agentops-ai


Nameagentops-ai JSON
Version 1.1.5 PyPI version JSON
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SummaryAI-powered QA co-pilot: Multi-agent system for requirements-driven test automation
upload_time2025-07-09 15:01:02
maintainerNone
docs_urlNone
authorNone
requires_python>=3.8
licenseNone
keywords testing requirements ai automation code-analysis test-generation qa quality-assurance multi-agent openai pytest test-automation requirements-traceability
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            # AgentOps ๐Ÿค–

[![PyPI version](https://badge.fury.io/py/agentops-ai.svg)](https://badge.fury.io/py/agentops-ai)
[![Python 3.8+](https://img.shields.io/badge/python-3.8+-blue.svg)](https://www.python.org/downloads/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![GitHub stars](https://img.shields.io/github/stars/knaig/agentops_ai?style=social)](https://github.com/knaig/agentops_ai)

**The world's first AI-powered QA co-pilot** that automatically generates comprehensive test suites from your code using a sophisticated multi-agent AI system. Think of it as having a team of expert QA engineers working on your codebase 24/7.

## โœจ Features

- ๐Ÿค– **6 AI Agents** working in harmony for comprehensive analysis
- ๐Ÿ“ **Automated Requirements Extraction** from code changes
- ๐Ÿงช **Intelligent Test Generation** with edge case detection
- ๐Ÿ”„ **Delta-Based Updates** - only analyze what changed
- ๐Ÿ“Š **Requirements Traceability** matrix for full coverage
- ๐Ÿš€ **90% Time Savings** compared to manual test writing
- ๐ŸŽฏ **Zero Configuration** setup with sensible defaults
- ๐Ÿ”ง **Rich CLI** with progressive help and examples

## ๐Ÿš€ Quick Start

### CLI Usage

```bash
# Install AgentOps
pip install agentops-ai

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

# Initialize and run the multi-agent workflow
agentops init
agentops multi-agent-run examples/demo-project/calc.py
agentops run --all
```

### Python API Usage

```python
from agentops_ai import AgentOps

# Initialize AgentOps for your project
agentops = AgentOps("./my_project")

# Analyze a Python file
result = agentops.analyze_file("src/my_module.py")

# Generate tests
result = agentops.generate_tests("src/my_module.py")

# Run tests
result = agentops.run_tests("src/my_module.py")
```

๐Ÿ“– **For detailed API documentation, see [docs/api-reference.md](docs/api-reference.md)**

## ๐ŸŽฏ What's New (v0.3.0)

### โœ… Enhanced Discoverability
- **Rich CLI Help System** with progressive disclosure and examples
- **Interactive Onboarding** with `agentops welcome` command
- **System Health Checks** with `agentops check` command
- **Project Status Monitoring** with `agentops status` command
- **Version Information** with `agentops version` command

### โœ… Multi-Agent AI System
AgentOps uses a **multi-agent AI system** that automatically handles the entire workflow:
- **CodeAnalyzer Agent**: Deep code structure analysis
- **RequirementsEngineer Agent**: LLM-powered requirement extraction  
- **TestArchitect Agent**: Comprehensive test strategy design
- **TestGenerator Agent**: High-quality test code generation
- **QualityAssurance Agent**: Test validation and scoring
- **IntegrationSpecialist Agent**: CI/CD and IDE integration

### โœ… Enhanced Reliability
- **Import Issues Fixed**: Resolved CLI import errors for cross-project usage
- **Error Recovery**: Robust error handling with automatic recovery mechanisms
- **Type Safety**: Enhanced type annotations and IDE support

### โœ… Simplified Workflow
- **One Command**: `agentops multi-agent-run` replaces the old multi-step process
- **Automatic**: No manual approval steps required
- **Intelligent**: AI agents handle all decision-making

## ๐ŸŽฏ What's New (December 2024)

### โœ… Multi-Agent AI System
AgentOps now uses a **multi-agent AI system** that automatically handles the entire workflow:
- **CodeAnalyzer Agent**: Deep code structure analysis
- **RequirementsEngineer Agent**: LLM-powered requirement extraction  
- **TestArchitect Agent**: Comprehensive test strategy design
- **TestGenerator Agent**: High-quality test code generation
- **QualityAssurance Agent**: Test validation and scoring
- **IntegrationSpecialist Agent**: CI/CD and IDE integration

