agentops-ai 1.4.1

AI-powered development platform: Multi-agent system for requirements discovery, conversational document building, and test automation

AgentOps: AI-Powered Requirements-Driven Test Automation

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

# 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

# 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:

# 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:

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:

# 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:

# 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:

# 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:

# 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

# 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

# Run analysis
agentops runner myfile.py

Generated Requirements (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:

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

# agentops_ai/agentops_core/config.py
LLM_MODEL = "gpt-4"
LLM_TEMPERATURE = 0.1
OUTPUT_DIR = ".agentops"
LOG_LEVEL = "INFO"

Python API

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:

# Set your OpenAI API key
export OPENAI_API_KEY="your-key-here"

Permission Errors:

# Ensure write permissions
chmod 755 .agentops/

Import Errors:

# Reinstall dependencies
pip install -r requirements.txt

Getting Help

# Show detailed help
agentops help

# Check version
agentops version

# Validate setup
agentops status

📚 Documentation

• Quick Start Guide
• Architecture Overview
• API Reference
• Integration Guide
• Changelog

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

📄 License

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

🆘 Support

• Issues: GitHub Issues
• Documentation: Project Docs
• Email: [e-mail]

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AgentOps: Bridging technical implementation and business requirements through AI-powered analysis. 🚀