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https://github.com/proffesor-for-testing/agentic-qe

Agentic QE Fleet is an open-source AI-powered QA/QE platform designed for use with Coding Agents (works best with Claude Code) featuring specialized agents and skills to support testing activities for a product at any stage of the SDLC. Free to use, fork, build, and contribute. Based on the Agentic QE Framework created by Dragan Spiridonov.
https://github.com/proffesor-for-testing/agentic-qe

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Agentic QE Fleet is an open-source AI-powered QA/QE platform designed for use with Coding Agents (works best with Claude Code) featuring specialized agents and skills to support testing activities for a product at any stage of the SDLC. Free to use, fork, build, and contribute. Based on the Agentic QE Framework created by Dragan Spiridonov.

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README 

          
# Agentic Quality Engineering Fleet






[![npm version](https://img.shields.io/npm/v/agentic-qe.svg)](https://www.npmjs.com/package/agentic-qe)

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

[![TypeScript](https://img.shields.io/badge/TypeScript-5.0+-blue.svg)](https://www.typescriptlang.org/)

[![Monthly Downloads](https://img.shields.io/npm/dm/agentic-qe)](https://www.npmjs.com/package/agentic-qe)

[![Total Downloads](https://img.shields.io/npm/dt/agentic-qe?label=total%20downloads)](https://www.npmjs.com/package/agentic-qe)



[Release Notes](docs/releases/README.md) | [Changelog](CHANGELOG.md) | [Issues](https://github.com/proffesor-for-testing/agentic-qe/issues) | [Discussions](https://github.com/proffesor-for-testing/agentic-qe/discussions)



**AI-powered quality engineering agents that generate tests, find coverage gaps, detect flaky tests, and learn your codebase patterns — across 11 coding agent platforms.**






---



## What AQE Does For You



- **Generates comprehensive tests automatically** — unit, integration, property-based, and BDD scenarios for your codebase with framework-specific output (Jest, Vitest, Playwright, Cypress, pytest, JUnit, Go, Rust, Swift, Flutter, and more)

- **Finds coverage gaps and prioritizes what to test** — risk-weighted analysis identifies the most impactful untested code paths

- **Detects and fixes flaky tests** — ML-powered detection with root cause analysis and stabilization recommendations

- **Learns your codebase patterns over time** — remembered patterns are reused across sessions and projects, improving with every interaction

- **Coordinates 60 specialized QE agents** — from test generation to security scanning to chaos engineering, orchestrated by a central coordinator

- **Reduces AI costs with intelligent routing** — automatically routes tasks to the right model tier (fast/cheap for simple tasks, powerful for complex ones)

- **Works with your existing tools** — integrates with 11 coding agent platforms and your existing CI/CD pipeline



---



## Quick Start



```bash

# Install

npm install -g agentic-qe



# Initialize your project (auto-detects tech stack, configures MCP)

cd your-project && aqe init --auto



# That's it — MCP tools are available immediately in Claude Code

# For other clients: aqe-mcp

```



After init, your coding agent can use AQE tools directly. For example in Claude Code:



```

"Generate tests for src/services/UserService.ts with 90% coverage target"

"Find coverage gaps in src/ and prioritize by risk"

"Run security scan on the authentication module"

"Analyze why tests in auth/ are flaky and suggest fixes"

```



### Windows install



`agentic-qe` runs on Windows, but several of its performance-oriented native

dependencies (`hnswlib-node` for HNSW search, `@ruvector/gnn` for graph

neural networks, `@ruvector/rvf-node` for the RVF pattern store) ship as

optional native modules. `hnswlib-node` in particular has no prebuilt

binaries and compiles from source via `node-gyp`. `npm install` does not

fail when these modules can't build — they're declared optional and AQE

falls back to JavaScript paths at runtime.



