Structured Evaluation

A reusable evaluation framework for LLM-as-Judge and multi-agent workflows.

Overview

structured-evaluation provides standardized types for evaluation reports, enabling:

• LLM-as-Judge assessments with weighted category scores and severity-based findings
• GO/NO-GO summary reports for deterministic checks (CI, tests, validation)
• Multi-agent coordination with DAG-based report aggregation

Installation

go get github.com/agentplexus/structured-evaluation

Packages

Package	Description
evaluation	EvaluationReport, CategoryScore, Finding, Severity types
summary	SummaryReport, TeamSection, TaskResult for GO/NO-GO checks
combine	DAG-based report aggregation using Kahn's algorithm
render/box	Box-format terminal renderer for summary reports
render/detailed	Detailed terminal renderer for evaluation reports
schema	JSON Schema generation and embedding

Report Types

Evaluation Report (LLM-as-Judge)

For subjective quality assessments with detailed findings:

import "github.com/agentplexus/structured-evaluation/evaluation"

report := evaluation.NewEvaluationReport("prd", "document.md")
report.AddCategory(evaluation.NewCategoryScore("problem_definition", 0.20, 8.5, "Clear problem statement"))
report.AddFinding(evaluation.Finding{
    Severity:       evaluation.SeverityMedium,
    Category:       "metrics",
    Title:          "Missing baseline metrics",
    Recommendation: "Add current baseline measurements",
})
report.Finalize("sevaluation check document.md")

Summary Report (GO/NO-GO)

For deterministic checks with pass/fail status:

import "github.com/agentplexus/structured-evaluation/summary"

report := summary.NewSummaryReport("my-service", "v1.0.0", "Release Validation")
report.AddTeam(summary.TeamSection{
    ID:   "qa",
    Name: "Quality Assurance",
    Tasks: []summary.TaskResult{
        {ID: "unit-tests", Status: summary.StatusGo, Detail: "Coverage: 92%"},
        {ID: "e2e-tests", Status: summary.StatusWarn, Detail: "2 flaky tests"},
    },
})

Severity Levels

Following InfoSec conventions:

Severity	Icon	Blocking	Description
Critical	🔴	Yes	Must fix before approval
High	🔴	Yes	Must fix before approval
Medium	🟡	No	Should fix, tracked
Low	🟢	No	Nice to fix
Info	⚪	No	Informational only

Pass Criteria

Default criteria (zero blocking findings, minimum score):

criteria := evaluation.DefaultPassCriteria()
// MaxCritical: 0, MaxHigh: 0, MaxMedium: -1 (unlimited), MinScore: 7.0

criteria := evaluation.StrictPassCriteria()
// MaxCritical: 0, MaxHigh: 0, MaxMedium: 3, MinScore: 8.0

CLI Tool

# Install
go install github.com/agentplexus/structured-evaluation/cmd/sevaluation@latest

# Render reports
sevaluation render report.json --format=detailed
sevaluation render report.json --format=box
sevaluation render report.json --format=json

# Check pass/fail (exit code 0/1)
sevaluation check report.json

# Validate structure
sevaluation validate report.json

# Generate JSON Schema
sevaluation schema generate -o ./schema/

DAG-Based Aggregation

For multi-agent workflows with dependencies:

import "github.com/agentplexus/structured-evaluation/combine"

results := []combine.AgentResult{
    {TeamID: "qa", Tasks: qaTasks},
    {TeamID: "security", Tasks: secTasks, DependsOn: []string{"qa"}},
    {TeamID: "release", Tasks: relTasks, DependsOn: []string{"qa", "security"}},
}

report := combine.AggregateResults(results, "my-project", "v1.0.0", "Release")
// Teams are topologically sorted: qa → security → release

JSON Schema

Schemas are embedded for runtime validation:

import "github.com/agentplexus/structured-evaluation/schema"

evalSchema := schema.EvaluationSchemaJSON
summarySchema := schema.SummarySchemaJSON

Rubrics (v0.2.0)

Define explicit scoring criteria for consistent evaluations:

rubric := evaluation.NewRubric("quality", "Output quality").
    AddRangeAnchor(8, 10, "Excellent", "Near perfect").
    AddRangeAnchor(5, 7.9, "Good", "Acceptable").
    AddRangeAnchor(0, 4.9, "Poor", "Needs work")

// Use default PRD rubric
rubricSet := evaluation.DefaultPRDRubricSet()

Judge Metadata (v0.2.0)

Track LLM judge configuration for reproducibility:

judge := evaluation.NewJudgeMetadata("claude-3-opus").
    WithProvider("anthropic").
    WithPrompt("prd-eval-v1", "1.0").
    WithTemperature(0.0).
    WithTokenUsage(1500, 800)

report.SetJudge(judge)

Pairwise Comparison (v0.2.0)

Compare two outputs instead of absolute scoring:

comparison := evaluation.NewPairwiseComparison(input, outputA, outputB)
comparison.SetWinner(evaluation.WinnerA, "A is more accurate", 0.9)

// Aggregate multiple comparisons
result := evaluation.ComputePairwiseResult(comparisons)
// result.WinRateA, result.OverallWinner

Multi-Judge Aggregation (v0.2.0)

Combine evaluations from multiple judges:

result := evaluation.AggregateEvaluations(evaluations, evaluation.AggregationMean)

// Methods: AggregationMean, AggregationMedian, AggregationConservative, AggregationMajority
// result.Agreement - inter-judge agreement (0-1)
// result.Disagreements - categories with significant disagreement
// result.ConsolidatedDecision - final aggregated decision

OmniObserve Integration

Export evaluations to Opik, Phoenix, or Langfuse:

import "github.com/agentplexus/omniobserve/integrations/sevaluation"

// Export to observability platform
err := sevaluation.Export(ctx, provider, traceID, report)

Integration

Designed to work with:

• github.com/agentplexus/omniobserve - LLM observability (Opik, Phoenix, Langfuse)
• github.com/grokify/structured-requirements - PRD evaluation templates
• github.com/agentplexus/multi-agent-spec - Agent coordination
• github.com/grokify/structured-changelog - Release validation

License

MIT License - see LICENSE for details.