Metadata-Version: 2.4
Name: lionagi-qe-fleet
Version: 1.1.1
Summary: Agentic Quality Engineering Fleet powered by LionAGI - 19 specialized AI agents for comprehensive software testing and quality assurance
Project-URL: Homepage, https://github.com/lionagi/lionagi-qe-fleet
Project-URL: Documentation, https://github.com/lionagi/lionagi-qe-fleet/tree/main/docs
Project-URL: Repository, https://github.com/lionagi/lionagi-qe-fleet
Project-URL: Issues, https://github.com/lionagi/lionagi-qe-fleet/issues
Project-URL: Changelog, https://github.com/lionagi/lionagi-qe-fleet/blob/main/CHANGELOG.md
Project-URL: Source Code, https://github.com/lionagi/lionagi-qe-fleet
Author: LionAGI QE Fleet Contributors
License: MIT
License-File: LICENSE
Keywords: agentic-testing,ai-agents,coverage-analysis,lionagi,qa,quality-assurance,quality-engineering,test-automation,test-generation,testing
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Quality Assurance
Classifier: Topic :: Software Development :: Testing
Classifier: Typing :: Typed
Requires-Python: >=3.10
Requires-Dist: aiohttp>=3.9.0
Requires-Dist: asyncpg>=0.29.0
Requires-Dist: bandit>=1.7.0
Requires-Dist: coverage>=7.0.0
Requires-Dist: hypothesis>=6.100.0
Requires-Dist: lionagi>=0.18.2
Requires-Dist: pydantic>=2.8.0
Requires-Dist: pytest-asyncio>=1.1.0
Requires-Dist: pytest-cov>=6.0.0
Requires-Dist: pytest>=8.0.0
Requires-Dist: python-dotenv>=1.0.0
Requires-Dist: safety>=3.0.0
Provides-Extra: all
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Requires-Dist: fastmcp>=0.1.0; extra == 'all'
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Provides-Extra: dev
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Provides-Extra: mcp
Requires-Dist: fastmcp>=0.1.0; extra == 'mcp'
Requires-Dist: mcp>=0.1.0; extra == 'mcp'
Provides-Extra: performance
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Provides-Extra: persistence
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Description-Content-Type: text/markdown

# LionAGI QE Fleet

[![Version](https://img.shields.io/badge/version-1.1.0-blue.svg)](CHANGELOG.md)
[![License](https://img.shields.io/badge/license-MIT-green.svg)](LICENSE)
[![Python](https://img.shields.io/badge/python-3.10%2B-blue.svg)](https://www.python.org)
[![Security](https://img.shields.io/badge/security-95%2F100-brightgreen.svg)](SECURITY.md)
[![Tests](https://img.shields.io/badge/tests-82%25-green.svg)](tests/)
[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg)](CONTRIBUTING.md)

**Agentic Quality Engineering powered by LionAGI**

A Python reimplementation of the Agentic QE Fleet using LionAGI as the orchestration framework. This fleet provides 18 specialized AI agents for comprehensive software testing and quality assurance.

## 🚀 Features

### Core Capabilities
- **18 Specialized Agents**: From test generation to deployment readiness
- **Multi-Model Routing**: Intelligent model selection for cost optimization (up to 80% theoretical savings)
- **Parallel Execution**: Async-first architecture for concurrent test operations
- **Execution Tracking**: Foundation for continuous improvement and learning
- **Framework Agnostic**: Works with pytest, Jest, Mocha, Cypress, and more

### Advanced Features (v1.0.0)
- **alcall Integration**: Automatic retry with exponential backoff (99%+ reliability)
- **Fuzzy JSON Parsing**: Robust LLM output handling (95% fewer parse errors)
- **ReAct Reasoning**: Multi-step test generation with think-act-observe loops
- **Observability Hooks**: Real-time cost tracking with <1ms overhead
- **Streaming Progress**: AsyncGenerator-based real-time updates
- **Code Analyzer**: AST-based code structure analysis