### โœ… Enhanced Reliability
- **Import Issues Fixed**: Resolved CLI import errors for cross-project usage
- **Error Recovery**: Robust error handling with automatic recovery mechanisms
- **Type Safety**: Enhanced type annotations and IDE support

### โœ… Simplified Workflow
- **One Command**: `agentops multi-agent-run` replaces the old multi-step process
- **Automatic**: No manual approval steps required
- **Intelligent**: AI agents handle all decision-making

## ๐Ÿ“ Project Structure

```
AgentOps/
โ”œโ”€โ”€ agentops_ai/              # Main package
โ”‚   โ”œโ”€โ”€ agentops_agents/      # Multi-agent system
โ”‚   โ”œโ”€โ”€ agentops_cli/         # Command-line interface
โ”‚   โ”œโ”€โ”€ agentops_core/        # Core business logic
โ”‚   โ”œโ”€โ”€ docs/                 # Documentation
โ”‚   โ”œโ”€โ”€ prompts/              # LLM prompt templates
โ”‚   โ””โ”€โ”€ .tours/               # CodeTour files
โ”œโ”€โ”€ examples/                 # Example projects
โ”‚   โ””โ”€โ”€ demo-project/         # Demo project
โ”œโ”€โ”€ .github/                  # CI/CD workflows
โ”œโ”€โ”€ .private/                 # Internal documentation
โ””โ”€โ”€ docs/                     # Project documentation
```

## ๐Ÿ› ๏ธ Development

### Prerequisites

- Python 3.11+
- Poetry
- OpenAI API key

### Setup

```bash
# Clone and install
git clone <repository-url>
cd AgentOps
pip install -e .

# Set environment variables
export OPENAI_API_KEY="your-api-key"
```

### Running Tests

```bash
# Run all tests
pytest

# Run with coverage
pytest --cov=agentops_ai

# Run linting
ruff check agentops_ai
black --check agentops_ai
```

### Documentation

```bash
# Start documentation server
cd agentops_ai/docs
mkdocs serve
```

## ๐Ÿ“š Documentation

- **[Documentation Overview](docs/README.md)** - Complete documentation structure and navigation
- **[Quick Start Guide](docs/user-guides/01_QUICK_START.md)** - Get up and running with multi-agent system
- **[AgentOps Runner Guide](docs/user-guides/AGENTOPS_RUNNER_GUIDE.md)** - Complete guide for the runner script
- **[Architecture Overview](docs/developer-guides/02_ARCHITECTURE_OVERVIEW.md)** - Multi-agent system design
- **[Multi-Agent Differences](docs/developer-guides/04_MULTI_AGENT_DIFF.md)** - Legacy vs. new system comparison
- **[API Reference](docs/api-reference/)** - Complete API docs
- **[Readiness Checklist](docs/developer-guides/06_READINESS_CHECKLIST.md)** - Engineer onboarding
- **[Recent Improvements](docs/changelog/IMPORT_FIX_AND_IMPROVEMENTS.md)** - Latest fixes and enhancements

## ๐ŸŽฏ Multi-Agent Workflow

AgentOps now follows a **single-command automation** workflow:

```bash
# Initialize project
agentops init

# Run complete multi-agent workflow
agentops multi-agent-run path/to/your_file.py

# Execute generated tests
agentops run --all
```

### What Each Agent Does
1. **CodeAnalyzer**: Analyzes code structure and dependencies
2. **RequirementsEngineer**: Extracts functional requirements using LLM
3. **TestArchitect**: Designs comprehensive test strategy
4. **TestGenerator**: Creates high-quality test code
5. **QualityAssurance**: Validates and scores test quality
6. **IntegrationSpecialist**: Sets up CI/CD and IDE integrations

