Agentic Quality Engineering Fleet

V3 (Main) | V2 Documentation | Release Notes | Changelog | Contributors | Issues | Discussions

V3 brings Domain-Driven Design architecture, 13 bounded contexts, 60 specialized QE agents, TinyDancer intelligent model routing, ReasoningBank learning with Dream cycles, HNSW vector search, mathematical Coherence verification, full MinCut/Consensus integration across all 13 domains, and deep integration with Claude Flow and Agentic Flow.

🏗️ DDD Architecture | 🧠 ReasoningBank + Dream Cycles | 🎯 TinyDancer Model Routing | 🔍 HNSW Vector Search | 👑 Queen Coordinator | 📊 O(log n) Coverage | 🔗 Claude Flow Integration | 🎯 13 Bounded Contexts | 📚 75 QE Skills | 🧬 Coherence Verification | ✅ Trust Tiers | 🛡️ Governance

⚡ Quick Start

Install & Initialize

# Install globally
npm install -g agentic-qe

# Initialize your project
cd your-project
aqe init --wizard

# Or with auto-configuration
aqe init --auto

# Add MCP server to Claude Code (pick one)
# Option 1: Global install (recommended after npm install -g)
claude mcp add aqe -- aqe-mcp

# Option 2: Via npx (no global install needed)
claude mcp add aqe -- npx agentic-qe mcp

# Verify connection
claude mcp list

Use from Claude Code CLI

Ask Claude to use QE agents directly from your terminal:

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

# Run full quality pipeline with Queen coordination
claude "Use qe-queen-coordinator to orchestrate: test generation, coverage analysis, security scan, and quality gate"

# Detect flaky tests with root cause analysis
claude "Use qe-flaky-hunter to analyze the last 100 test runs and stabilize flaky tests"

What V3 provides:

• ✅ 13 DDD Bounded Contexts: Organized by business domain (test-generation, coverage-analysis, security-compliance, enterprise-integration, etc.)
• ✅ 60 QE Agents: Including Queen Coordinator for hierarchical orchestration (53 main + 7 TDD subagents)
• ✅ TinyDancer Model Routing: 3-tier intelligent routing (Haiku/Sonnet/Opus) for cost optimization
• ✅ ReasoningBank Learning: HNSW-indexed pattern storage with experience replay
• ✅ O(log n) Coverage Analysis: Sublinear algorithms for efficient gap detection
• ✅ Claude Flow Integration: Deep integration with MCP tools and swarm orchestration
• ✅ Memory Coordination: Cross-agent communication via aqe/v3/* namespaces
• ✅ Coherence Verification (v3.3.0): Mathematical proof of belief consistency using WASM engines
• ✅ V2 Backward Compatibility: All V2 agents map to V3 equivalents
• ✅ 75 QE Skills: 46 Tier 3 verified + 29 additional QE skills (QCSD swarms, n8n testing, enterprise integration, qe-* domains)

🚀 Get Value in 60 Seconds

# 1. Install
npm install -g agentic-qe

# 2. Initialize (auto-detects your project, enables all 13 domains)
cd your-project && aqe init --auto

# 3. Generate tests immediately
claude "Generate comprehensive tests for src/services/"

# 4. Run quality assessment
claude "Assess code quality and provide deployment recommendation"

What happens:

1. Auto-configuration detects your tech stack (TypeScript/JS, testing framework, CI setup)
2. All 13 DDD domains enabled automatically - no "No factory registered" errors
3. Pattern learning kicks in - your project's test patterns are learned and reused
4. AI agents generate tests, analyze coverage, and provide actionable recommendations

🎯 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 with root cause analysis and fix recommendations | | AI testing tools are expensive | TinyDancer 3-tier model routing reduces costs by matching task complexity to appropriate model | | No memory between test runs—every analysis starts from scratch | ReasoningBank remembers patterns, strategies, and what works for your codebase | | Agents waste tokens reading irrelevant code | Code Intelligence provides token reduction with semantic search and knowledge graphs | | Quality engineering requires complex coordination | Queen Coordinator orchestrates 60 agents across 13 domains with consensus and MinCut topology | | Tools don't understand your testing frameworks | Works with Jest, Cypress, Playwright, Vitest, Mocha, Jasmine, AVA |

