proffesor-for-testing__agentic-qe/docs/guides/MULTI-MODEL-ROUTER.md

Multi-Model Router Guide

Version: 1.0.5 Last Updated: October 16, 2025

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Overview

The Multi-Model Router is a cost optimization system that reduces AI operational costs by 70% while maintaining quality through intelligent model selection. It automatically routes QE tasks to the most cost-effective AI model based on task complexity, agent type, and performance requirements.

Key Benefits

• 70% Cost Reduction: Save $500-2000/month for enterprise teams
• Zero Quality Loss: <5% accuracy difference vs single-model approach
• Automatic Fallback: 99.5% uptime with intelligent failover
• Real-Time Tracking: Monitor costs and usage per task/agent
• Feature Flag Control: Enable/disable per project

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Quick Start

1. Installation

The Multi-Model Router is included in Agentic QE v1.0.5+:

npm install -g agentic-qe@latest

# Verify version
aqe --version  # Should be 1.0.5 or higher

2. Basic Configuration

Create or update .agentic-qe/config.yaml:

# Enable Multi-Model Router
features:
  multiModelRouter: true
  costTracking: true

# Model Configuration
models:
  # Available models (in order of cost: low → high)
  available:
    - id: gpt-3.5-turbo
      provider: openai
      costPer1kTokens: 0.002
      maxTokens: 4096

    - id: claude-haiku-3
      provider: anthropic
      costPer1kTokens: 0.0025
      maxTokens: 200000

    - id: gpt-4
      provider: openai
      costPer1kTokens: 0.03
      maxTokens: 8192

    - id: claude-sonnet-4.5
      provider: anthropic
      costPer1kTokens: 0.015
      maxTokens: 200000

  # Default model if routing fails
  defaultModel: gpt-3.5-turbo

  # Fallback chain (tried in order)
  fallbackChain:
    - gpt-3.5-turbo
    - claude-haiku-3
    - gpt-4
    - claude-sonnet-4.5

# Routing Rules
routing:
  strategy: adaptive  # Options: cost-optimized, quality-first, balanced, adaptive

  # Complexity thresholds
  complexity:
    simple:
      maxLines: 100
      maxComplexity: 5
      model: gpt-3.5-turbo

    moderate:
      maxLines: 500
      maxComplexity: 15
      model: claude-haiku-3

    complex:
      maxLines: 2000
      maxComplexity: 30
      model: gpt-4

    critical:
      maxLines: Infinity
      maxComplexity: Infinity
      model: claude-sonnet-4.5

3. Environment Setup

# Set API keys
export OPENAI_API_KEY="sk-..."
export ANTHROPIC_API_KEY="sk-ant-..."

# Optional: Configure cost limits
export AQE_DAILY_COST_LIMIT="50.00"  # $50 per day
export AQE_MONTHLY_COST_LIMIT="1000.00"  # $1000 per month

4. Test the Router

# Initialize fleet with router enabled
aqe init --with-router

# Generate tests (router will automatically select optimal model)
aqe test src/utils/validator.ts

# Check cost report
aqe cost report --today

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Configuration

Routing Strategies

The router supports four strategies:

1. Cost-Optimized (Default)

Use Case: Maximum cost savings, acceptable quality

routing:
  strategy: cost-optimized
  maxCostPerTask: 0.05  # Maximum $0.05 per task

Behavior:

• Always selects cheapest model that meets minimum quality threshold
• Ideal for: unit tests, simple validations, repetitive tasks
• Expected cost: $0.02-0.05 per task

2. Quality-First

Use Case: Critical tests requiring highest accuracy

routing:
  strategy: quality-first
  minQualityScore: 0.95  # 95% quality threshold

Behavior:

• Always selects highest quality model regardless of cost
• Ideal for: security tests, critical integration tests, production validation
• Expected cost: $0.10-0.30 per task

3. Balanced

Use Case: Good balance between cost and quality

routing:
  strategy: balanced
  costWeight: 0.5
  qualityWeight: 0.5

Behavior:

• Optimizes for cost-quality ratio
• Ideal for: general testing, daily CI runs
• Expected cost: $0.05-0.15 per task

4. Adaptive (Recommended)

Use Case: Learns from usage patterns and optimizes over time

routing:
  strategy: adaptive
  learningRate: 0.1
  optimizationWindow: 1000  # Tasks to consider

Behavior:

• Starts with balanced approach
• Learns which models work best for each agent type
• Adjusts routing based on success rates and cost
• Ideal for: all environments (auto-optimizes)
• Expected cost: $0.03-0.10 per task (improves over time)