## ๐Ÿ”ง CLI Commands

### Core Workflow
```bash
agentops init                    # Initialize project structure
agentops multi-agent-run <file>  # Run complete multi-agent workflow
agentops run --file <file>       # Run tests for specific file
agentops run --all               # Run all generated tests
```

### Discovery & Help
```bash
agentops welcome                 # Interactive welcome and quick start guide
agentops help                    # Comprehensive help documentation
agentops examples                # Practical examples and use cases
agentops commands                # List all available commands
agentops version                 # Show version and system information
agentops check                   # Verify system requirements and setup
agentops status                  # Show project status and configuration
```

### Management & Analysis
```bash
agentops config                  # View and edit configuration
agentops report --check-changes  # Generate analysis report
agentops traceability --open     # View requirements-to-tests matrix
agentops integration setup       # Configure CI/CD and IDE integrations
```

### Getting Started
```bash
# First time? Start here:
agentops welcome                 # Interactive onboarding
agentops check                   # Verify your setup
agentops init                    # Initialize your project
agentops multi-agent-run myfile.py  # Run your first workflow
```

## ๐Ÿ—๏ธ Architecture

AgentOps uses a modern multi-agent architecture:

- **Multi-Agent Layer**: Specialized AI agents for each task
- **LangGraph Orchestration**: Modern AI workflow management
- **CLI Layer**: Command-line interface with Click
- **Core Engine**: Business logic and state management
- **Service Layer**: LLM-based analysis and generation
- **Storage Layer**: SQLite database for requirements

## ๐Ÿ›ก๏ธ Error Recovery

The system includes robust error recovery mechanisms:
- **LLM API Errors**: Automatic fallback strategies
- **JSON Parsing Errors**: Default structure provision
- **File System Errors**: Directory creation retry
- **Code Analysis Errors**: File reload and minimal analysis
- **Import Resolution Errors**: Basic import addition

## ๐Ÿค Contributing

1. Read the [Architecture Overview](agentops_ai/docs/02_ARCHITECTURE_OVERVIEW.md)
2. Complete the [Readiness Checklist](agentops_ai/docs/06_READINESS_CHECKLIST.md)
3. Review [Recent Improvements](docs/IMPORT_FIX_AND_IMPROVEMENTS.md)
4. Explore the CodeTours in `.tours/`
5. Follow the development workflow

## ๐Ÿ” Discoverability Features

AgentOps is designed to be **highly discoverable** with multiple ways to learn and explore:

### ๐ŸŽฏ Interactive Onboarding
```bash
agentops welcome    # Start here for new users
agentops check      # Verify your system setup
agentops status     # Check your project status
```

### ๐Ÿ“š Progressive Help System
```bash
agentops --help     # Main help with command categories
agentops help       # Comprehensive documentation
agentops examples   # Real-world use cases
agentops commands   # All commands organized by function
```

### ๐Ÿš€ Command Discovery
- **Tab Completion**: Works with bash, zsh, fish shells
- **Command Suggestions**: Get help when you make typos
- **Progressive Disclosure**: Essential info first, details on demand
- **Rich Examples**: Every command includes practical examples

### ๐Ÿ“Š System Monitoring
- **Health Checks**: Verify dependencies and configuration
- **Status Monitoring**: Track project progress and health
- **Version Information**: Check updates and compatibility
- **Error Diagnostics**: Clear error messages with solutions

## ๐Ÿ“„ License

MIT License - see [LICENSE](LICENSE) file for details.

---

**Built for vibe coders who want to ship fast without sacrificing quality! ๐Ÿš€**

**Latest**: Multi-agent AI system with enhanced discoverability and CLI experience โœ…

## ๐ŸŒŸ Star Us!

If AgentOps helps you ship faster, please [star us on GitHub](https://github.com/knaig/agentops_ai) โญ 

            