**Important caveat about the fallback.** The pure-JavaScript HNSW fallback

(`ProgressiveHnswBackend`) is correct but degrades to O(N) brute-force search

when neither `hnswlib-node` nor `@ruvector/gnn` is available. That's fine for

small projects but **unsuitable for large indexes** (tens of thousands of

vectors and up). If you plan to run AQE against a sizeable codebase on

Windows, install the native build toolchain so `hnswlib-node` compiles:



- **Python 3** (added to `PATH`), and

- **Visual Studio 2022 Build Tools** with the *Desktop development with C++*

  workload — works with any node-gyp version, **or**

- **Visual Studio 2026** with the same workload **plus** an upgraded npm

  (`npm install -g npm@latest` — VS 2026 detection requires npm ≥ 11.6.3,

  shipped via node-gyp ≥ 12.1.0). Node 22 LTS still ships npm 10.x by

  default, which cannot detect VS 2026; either upgrade npm globally or use

  VS 2022 Build Tools instead.



If the native dep fails to build you'll see a `node-gyp` warning during

install (e.g. `gyp ERR! find VS`); the install itself completes. To verify

which backend is active:



```bash

aqe health

# Look for "HNSW backend: native" (hnswlib-node) or "HNSW backend: js"

# (ProgressiveHnswBackend — see caveat above).

```



Linux and macOS users: no extra setup required. The native binary compiles

out of the box.



---



## Claude Code Plugin (Alternative Install)



If you only need a slim, scoped fleet inside Claude Code — without the full `aqe init` setup — install the **`agentic-qe-fleet`** plugin. It bundles 11 specialized QE agents, 9 slash commands, 9 skills, and auto-registers the MCP server.



### Install from a local checkout



```bash

git clone https://github.com/proffesor-for-testing/agentic-qe.git

claude --plugin-dir ./agentic-qe/plugins/agentic-qe-fleet

```



### Install from the marketplace



In any Claude Code session:



```

/plugin marketplace add proffesor-for-testing/agentic-qe

/plugin install agentic-qe-fleet

```



### What you get



| Asset | Count | Notes |

|---|---|---|

| **Agents** (Task tool) | 11 | Model-routed: 6 on Opus (heavy reasoning), 5 on Sonnet (focused execution) |

| **Slash commands** | 9 | `/aqe-analyze`, `/aqe-execute`, `/aqe-generate`, `/aqe-optimize`, `/aqe-chaos`, `/aqe-fleet-status`, `/aqe-report`, `/aqe-benchmark`, `/aqe-costs` |

| **Skills** | 9 | All trust-tier 2 or 3 (validated/verified). Tier-1 untested skills excluded per policy. |

| **MCP server** | 1 | Auto-registers via `npx -y agentic-qe@latest mcp` — no separate `claude mcp add` |



**Bundled agents:** `qe-test-architect`, `qe-coverage-specialist`, `qe-flaky-hunter`, `qe-chaos-engineer`, `qe-fleet-commander`, `qe-quality-gate`, `qe-security-scanner`, `qe-performance-tester`, `qe-regression-analyzer`, `qe-tdd-specialist`, `qe-requirements-validator`.



**Bundled skills:** `qe-test-generation`, `qe-coverage-analysis`, `qe-test-execution`, `qe-chaos-resilience`, `qe-quality-assessment`, `chaos-engineering-resilience`, `mutation-testing`, `risk-based-testing`, `tdd-london-chicago`.



### Use it



After loading the plugin, the slash commands and agents are available immediately:



```

/aqe-fleet-status                 # health and metrics

/aqe-generate src/services/Auth.ts

/aqe-analyze src/                 # coverage gap analysis

```



Or invoke an agent through the Task tool:



```

"Use qe-test-architect to generate tests for src/services/PaymentService.ts"

"Use qe-flaky-hunter to find and stabilize flaky tests in tests/integration/"

"Use qe-chaos-engineer to inject network partitions into the order workflow"

```



### Plugin vs `aqe init` — which to use?



| | **Plugin** | **`aqe init`** |

|---|---|---|

| Setup | One slash command | Full project setup |

| Scope | 11 agents, 9 skills | 60 agents, 85 skills |

| Persistent learning DB | No (uses MCP server's) | Yes (`.agentic-qe/memory.db`) |

| Cross-platform support | Claude Code only | 11 platforms (Cursor, Copilot, Cline, etc.) |

| Use when | Quick start, single Claude Code project | Production team setup, multi-platform, full fleet |



You can run both — the plugin's MCP server uses the same `agentic-qe` package, so installing both gives you the full fleet via `aqe init` and the slash-command shortcuts via the plugin.