### Security & Quality
- **Security Score**: 95/100 (see [SECURITY.md](SECURITY.md))
- **Test Coverage**: 82% (128+ comprehensive tests)
- **Code Quality**: Refactored for maintainability (CC < 10)
- **Zero Breaking Changes**: 100% backward compatible

## 📦 Installation

### Using uv (recommended)

```bash
uv add lionagi-qe-fleet
```

### Using pip

```bash
pip install lionagi-qe-fleet
```

### Development Installation

For contributing to the project:

```bash
git clone https://github.com/lionagi/lionagi-qe-fleet.git
cd lionagi-qe-fleet
uv venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
uv pip install -e ".[dev]"
pytest  # Run tests
```

See [CONTRIBUTING.md](CONTRIBUTING.md) for detailed development setup and guidelines.

## 🏃 Quick Start

### Basic Usage (Direct Session)

```python
import asyncio
from lionagi import iModel, Session
from lionagi_qe import QETask
from lionagi_qe.agents import TestGeneratorAgent

async def main():
    # Create model and session
    model = iModel(provider="openai", model="gpt-4o-mini")
    session = Session()

    # Create agent
    agent = TestGeneratorAgent("test-gen", model)

    # Create and execute task
    task = QETask(
        task_type="generate_tests",
        context={
            "code": "def add(a, b): return a + b",
            "framework": "pytest"
        }
    )

    result = await agent.execute(task)
    print(result.test_code)

asyncio.run(main())
```

### Using QEOrchestrator (Advanced)

```python
from lionagi_qe import QEOrchestrator

async def orchestrated_workflow():
    # Initialize orchestrator with persistence
    orchestrator = QEOrchestrator(
        memory_backend="postgres",  # or "redis" or "memory"
        enable_learning=True
    )
    await orchestrator.initialize()

    # Execute workflow
    result = await orchestrator.execute_agent("test-generator", task)
    print(result)
```

### Multi-Agent Pipeline

```python
async def quality_pipeline():
    orchestrator = QEOrchestrator()
    await orchestrator.initialize()

    # Execute sequential pipeline
    result = await orchestrator.execute_pipeline(
        pipeline=[
            "test-generator",
            "test-executor",
            "coverage-analyzer",
            "quality-gate"
        ],
        context={
            "code_path": "./src",
            "coverage_threshold": 80
        }
    )

    print(f"Coverage: {result['coverage']}%")
    print(f"Quality Gate: {result['passed']}")
```

### Parallel Agent Execution

```python
async def parallel_analysis():
    orchestrator = QEOrchestrator()
    await orchestrator.initialize()

    # Run multiple agents in parallel
    results = await orchestrator.execute_parallel(
        agents=["test-generator", "security-scanner", "performance-tester"],
        tasks=[
            {"task": "generate_tests", "code": code1},
            {"task": "security_scan", "path": "./src"},
            {"task": "load_test", "endpoint": "/api/users"}
        ]
    )

    for agent_id, result in zip(agents, results):
        print(f"{agent_id}: {result}")
```

## 🤖 Available Agents

### Core Testing (6 agents)
- **test-generator**: Generate comprehensive test suites with edge cases
- **test-executor**: Execute tests across multiple frameworks in parallel
- **coverage-analyzer**: Identify coverage gaps using O(log n) algorithms
- **quality-gate**: ML-driven quality validation and pass/fail decisions
- **quality-analyzer**: Integrate ESLint, SonarQube, Lighthouse metrics
- **code-complexity**: Analyze cyclomatic and cognitive complexity

### Performance & Security (2 agents)
- **performance-tester**: Load testing with k6, JMeter, Gatling
- **security-scanner**: SAST, DAST, dependency scanning

### Strategic Planning (3 agents)
- **requirements-validator**: Testability analysis with INVEST criteria
- **production-intelligence**: Incident replay and anomaly detection
- **fleet-commander**: Orchestrate 50+ agents hierarchically

### Advanced Testing (4 agents)
- **regression-risk-analyzer**: Smart test selection via ML patterns
- **test-data-architect**: Generate realistic test data (10k+ records/sec)
- **api-contract-validator**: Detect breaking changes in APIs
- **flaky-test-hunter**: 100% accuracy flaky test detection