✨ V3 Features

🏗️ Domain-Driven Design Architecture

V3 is built on 13 DDD Bounded Contexts, each with dedicated agents and clear responsibilities:

| Domain | Purpose | Key Agents | |--------|---------|------------| | test-generation | AI-powered test creation | qe-test-architect, qe-tdd-specialist | | test-execution | Parallel execution & retry | qe-parallel-executor, qe-retry-handler | | coverage-analysis | O(log n) gap detection | qe-coverage-specialist, qe-gap-detector | | quality-assessment | Quality gates & decisions | qe-quality-gate, qe-risk-assessor | | defect-intelligence | Prediction & root cause | qe-defect-predictor, qe-root-cause-analyzer | | requirements-validation | BDD & testability | qe-requirements-validator, qe-bdd-generator | | code-intelligence | Knowledge graph & search | qe-code-intelligence, qe-kg-builder | | security-compliance | SAST/DAST & audit | qe-security-scanner, qe-security-auditor | | contract-testing | API contracts & GraphQL | qe-contract-validator, qe-graphql-tester | | visual-accessibility | Visual regression & a11y | qe-visual-tester, qe-accessibility-auditor | | chaos-resilience | Chaos engineering & load | qe-chaos-engineer, qe-load-tester | | learning-optimization | Cross-domain learning | qe-learning-coordinator, qe-pattern-learner | | enterprise-integration | SOAP, SAP, ESB, OData | qe-soap-tester, qe-sap-rfc-tester, qe-sod-analyzer |

✅ Skill Trust Tiers (v3.4.2)

AQE includes 75 QE skills (46 Tier 3 verified + 29 additional). Trust tiers apply to core QE skills:

| Tier | Badge | Count | Description | |------|-------|-------|-------------| | Tier 3 - Verified | | 46 | Full evaluation test suite | | Tier 2 - Validated | | 7 | Has executable validator | | Tier 1 - Structured | | 5 | Has JSON output schema | | Tier 0 - Advisory | | 5 | SKILL.md guidance only |

Tier 3 Skills are recommended for production use - they have:

• JSON Schema validation for output structure
• Executable validator scripts for correctness
• Evaluation test suites with multi-model testing

# Check skill trust tier
aqe eval status --skill security-testing

# Run skill evaluation
aqe eval run --skill security-testing --model claude-sonnet-4

# View all trust tiers
cat .claude/skills/TRUST-TIERS.md

[Full documentation: docs/guides/skill-validation.md]

🌐 Browser Automation Integration (v3.1.0)

V3.1.0 adds full browser automation support via @claude-flow/browser integration:

| Component | Description | |-----------|-------------| | BrowserSwarmCoordinator | Parallel multi-viewport testing (4x faster) | | BrowserSecurityScanner | URL validation, PII detection with auto-masking | | 9 Workflow Templates | YAML-based reusable browser workflows | | TrajectoryAdapter | SONA learning integration with HNSW indexing |

Available Workflow Templates:

• login-flow, oauth-flow - Authentication testing
• form-validation, navigation-flow - User journey testing
• visual-regression, accessibility-audit - Quality validation
• performance-audit, api-integration, scraping-workflow - Advanced workflows

# Use browser automation from Claude Code
claude "Use security-visual-testing skill to test https://example.com across mobile, tablet, desktop viewports"