---



## Platform Support



AQE works with **11 coding agent platforms** through a single MCP server:



| Platform | Setup |

|----------|-------|

| **Claude Code** | `aqe init --auto` (built-in) |

| **GitHub Copilot** | `aqe init --auto --with-copilot` |

| **Cursor** | `aqe init --auto --with-cursor` |

| **Cline** | `aqe init --auto --with-cline` |

| **OpenCode** | `aqe init --auto --with-opencode` |

| **AWS Kiro** | `aqe init --auto --with-kiro` |

| **Kilo Code** | `aqe init --auto --with-kilocode` |

| **Roo Code** | `aqe init --auto --with-roocode` |

| **OpenAI Codex CLI** | `aqe init --auto --with-codex` |

| **Windsurf** | `aqe init --auto --with-windsurf` |

| **Continue.dev** | `aqe init --auto --with-continuedev` |



```bash

# Set up all platforms at once

aqe init --auto --with-all-platforms



# Or add a platform later

aqe platform setup cursor

aqe platform list       # show install status

aqe platform verify cursor  # validate config

```



For detailed per-platform instructions, see [Platform Setup Guide](docs/platform-setup-guide.md).



---



## Usage Examples



### Generate Tests



```bash

claude "Use qe-test-architect to create tests for PaymentService with 95% coverage target"

```



Output:

```

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

```



### Full Quality Pipeline



```bash

claude "Use qe-queen-coordinator to run full quality assessment:

1. Generate tests for src/services/*.ts

2. Analyze coverage gaps with risk scoring

3. Run security scan

4. Validate quality gate at 90% threshold

5. Provide deployment recommendation"

```



The Queen Coordinator spawns domain-specific agents, runs them in parallel, and synthesizes a final recommendation.



### TDD Workflow



```bash

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

```



Coordinates 5 subagents: write failing tests → implement minimal code → refactor → code review → security review.



### Security Audit



```bash

claude "Coordinate security audit:

- SAST/DAST scanning with qe-security-scanner

- Dependency vulnerability scanning with qe-dependency-mapper

- API security with qe-contract-validator

- Chaos resilience testing with qe-chaos-engineer"

```



---



## 60 QE Agents



The fleet is organized into **13 domains**, coordinated by the **qe-queen-coordinator**:



| Domain | Agents | What They Do |

|--------|--------|-------------|

| **Test Generation** | test-architect, tdd-specialist, mutation-tester, property-tester | Generate tests, TDD workflows, validate test effectiveness |

| **Test Execution** | parallel-executor, retry-handler, integration-tester | Run tests in parallel, handle retries, integration testing |

| **Coverage Analysis** | coverage-specialist, gap-detector | Find untested code, prioritize by risk |

| **Quality Assessment** | quality-gate, risk-assessor, deployment-advisor, devils-advocate | Go/no-go decisions, risk scoring, adversarial review |

| **Defect Intelligence** | defect-predictor, root-cause-analyzer, flaky-hunter, regression-analyzer | Predict bugs, find root causes, fix flaky tests |

| **Requirements** | requirements-validator, bdd-generator | Validate testability, generate BDD scenarios |

| **Code Intelligence** | code-intelligence, kg-builder, dependency-mapper, impact-analyzer | Knowledge graphs, semantic search, change impact |

| **Security** | security-scanner, security-auditor, pentest-validator | SAST/DAST, compliance audits, exploit validation |

| **Contracts** | contract-validator, graphql-tester | API contracts, GraphQL schema testing |

| **Visual & A11y** | visual-tester, accessibility-auditor, responsive-tester | Visual regression, WCAG compliance, viewport testing |

| **Chaos & Performance** | chaos-engineer, load-tester, performance-tester | Fault injection, load testing, performance validation |

| **Learning** | learning-coordinator, pattern-learner, transfer-specialist, metrics-optimizer | Cross-project learning, pattern discovery |

| **Enterprise** | soap-tester, sap-rfc-tester, sap-idoc-tester, sod-analyzer, odata-contract-tester, middleware-validator, message-broker-tester | SAP, SOAP, ESB, OData, JMS/AMQP/Kafka |



Plus **7 TDD subagents** (red, green, refactor, code/integration/performance/security reviewers) and the **fleet-commander** for large-scale orchestration.