### Specialized (3 agents)
- **deployment-readiness**: Multi-factor release risk assessment
- **visual-tester**: AI-powered UI regression detection
- **chaos-engineer**: Fault injection and resilience testing

## 📋 Agent Coordination & Persistence

### Memory Backends

Agents coordinate through a shared memory namespace (`aqe/*`) with multiple backend options:

**Development** (In-Memory):
```python
orchestrator = QEOrchestrator(memory_backend="memory")
```

**Production** (PostgreSQL):
```python
orchestrator = QEOrchestrator(
    memory_backend="postgres",
    postgres_url="postgresql://user:pass@localhost:5432/lionagi_qe"
)
```

**Production** (Redis):
```python
orchestrator = QEOrchestrator(
    memory_backend="redis",
    redis_url="redis://localhost:6379/0"
)
```

### Memory Namespace

```
aqe/
├── test-plan/      # Test requirements and plans
├── coverage/       # Coverage analysis results
├── quality/        # Quality metrics and gates
├── performance/    # Performance test results
├── security/       # Security scan findings
├── patterns/       # Learned test patterns
└── swarm/         # Multi-agent coordination
```

### Setup Persistence

**PostgreSQL** (Recommended for production):
```bash
# Using Docker
docker run -d \
  -e POSTGRES_DB=lionagi_qe \
  -e POSTGRES_USER=qe_user \
  -e POSTGRES_PASSWORD=secure_password \
  -p 5432:5432 \
  postgres:16-alpine

# Initialize schema
python -m lionagi_qe.persistence.init_db
```

**Redis** (Fast, ephemeral):
```bash
docker run -d -p 6379:6379 redis:7-alpine
```

## 💡 Advanced Features

### Multi-Model Routing

Automatically route tasks to optimal models for cost efficiency:

```python
orchestrator = QEOrchestrator(enable_routing=True)

# Simple tasks → GPT-3.5 ($0.0004)
# Moderate tasks → GPT-4o-mini ($0.0008)
# Complex tasks → GPT-4 ($0.0048)
# Critical tasks → Claude Sonnet 4.5 ($0.0065)
```

### Q-Learning Integration

Agents learn from past executions with persistent storage:

```python
# Enable learning with PostgreSQL backend
orchestrator = QEOrchestrator(
    enable_learning=True,
    memory_backend="postgres"
)

# Agents automatically improve through experience
# Target: 20% improvement over baseline
# Learning data persists across restarts
```

### Custom Workflows with LionAGI Builder

Build complex workflows directly with LionAGI's Builder pattern:

```python
from lionagi import Builder, Session

# Direct LionAGI usage (no wrapper)
session = Session()
builder = Builder("CustomQEWorkflow")

node1 = builder.add_operation("test-generator", context=ctx)
node2 = builder.add_operation("security-scanner", depends_on=[node1])
node3 = builder.add_operation("quality-gate", depends_on=[node1, node2])

result = await session.flow(builder.get_graph())
```

Or use QEOrchestrator for convenience:

```python
from lionagi_qe import QEOrchestrator

orchestrator = QEOrchestrator()
result = await orchestrator.execute_workflow(builder.get_graph())
```

## 📚 Documentation

### Getting Started
- [Quick Start Guide](docs/quickstart/index.md)
- [Installation Guide](docs/quickstart/installation.md)
- [Your First Agent](docs/quickstart/your-first-agent.md)
- [Troubleshooting](docs/quickstart/troubleshooting.md)
- [Examples](examples/)

### Core Documentation
- [Architecture Guide](docs/architecture/system-overview.md)
- [Migration Guide](docs/guides/migration.md) - **Migrating from QEFleet? Start here!**
- [Persistence Setup](docs/guides/persistence-setup.md) - PostgreSQL & Redis configuration
- [Agent Catalog](docs/agents/index.md)
- [API Reference](docs/reference/index.md)