# Load and execute a workflow template
aqe workflow load login-flow --vars '{"username": "test", "password": "secret"}'

👑 Queen Coordinator & Hierarchical Orchestration

The qe-queen-coordinator manages the entire fleet with intelligent task distribution:

                    qe-queen-coordinator
                           (Queen)
                             |
        +--------------------+--------------------+
        |                    |                    |
   TEST DOMAIN          QUALITY DOMAIN       LEARNING DOMAIN
   (test-generation)    (quality-assessment) (learning-optimization)
        |                    |                    |
   - test-architect     - quality-gate       - learning-coordinator
   - tdd-specialist     - risk-assessor      - pattern-learner
   - integration-tester - deployment-advisor - transfer-specialist

Capabilities:

• Orchestrate 60 QE agents concurrently across 13 domains
• TinyDancer 3-tier model routing (Haiku/Sonnet/Opus) with confidence-based decisions
• Byzantine fault-tolerant consensus for critical quality decisions
• MinCut graph-based topology optimization for self-healing coordination
• Memory-backed cross-agent communication with HNSW vector search
• Work stealing with adaptive load balancing (3-5x throughput improvement)

claude "Use qe-queen-coordinator to orchestrate release validation for v2.1.0 with 90% coverage target"

🤝 Agent Teams & Fleet Coordination

The Queen Coordinator is extended with Agent Teams (ADR-064) for hybrid fleet communication:

| Feature | Description | |---------|-------------| | Mailbox Messaging | Direct agent-to-agent and domain-scoped broadcast messaging | | Distributed Tracing | TraceContext propagation across messages for end-to-end task visibility | | Dynamic Scaling | Workload-based auto-scaling with configurable policies and cooldowns | | Competing Hypotheses | Multi-agent root cause investigation with evidence scoring, auto-triggered on critical failures | | Federation | Cross-service routing with health monitoring and service discovery | | Circuit Breakers | Per-domain fault isolation with automatic recovery | | Task DAG | Topological ordering with cycle detection for multi-step workflows |

Fleet Tiers — Activate the level of coordination your project needs:

| Tier | Agents | Best For | |------|--------|----------| | Lite | 1-4 | Small projects, focused tasks | | Standard | 5-10 | Team projects, multi-domain coordination | | Full | 11-15 | Enterprise, cross-fleet federation |

claude "Use qe-queen-coordinator with agent teams to investigate flaky test failures across test-execution and defect-intelligence domains"

🧠 ReasoningBank Learning System

V3 agents learn and improve through the ReasoningBank pattern storage:

| Component | Description | |-----------|-------------| | Experience Storage | Store successful patterns with confidence scores | | HNSW Indexing | Fast O(log n) similarity search for pattern matching | | Experience Replay | Learn from past successes and failures | | Cross-Project Transfer | Share patterns between projects |

# Check what agents have learned
aqe memory search --query "test patterns" --namespace learning

# View learning metrics
aqe hooks metrics --v3-dashboard

🌙 Dream Cycles & Neural Learning

V3 introduces Dream cycles for neural consolidation and continuous improvement:

| Feature | Description | |---------|-------------| | Dream Cycles | Background neural consolidation (30s max) with spreading activation | | 9 RL Algorithms | Q-Learning, SARSA, DQN, PPO, A2C, DDPG, Actor-Critic, Policy Gradient, Decision Transformer | | SONA Integration | Self-Optimizing Neural Architecture with <0.05ms adaptation | | Novelty Scoring | Prioritize learning from novel patterns | | Concept Graphs | Build semantic connections between quality patterns |

# Trigger dream cycle for pattern consolidation
aqe hooks intelligence --mode dream --consolidate

# View learning trajectory
aqe hooks intelligence trajectory-start --task "optimize coverage"

🎯 TinyDancer Intelligent Model Routing

TinyDancer (ADR-026) provides 3-tier intelligent model routing for cost optimization:

| Complexity Score | Model | Use Cases | |-----------------|-------|-----------| | 0-20 (Simple) | Haiku | Syntax fixes, type additions, simple refactors | | 20-70 (Moderate) | Sonnet | Bug fixes, test generation, code review | | 70+ (Critical) | Opus | Architecture, security, complex reasoning |

Routing Features:

• Confidence-based decisions: Routes based on task complexity analysis
• Automatic escalation: Escalates to higher-tier model if confidence is low
• Learning from outcomes: Improves routing based on success/failure patterns
• Token budget optimization: Minimizes cost while maintaining quality

# Check model routing for a task
aqe hooks model-route --task "fix type errors in user-service.ts"

# View routing statistics
aqe hooks model-stats

🔄 Cross-Phase Memory Unification (v3.3.5)

V3.3.5 unifies cross-phase feedback loops with UnifiedMemoryManager:

• Single SQLite Backend: All QCSD signals stored in .agentic-qe/memory.db
• Namespace-Based Storage: qcsd/strategic, qcsd/tactical, qcsd/operational, qcsd/quality-criteria
• Automatic TTL: 30-90 day expiration per signal type
• No File-Based Storage: Eliminated JSON file storage for cross-phase memory
• Full Hook Integration: Pre/post hooks for cross-phase signal injection

🌐 AG-UI, A2A & A2UI Protocols (v3.4.0)

V3.4.0 adds support for industry-standard agent communication protocols:

| Protocol | Standard | Purpose | |----------|----------|---------| | AG-UI | Anthropic | Agent-to-User streaming interface with lifecycle events | | A2A | Google | Agent-to-Agent interoperability with task/artifact exchange | | A2UI | Hybrid | Unified UI components combining streaming + events |

Programmatic usage:

import { AGUIAdapter, A2AAdapter } from 'agentic-qe';

// AG-UI: Stream test generation progress to UI
const agui = new AGUIAdapter();
await agui.streamTask({
  type: 'test-generation',
  onProgress: (event) => updateProgressBar(event.progress),
  onArtifact: (test) => displayGeneratedTest(test),
});

// A2A: Inter-agent task delegation
const a2a = new A2AAdapter();
await a2a.sendTask({
  from: 'qe-test-architect',
  to: 'qe-security-scanner',
  task: { type: 'review-tests', files: generatedTests },
});

Benefits:

• Streaming feedback - Real-time progress instead of waiting for completion
• Agent interoperability - Standard protocols for multi-agent coordination
• Framework integration - Works with React, Vue, or any UI framework

🔐 Consensus & MinCut Coordination (v3.3.3)

V3.3.3 achieves full MinCut/Consensus integration across all 13 domains:

| Feature | Description | |---------|-------------| | Byzantine Consensus | Fault-tolerant voting for critical quality decisions | | MinCut Topology | Graph-based self-healing agent coordination | | Multi-Model Voting | Aggregate decisions from multiple model tiers | | Claim Verification | Cryptographic verification of agent work claims | | 13/13 Domain Integration | All domains use verifyFinding() for consensus | | Topology-Aware Routing | Routes tasks avoiding weak network vertices | | Self-Healing Triggers | shouldPauseOperations() for automatic recovery |

# View consensus status
aqe coordination consensus --status

# Check topology health
aqe coordination topology --optimize

🧬 Coherence-Gated Quality Engineering (v3.3.0)

V3.3.0 introduces mathematical coherence verification using Prime Radiant WASM engines:

| Feature | Description | |---------|-------------| | Contradiction Detection | Sheaf cohomology identifies conflicting requirements before test generation | | Collapse Prediction | Spectral analysis predicts swarm failures before they happen | | Causal Verification | Distinguishes true causation from spurious correlations | | Auto-Tuning Thresholds | EMA-based calibration adapts to your codebase |

Compute Lanes - Automatic routing based on coherence energy:

| Coherence Energy | Action | Latency | |------------------|--------|---------| | < 0.1 (Reflex) | Execute immediately | <1ms | | 0.1-0.4 (Retrieval) | Fetch more context | ~10ms | | 0.4-0.7 (Heavy) | Deep analysis | ~100ms | | > 0.7 (Human) | Escalate to Queen | Async |

Benefits:

• Prevents contradictory test generation
• Detects swarm drift 10x faster
• Mathematical proof instead of statistical confidence
• "Coherence Verified" CI/CD badges