---



## 75 QE Skills



Agents automatically apply relevant skills from the skill library. Skills are rated by **trust tier**:



| Tier | Count | Meaning |

|------|-------|---------|

| **Tier 3 — Verified** | 49 | Full evaluation test suite, production-ready |

| **Tier 2 — Validated** | 7 | Has executable validator |

| **Tier 1 — Structured** | 5 | Has JSON output schema |

| **Tier 0 — Advisory** | 5 | Guidance only |



View all 75 skills



**Core Testing (12):** agentic-quality-engineering, holistic-testing-pact, context-driven-testing, tdd-london-chicago, xp-practices, risk-based-testing, test-automation-strategy, refactoring-patterns, shift-left-testing, shift-right-testing, regression-testing, verification-quality



**Specialized Testing (13):** accessibility-testing, mobile-testing, database-testing, contract-testing, chaos-engineering-resilience, visual-testing-advanced, security-visual-testing, compliance-testing, compatibility-testing, localization-testing, mutation-testing, performance-testing, security-testing



**Browser Automation (1):** qe-browser (Vibium engine — assert, batch, visual-diff, prompt-injection scanning, semantic intents; see [ADR-091](docs/implementation/adrs/ADR-091-qe-browser-skill-vibium-engine.md))



**Domain Skills (11):** qe-test-generation, qe-test-execution, qe-coverage-analysis, qe-quality-assessment, qe-defect-intelligence, qe-requirements-validation, qe-code-intelligence, qe-visual-accessibility, qe-chaos-resilience, qe-learning-optimization, qe-iterative-loop



**Strategic (8):** six-thinking-hats, brutal-honesty-review, sherlock-review, cicd-pipeline-qe-orchestrator, bug-reporting-excellence, consultancy-practices, quality-metrics, pair-programming



**Testing Techniques (9):** exploratory-testing-advanced, test-design-techniques, test-data-management, test-environment-management, test-reporting-analytics, testability-scoring, technical-writing, code-review-quality, api-testing-patterns



**On-Demand Hooks (5):** strict-tdd, no-skip, coverage-guard, freeze-tests, security-watch



**Runbooks & Analysis (5):** test-failure-investigator, coverage-drop-investigator, e2e-flow-verifier, test-metrics-dashboard, skill-stats



**n8n Workflow Testing (5):** n8n-workflow-testing-fundamentals, n8n-expression-testing, n8n-security-testing, n8n-trigger-testing-strategies, n8n-integration-testing-patterns



**QCSD Swarms (5):** qcsd-ideation-swarm, qcsd-refinement-swarm, qcsd-development-swarm, qcsd-cicd-swarm, qcsd-production-swarm



**Accessibility (2):** a11y-ally, accessibility-testing



**Enterprise Integration (5):** enterprise-integration-testing, middleware-testing-patterns, observability-testing-patterns, wms-testing-patterns, pentest-validation



**Validation (1):** validation-pipeline



---



## How It Works



### Agent Coordination



The **Queen Coordinator** orchestrates agents across all 13 domains. When you ask for a quality assessment, the Queen decomposes the task, spawns the right agents, coordinates their work in parallel, and synthesizes results. Agents communicate through shared memory namespaces and use consensus protocols for critical quality decisions.



### Pattern Learning



AQE learns from every interaction. Successful test patterns, coverage strategies, and defect indicators are stored and indexed for fast retrieval. When generating tests for a new service, AQE searches for similar patterns from past sessions — even across different projects. Patterns improve over time through experience replay and dream cycles (background consolidation).



```bash

aqe learning stats      # view learning statistics

aqe learning dream      # trigger pattern consolidation

aqe brain export        # export learned patterns for sharing

```



### Intelligent Model Routing



**TinyDancer** routes tasks to the right model tier to minimize cost without sacrificing quality:



| Task Complexity | Model | Examples |

|----------------|-------|---------|

| Simple (0-20) | Haiku | Type additions, simple refactors |

| Moderate (20-70) | Sonnet | Bug fixes, test generation |

| Critical (70+) | Opus | Architecture, security, complex reasoning |



### Quality Gates



Anti-sycophancy scoring catches hollow tests. Tautological assertions (`expect(true).toBe(true)`) are rejected. Edge cases from historical patterns are injected into test generation. See [Loki-mode features](docs/loki-mode-features.md).