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    "description": "# AgentOps \ud83e\udd16\n\n[![PyPI version](https://badge.fury.io/py/agentops-ai.svg)](https://badge.fury.io/py/agentops-ai)\n[![Python 3.8+](https://img.shields.io/badge/python-3.8+-blue.svg)](https://www.python.org/downloads/)\n[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)\n[![GitHub stars](https://img.shields.io/github/stars/knaig/agentops_ai?style=social)](https://github.com/knaig/agentops_ai)\n\n**The world's first AI-powered QA co-pilot** that automatically generates comprehensive test suites from your code using a sophisticated multi-agent AI system. Think of it as having a team of expert QA engineers working on your codebase 24/7.\n\n## \u2728 Features\n\n- \ud83e\udd16 **6 AI Agents** working in harmony for comprehensive analysis\n- \ud83d\udcdd **Automated Requirements Extraction** from code changes\n- \ud83e\uddea **Intelligent Test Generation** with edge case detection\n- \ud83d\udd04 **Delta-Based Updates** - only analyze what changed\n- \ud83d\udcca **Requirements Traceability** matrix for full coverage\n- \ud83d\ude80 **90% Time Savings** compared to manual test writing\n- \ud83c\udfaf **Zero Configuration** setup with sensible defaults\n- \ud83d\udd27 **Rich CLI** with progressive help and examples\n\n## \ud83d\ude80 Quick Start\n\n### CLI Usage\n\n```bash\n# Install AgentOps\npip install agentops-ai\n\n# Set up your OpenAI API key\nexport OPENAI_API_KEY=\"your-api-key-here\"\n\n# Initialize and run the multi-agent workflow\nagentops init\nagentops multi-agent-run examples/demo-project/calc.py\nagentops run --all\n```\n\n### Python API Usage\n\n```python\nfrom agentops_ai import AgentOps\n\n# Initialize AgentOps for your project\nagentops = AgentOps(\"./my_project\")\n\n# Analyze a Python file\nresult = agentops.analyze_file(\"src/my_module.py\")\n\n# Generate tests\nresult = agentops.generate_tests(\"src/my_module.py\")\n\n# Run tests\nresult = agentops.run_tests(\"src/my_module.py\")\n```\n\n\ud83d\udcd6 **For detailed API documentation, see [docs/api-reference.md](docs/api-reference.md)**\n\n## \ud83c\udfaf What's New (v0.3.0)\n\n### \u2705 Enhanced Discoverability\n- **Rich CLI Help System** with progressive disclosure and examples\n- **Interactive Onboarding** with `agentops welcome` command\n- **System Health Checks** with `agentops check` command\n- **Project Status Monitoring** with `agentops status` command\n- **Version Information** with `agentops version` command\n\n### \u2705 Multi-Agent AI System\nAgentOps uses a **multi-agent AI system** that automatically handles the entire workflow:\n- **CodeAnalyzer Agent**: Deep code structure analysis\n- **RequirementsEngineer Agent**: LLM-powered requirement extraction  \n- **TestArchitect Agent**: Comprehensive test strategy design\n- **TestGenerator Agent**: High-quality test code generation\n- **QualityAssurance Agent**: Test validation and scoring\n- **IntegrationSpecialist Agent**: CI/CD and IDE integration\n\n### \u2705 Enhanced Reliability\n- **Import Issues Fixed**: Resolved CLI import errors for cross-project usage\n- **Error Recovery**: Robust error handling with automatic recovery mechanisms\n- **Type Safety**: Enhanced type annotations and IDE support\n\n### \u2705 Simplified Workflow\n- **One Command**: `agentops multi-agent-run` replaces the old multi-step process\n- **Automatic**: No manual approval steps required\n- **Intelligent**: AI agents handle all decision-making\n\n## \ud83c\udfaf What's New (December 2024)\n\n### \u2705 Multi-Agent AI System\nAgentOps now uses a **multi-agent AI system** that automatically handles the entire workflow:\n- **CodeAnalyzer Agent**: Deep code structure analysis\n- **RequirementsEngineer Agent**: LLM-powered requirement extraction  \n- **TestArchitect Agent**: Comprehensive test strategy design\n- **TestGenerator Agent**: High-quality test code