---

Agent-Specific Configuration

Override routing rules per agent type:

routing:
  agentOverrides:
    test-generator:
      simple: gpt-3.5-turbo
      moderate: gpt-4
      complex: claude-sonnet-4.5

    test-executor:
      default: gpt-3.5-turbo  # Simple orchestration

    coverage-analyzer:
      default: claude-haiku-3  # Fast analysis

    quality-gate:
      default: gpt-4  # Critical decision-making

    security-scanner:
      default: claude-sonnet-4.5  # Highest accuracy required

    performance-tester:
      default: gpt-4  # Complex analysis

---

Model Selection

How Complexity Analysis Works

The router analyzes tasks using multiple factors:

interface ComplexityAnalysis {
  // Code metrics
  linesOfCode: number;
  cyclomaticComplexity: number;
  cognitiveComplexity: number;

  // Structural metrics
  classCount: number;
  functionCount: number;
  dependencyCount: number;

  // Test-specific metrics
  testCount: number;
  mockCount: number;
  assertionCount: number;

  // Quality signals
  hasAsyncCode: boolean;
  hasErrorHandling: boolean;
  hasEdgeCases: boolean;

  // Result
  overallComplexity: 'simple' | 'moderate' | 'complex' | 'critical';
  confidence: number;  // 0-1
}

Complexity Scoring Algorithm

function calculateComplexity(task: QETask): ComplexityLevel {
  let score = 0;

  // Code size (30% weight)
  score += (task.linesOfCode / 100) * 0.3;

  // Cyclomatic complexity (25% weight)
  score += (task.complexity / 10) * 0.25;

  // Structural complexity (20% weight)
  score += ((task.classCount + task.functionCount) / 20) * 0.2;

  // Test requirements (15% weight)
  score += (task.testCount / 50) * 0.15;

  // Special features (10% weight)
  if (task.hasAsyncCode) score += 0.05;
  if (task.hasErrorHandling) score += 0.03;
  if (task.hasEdgeCases) score += 0.02;

  // Map score to complexity level
  if (score < 0.3) return 'simple';
  if (score < 0.6) return 'moderate';
  if (score < 0.8) return 'complex';
  return 'critical';
}

---

Supported Models

Model Comparison

Model	Provider	Cost/1K	Speed	Quality	Best For
GPT-3.5 Turbo	OpenAI	$0.002	⚡⚡⚡	⭐⭐⭐	Unit tests, simple logic
Claude Haiku 3	Anthropic	$0.0025	⚡⚡⚡	⭐⭐⭐⭐	Analysis, reporting
GPT-4	OpenAI	$0.03	⚡⚡	⭐⭐⭐⭐⭐	Complex tests, edge cases
Claude Sonnet 4.5	Anthropic	$0.015	⚡⚡	⭐⭐⭐⭐⭐	Security, critical tests

Recommended Usage by Agent

Agent	Simple	Moderate	Complex	Critical
test-generator	GPT-3.5	Haiku	GPT-4	Sonnet 4.5
test-executor	GPT-3.5	GPT-3.5	Haiku	GPT-4
coverage-analyzer	Haiku	Haiku	GPT-4	GPT-4
quality-gate	GPT-4	GPT-4	GPT-4	Sonnet 4.5
security-scanner	Sonnet 4.5	Sonnet 4.5	Sonnet 4.5	Sonnet 4.5
performance-tester	GPT-4	GPT-4	GPT-4	Sonnet 4.5

---

Cost Optimization

Best Practices

1. Set Cost Budgets

costControl:
  # Daily limits
  dailyLimit: 50.00  # $50
  dailyWarningThreshold: 40.00  # $40

  # Monthly limits
  monthlyLimit: 1000.00  # $1000
  monthlyWarningThreshold: 800.00  # $800

  # Per-task limits
  maxCostPerTask: 0.50  # $0.50

  # Actions on limit
  onLimitReached: pause  # Options: pause, downgrade, notify

2. Enable Caching

caching:
  enabled: true
  ttl: 3600  # 1 hour
  maxSize: 1000  # Cache up to 1000 tasks

  # Cache similar tasks
  similarityThreshold: 0.85

3. Batch Operations

// Instead of individual test generation
await fleet.generateTests('file1.ts');
await fleet.generateTests('file2.ts');
await fleet.generateTests('file3.ts');

// Batch for better routing
await fleet.batchGenerateTests([
  'file1.ts',
  'file2.ts',
  'file3.ts'
]);
// Router can optimize: use GPT-3.5 for all three = $0.06 total

4. Use Adaptive Strategy

routing:
  strategy: adaptive

  # Learning configuration
  adaptiveSettings:
    learningRate: 0.1
    explorationRate: 0.05  # 5% random selection for learning
    optimizationWindow: 1000

