategy(Enum):
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SIMPLE_AVERAGE = "simple_average" # Equal weight to all tasks
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WEIGHTED_BY_COMPLEXITY = "weighted_by_complexity" # Weight by task complexity
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BEST_PERFORMANCE = "best_performance" # Use best scores across tasks
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WORST_PERFORMANCE = "worst_performance" # Use worst scores across tasks
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class AgentAggregatedEvaluationResult(BaseModel):
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agent_id: str = Field(
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default="",
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description="ID of the agent"
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)
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agent_role: str = Field(
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default="",
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description="Role of the agent"
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)
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task_count: int = Field(
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default=0,
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description="Number of tasks included in this aggregation"
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)
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aggregation_strategy: AggregationStrategy = Field(
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default=AggregationStrategy.SIMPLE_AVERAGE,
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description="Strategy used for aggregation"
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)
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metrics: Dict[MetricCategory, EvaluationScore] = Field(
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default_factory=dict,
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description="Aggregated metrics across all tasks"
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)
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task_results: List[str] = Field(
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default_factory=list,
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description="IDs of tasks included in this aggregation"
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)
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overall_score: Optional[float] = Field(
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default=None,
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description="Overall score for this agent"
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)
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def __str__(self) -> str:
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result = f"Agent Evaluation: {self.agent_role}\n"
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result += f"Strategy: {self.aggregation_strategy.value}\n"
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result += f"Tasks evaluated: {self.task_count}\n"
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for category, score in self.metrics.items():
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result += f"\n\n- {category.value.upper()}: {score.score}/10\n"
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if score.feedback:
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detailed_feedback = "\n ".join(score.feedback.split('\n'))
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result += f" {detailed_feedback}\n"
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return result |