generation\n- **QualityAssurance Agent**: Test validation and scoring\n- **IntegrationSpecialist Agent**: CI/CD and IDE integration\n\n### \u2705 Enhanced Reliability\n- **Import Issues Fixed**: Resolved CLI import errors for cross-project usage\n- **Error Recovery**: Robust error handling with automatic recovery mechanisms\n- **Type Safety**: Enhanced type annotations and IDE support\n\n### \u2705 Simplified Workflow\n- **One Command**: `agentops multi-agent-run` replaces the old multi-step process\n- **Automatic**: No manual approval steps required\n- **Intelligent**: AI agents handle all decision-making\n\n## \ud83d\udcc1 Project Structure\n\n```\nAgentOps/\n\u251c\u2500\u2500 agentops_ai/              # Main package\n\u2502   \u251c\u2500\u2500 agentops_agents/      # Multi-agent system\n\u2502   \u251c\u2500\u2500 agentops_cli/         # Command-line interface\n\u2502   \u251c\u2500\u2500 agentops_core/        # Core business logic\n\u2502   \u251c\u2500\u2500 docs/                 # Documentation\n\u2502   \u251c\u2500\u2500 prompts/              # LLM prompt templates\n\u2502   \u2514\u2500\u2500 .tours/               # CodeTour files\n\u251c\u2500\u2500 examples/                 # Example projects\n\u2502   \u2514\u2500\u2500 demo-project/         # Demo project\n\u251c\u2500\u2500 .github/                  # CI/CD workflows\n\u251c\u2500\u2500 .private/                 # Internal documentation\n\u2514\u2500\u2500 docs/                     # Project documentation\n```\n\n## \ud83d\udee0\ufe0f Development\n\n### Prerequisites\n\n- Python 3.11+\n- Poetry\n- OpenAI API key\n\n### Setup\n\n```bash\n# Clone and install\ngit clone <repository-url>\ncd AgentOps\npip install -e .\n\n# Set environment variables\nexport OPENAI_API_KEY=\"your-api-key\"\n```\n\n### Running Tests\n\n```bash\n# Run all tests\npytest\n\n# Run with coverage\npytest --cov=agentops_ai\n\n# Run linting\nruff check agentops_ai\nblack --check agentops_ai\n```\n\n### Documentation\n\n```bash\n# Start documentation server\ncd agentops_ai/docs\nmkdocs serve\n```\n\n## \ud83d\udcda Documentation\n\n- **[Documentation Overview](docs/README.md)** - Complete documentation structure and navigation\n- **[Quick Start Guide](docs/user-guides/01_QUICK_START.md)** - Get up and running with multi-agent system\n- **[AgentOps Runner Guide](docs/user-guides/AGENTOPS_RUNNER_GUIDE.md)** - Complete guide for the runner script\n- **[Architecture Overview](docs/developer-guides/02_ARCHITECTURE_OVERVIEW.md)** - Multi-agent system design\n- **[Multi-Agent Differences](docs/developer-guides/04_MULTI_AGENT_DIFF.md)** - Legacy vs. new system comparison\n- **[API Reference](docs/api-reference/)** - Complete API docs\n- **[Readiness Checklist](docs/developer-guides/06_READINESS_CHECKLIST.md)** - Engineer onboarding\n- **[Recent Improvements](docs/changelog/IMPORT_FIX_AND_IMPROVEMENTS.md)** - Latest fixes and enhancements\n\n## \ud83c\udfaf Multi-Agent Workflow\n\nAgentOps now follows a **single-command automation** workflow:\n\n```bash\n# Initialize project\nagentops init\n\n# Run complete multi-agent workflow\nagentops multi-agent-run path/to/your_file.py\n\n# Execute generated tests\nagentops run --all\n```\n\n### What Each Agent Does\n1. **CodeAnalyzer**: Analyzes code structure and dependencies\n2. **RequirementsEngineer**: Extracts functional requirements using LLM\n3. **TestArchitect**: Designs comprehensive test strategy\n4. **TestGenerator**: Creates high-quality test code\n5. **QualityAssurance**: Validates and scores test quality\n6. **IntegrationSpecialist**: Sets up CI/CD and IDE integrations\n\n## \ud83d\udd27 CLI Commands\n\n### Core Workflow\n```bash\nagentops init                    # Initialize project structure\nagentops multi-agent-run <file>  # Run complete multi-agent workflow\nagentops run --file <file>       # Run tests for specific file\nagentops run --all               # Run all