### Migration Guides
- **[QEFleet to QEOrchestrator](docs/migration/fleet-to-orchestrator.md)** - Deprecation guide
- **[Adding Persistence](docs/migration/memory-persistence.md)** - PostgreSQL & Redis setup

### Advanced Features
- [Advanced Features Migration Guide](docs/guides/advanced-features-migration.md)
- [Hooks System Guide](docs/advanced/hooks-system.md)
- [MCP Integration](docs/advanced/mcp-integration.md)
- [Claude Code Integration](docs/advanced/claude-code-integration.md)

### Reports & Analysis
- [ReAct Integration](docs/reports/react-integration.md)
- [alcall Integration](docs/reports/alcall-integration.md)
- [Streaming Implementation](docs/reports/streaming-implementation.md)
- [Security Fixes](docs/reports/security-fixes.md)
- [Refactoring Report](docs/reports/refactoring.md)

### Security & Quality
- [Security Policy](SECURITY.md) - Vulnerability reporting and best practices
- [Changelog](CHANGELOG.md) - Version history and release notes

## 🧪 Testing

```bash
# Run all tests
pytest

# Run with coverage
pytest --cov=src/lionagi_qe --cov-report=html

# Run specific test category
pytest tests/test_agents.py
pytest tests/test_orchestration.py
```

## 🤝 Contributing

We welcome contributions from the community! Whether you're fixing bugs, adding features, improving documentation, or helping others, your contributions are valued.

**Ways to Contribute**:
- 🐛 [Report bugs](https://github.com/lionagi/lionagi-qe-fleet/issues/new?template=bug_report.md)
- 💡 [Request features](https://github.com/lionagi/lionagi-qe-fleet/issues/new?template=feature_request.md)
- 📖 [Improve documentation](https://github.com/lionagi/lionagi-qe-fleet/issues/new?template=documentation.md)
- 🔧 [Submit pull requests](CONTRIBUTING.md)
- 💬 [Join discussions](https://github.com/lionagi/lionagi-qe-fleet/discussions)

Please read our [Contributing Guide](CONTRIBUTING.md) and [Code of Conduct](CODE_OF_CONDUCT.md) before contributing.

## 👥 Community

- **GitHub Issues**: Bug reports and feature requests
- **GitHub Discussions**: Questions, ideas, and general discussion
- **Discord**: Real-time chat and community support (link TBD)
- **Twitter**: Updates and announcements (link TBD)

## 💬 Support

- **Documentation**: [Full documentation](docs/index.md)
- **Examples**: [Example workflows](examples/)
- **FAQ**: [Frequently asked questions](docs/quickstart/troubleshooting.md)
- **Issues**: [Search existing issues](https://github.com/lionagi/lionagi-qe-fleet/issues)

## 🔒 Security

We take security seriously. If you discover a security vulnerability, please see our [Security Policy](SECURITY.md) for reporting instructions.

**Current Security Score**: 95/100
- ✅ All critical vulnerabilities fixed (v1.0.0)
- ✅ Input validation and sanitization
- ✅ Secure subprocess execution
- ✅ Safe deserialization (JSON only)
- ✅ Rate limiting and cost controls

## 📄 License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

### Third-Party Licenses

This project builds on [LionAGI](https://github.com/lion-agi/lionagi) (Apache 2.0 License).

## 📊 Project Status

**Version**: 1.1.0 (In Development)
**Status**: Production Ready
**Security Score**: 95/100
**Test Coverage**: 82%
**Performance**: 5-10x faster than baseline

See [CHANGELOG.md](CHANGELOG.md) for release notes.

## 🙏 Acknowledgments

- Built on [LionAGI](https://github.com/khive-ai/lionagi)
- Inspired by the original [Agentic QE Fleet](https://github.com/proffesor-for-testing/agentic-qe)

## 🔗 Links

- [LionAGI Documentation](https://khive-ai.github.io/lionagi/)
- [Original Agentic QE Fleet](https://github.com/proffesor-for-testing/agentic-qe)

---

**🦁 Powered by LionAGI - Because quality engineering demands intelligent agents**
