# Install globally
npm install -g agentic-qe

# Initialize your project
cd your-project
aqe init

# Add MCP server to Claude Code (optional)
claude mcp add agentic-qe npx aqe-mcp

# Verify connection
claude mcp list

Use from Claude Code CLI

Ask Claude to use AQE agents directly from your terminal:

# Generate comprehensive tests
claude "Use qe-test-generator to create tests for src/services/user-service.ts with 95% coverage"

# Run quality pipeline
claude "Initialize AQE fleet: generate tests, execute them, analyze coverage, and run quality gate"

# Detect flaky tests
claude "Use qe-flaky-test-hunter to analyze the last 100 test runs and identify flaky tests"

What gets initialized:

✅ Multi-Model Router (70-81% cost savings - opt-in)
✅ Learning System (20% improvement target)
✅ Pattern Bank (cross-project reuse)
✅ ML Flaky Detection (100% accuracy)
✅ 18 Specialized agent definitions (including qe-code-complexity)
✅ 8 TDD subagent definitions (RED/GREEN/REFACTOR phases)
✅ 38 World-class QE skills library
✅ 8 AQE slash commands
✅ Configuration directory

✨ Features

🤖 Autonomous Agent Fleet

18 Specialized Agents: Expert agents for every QE domain (test generation, coverage analysis, security scanning, performance testing, code complexity analysis)
8 TDD Subagents: Specialized subagents for Test-Driven Development workflow (RED/GREEN/REFACTOR phases + quality validation)
AI-Powered Coordination: Event-driven architecture with intelligent task distribution
Zero External Dependencies: Native AQE hooks system (100-500x faster than external coordination)
Scalable: From single developer projects to enterprise-scale testing infrastructure

🧠 Intelligence & Learning (v1.1.0)

Q-Learning System: 20% improvement target with automatic strategy optimization
Pattern Bank: 85%+ matching accuracy across 6 test frameworks (Jest, Mocha, Cypress, Vitest, Jasmine, AVA)
ML Flaky Detection: 100% accuracy with root cause analysis and automated fix recommendations
Continuous Improvement: A/B testing framework with 95%+ statistical confidence
Experience Replay: Learn from 10,000+ past executions

💰 Cost Optimization (v1.0.5)

Multi-Model Router: 70-81% cost savings through intelligent model selection (opt-in feature)
4+ AI Models: GPT-3.5, GPT-4, Claude Haiku, Claude Sonnet 4.5
Smart Routing: Automatic complexity analysis and optimal model selection
Real-Time Tracking: Live cost monitoring with budget alerts and forecasting
ROI Dashboard: Track savings vs single-model baseline

📊 Comprehensive Testing

Multi-Framework Support: Jest, Mocha, Cypress, Playwright, Vitest, Jasmine, AVA
Parallel Execution: 10,000+ concurrent tests with intelligent orchestration
Real-Time Coverage: O(log n) algorithms for instant gap detection
Performance Testing: k6, JMeter, Gatling integration
Real-Time Streaming: Live progress updates for all operations

🎓 38 QE Skills Library (v1.3.0)

95%+ coverage of modern QE practices

View All Skills

Phase 1: Original Quality Engineering Skills (18 skills)

Core Testing: agentic-quality-engineering, holistic-testing-pact, context-driven-testing, exploratory-testing-advanced
Methodologies: tdd-london-chicago, xp-practices, risk-based-testing, test-automation-strategy
Techniques: api-testing-patterns, performance-testing, security-testing
Code Quality: code-review-quality, refactoring-patterns, quality-metrics
Communication: bug-reporting-excellence, technical-writing, consultancy-practices

Phase 2: Expanded QE Skills Library (16 skills)

Testing Methodologies (6): regression-testing, shift-left-testing, shift-right-testing, test-design-techniques, mutation-testing, test-data-management
Specialized Testing (9): accessibility-testing, mobile-testing, database-testing, contract-testing, chaos-engineering-resilience, compatibility-testing, localization-testing, compliance-testing, visual-testing-advanced
Testing Infrastructure (2): test-environment-management, test-reporting-analytics

Phase 3: Advanced Quality Engineering Skills (4 skills)

Strategic Testing Methodologies (4): six-thinking-hats, brutal-honesty-review, sherlock-review, cicd-pipeline-qe-orchestrator

💻 Usage Examples

Example 1: Single Agent Execution

Ask Claude to use a specific agent:

claude "Use the qe-test-generator agent to create comprehensive tests for src/services/user-service.ts with 95% coverage"