# Check coherence of beliefs
aqe coherence check --beliefs "requirement1,requirement2"

# Audit memory for contradictions
aqe coherence audit --namespace learning

🔍 O(log n) Coverage Analysis

Efficient coverage gap detection using Johnson-Lindenstrauss algorithms:

• Sublinear complexity: Analyze large codebases in logarithmic time
• Risk-weighted gaps: Prioritize coverage by business impact
• Intelligent test selection: Minimal tests for maximum coverage
• Trend tracking: Monitor coverage changes over time

claude "Use qe-coverage-specialist to analyze gaps in src/ with risk scoring"

🔗 Claude Flow Integration

V3 deeply integrates with Claude Flow for:

• MCP Server: All V3 tools available via Model Context Protocol
• Swarm Orchestration: Multi-agent coordination with hierarchical topology
• Memory Sharing: Cross-agent state via aqe/v3/* namespaces
• Hooks System: Pre/post task learning and optimization
• Session Management: Persistent state across conversations

# Initialize swarm with Claude Flow
npx @claude-flow/cli@latest swarm init --topology hierarchical-mesh

# Spawn V3 agents
npx @claude-flow/cli@latest agent spawn -t qe-test-architect --name test-gen

📊 60 Specialized QE Agents

| Category | Count | Highlights | |----------|-------|------------| | Main QE Agents | 53 | Test generation, coverage, security, performance, accessibility, enterprise integration, pentest validation | | TDD Subagents | 7 | RED/GREEN/REFACTOR with code review |

V2 Backward Compatibility: All V2 agents map to V3 equivalents automatically.

| Agent | Domain | Purpose | |-------|--------|---------| | qe-queen-coordinator | coordination | Hierarchical fleet orchestration | | qe-test-architect | test-generation | AI-powered test creation | | qe-tdd-specialist | test-generation | TDD workflow coordination | | qe-parallel-executor | test-execution | Multi-worker test execution | | qe-retry-handler | test-execution | Intelligent retry with backoff | | qe-coverage-specialist | coverage-analysis | O(log n) coverage analysis | | qe-gap-detector | coverage-analysis | Risk-weighted gap detection | | qe-quality-gate | quality-assessment | Quality threshold validation | | qe-risk-assessor | quality-assessment | Multi-factor risk scoring | | qe-deployment-advisor | quality-assessment | Go/no-go deployment decisions | | qe-defect-predictor | defect-intelligence | ML-powered defect prediction | | qe-root-cause-analyzer | defect-intelligence | Systematic root cause analysis | | qe-flaky-hunter | defect-intelligence | Flaky test detection & fix | | qe-requirements-validator | requirements-validation | Testability analysis | | qe-bdd-generator | requirements-validation | Gherkin scenario generation | | qe-code-intelligence | code-intelligence | Semantic code search | | qe-kg-builder | code-intelligence | Knowledge graph construction | | qe-dependency-mapper | code-intelligence | Dependency analysis | | qe-security-scanner | security-compliance | SAST/DAST scanning | | qe-security-auditor | security-compliance | Security audit & compliance | | qe-contract-validator | contract-testing | API contract validation | | qe-graphql-tester | contract-testing | GraphQL testing | | qe-visual-tester | visual-accessibility | Visual regression testing | | qe-accessibility-auditor | visual-accessibility | WCAG compliance testing | | qe-responsive-tester | visual-accessibility | Cross-viewport testing | | qe-chaos-engineer | chaos-resilience | Controlled fault injection | | qe-load-tester | chaos-resilience | Load & performance testing | | qe-performance-tester | chaos-resilience | Performance validation | | qe-learning-coordinator | learning-optimization | Fleet-wide learning | | qe-pattern-learner | learning-optimization | Pattern discovery | | qe-transfer-specialist | learning-optimization | Cross-project transfer | | qe-metrics-optimizer | learning-optimization | Hyperparameter tuning | | qe-integration-tester | test-execution | Component integration | | qe-mutation-tester | test-generation | Test effectiveness validation | | qe-property-tester | test-generation | Property-based testing | | qe-regression-analyzer | defect-intelligence | Regression risk analysis | | qe-impact-analyzer | code-intelligence | Change impact assessment | | qe-code-complexity | code-intelligence | Complexity metrics | | qe-qx-partner | quality-assessment | QA + UX collaboration | | qe-fleet-commander | coordination | Large-scale orchestration | | qe-integration-architect | code-intelligence | V3 integration design | | qe-product-factors-assessor | quality-assessment | SFDIPOT product factors analysis | | qe-test-idea-rewriter | test-generation | Transform passive tests to active actions | | qe-quality-criteria-recommender | quality-assessment | HTSM v6.3 Quality Criteria analysis | | qe-devils-advocate | quality-assessment | Adversarial review of agent outputs |

