Agentic Quality Engineering Fleet

---

⚡ Quick Start

$3

``bash


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
`

$3

Ask Claude to use QE agents directly from your terminal:

`bash


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.)
- ✅ 59 QE Agents: Including Queen Coordinator for hierarchical orchestration (52 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

`bash


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 59 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

$3

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 |

---

$3

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 | !Tier 3 | 46 | Full evaluation test suite |
| Tier 2 - Validated | !Tier 2 | 7 | Has executable validator |
| Tier 1 - Structured | !Tier 1 | 5 | Has JSON output schema |
| Tier 0 - Advisory | !Tier 0 | 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

`bash


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]

---

$3

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

`bash


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"}'
`

---

$3

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 59 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)

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

---

$3

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 |

`bash


Check what agents have learned

aqe memory search --query "test patterns" --namespace learning

View learning metrics

aqe hooks metrics --v3-dashboard
`

---

$3

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 |

`bash


Trigger dream cycle for pattern consolidation

aqe hooks intelligence --mode dream --consolidate

View learning trajectory

aqe hooks intelligence trajectory-start --task "optimize coverage"
`

---

$3

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

`bash


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
`

---

$3

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

$3

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:

`typescript
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

---

$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 |

`bash


View consensus status

aqe coordination consensus --status

Check topology health

aqe coordination topology --optimize
`

---

$3

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

`bash


Check coherence of beliefs

aqe coherence check --beliefs "requirement1,requirement2"

Audit memory for contradictions

aqe coherence audit --namespace learning
`

---

$3

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

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

---

$3

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

`bash


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
`

---

$3

| Category | Count | Highlights |
|----------|-------|------------|
| Main QE Agents | 52 | 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.

📋 View All Main QE Agents (52)

| 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 |

🔧 TDD Subagents (7)

| 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

$3

`bash
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

$3

`bash
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)
`

$3

`bash
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

$3

`bash
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.

View All 75 QE Skills

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:

`bash


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 |

`bash


Configure provider


export GROQ_API_KEY="gsk_..."
aqe init --auto
`

---

📖 Documentation

$3

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

$3

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

$3

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

$3

- 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/ # 59 QE agent definitions (52 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
`

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🚀 Development

$3

`bash


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
`

$3

| 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 |

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🤝 Contributing

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

---

📞 Support

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

---

📝 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-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

---

🙏 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)

---