---



## CLI Reference



```bash

aqe init [--auto]              # Initialize project

aqe agent list                 # List available agents

aqe fleet status               # Fleet health and coordination

aqe learning stats             # Learning statistics

aqe learning dream             # Trigger dream cycle

aqe brain export/import        # Portable intelligence

aqe platform list/setup/verify # Manage coding agent platforms

aqe health                     # System health check



# Code intelligence

aqe code index src/                  # Index codebase into knowledge graph

aqe code index src/ --incremental    # Incremental index (changed files only)

aqe code index . --git-since HEAD~5  # Index files changed in last 5 commits

aqe code search "authentication"     # Semantic code search

aqe code impact src/                 # Change impact analysis

aqe code deps src/                   # Dependency mapping

aqe code complexity src/             # Complexity metrics and hotspots

```



---



## LLM Providers



| 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

export GROQ_API_KEY="gsk_..."  # or any supported provider

aqe init --auto

```



---



## Documentation



| Guide | Description |

|-------|-------------|

| [Platform Setup](docs/platform-setup-guide.md) | Per-platform configuration instructions |

| [Skill Validation](docs/guides/skill-validation.md) | Trust tiers and evaluation system |

| [Learning System](docs/guides/reasoningbank-learning-system.md) | ReasoningBank pattern learning |

| [Code Intelligence](docs/guides/fleet-code-intelligence-integration.md) | Knowledge graph and semantic search |

| [Loki-mode Features](docs/loki-mode-features.md) | Anti-sycophancy and quality gates |

| [Release Notes](docs/releases/README.md) | Version history and changelogs |

| [Architecture Glossary](docs/v3-technical-architecture-glossary.md) | Technical terms and concepts |



---



## Development



```bash

git clone https://github.com/proffesor-for-testing/agentic-qe.git

cd agentic-qe

npm install

npm run build

npm test -- --run

```



| Script | Description |

|--------|-------------|

| `npm run build` | Compile TypeScript + CLI + MCP bundles |

| `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](CONTRIBUTING.md) for details.



---



## Support



- **Documentation**: [docs/](docs/)

- **Issues**: [GitHub Issues](https://github.com/proffesor-for-testing/agentic-qe/issues)

- **Discussions**: [GitHub Discussions](https://github.com/proffesor-for-testing/agentic-qe/discussions)



---



## License



MIT — see [LICENSE](LICENSE).



---



## Contributors



| 
**[@proffesor-for-testing](https://github.com/proffesor-for-testing)**
Project Lead | 
**[@fndlalit](https://github.com/fndlalit)**
QX Partner, Testability | 
**[@shaal](https://github.com/shaal)**
Core Development | 
**[@mondweep](https://github.com/mondweep)**
Architecture |

|:---:|:---:|:---:|:---:|



[View all contributors](CONTRIBUTORS.md) | [Become a contributor](CONTRIBUTING.md)



---



## Support the Project



If you find AQE valuable, consider supporting its development:



| | Monthly | Annual (Save $10) |

|---|:---:|:---:|

| **Price** | $5/month | $50/year |

| **Subscribe** | [**Monthly**](https://www.paypal.com/webapps/billing/plans/subscribe?plan_id=P-88G03706DU8150205NEYZZAY) | [**Annual**](https://www.paypal.com/webapps/billing/plans/subscribe?plan_id=P-39189175UE6623540NEYZ2CI) |



[View sponsorship details](FUNDING.md)



---



## Acknowledgments



- **[Claude Flow](https://github.com/ruvnet/claude-flow)** by [@ruvnet](https://github.com/ruvnet) — Multi-agent orchestration and MCP integration

- **[Agentic Flow](https://github.com/ruvnet/agentic-flow)** by [@ruvnet](https://github.com/ruvnet) — Agent patterns and learning systems

- Built with TypeScript, Node.js, and better-sqlite3

- Compatible with Jest, Cypress, Playwright, Vitest, Mocha, pytest, JUnit, and more



---






**Made with care by the Agentic QE Team**



[Star us on GitHub](https://github.com/proffesor-for-testing/agentic-qe) | [Sponsor](FUNDING.md) | [Contributors](CONTRIBUTORS.md)