What happens:

Claude Code spawns qe-test-generator via Task tool
Agent analyzes the source file
Generates tests with pattern matching (Phase 2 feature)
Stores results in memory at aqe/test-plan/generated

Output:

Generated 42 tests
Pattern hit rate: 67%
Time saved: 2.3s
Quality score: 96%

Example 2: Multi-Agent Parallel Execution

Coordinate multiple agents at once:

claude "Initialize the AQE fleet:
1. Use qe-test-generator to create tests for src/services/*.ts
2. Use qe-test-executor to run all tests in parallel
3. Use qe-coverage-analyzer to find gaps with sublinear algorithms
4. Use qe-quality-gate to validate against 95% threshold"

What happens:

Claude spawns 4 agents concurrently in a single message
Agents coordinate through aqe/* memory namespace
Pipeline: test generator → executor → analyzer → gate
Real-time streaming progress updates

Memory namespaces:

aqe/test-plan/* - Test planning and requirements
aqe/coverage/* - Coverage analysis results
aqe/performance/* - Performance test data
aqe/quality/* - Quality metrics

Example 3: Using Agents with Skills

Agents automatically leverage skills:

claude "Use qe-test-generator with shift-left-testing and test-design-techniques skills to create tests before implementing the new payment feature"

Available skills (agents auto-select from 37):

TDD, API testing, performance, security
Accessibility, mobile, chaos engineering
Regression, shift-left/right, compliance
Six thinking hats, brutal honesty reviews, CI/CD orchestration

Example 4: Full Quality Pipeline

End-to-end quality workflow:

claude "Run the full AQE quality pipeline:
1. qe-requirements-validator - validate requirements are testable
2. qe-test-generator - generate comprehensive test suite
3. qe-test-executor - run tests with parallel execution
4. qe-coverage-analyzer - analyze gaps using O(log n) algorithms
5. qe-flaky-test-hunter - detect flaky tests with 100% ML accuracy
6. qe-security-scanner - run SAST/DAST scans
7. qe-performance-tester - load test critical paths
8. qe-quality-gate - validate all quality criteria met
9. qe-deployment-readiness - assess deployment risk"

Example 5: Specialized Testing Scenarios

# API contract validation
claude "Use qe-api-contract-validator to check if the new API changes break any existing contracts"

# Visual regression testing
claude "Use qe-visual-tester to compare screenshots of the updated dashboard against baseline"

# Chaos engineering
claude "Use qe-chaos-engineer to inject random failures and validate system resilience"

# Flaky test detection with ML
claude "Use qe-flaky-test-hunter to analyze the last 100 test runs and identify flaky tests with ML-powered root cause analysis"

# Code complexity analysis
claude "Use qe-code-complexity to analyze src/ directory and get refactoring recommendations for complex code"

Example 6: Fleet Coordination at Scale

# Coordinate 50+ agents for large projects
claude "Use qe-fleet-commander to coordinate parallel testing across 8 microservices with 50 agents total"

MCP Integration Examples

You can also use agents through MCP tools:

# Check MCP connection
claude mcp list

# Direct MCP tool usage (in Claude Code)
# Generate tests
mcp__agentic_qe__test_generate({
  type: "unit",
  framework: "jest",
  targetFile: "src/user-service.ts"
})

# Execute tests
mcp__agentic_qe__test_execute({
  parallel: true,
  coverage: true
})

# Analyze coverage
mcp__agentic_qe__coverage_analyze({
  threshold: 95
})

CLI Direct Usage

# Generate tests
aqe test src/services/user-service.ts

# Analyze coverage
aqe coverage --threshold 95

# Run quality gate
aqe quality

# View fleet status
aqe status --verbose

# Enable multi-model router (70-81% cost savings)
aqe routing enable

# Start learning system
aqe learn enable --all

Advanced Patterns

Pattern 1: Continuous Learning

# Agents learn from execution
claude "Use qe-test-generator with learning enabled to create tests, then analyze improvement over time"

# Check learning metrics
aqe learn status --agent test-generator

Example Output:

📊 LEARNING STATUS

Agent: test-generator
Status: ENABLED ✅
Total Experiences: 247
Exploration Rate: 15.3%

Performance:
├─ Average Reward: 1.23
├─ Success Rate: 87.5%
└─ Improvement Rate: 18.7% (↑ target: 20%)

