Implementation:Microsoft Autogen Studio Eval Datamodel

Sources	python/packages/autogen-studio/autogenstudio/datamodel/eval.py
Domains	Evaluation, Data_Modeling, Agent_Systems
Last Updated	2026-02-11

Overview

Description

The Studio Eval Datamodel module defines the core Pydantic data models used throughout AutoGen Studio's evaluation system. This module provides structured representations for evaluation tasks, results, scoring mechanisms, and criteria. It serves as the foundational data layer for AutoGen Studio's evaluation capabilities, enabling type-safe interactions between evaluation runners and judges.

The module includes six primary model classes:

• EvalTask - Defines a task to be evaluated with input, metadata, and optional expected outputs
• EvalRunResult - Captures the result of running an evaluation task including status, timing, and errors
• EvalDimensionScore - Represents a score for a single evaluation dimension with reasoning
• EvalScore - Composite scoring structure aggregating multiple dimension scores
• EvalJudgeCriteria - Specifies criteria for judging evaluation results along specific dimensions
• EvalRunStatus - Enum defining possible evaluation run states

Usage

These data models are used throughout the AutoGen Studio evaluation system. Evaluation runners create EvalRunResult objects from EvalTask inputs, while judges consume these results along with EvalJudgeCriteria to produce EvalScore outputs. The models leverage Pydantic for validation and serialization, ensuring data consistency across the evaluation pipeline.

Code Reference

Source Location

python/packages/autogen-studio/autogenstudio/datamodel/eval.py

Signature

# Core Pydantic Models
class EvalTask(BaseModel):
    task_id: UUID | str = Field(default_factory=uuid4)
    input: str | Sequence[str | Image]
    name: str = ""
    description: str = ""
    expected_outputs: Optional[List[Any]] = None
    metadata: Dict[str, Any] = {}

class EvalRunResult(BaseModel):
    result: TaskResult | None = None
    status: bool = False
    start_time: Optional[datetime] = Field(default=datetime.now())
    end_time: Optional[datetime] = None
    error: Optional[str] = None

class EvalDimensionScore(BaseModel):
    dimension: str
    score: float
    reason: str
    max_value: float
    min_value: float

class EvalScore(BaseModel):
    overall_score: Optional[float] = None
    dimension_scores: List[EvalDimensionScore] = []
    reason: Optional[str] = None
    max_value: float = 10.0
    min_value: float = 0.0
    metadata: Dict[str, Any] = {}

class EvalJudgeCriteria(BaseModel):
    dimension: str
    prompt: str
    max_value: float = 10.0
    min_value: float = 0.0
    metadata: Dict[str, Any] = {}

class EvalRunStatus(str, Enum):
    PENDING = "pending"
    RUNNING = "running"
    COMPLETED = "completed"
    FAILED = "failed"
    CANCELED = "canceled"

class EvalResult(BaseModel):
    task_id: UUID | str
    status: EvalRunStatus = EvalRunStatus.PENDING
    start_time: Optional[datetime] = Field(default=datetime.now())
    end_time: Optional[datetime] = None

Import

from autogenstudio.datamodel.eval import (
    EvalTask,
    EvalRunResult,
    EvalDimensionScore,
    EvalScore,
    EvalJudgeCriteria,
    EvalRunStatus,
    EvalResult
)

I/O Contract

Inputs

Model	Field	Type	Description
EvalTask	task_id	str	Unique identifier for the task (auto-generated if not provided)
	input	Sequence[str | Image]	Task input - can be text string or sequence of strings/images
	name	str	Human-readable name for the task
	description	str	Detailed description of the task
	expected_outputs	Optional[List[Any]]	Expected outputs for validation purposes
	metadata	Dict[str, Any]	Additional metadata about the task
EvalJudgeCriteria	dimension	str	Name of the evaluation dimension (e.g., "relevance", "accuracy")
	prompt	str	Prompt text for judges to evaluate this dimension
	max_value	float	Maximum score value (default: 10.0)
	min_value	float	Minimum score value (default: 0.0)
	metadata	Dict[str, Any]	Additional metadata for the criteria

