# AgentOps: AI-Powered Requirements-Driven Test Automation
[](https://pypi.org/project/agentops-ai/)
[](https://www.python.org/downloads/)
[](https://opensource.org/licenses/MIT)
AgentOps is a next-generation, AI-powered development platform that automatically generates requirements and tests from your codebase. Using advanced multi-agent AI systems, AgentOps bridges the gap between technical implementation and business requirements through bidirectional analysis.
## 🚀 Quick Start
### Installation
```bash
# Install from PyPI
pip install agentops-ai
# Or install from source
git clone https://github.com/knaig/agentops_ai.git
cd agentops_ai
pip install -e .
```
### Basic Usage
```bash
# 1. Initialize your project
agentops init
# 2. Run complete analysis on your code
agentops runner myfile.py
# 3. Check results and status
agentops status
# 4. View traceability matrix
agentops traceability
```
That's it! AgentOps will automatically:
- ✅ Analyze your code structure
- ✅ Generate business-focused requirements
- ✅ Create comprehensive test cases
- ✅ Execute tests and provide results
- ✅ Generate traceability matrices
## 📋 Available Commands
| Command | Description | Example |
|---------|-------------|---------|
| `agentops init` | Initialize project structure | `agentops init` |
| `agentops runner <file>` | Complete workflow execution | `agentops runner src/main.py` |
| `agentops tests <file>` | Generate test suites | `agentops tests src/main.py` |
| `agentops analyze <file>` | Deep requirements analysis | `agentops analyze src/main.py` |
| `agentops status` | Check project status | `agentops status` |
| `agentops traceability` | Generate traceability matrix | `agentops traceability` |
| `agentops onboarding` | Interactive setup guide | `agentops onboarding` |
| `agentops version` | Show version information | `agentops version` |
| `agentops help` | Show detailed help | `agentops help` |
### 🚀 Power User Options
| Option | Description | Example |
|--------|-------------|---------|
| `--all` | Process all Python files | `agentops runner --all` |
| `--auto-approve` | Skip manual review | `agentops runner myfile.py --auto-approve` |
| `-v, --verbose` | Detailed output | `agentops runner myfile.py -v` |
**Combined Usage:**
```bash
# Batch process entire project with automation
agentops runner --all --auto-approve -v
# Generate tests for all files
agentops tests --all --auto-approve
# Detailed analysis with verbose output
agentops analyze myfile.py -v
```
## 🎯 Enhanced CLI Experience
### Interactive Onboarding
New users can get started quickly with the interactive onboarding:
```bash
agentops onboarding
```
This will:
- ✅ Check your installation and API key setup
- ✅ Create sample files for demonstration
- ✅ Run a complete demo workflow
- ✅ Show you all available commands and options
- ✅ Guide you through next steps
### Batch Processing
Process your entire project with a single command:
```bash
# Analyze all Python files in your project
agentops runner --all
# Generate tests for all files
agentops tests --all
# With automation and verbose output
agentops runner --all --auto-approve -v
```
### CI/CD Integration
Perfect for automated workflows:
```bash
# Fully automated analysis
agentops runner --all --auto-approve
# Check project status
agentops status -v
```
## 🔧 Configuration
### Environment Setup
Create a `.env` file in your project root:
```bash
# Required: OpenAI API Key
OPENAI_API_KEY=your_openai_api_key_here
# Optional: Custom settings
AGENTOPS_OUTPUT_DIR=.agentops
AGENTOPS_LOG_LEVEL=INFO
```
### API Key Validation
Validate your API keys before running:
```bash
# Run the validation script
./scripts/validate_api_keys.sh
```
## 📁 Generated Artifacts
After running AgentOps, you'll find these artifacts in your project:
```
.agentops/
├── requirements/
│ ├── requirements.gherkin # Requirements in Gherkin format
│ └── requirements.md # Requirements in Markdown format
├── tests/
│ ├── test_main.py # Generated test files
│ └── test_coverage.xml # Test coverage reports
├── traceability/
│ └── traceability_matrix.md # Requirements-to-tests mapping
└── reports/
└── analysis_report.json # Detailed analysis results
```
## 🎯 Key Features
### 🤖 Multi-Agent AI System
- **Code Analyzer**: Deep code structure and dependency analysis
- **Requirements Engineer**: Business-focused requirement extraction
- **Test Architect**: Comprehensive test strategy design
- **Test Generator**: High-quality, maintainable test code
- **Quality Assurance**: Automated test validation and scoring
### 📊 Requirements-Driven Testing
- **Bidirectional Analysis**: Code → Requirements → Tests
- **Business Context**: Requirements focus on real-world value
- **Traceability**: Complete mapping from requirements to tests
- **Multiple Formats**: Gherkin (BDD) and Markdown outputs
### 🔄 Streamlined Workflow
- **Single Command**: `agentops runner` does everything
- **Automatic Export**: Requirements and traceability always up-to-date
- **Error Recovery**: Robust handling of common issues
- **Progress Tracking**: Real-time status updates
## 📖 Examples
### Basic Python File Analysis
```python
# myfile.py
def calculate_total(items, tax_rate=0.1):
"""Calculate total with tax for a list of items."""