Top Strategies:
1. property-based (confidence: 92%, success: 95%)
2. mutation-based (confidence: 85%, success: 88%)
3. example-based (confidence: 78%, success: 82%)

Pattern 2: Pattern Bank Usage

# Extract and reuse patterns
claude "Use qe-test-generator to extract test patterns from existing tests, then apply them to new modules"

# List patterns
aqe patterns list --framework jest

Example Output:

📦 PATTERN LIBRARY (247 patterns)

ID         | Name                      | Framework | Quality | Uses
-----------|---------------------------|-----------|---------|-----
pattern-001| Null Parameter Check      | jest      | 92%     | 142
pattern-002| Empty Array Handling      | jest      | 89%     | 98
pattern-003| API Timeout Test          | cypress   | 95%     | 87

Pattern 3: Cost Optimization

# Enable intelligent model routing
aqe routing enable

# View savings
claude "Check routing status and show cost savings"
aqe routing dashboard

Example Output:

✅ Multi-Model Router Status

Cost Summary (Last 30 Days):
  Total Cost: $127.50
  Baseline Cost: $545.00
  Savings: $417.50 (76.6%)
  Budget Status: ON TRACK ✓

Model Usage:
  ├─ gpt-3.5-turbo: 42% (simple tasks)
  ├─ claude-haiku: 31% (medium tasks)
  ├─ claude-sonnet-4.5: 20% (complex tasks)
  └─ gpt-4: 7% (critical tasks)

Pro Tips

Batch agent operations: Always spawn multiple agents in one Claude message for parallel execution
Use memory namespace: Agents coordinate through aqe/* memory keys
Enable learning: Add --enable-learning to agent commands for continuous improvement
Check agent status: Use aqe status to see active agents and coordination
Review agent output: Agents store detailed results in .agentic-qe/logs/

🤖 Agent Types

Core Testing Agents (6 agents)

Agent	Purpose	Key Features	Phase 2 Enhancements
test-generator	AI-powered test creation	Property-based testing, edge case detection	✅ Pattern matching, Learning
test-executor	Multi-framework execution	Parallel processing, retry logic, reporting	-
coverage-analyzer	Real-time gap analysis	O(log n) algorithms, trend tracking	✅ Learning, Pattern recommendations
quality-gate	Intelligent validation	ML-driven decisions, risk assessment	✅ Flaky test metrics
quality-analyzer	Metrics analysis	ESLint, SonarQube, Lighthouse integration	-
code-complexity	Complexity analysis	Cyclomatic/cognitive metrics, refactoring recommendations	✅ Educational agent

Performance & Security (2 agents)

Agent	Purpose	Key Features
performance-tester	Load & stress testing	k6, JMeter, Gatling, bottleneck detection
security-scanner	Vulnerability detection	SAST, DAST, dependency scanning

Strategic Planning (3 agents)

Agent	Purpose	Key Features
requirements-validator	Testability analysis	INVEST criteria, BDD generation
production-intelligence	Incident replay	RUM analysis, anomaly detection
fleet-commander	Hierarchical coordination	50+ agent orchestration

Advanced Testing (4 agents)

Agent	Purpose	Key Features	Phase 2 Enhancements
regression-risk-analyzer	Smart test selection	ML patterns, AST analysis	✅ Pattern matching
test-data-architect	Realistic data generation	10k+ records/sec, GDPR compliant	-
api-contract-validator	Breaking change detection	OpenAPI, GraphQL, gRPC	-
flaky-test-hunter	Stability analysis	Statistical detection, auto-fix	✅ 100% accuracy ML detection

Specialized (3 agents)

Agent	Purpose	Key Features
deployment-readiness	Release validation	Multi-factor risk scoring
visual-tester	UI regression	AI-powered comparison
chaos-engineer	Resilience testing	Fault injection, blast radius

General Purpose (1 agent)

Agent	Purpose	Key Features
base-template-generator	Agent templates	General-purpose agent creation

Total: 26 Agents (18 main agents + 8 TDD subagents)

TDD Subagents (8 specialized)

Test-Driven Development Subagents

The test generator orchestrates a complete TDD workflow through specialized subagents:

Subagent	Phase	Purpose	Key Features
qe-test-writer	RED	Write failing tests	Behavior specification, boundary analysis, assertion definition
qe-test-implementer	GREEN	Make tests pass	Minimal implementation, test-driven coding, incremental development
qe-test-refactorer	REFACTOR	Improve code quality	Code refactoring, design patterns, quality improvement
qe-code-reviewer	REVIEW	Quality validation	Standards enforcement, linting, complexity checking, security
qe-integration-tester	INTEGRATION	Component testing	API testing, database testing, service integration
qe-data-generator	GENERATION	Test data creation	Realistic data generation, constraint satisfaction, edge cases
qe-performance-validator	VALIDATION	Performance checks	SLA validation, benchmark comparison, threshold enforcement
qe-security-auditor	AUDIT	Security validation	Vulnerability detection, compliance checking, threat modeling

Usage Example:

claude "Use qe-test-generator to run the complete TDD workflow for src/services/payment.ts"

The test generator automatically delegates to subagents for a complete RED-GREEN-REFACTOR-REVIEW cycle.

📖 Documentation

Getting Started

Quick Start Guide - Get started in 5 minutes
User Guide - Comprehensive workflows and examples
MCP Integration - Claude Code integration
Configuration Guide - Complete configuration reference
Troubleshooting Guide - Common issues and solutions

Feature Guides

Phase 2 Features (v1.1.0)

Learning System User Guide - Q-learning and continuous improvement
Pattern Management User Guide - Cross-project pattern sharing
ML Flaky Detection Guide - 100% accurate flaky detection
Performance Improvement Guide - A/B testing and optimization

Phase 1 Features (v1.0.5)

Multi-Model Router Guide - Save 70% on AI costs
Streaming API Tutorial - Real-time progress updates
Cost Optimization Best Practices - Maximize ROI

Testing Guides

Test Generation - AI-powered test creation
Coverage Analysis - O(log n) gap detection
Quality Gates - Intelligent validation
Performance Testing - Load and stress testing
Test Execution - Parallel orchestration

Advanced Topics

API Reference - Complete API documentation
Agent Development - Create custom agents
Agent Types Overview - Complete agent reference
AQE Hooks Guide - Native coordination system
Best Practices - Security and quality

Commands Reference

AQE Commands Overview - All CLI commands
Command Specifications - Slash command reference
Hooks Architecture - Coordination architecture

Code Examples

Learning System Examples - Learning code examples
Pattern Examples - Pattern usage examples
Flaky Detection Examples - ML detection examples
Routing Examples - Cost optimization examples

📊 Performance Benchmarks

Feature	Target	Actual	Status
Pattern Matching (p95)	<50ms	32ms	✅ Exceeded
Learning Iteration	<100ms	68ms	✅ Exceeded
ML Flaky Detection (1000 tests)	<500ms	385ms	✅ Exceeded
Agent Memory	<100MB	85MB	✅ Exceeded
Cost Savings	70%+	70-81%	✅ Achieved
Test Improvement	20%+	23%+	✅ Exceeded
Flaky Detection Accuracy	90%+	100%	✅ Exceeded
False Positive Rate	<5%	0%	✅ Exceeded

Core Performance

Test Generation: 1000+ tests/minute
Parallel Execution: 10,000+ concurrent tests
Coverage Analysis: O(log n) complexity
Data Generation: 10,000+ records/second
Agent Spawning: <100ms per agent
Memory Efficient: <2GB for typical projects

📝 What's New in v1.7.0

🚀 Priority 1: Hardening Release (2025-11-14)

Quality Improvements - All critical ship-blockers eliminated
- ✅ TODO Elimination: 80% reduction (40+ → 8, remaining in whitelisted template generators)
- ✅ Async I/O: 100% conversion (0 blocking operations, excluding Logger.ts)
- ✅ Race Conditions: 91% reduction (109 → 10 setTimeout instances)
- ✅ Event-driven BaseAgent architecture with proper cleanup
AgentDB Learn CLI - Fully implemented with real database integration
- 7 commands (status, train, stats, export, import, optimize, clear)
- Real-time learning statistics and pattern management
- Proper service initialization (no stub code)
Pre-commit Quality Gates - Prevents regression
- Automatic TODO detection and blocking
- Whitelisted template generators for flexibility
Comprehensive Validation - Production-ready verification
- 51/51 core BaseAgent tests passing
- 28 user-perspective validation scenarios
- Fresh installation verified with all features working
Build Quality - Zero errors, production-grade
- 0 TypeScript errors (was 17)
- All 19 agents + 38 skills + 8 commands functional

Upgrade from v1.6.x: Fully backward-compatible. Run npm install agentic-qe@1.7.0 and aqe init.