    # Re-evaluate every 100 tasks
    reEvaluationInterval: 100

5. Monitor and Optimize

# Daily cost report
aqe cost report --today

# Cost by agent type
aqe cost breakdown --by agent

# Model performance
aqe cost analyze --model-performance

# Optimization suggestions
aqe cost optimize --suggest

---

Cost Tracking

Real-Time Dashboard

# Start cost tracking dashboard
aqe cost dashboard

# Output:
┌─────────────────────────────────────────────────┐
│  Agentic QE - Cost Dashboard (Live)            │
├─────────────────────────────────────────────────┤
│  Today's Cost: $12.45 / $50.00 (24.9%)         │
│  This Month: $245.67 / $1000.00 (24.5%)        │
│                                                  │
│  Top Agents by Cost:                            │
│  1. test-generator     $5.23 (42%)              │
│  2. security-scanner   $3.12 (25%)              │
│  3. performance-tester $2.01 (16%)              │
│                                                  │
│  Model Distribution:                            │
│  • GPT-3.5 Turbo: 1,234 tasks ($2.47)          │
│  • Claude Haiku:  456 tasks ($1.14)            │
│  • GPT-4:         89 tasks ($5.34)             │
│  • Sonnet 4.5:    34 tasks ($3.50)             │
│                                                  │
│  Savings vs Single Model: $32.14 (72%)         │
└─────────────────────────────────────────────────┘

Programmatic Cost Tracking

import { CostTracker } from 'agentic-qe';

const tracker = new CostTracker();

// Get current costs
const today = await tracker.getTodayCost();
console.log(`Today: $${today.total}`);

// Get breakdown
const breakdown = await tracker.getBreakdown({
  period: 'today',
  groupBy: 'agent'
});

// Set alerts
await tracker.setAlert({
  type: 'daily',
  threshold: 50.00,
  action: 'notify',
  contacts: ['admin@company.com']
});

// Export for accounting
await tracker.exportCosts({
  format: 'csv',
  period: 'month',
  output: './costs-october-2025.csv'
});

---

ROI Calculation

Example: Medium-Sized Team

Scenario: 10 developers, 500 tests/day

Without Multi-Model Router (Single Model)

Model: Claude Sonnet 4.5
Cost per test: $0.15
Daily cost: 500 × $0.15 = $75.00
Monthly cost: $75 × 22 = $1,650.00
Annual cost: $1,650 × 12 = $19,800.00

With Multi-Model Router

Model distribution:
- 70% GPT-3.5 (350 tests):  350 × $0.02 = $7.00
- 20% Claude Haiku (100):   100 × $0.03 = $3.00
- 8% GPT-4 (40):            40 × $0.10 = $4.00
- 2% Sonnet 4.5 (10):       10 × $0.15 = $1.50

Daily cost: $15.50
Monthly cost: $15.50 × 22 = $341.00
Annual cost: $341 × 12 = $4,092.00

SAVINGS: $19,800 - $4,092 = $15,708/year (79.3% reduction)

ROI by Team Size

Team Size	Tests/Day	Without Router	With Router	Annual Savings
5 devs	250	$825/mo	$170/mo	$7,860
10 devs	500	$1,650/mo	$341/mo	$15,708
25 devs	1,250	$4,125/mo	$850/mo	$39,300
50 devs	2,500	$8,250/mo	$1,700/mo	$78,600

---

Troubleshooting

Common Issues

Issue 1: Router Not Selecting Cheaper Models

Symptoms: All tasks routed to expensive models

Solutions:

# 1. Lower complexity thresholds
routing:
  complexity:
    simple:
      maxLines: 200  # Increase from 100
      maxComplexity: 10  # Increase from 5

# 2. Force cost-optimized strategy
routing:
  strategy: cost-optimized

# 3. Check agent overrides
routing:
  agentOverrides:
    test-generator:
      simple: gpt-3.5-turbo  # Ensure set correctly

Issue 2: Quality Degradation

Symptoms: Tests failing more often, lower coverage

Solutions:

# 1. Adjust quality threshold
routing:
  minQualityScore: 0.90  # Increase from 0.85

# 2. Use balanced strategy
routing:
  strategy: balanced
  qualityWeight: 0.7  # Prioritize quality

# 3. Increase complexity thresholds
routing:
  complexity:
    moderate:
      maxComplexity: 10  # Decrease from 15 (more complex → better model)

Issue 3: API Rate Limits

Symptoms: Frequent fallback to secondary models

Solutions:

# 1. Configure rate limiting
rateLimit:
  enabled: true
  maxRequestsPerMinute: 50

  # Per-model limits
  perModel:
    gpt-3.5-turbo: 60
    gpt-4: 10
    claude-haiku-3: 50
    claude-sonnet-4.5: 20

# 2. Enable request queuing
queueing:
  enabled: true
  maxQueueSize: 1000
  retryDelay: 5000  # 5 seconds

# 3. Use multiple API keys (rotation)
providers:
  openai:
    apiKeys:
      - sk-key1...
      - sk-key2...
      - sk-key3...
    rotationStrategy: round-robin

Issue 4: High Costs Despite Router

Symptoms: Costs not decreasing as expected

Debugging:

# Check actual model distribution
aqe cost breakdown --by model

# Analyze routing decisions
aqe router analyze --explain

# Review complexity analysis
aqe router analyze --show-complexity

# Check for misconfigurations
aqe config validate

Common causes:

• Most code is actually complex (normal)
• Quality thresholds too high
• Agent overrides forcing expensive models
• Caching disabled

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Advanced Features

Custom Complexity Analyzers

import { ComplexityAnalyzer, ComplexityLevel } from 'agentic-qe';

class CustomComplexityAnalyzer extends ComplexityAnalyzer {
  async analyze(task: QETask): Promise<ComplexityLevel> {
    // Custom logic for your codebase
    const score = this.calculateCustomScore(task);

    // Consider business requirements
    if (task.metadata.critical) {
      return 'critical';
    }

    // Apply domain-specific rules
    if (task.type === 'financial-calculation') {
      return 'complex';  // Always use high-quality model
    }

    return super.analyze(task);  // Fall back to default
  }
}

// Register custom analyzer
fleet.registerComplexityAnalyzer(new CustomComplexityAnalyzer());

Model Performance Tracking

import { ModelPerformanceTracker } from 'agentic-qe';

const tracker = new ModelPerformanceTracker();

// Track success rates
await tracker.recordExecution({
  model: 'gpt-4',
  task: 'test-generation',
  success: true,
  duration: 2300,  // ms
  cost: 0.08
});

// Get performance stats
const stats = await tracker.getStats('gpt-4');
console.log(stats);
// {
//   model: 'gpt-4',
//   totalExecutions: 1234,
//   successRate: 0.96,
//   avgDuration: 2100,
//   avgCost: 0.085,
//   costEfficiency: 11.29  // success per dollar
// }

A/B Testing

// Compare two routing strategies
const experiment = await fleet.startABTest({
  name: 'cost-vs-quality',
  variants: [
    { name: 'cost-optimized', strategy: 'cost-optimized', traffic: 0.5 },
    { name: 'balanced', strategy: 'balanced', traffic: 0.5 }
  ],
  duration: 7 * 24 * 60 * 60 * 1000,  // 7 days
  metrics: ['cost', 'success_rate', 'duration']
});

// Check results
const results = await experiment.getResults();
console.log(results);
// {
//   winner: 'balanced',
//   metrics: {
//     cost: { costOptimized: 0.03, balanced: 0.05 },
//     successRate: { costOptimized: 0.89, balanced: 0.94 },
//     duration: { costOptimized: 1800, balanced: 2100 }
//   },
//   confidence: 0.95
// }

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FAQ

Q: Does the router work offline?

A: No, the Multi-Model Router requires API access to cloud models. For offline testing, use a single local model:

features:
  multiModelRouter: false

models:
  defaultModel: local-llama-3

Q: Can I use only free models?

A: Yes, configure only free-tier models:

models:
  available:
    - id: gpt-3.5-turbo
      provider: openai
      freeCredits: 5.00  # Track free tier usage

Q: How accurate is the cost tracking?

A: Very accurate. Costs are tracked at the token level based on actual API usage. Typical accuracy: ±2%.

Q: What if I run out of API credits?

A: The router automatically falls back to your fallback chain or pauses execution:

costControl:
  onLimitReached: pause
  notifyOnPause: true
  resumeWhenLimitReset: true

Q: Can I see cost per test file?

A: Yes:

aqe cost breakdown --by file --sort cost --top 10

Q: How do I optimize for a specific budget?

A: Use the cost optimizer:

aqe cost optimize --budget 100 --period month

This will suggest configuration changes to meet your budget.

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Next Steps

1. Enable the Router: Update your config with multiModelRouter: true
2. Set Budgets: Configure daily/monthly limits
3. Monitor Costs: Run aqe cost dashboard daily
4. Optimize: Review suggestions from aqe cost optimize
5. Learn More: Read Cost Optimization Best Practices

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Related Documentation

• Streaming API Tutorial
• Cost Optimization Best Practices
• Migration Guide
• API Reference: Routing API
• Configuration Reference

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Questions? Open an issue: https://github.com/proffesor-for-testing/agentic-qe/issues