subtotal = sum(items)
tax = subtotal * tax_rate
return subtotal + tax
def apply_discount(total, discount_percent):
"""Apply percentage discount to total."""
discount = total * (discount_percent / 100)
return total - discount
```
```bash
# Run analysis
agentops runner myfile.py
```
**Generated Requirements (Gherkin):**
```gherkin
Feature: Shopping Cart Calculations
Scenario: Calculate total with tax
Given a list of items with prices [10, 20, 30]
And a tax rate of 10%
When I calculate the total
Then the result should be 66.0
Scenario: Apply discount to total
Given a total amount of 100
And a discount of 15%
When I apply the discount
Then the final amount should be 85.0
```
**Generated Tests:**
```python
def test_calculate_total_with_tax():
items = [10, 20, 30]
result = calculate_total(items, tax_rate=0.1)
assert result == 66.0
def test_apply_discount():
result = apply_discount(100, 15)
assert result == 85.0
```
## 🛠️ Advanced Usage
### Custom Configuration
```python
# agentops_ai/agentops_core/config.py
LLM_MODEL = "gpt-4"
LLM_TEMPERATURE = 0.1
OUTPUT_DIR = ".agentops"
LOG_LEVEL = "INFO"
```
### Python API
```python
from agentops_ai.agentops_core.orchestrator import AgentOrchestrator
# Create orchestrator
orchestrator = AgentOrchestrator()
# Run analysis
result = orchestrator.run_workflow("myfile.py")
# Access results
print(f"Requirements: {len(result.requirements)}")
print(f"Tests generated: {len(result.test_code)} lines")
print(f"Quality score: {result.quality_score}")
```
## 🔍 Troubleshooting
### Common Issues
**API Key Errors:**
```bash
# Set your OpenAI API key
export OPENAI_API_KEY="your-key-here"
```
**Permission Errors:**
```bash
# Ensure write permissions
chmod 755 .agentops/
```
**Import Errors:**
```bash
# Reinstall dependencies
pip install -r requirements.txt
```
### Getting Help
```bash
# Show detailed help
agentops help
# Check version
agentops version
# Validate setup
agentops status
```
## 📚 Documentation
- [Quick Start Guide](docs/02_testing/01_quick_start.md)
- [Architecture Overview](docs/02_testing/02_architecture_overview.md)
- [API Reference](docs/02_testing/03_api_reference.md)
- [Integration Guide](docs/02_testing/03_integration_guide.md)
- [Changelog](docs/01_project_management/03_changelog.md)
## 🤝 Contributing
We welcome contributions! Please see our [Contributing Guide](docs/05_development/01_contributing.md) for details.
