Agentic Quality Engineering Fleet

Version 2.5.4 | Changelog | Contributors | Issues | Discussions

AI-powered test automation that learns from every task, switches between 300+ AI models on-the-fly, scores code testability, visualizes agent activity in real-time, and improves autonomously overnight — with built-in safety guardrails and full observability.

🎨 Real-Time Visualization | 📊 Testability Scoring | 🧠 QE Agent Learning | 🚀 QUIC Transport | 📋 Constitution System | 📚 41 QE Skills | 🎯 Flaky Detection | 💰 Multi-Model Router

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⚡ Quick Start

Install & Initialize

# 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:

• ✅ Real-time dashboards, interactive graphs (v1.9.0)
• ✅ OpenTelemetry, Event Store, Constitution System
• ✅ Multi-Model Router (70-81% cost savings - opt-in)
• ✅ Learning System (20% improvement target)
• ✅ Pattern Bank (cross-project reuse)
• ✅ ML Flaky Detection (90%+ accuracy with root cause analysis)
• ✅ 19 Specialized agent definitions (including qe-code-complexity)
• ✅ 11 TDD subagent definitions (RED/GREEN/REFACTOR phases + specialized)
• ✅ 41 World-class QE skills library (accessibility, shift-left/right, verification, visual testing, XP practices, testability-scoring by @fndlalit)
• ✅ 8 AQE slash commands
• ✅ Modular init system with comprehensive project setup

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🎯 Why AQE?

Problem	AQE Solution
Writing comprehensive tests is tedious and time-consuming	AI agents generate tests automatically with pattern reuse across projects
Test suites become slow and expensive at scale	Sublinear O(log n) algorithms for coverage analysis and intelligent test selection
Flaky tests waste developer time debugging false failures	ML-powered detection (90%+ accuracy) with root cause analysis and fix recommendations
AI testing tools are expensive	Multi-model routing cuts costs by up to 70-81% by matching task complexity to model
No memory between test runs—every analysis starts from scratch	Self-learning system remembers patterns, strategies, and what works for your codebase
Tools don't understand your testing frameworks	Works with Jest, Cypress, Playwright, Vitest, Mocha, Jasmine, AVA

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✨ Features

Real-Time Visualization (since v1.9.0)

Observability Dashboards

📊 Interactive React Frontend

• MindMap Component: Cytoscape.js-powered graph visualization with 6 layout algorithms

  • Support for 1000+ nodes with <500ms render time
  • Expand/collapse, zoom/pan, search/filter
  • Real-time WebSocket updates
  • Export to PNG/JSON

• QualityMetrics Panel: Recharts-based quality visualization

  • 7-dimension radar chart (coverage, security, performance, etc.)
  • Trend analysis with historical data
  • Token usage and cost tracking
  • Auto-refresh every 30 seconds

• Timeline View: Virtual scrolling for event history

  • Handle 1000+ events with react-window
  • Color-coded event types
  • Advanced filtering (agent, type, session, time range)
  • Drill-down detail panels

🔌 Backend API Services

• REST API: 6 endpoints for historical data queries

  • Event history with pagination
  • Aggregated metrics
  • Graph visualization data
  • Agent activity history

• WebSocket Server: Real-time streaming with backpressure

  • Event streaming with client subscriptions
  • Heartbeat mechanism
  • Connection management
  • <50ms latency

📈 Grafana Dashboards

• Executive Dashboard: Quality trends and cost analysis
• Developer Dashboard: Trace explorer and debugging tools
• QA Dashboard: Test metrics and coverage visualization

Performance:

• ✅ 185 events/sec write throughput (186% of target)
• ✅ <1ms query latency (99% better than target)
• ✅ <100ms render time for 100 nodes
• ✅ <500ms render time for 1000 nodes

Quick Start:

# Start visualization services
node scripts/start-visualization-services.ts

# Start frontend dev server
cd frontend && npm run dev

# Open in browser
open http://localhost:3000

Documentation: See docs/PHASE3-COMPLETE.md for full details

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🤖 Autonomous Agent Fleet

• 20 Specialized Agents: Expert agents for every QE domain (test generation, coverage analysis, security scanning, performance testing, code complexity analysis, QX analysis, accessibility)
• 11 TDD Subagents: Specialized subagents for Test-Driven Development workflow (RED/GREEN/REFACTOR phases + quality validation + analysis)
• 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

• QE Agent Learning System: Q-Learning integrated with AgentDB's 9 RL algorithms, 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: 90%+ 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

🧠 Self-Learning System (from v2.2.0)

AQE agents learn from every interaction and improve over time. Unlike traditional tools that start from scratch each run, the Self-Learning System builds institutional knowledge for your codebase.

How It Works:

┌─────────────────────────────────────────────────────────────────┐
│                    Self-Learning Pipeline                        │
├─────────────────────────────────────────────────────────────────┤
│                                                                  │
│   ┌──────────┐    ┌──────────┐    ┌──────────┐    ┌──────────┐  │
│   │  Agent   │───▶│ Execute  │───▶│ Evaluate │───▶│  Learn   │  │
│   │  Action  │    │   Task   │    │  Result  │    │ & Store  │  │
│   └──────────┘    └──────────┘    └──────────┘    └──────────┘  │
│        │                                               │         │
│        │         ┌─────────────────────────────┐       │         │
│        └────────▶│     Pattern Bank (AgentDB)  │◀──────┘         │
│                  │  • Successful strategies    │                 │
│                  │  • Framework patterns       │                 │
│                  │  • Q-values per action      │                 │
│                  └─────────────────────────────┘                 │
│                                                                  │
└─────────────────────────────────────────────────────────────────┘

Reinforcement Learning Algorithms:

• Q-Learning: Default algorithm - learns optimal action-value functions
• SARSA: On-policy learning for safer exploration
• Actor-Critic (A2C): Combines value and policy learning
• PPO: Advanced policy optimization for complex scenarios

What Gets Learned:

• Which test patterns work best for your framework (Jest, Cypress, etc.)
• Optimal strategies for your codebase structure
• Common failure patterns and how to prevent them
• Cost-effective model routing decisions

CLI Commands:

# Check learning status for an agent
aqe learn status --agent qe-test-generator

# List learned patterns for a framework
aqe patterns list --framework jest

# View algorithm performance
aqe learn metrics --algorithm q-learning

# Switch learning algorithm
aqe learn set-algorithm --agent qe-test-generator --algorithm sarsa

Persistence: All learning is stored in AgentDB (SQLite-based) and persists across sessions. Your agents get smarter with every run.

💰 Cost Optimization

• 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

🎓 41 QE Skills Library

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 (7): regression-testing, shift-left-testing, shift-right-testing, test-design-techniques, mutation-testing, test-data-management, verification-quality
• 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

Total: 41 QE Skills - Includes accessibility testing, shift-left/right testing, verification & quality assurance, visual testing advanced, XP practices, and technical writing

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💻 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:

1. Claude Code spawns qe-test-generator via Task tool
2. Agent analyzes the source file
3. Generates tests with pattern matching (Phase 2 feature)
4. 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:

1. Claude spawns 4 agents concurrently in a single message
2. Agents coordinate through aqe/* memory namespace
3. Pipeline: test generator → executor → analyzer → gate
4. 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 40):

• TDD, API testing, performance, security
• Accessibility, mobile, chaos engineering, visual testing
• Regression, shift-left/right, compliance, verification
• Six thinking hats, brutal honesty reviews, CI/CD orchestration
• XP practices, technical writing, refactoring patterns

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 ML-powered analysis
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
})

All 85 MCP Tools Available:

• Fleet Management (9 tools): init, spawn, status, coordinate, orchestrate
• Test Generation (2 tools): generate enhanced, execute
• Test Execution (3 tools): execute, parallel, stream
• Coverage Analysis (4 tools): analyze with risk scoring, detect gaps ML, trends, recommendations
• Quality Gates (3 tools): execute, validate metrics, generate report
• Flaky Test Detection (2 tools): detect statistical, analyze patterns
• Performance Testing (3 tools): benchmark, analyze bottlenecks, generate a report
• Security Scanning (2 tools): comprehensive scan, detect vulnerabilities
• Visual Testing (3 tools): compare screenshots, accessibility validation, regression detection
• API Contract Testing (3 tools): validate, breaking changes, versioning
• Test Data Management (3 tools): generate, mask, analyze schema
• Code Quality (2 tools): complexity analysis, metrics
• Memory & Collaboration (5 tools): store, retrieve, query, share, backup
• Blackboard System (2 tools): post, read
• Consensus Mechanisms (2 tools): propose, vote
• Workflow Management (4 tools): create, execute, checkpoint, resume
• Event System (2 tools): emit, subscribe
• Regression Analysis (2 tools): analyze risk, select tests
• Production Monitoring (2 tools): incident replay, RUM analysis
• Mutation Testing (1 tool): execute
• Deployment Readiness (1 tool): check
• Artifact Management (1 tool): manifest
• Task Management (1 tool): status
• Learning System (4 tools): store experience, store Q-value, store pattern, query

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

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

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🤖 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	✅ 90%+ accuracy ML detection

Specialized (4 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
a11y-ally	Accessibility testing	WCAG 2.2, AI video analysis, EU compliance

General Purpose (1 agent)

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

Total: 31 Agents (20 main agents + 11 TDD subagents)

TDD Subagents (11 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
qe-flaky-investigator	ANALYSIS	Flaky test detection	Pattern detection, timing analysis, stabilization fixes
qe-coverage-gap-analyzer	ANALYSIS	Coverage gaps	Risk scoring, gap detection, test recommendations
qe-test-data-architect-sub	GENERATION	High-volume data	Schema-aware generation, relationship preservation

Coordination Protocol: All subagents use a unified coordination protocol with cycle-based memory namespaces (aqe/tdd/cycle-{id}/*) ensuring tests written in RED are the same tests validated in GREEN and refactored in REFACTOR. See Coordination Guide.

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.

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📖 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

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📊 Performance

For detailed performance benchmarks and metrics, see docs/PERFORMANCE.md.

Core Performance

• Test Generation: Pattern-based with cross-project reuse
• Parallel Execution: Multi-framework concurrent tests
• Coverage Analysis: O(log n) sublinear algorithms
• Data Generation: 10,000+ records/second with GDPR compliance
• Agent Spawning: <100ms per agent
• Flaky Detection: 90%+ accuracy with ML-powered root cause analysis

---

🚀 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/          # 19 main agent definitions
│   │   └── subagents/   # 11 TDD subagent definitions
│   ├── skills/          # 40 QE skill definitions
│   └── commands/        # 8 AQE slash commands
└── config/              # Configuration files

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🤝 Contributing

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

Quick Contribution Guide

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

Development Guidelines

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

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📞 Support

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

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📝 License

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

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👥 Contributors

Thanks to all the amazing people who have contributed to Agentic QE Fleet!

@proffesor-for-testing Project Lead	@fndlalit QX Partner, Testability	@shaal Core Development	@mondweep Architecture

View all contributors | Become a contributor

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💖 Support the Project

If you find Agentic QE Fleet valuable, consider supporting its development:

	Monthly	Annual (Save $10)
Price	$5/month	$50/year
Benefits	Sponsor recognition, Priority support	All monthly + Featured in README, Roadmap input
Subscribe	Monthly	Annual

View sponsorship details

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🙏 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)

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Made with ❤️ by the Agentic QE Team

⭐ Star us on GitHub | 💖 Sponsor | 👥 Contributors