Outputs

Model	Field	Type	Description
EvalRunResult	result	None	The actual task result from autogen_agentchat
	status	bool	Success/failure status of the run
	start_time	Optional[datetime]	When the evaluation started (auto-generated)
	end_time	Optional[datetime]	When the evaluation completed
	error	Optional[str]	Error message if status is False
EvalDimensionScore	dimension	str	Name of the evaluated dimension
	score	float	Numerical score for this dimension
	reason	str	Textual explanation for the score
	max_value	float	Maximum possible score
	min_value	float	Minimum possible score
EvalScore	overall_score	Optional[float]	Aggregate score across all dimensions
	dimension_scores	List[EvalDimensionScore]	Individual scores for each evaluation dimension
	reason	Optional[str]	Overall reasoning for the composite score
	max_value	float	Maximum possible overall score
	min_value	float	Minimum possible overall score
	metadata	Dict[str, Any]	Additional metadata about the scoring

Usage Examples

Creating an Evaluation Task

from autogenstudio.datamodel.eval import EvalTask

# Simple text task
task = EvalTask(
    name="French Capital Query",
    description="Test the agent's knowledge of geography",
    input="What is the capital of France?",
    expected_outputs=["Paris"],
    metadata={"category": "geography", "difficulty": "easy"}
)

# Multi-modal task with images
from autogen_core import Image

visual_task = EvalTask(
    name="Image Analysis",
    input=[
        "Describe what you see in this image:",
        Image.from_file("path/to/image.png")
    ],
    metadata={"task_type": "vision"}
)

Defining Judge Criteria

from autogenstudio.datamodel.eval import EvalJudgeCriteria

criteria = [
    EvalJudgeCriteria(
        dimension="relevance",
        prompt="Evaluate how relevant the response is to the query.",
        max_value=10.0,
        min_value=0.0
    ),
    EvalJudgeCriteria(
        dimension="accuracy",
        prompt="Evaluate the factual accuracy of the response.",
        max_value=10.0,
        min_value=0.0
    ),
    EvalJudgeCriteria(
        dimension="completeness",
        prompt="Evaluate whether the response fully addresses the query.",
        max_value=10.0,
        min_value=0.0
    )
]

Working with Evaluation Results

from autogenstudio.datamodel.eval import EvalRunResult, EvalScore, EvalDimensionScore
from datetime import datetime

# Creating a result
result = EvalRunResult(
    status=True,
    start_time=datetime.now(),
    end_time=datetime.now(),
    result=task_result  # TaskResult from autogen_agentchat
)

# Creating dimension scores
dimension_scores = [
    EvalDimensionScore(
        dimension="relevance",
        score=9.0,
        reason="Response directly addresses the query",
        max_value=10.0,
        min_value=0.0
    ),
    EvalDimensionScore(
        dimension="accuracy",
        score=10.0,
        reason="Factually correct response",
        max_value=10.0,
        min_value=0.0
    )
]

# Composite score
eval_score = EvalScore(
    overall_score=9.5,
    dimension_scores=dimension_scores,
    reason="High quality response with accurate and relevant information"
)

Using EvalRunStatus

from autogenstudio.datamodel.eval import EvalRunStatus, EvalResult

# Track evaluation progress
eval_result = EvalResult(
    task_id="task-123",
    status=EvalRunStatus.PENDING,
    start_time=datetime.now()
)

# Update status as evaluation progresses
eval_result.status = EvalRunStatus.RUNNING

# Mark as completed
eval_result.status = EvalRunStatus.COMPLETED
eval_result.end_time = datetime.now()

Related Pages

• Studio Eval Judges - Judge implementations that consume these data models
• Studio Eval Runners - Runner implementations that produce EvalRunResult
• Studio Datamodel Pydantic Types - Additional Pydantic models for AutoGen Studio
• Evaluation Domain - All evaluation-related implementations
• Data Modeling Domain - Data structure implementations