## 📄 License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## 🆘 Support
- **Issues**: [GitHub Issues](https://github.com/knaig/agentops_ai/issues)
- **Documentation**: [Project Docs](docs/)
- **Email**: agentops_info@protonmail.com
---
**AgentOps**: Bridging technical implementation and business requirements through AI-powered analysis. 🚀
Raw data
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"description": "# AgentOps: AI-Powered Requirements-Driven Test Automation\n\n[](https://pypi.org/project/agentops-ai/)\n[](https://www.python.org/downloads/)\n[](https://opensource.org/licenses/MIT)\n\nAgentOps is a next-generation, AI-powered development platform that automatically generates requirements and tests from your codebase. Using advanced multi-agent AI systems, AgentOps bridges the gap between technical implementation and business requirements through bidirectional analysis.\n\n## \ud83d\ude80 Quick Start\n\n### Installation\n\n```bash\n# Install from PyPI\npip install agentops-ai\n\n# Or install from source\ngit clone https://github.com/knaig/agentops_ai.git\ncd agentops_ai\npip install -e .\n```\n\n### Basic Usage\n\n```bash\n# 1. Initialize your project\nagentops init\n\n# 2. Run complete analysis on your code\nagentops runner myfile.py\n\n# 3. Check results and status\nagentops status\n\n# 4. View traceability matrix\nagentops traceability\n```\n\nThat's it! AgentOps will automatically:\n- \u2705 Analyze your code structure\n- \u2705 Generate business-focused requirements\n- \u2705 Create comprehensive test cases\n- \u2705 Execute tests and provide results\n- \u2705 Generate traceability matrices\n\n## \ud83d\udccb Available Commands\n\n| Command | Description | Example |\n|---------|-------------|---------|\n| `agentops init` | Initialize project structure | `agentops init` |\n| `agentops runner <file>` | Complete workflow execution | `agentops runner src/main.py` |\n| `agentops tests <file>` | Generate test suites | `agentops tests src/main.py` |\n| `agentops analyze <file>` | Deep requirements analysis | `agentops analyze src/main.py` |\n| `agentops status` | Check project status | `agentops status` |\n| `agentops traceability` | Generate traceability matrix | `agentops traceability` |\n| `agentops onboarding` | Interactive setup guide | `agentops onboarding` |\n| `agentops version` | Show version information | `agentops version` |\n| `agentops help` | Show detailed help | `agentops help` |\n\n### \ud83d\ude80 Power User Options\n\n| Option | Description | Example |\n|--------|-------------|---------|\n| `--all` | Process all Python files | `agentops runner --all` |\n| `--auto-approve` | Skip manual review | `agentops runner myfile.py --auto-approve` |\n| `-v, --verbose` | Detailed output | `agentops runner myfile.py -v` |\n\n**Combined Usage:**\n```bash\n# Batch process entire project with automation\nagentops runner --all --auto-approve -v\n\n# Generate tests for all files\nagentops tests --all --auto-approve\n\n# Detailed analysis with verbose output\nagentops analyze myfile.py -v\n```\n\n## \ud83c\udfaf Enhanced CLI Experience\n\n### Interactive Onboarding\nNew users can get started quickly with the interactive onboarding:\n\n```bash\nagentops onboarding\n```\n\nThis will:\n- \u2705 Check your installation and API key setup\n- \u2705 Create sample files for demonstration\n- \u2705 Run a complete demo workflow\n- \u2705 Show you all available commands and options\n- \u2705 Guide you through next steps\n\n### Batch Processing\nProcess your entire project with a single command:\n\n```bash\n# Analyze all Python files in your project\nagentops runner --all\n\n# Generate tests for all files\nagentops tests --all\n\n# With automation and verbose output\nagentops runner --all --auto-approve -v\n```\n\n### CI/CD Integration\nPerfect for automated workflows:\n\n```bash\n# Fully automated analysis\nagentops runner --all --auto-approve\n\n# Check project status\nagentops status -v\n```\n\n## \ud83d\udd27 Configuration\n\n### Environment Setup\n\nCreate a `.env` file in your project root:\n\n```bash\n# Required: OpenAI API Key\nOPENAI_API_KEY=your_openai_api_key_here\n\n# Optional: Custom settings\nAGENTOPS_OUTPUT_DIR=.agentops\nAGENTOPS_LOG_LEVEL=INFO\n```\n\n### API Key Validation\n\nValidate your API keys before running:\n\n```bash\n# Run the validation script\n./scripts/validate_api_keys.sh\n```\n\n## \ud83d\udcc1 Generated Artifacts\n\nAfter running AgentOps, you'll find these artifacts in your project:\n\n```\n.agentops/\n\u251c\u2500\u2500 requirements/\n\u2502 \u251c\u2500\u2500 requirements.gherkin # Requirements in Gherkin format\n\u2502 \u2514\u2500\u2500 