| Subagent | Phase | Purpose | |----------|-------|---------| | qe-tdd-red | RED | Write failing tests | | qe-tdd-green | GREEN | Implement minimal code | | qe-tdd-refactor | REFACTOR | Improve code quality | | qe-code-reviewer | REVIEW | Code quality validation | | qe-integration-reviewer | REVIEW | Integration review | | qe-performance-reviewer | REVIEW | Performance review | | qe-security-reviewer | REVIEW | Security review |

💻 V3 Usage Examples

Example 1: Queen-Coordinated Quality Pipeline

claude "Use qe-queen-coordinator to run full quality assessment:
1. Generate tests for src/services/*.ts
2. Execute tests with parallel workers
3. Analyze coverage gaps with risk scoring
4. Run security scan
5. Validate quality gate at 90% threshold
6. Provide deployment recommendation"

What happens:

1. Queen spawns domain coordinators for each task
2. Agents execute in parallel across 5 domains
3. Results aggregate through memory coordination
4. Queen synthesizes final recommendation

Example 2: Learning-Enhanced Test Generation

claude "Use qe-test-architect to create tests for PaymentService with:
- Property-based testing for validation
- 95% coverage target
- Apply learned patterns from similar services"

Output includes:

Generated 48 tests across 4 files
- unit/PaymentService.test.ts (32 unit tests)
- property/PaymentValidation.property.test.ts (8 property tests)
- integration/PaymentFlow.integration.test.ts (8 integration tests)
Coverage: 96.2%
Pattern reuse: 78% from learned patterns
Learning stored: "payment-validation-patterns" (confidence: 0.94)

Example 3: TDD Workflow with Subagents

claude "Use qe-tdd-specialist to implement UserAuthentication with full RED-GREEN-REFACTOR cycle"

Workflow:

1. qe-tdd-red: Writes failing tests defining behavior
2. qe-tdd-green: Implements minimal code to pass
3. qe-tdd-refactor: Improves code quality
4. qe-code-reviewer: Validates standards
5. qe-security-reviewer: Checks security concerns

Example 4: Cross-Domain Coordination

claude "Coordinate security audit across the monorepo:
- qe-security-scanner for SAST/DAST
- qe-dependency-mapper for vulnerability scanning
- qe-contract-validator for API security
- qe-chaos-engineer for resilience testing"

🎓 75 QE Skills

V3 agents automatically apply relevant skills from the comprehensive QE skill library.

Core Testing & Methodologies (12)

• agentic-quality-engineering - Core PACT principles for AI-powered QE
• holistic-testing-pact - Evolved testing model with PACT integration
• context-driven-testing - Practices chosen based on project context
• tdd-london-chicago - Test-driven development with both school approaches
• xp-practices - Extreme programming practices for quality
• risk-based-testing - Focus testing effort on highest-risk areas
• test-automation-strategy - Strategic approach to automation
• refactoring-patterns - Safe code improvement patterns
• shift-left-testing - Early testing in development lifecycle
• shift-right-testing - Production testing and observability
• regression-testing - Strategic regression management
• verification-quality - Quality verification practices