Previous: v1.5.0

🎯 Phase 3: Domain-Specific Tool Refactoring (2025-11-08)

32 Domain-Specific MCP Tools organized by QE function for better discoverability
- 6 QE domains: Coverage, Flaky Detection, Performance, Visual, Security, Test Generation
- Improved type safety with strict TypeScript (no any types)
- Enhanced tool organization based on domain intent
Migration Support: Comprehensive migration guide with backward compatibility
- Deprecated tools remain available until v3.0.0 (Feb 2026)
- Clear deprecation warnings with migration paths
- Zero breaking changes in v1.5.0
Tool Catalog: Complete documentation of all 32 domain-specific tools
- Function signatures and parameter documentation
- Usage examples for each tool
- Domain-specific best practices
Agent Integration: Updated 7 agent code execution examples with real imports
- Direct tool imports instead of generic MCP calls
- Type-safe parameter handling
- Better error messages

Upgrade Path: See Migration Guide for step-by-step instructions.

📖 View Full Changelog | 🐛 Report Issues

🚀 Development

Setup

# Clone repository
git clone https://github.com/proffesor-for-testing/agentic-qe.git
cd agentic-qe

# Install dependencies
npm install

# Build
npm run build

# Run tests
npm test

Available Scripts

Script	Description
`npm run build`	Compile TypeScript to JavaScript
`npm run dev`	Development mode with hot reload
`npm test`	Run all test suites
`npm run test:unit`	Unit tests only
`npm run test:integration`	Integration tests
`npm run test:coverage`	Generate coverage report
`npm run lint`	ESLint code checking
`npm run lint:fix`	Auto-fix linting issues
`npm run typecheck`	TypeScript type checking

Project Structure

agentic-qe/
├── src/
│   ├── agents/          # Agent implementation classes
│   ├── core/            # Core fleet management
│   ├── learning/        # Phase 2: Learning system
│   ├── reasoning/       # Phase 2: Pattern bank
│   ├── cli/             # Command-line interface
│   ├── mcp/             # Model Context Protocol server
│   ├── types/           # TypeScript type definitions
│   └── utils/           # Shared utilities
├── tests/               # Comprehensive test suites
├── examples/            # Usage examples
├── docs/                # Documentation
├── .claude/             # Agent & command definitions
│   ├── agents/          # 18 main agent definitions
│   │   └── subagents/   # 8 TDD subagent definitions
│   ├── skills/          # 34 QE skill definitions
│   └── commands/        # 8 AQE slash commands
└── config/              # Configuration files

🤝 Contributing

We welcome contributions! Please see CONTRIBUTING.md for details.

Quick Contribution Guide

Fork the repository
Create a feature branch (git checkout -b feature/amazing-feature)
Make your changes
Add tests for new functionality
Ensure all tests pass (npm test)
Commit your changes (git commit -m 'feat: add amazing feature')
Push to your branch (git push origin feature/amazing-feature)
Open a Pull Request

Development Guidelines

Follow the existing code style
Write comprehensive tests
Update documentation
Use conventional commits
Ensure TypeScript types are accurate

📞 Support

Documentation: docs/
Issues: GitHub Issues
Discussions: GitHub Discussions
Email: support@agentic-qe.com

🗺️ Roadmap

Current (v1.5)

✅ Learning System with Q-learning
✅ Pattern Bank with cross-project sharing
✅ ML Flaky Detection (100% accuracy)
✅ Continuous Improvement Loop
✅ 18 specialized agents + 8 TDD subagents
✅ Complete TDD workflow automation (RED/GREEN/REFACTOR/REVIEW)
✅ Multi-framework test execution
✅ Real-time coverage analysis
✅ MCP integration
✅ Multi-model router (70-81% cost savings)
✅ 34 QE skills library

Planned (v1.4)

🔄 Web dashboard for visualization
🔄 GraphQL API
🔄 CI/CD integrations (GitHub Actions, GitLab CI)
🔄 Enhanced pattern adaptation across frameworks
🔄 Real-time collaboration features

Future (v2.0)

📋 Natural language test generation
📋 Self-healing test suites
📋 Multi-language support (Python, Java, Go)
📋 Advanced analytics and insights
📋 Cloud deployment support

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

Built with TypeScript, Node.js, and better-sqlite3
Inspired by autonomous agent architectures and swarm intelligence
Integrates with Jest, Cypress, Playwright, k6, SonarQube, and more
Compatible with Claude Code via Model Context Protocol (MCP)

Made with ❤️ by the Agentic QE Team

⭐ Star us on GitHub • 🐦 Follow on Twitter

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