requirements.md # Requirements in Markdown format\n\u251c\u2500\u2500 tests/\n\u2502 \u251c\u2500\u2500 test_main.py # Generated test files\n\u2502 \u2514\u2500\u2500 test_coverage.xml # Test coverage reports\n\u251c\u2500\u2500 traceability/\n\u2502 \u2514\u2500\u2500 traceability_matrix.md # Requirements-to-tests mapping\n\u2514\u2500\u2500 reports/\n \u2514\u2500\u2500 analysis_report.json # Detailed analysis results\n```\n\n## \ud83c\udfaf Key Features\n\n### \ud83e\udd16 Multi-Agent AI System\n- **Code Analyzer**: Deep code structure and dependency analysis\n- **Requirements Engineer**: Business-focused requirement extraction\n- **Test Architect**: Comprehensive test strategy design\n- **Test Generator**: High-quality, maintainable test code\n- **Quality Assurance**: Automated test validation and scoring\n\n### \ud83d\udcca Requirements-Driven Testing\n- **Bidirectional Analysis**: Code \u2192 Requirements \u2192 Tests\n- **Business Context**: Requirements focus on real-world value\n- **Traceability**: Complete mapping from requirements to tests\n- **Multiple Formats**: Gherkin (BDD) and Markdown outputs\n\n### \ud83d\udd04 Streamlined Workflow\n- **Single Command**: `agentops runner` does everything\n- **Automatic Export**: Requirements and traceability always up-to-date\n- **Error Recovery**: Robust handling of common issues\n- **Progress Tracking**: Real-time status updates\n\n## \ud83d\udcd6 Examples\n\n### Basic Python File Analysis\n\n```python\n# myfile.py\ndef calculate_total(items, tax_rate=0.1):\n \"\"\"Calculate total with tax for a list of items.\"\"\"\n subtotal = sum(items)\n tax = subtotal * tax_rate\n return subtotal + tax\n\ndef apply_discount(total, discount_percent):\n \"\"\"Apply percentage discount to total.\"\"\"\n discount = total * (discount_percent / 100)\n return total - discount\n```\n\n```bash\n# Run analysis\nagentops runner myfile.py\n```\n\n**Generated Requirements (Gherkin):**\n```gherkin\nFeature: Shopping Cart Calculations\n\nScenario: Calculate total with tax\n Given a list of items with prices [10, 20, 30]\n And a tax rate of 10%\n When I calculate the total\n Then the result should be 66.0\n\nScenario: Apply discount to total\n Given a total amount of 100\n And a discount of 15%\n When I apply the discount\n Then the final amount should be 85.0\n```\n\n**Generated Tests:**\n```python\ndef test_calculate_total_with_tax():\n items = [10, 20, 30]\n result = calculate_total(items, tax_rate=0.1)\n assert result == 66.0\n\ndef test_apply_discount():\n result = apply_discount(100, 15)\n assert result == 85.0\n```\n\n## \ud83d\udee0\ufe0f Advanced Usage\n\n### Custom Configuration\n\n```python\n# agentops_ai/agentops_core/config.py\nLLM_MODEL = \"gpt-4\"\nLLM_TEMPERATURE = 0.1\nOUTPUT_DIR = \".agentops\"\nLOG_LEVEL = \"INFO\"\n```\n\n### Python API\n\n```python\nfrom agentops_ai.agentops_core.orchestrator import AgentOrchestrator\n\n# Create orchestrator\norchestrator = AgentOrchestrator()\n\n# Run analysis\nresult = orchestrator.run_workflow(\"myfile.py\")\n\n# Access results\nprint(f\"Requirements: {len(result.requirements)}\")\nprint(f\"Tests generated: {len(result.test_code)} lines\")\nprint(f\"Quality score: {result.quality_score}\")\n```\n\n## \ud83d\udd0d Troubleshooting\n\n### Common Issues\n\n**API Key Errors:**\n```bash\n# Set your OpenAI API key\nexport OPENAI_API_KEY=\"your-key-here\"\n```\n\n**Permission Errors:**\n```bash\n# Ensure write permissions\nchmod 755 .agentops/\n```\n\n**Import Errors:**\n```bash\n# Reinstall dependencies\npip install -r requirements.txt\n```\n\n### Getting Help\n\n```bash\n# Show detailed help\nagentops help\n\n# Check version\nagentops version\n\n# Validate setup\nagentops status\n```\n\n## \ud83d\udcda Documentation\n\n- [Quick Start Guide](docs/02_testing/01_quick_start.md)\n- [Architecture Overview](docs/02_testing/02_architecture_overview.md)\n- [API Reference](docs/02_testing/03_api_reference.md)\n- [Integration Guide](docs/02_testing/03_integration_guide.md)\n- [Changelog](docs/01_project_management/03_changelog.md)\n\n## \ud83e\udd1d Contributing\n\nWe welcome contributions! Please see our [Contributing Guide](docs/05_development/01_contributing.md) for details.\n\n## \ud83d\udcc4 License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n\n## \ud83c\udd98 Support\n\n- **Issues**: [GitHub Issues](https://github.com/knaig/agentops_ai/issues)\n- **Documentation**: [Project Docs](docs/)\n- **Email**: agentops_info@protonmail.com\n\n---\n\n**AgentOps**: Bridging technical implementation and business requirements through AI-powered analysis. \ud83d\ude80 \n",
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