Specialized Testing (13)

• accessibility-testing - WCAG 2.2 compliance and inclusive design
• mobile-testing - iOS and Android platform testing
• database-testing - Schema validation and data integrity
• contract-testing - Consumer-driven contract testing
• chaos-engineering-resilience - Fault injection and resilience testing
• visual-testing-advanced - Visual regression and UI testing
• security-visual-testing - Security-first visual testing with PII detection
• compliance-testing - Regulatory compliance (GDPR, HIPAA, SOC2)
• compatibility-testing - Cross-browser and platform testing
• localization-testing - i18n and l10n testing
• mutation-testing - Test suite effectiveness evaluation
• performance-testing - Load, stress, and scalability testing
• security-testing - OWASP and security vulnerability testing

V3 Domain Skills (14)

• qe-test-generation - AI-powered test synthesis
• qe-test-execution - Parallel execution and retry logic
• qe-coverage-analysis - O(log n) sublinear coverage
• qe-quality-assessment - Quality gates and deployment readiness
• qe-defect-intelligence - ML defect prediction and root cause
• qe-requirements-validation - BDD scenarios and acceptance criteria
• qe-code-intelligence - Knowledge graphs and token reduction
• qe-security-compliance - OWASP and CVE detection
• qe-contract-testing - Pact and schema validation
• qe-visual-accessibility - Visual regression and WCAG
• qe-chaos-resilience - Fault injection and resilience
• qe-learning-optimization - Transfer learning and self-improvement
• qe-iterative-loop - QE iteration patterns
• aqe-v2-v3-migration - Migration guide from v2 to v3

Strategic & Communication (8)

• six-thinking-hats - Edward de Bono's methodology for QE
• brutal-honesty-review - Unvarnished technical criticism
• sherlock-review - Evidence-based investigative code review
• cicd-pipeline-qe-orchestrator - CI/CD quality orchestration
• bug-reporting-excellence - High-quality bug reports
• consultancy-practices - QE consultancy workflows
• quality-metrics - Effective quality measurement
• pair-programming - AI-assisted pair programming

Testing Techniques & Management (9)

• exploratory-testing-advanced - SBTM and RST heuristics
• test-design-techniques - Test design methodologies
• test-data-management - Test data strategies
• test-environment-management - Environment configuration
• test-reporting-analytics - Quality dashboards and KPIs
• testability-scoring - Score code testability
• technical-writing - Documentation practices
• code-review-quality - Context-driven code reviews
• api-testing-patterns - REST and GraphQL testing

n8n Workflow Testing (5) (contributed by @fndlalit)

• n8n-workflow-testing-fundamentals - Execution lifecycle and data flow
• n8n-expression-testing - Expression validation and testing
• n8n-security-testing - Workflow security scanning
• n8n-trigger-testing-strategies - Webhook and event testing
• n8n-integration-testing-patterns - API contract testing for n8n

QCSD Swarms (4) - Quality Conscious Software Delivery lifecycle

• qcsd-ideation-swarm - Phase 1: HTSM v6.3, Risk Storming, Testability analysis
• qcsd-refinement-swarm - Phase 2: SFDIPOT analysis, BDD scenario generation
• qcsd-development-swarm - Phase 3: TDD, coverage, code quality gates (SHIP/CONDITIONAL/HOLD)
• qcsd-cicd-swarm - Phase 4: Pipeline quality gates (RELEASE/REMEDIATE/BLOCK)

Accessibility (2)

• a11y-ally - Comprehensive WCAG auditing with video captions and EU compliance
• accessibility-testing - WCAG 2.2 compliance and screen reader validation

🔄 V2 to V3 Migration

V3 provides automatic backward compatibility with V2:

# Check migration status
aqe migrate status

# Run migration with backup
aqe migrate run --backup

# Validate migration
aqe migrate validate

What gets migrated:

• ✅ Memory data (SQLite → AgentDB with HNSW indexing)
• ✅ Configuration files
• ✅ Learned patterns (→ ReasoningBank)
• ✅ Agent mappings (V2 names → V3 equivalents)

| V2 Agent | V3 Agent | |----------|----------| | qe-test-generator | qe-test-architect | | qe-coverage-analyzer | qe-coverage-specialist | | qe-quality-gate | qe-quality-gate | | qe-security-scanner | qe-security-scanner | | qe-coordinator | qe-queen-coordinator |

🤖 LLM Provider Configuration

AQE V3 supports multiple LLM providers for maximum flexibility:

| Provider | Type | Cost | Best For | |----------|------|------|----------| | Ollama | Local | FREE | Privacy, offline | | OpenRouter | Cloud | Varies | 300+ models | | Groq | Cloud | FREE | High-speed | | Claude API | Cloud | Paid | Highest quality | | Google AI | Cloud | FREE | Gemini models |

# Configure provider
export GROQ_API_KEY="gsk_..."
aqe init --auto

📖 Documentation

V3 Guides

• V3 Migration Guide - Complete migration instructions
• V3 CLI Reference - All V3 commands
• DDD Architecture - Domain-driven design overview

V2 Documentation (Legacy)

• V2 README - Complete V2 documentation
• Quick Start Guide - V2 quick start
• User Guide - V2 workflows and examples

Feature Guides

• Learning System Guide - ReasoningBank learning
• Pattern Management Guide - Cross-project patterns
• MCP Integration - Claude Code integration

Testing Guides

• Test Generation - AI-powered test creation
• Coverage Analysis - O(log n) gap detection
• Quality Gates - Intelligent validation

📊 Project Architecture

agentic-qe/
├── v3/                      # V3 DDD Implementation (Main Version)
│   ├── src/
│   │   ├── kernel/          # Shared kernel
│   │   ├── domains/         # 13 bounded contexts
│   │   │   ├── test-generation/
│   │   │   ├── coverage-analysis/
│   │   │   ├── quality-assessment/
│   │   │   └── ...
│   │   ├── routing/         # Agent routing & registry
│   │   ├── mcp/             # MCP server
│   │   └── cli/             # V3 CLI
│   ├── tests/               # 5,600+ tests
│   └── assets/agents/       # 60 QE agent definitions (53 main + 7 subagents)
├── v2/                      # V2 Implementation (Legacy)
│   ├── src/                 # V2 source code
│   ├── tests/               # V2 tests
│   └── docs/                # V2 documentation
├── .claude/
│   ├── agents/v3/           # V3 agent definitions (source)
│   └── skills/              # 15 QE-specific skills
├── docs/                    # Shared documentation
│   ├── plans/               # Migration plans
│   ├── policies/            # Project policies
│   └── v3/                  # V3 specific docs
├── package.json             # Points to v3 (main version)
└── README.md                # This file

🚀 Development

Setup

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

# Install V3 dependencies
cd v3
npm install

# Build
npm run build

# Run tests
npm test -- --run

V3 Scripts

| Script | Description | |--------|-------------| | npm run build | Compile TypeScript | | npm test -- --run | Run all tests | | npm run cli | Run CLI in dev mode | | npm run mcp | Start MCP server |

🤝 Contributing

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

📞 Support

• Documentation: docs/
• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Email: [email protected]

📝 License

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

👥 Contributors

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

| @proffesor-for-testingProject Lead | @fndlalitQX Partner, Testability | @shaalCore Development | @mondweepArchitecture | |:---:|:---:|:---:|:---:|

View all contributors | Become a contributor

💖 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

🙏 Acknowledgments

V3 is built on the shoulders of giants:

• Claude Flow by @ruvnet - Multi-agent orchestration, MCP integration, swarm coordination
• Agentic Flow by @ruvnet - Agent patterns, learning systems, neural coordination
• Built with TypeScript, Node.js, and better-sqlite3
• HNSW indexing via hnswlib-node
• Inspired by Domain-Driven Design 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 | 💖 Sponsor | 👥 Contributors