generated tests\n```\n\n### Discovery & Help\n```bash\nagentops welcome                 # Interactive welcome and quick start guide\nagentops help                    # Comprehensive help documentation\nagentops examples                # Practical examples and use cases\nagentops commands                # List all available commands\nagentops version                 # Show version and system information\nagentops check                   # Verify system requirements and setup\nagentops status                  # Show project status and configuration\n```\n\n### Management & Analysis\n```bash\nagentops config                  # View and edit configuration\nagentops report --check-changes  # Generate analysis report\nagentops traceability --open     # View requirements-to-tests matrix\nagentops integration setup       # Configure CI/CD and IDE integrations\n```\n\n### Getting Started\n```bash\n# First time? Start here:\nagentops welcome                 # Interactive onboarding\nagentops check                   # Verify your setup\nagentops init                    # Initialize your project\nagentops multi-agent-run myfile.py  # Run your first workflow\n```\n\n## \ud83c\udfd7\ufe0f Architecture\n\nAgentOps uses a modern multi-agent architecture:\n\n- **Multi-Agent Layer**: Specialized AI agents for each task\n- **LangGraph Orchestration**: Modern AI workflow management\n- **CLI Layer**: Command-line interface with Click\n- **Core Engine**: Business logic and state management\n- **Service Layer**: LLM-based analysis and generation\n- **Storage Layer**: SQLite database for requirements\n\n## \ud83d\udee1\ufe0f Error Recovery\n\nThe system includes robust error recovery mechanisms:\n- **LLM API Errors**: Automatic fallback strategies\n- **JSON Parsing Errors**: Default structure provision\n- **File System Errors**: Directory creation retry\n- **Code Analysis Errors**: File reload and minimal analysis\n- **Import Resolution Errors**: Basic import addition\n\n## \ud83e\udd1d Contributing\n\n1. Read the [Architecture Overview](agentops_ai/docs/02_ARCHITECTURE_OVERVIEW.md)\n2. Complete the [Readiness Checklist](agentops_ai/docs/06_READINESS_CHECKLIST.md)\n3. Review [Recent Improvements](docs/IMPORT_FIX_AND_IMPROVEMENTS.md)\n4. Explore the CodeTours in `.tours/`\n5. Follow the development workflow\n\n## \ud83d\udd0d Discoverability Features\n\nAgentOps is designed to be **highly discoverable** with multiple ways to learn and explore:\n\n### \ud83c\udfaf Interactive Onboarding\n```bash\nagentops welcome    # Start here for new users\nagentops check      # Verify your system setup\nagentops status     # Check your project status\n```\n\n### \ud83d\udcda Progressive Help System\n```bash\nagentops --help     # Main help with command categories\nagentops help       # Comprehensive documentation\nagentops examples   # Real-world use cases\nagentops commands   # All commands organized by function\n```\n\n### \ud83d\ude80 Command Discovery\n- **Tab Completion**: Works with bash, zsh, fish shells\n- **Command Suggestions**: Get help when you make typos\n- **Progressive Disclosure**: Essential info first, details on demand\n- **Rich Examples**: Every command includes practical examples\n\n### \ud83d\udcca System Monitoring\n- **Health Checks**: Verify dependencies and configuration\n- **Status Monitoring**: Track project progress and health\n- **Version Information**: Check updates and compatibility\n- **Error Diagnostics**: Clear error messages with solutions\n\n## \ud83d\udcc4 License\n\nMIT License - see [LICENSE](LICENSE) file for details.\n\n---\n\n**Built for vibe coders who want to ship fast without sacrificing quality! \ud83d\ude80**\n\n**Latest**: Multi-agent AI system with enhanced discoverability and CLI experience \u2705\n\n## \ud83c\udf1f Star Us!\n\nIf AgentOps helps you ship faster, please [star us on GitHub](https://github.com/knaig/agentops_ai) \u2b50 \n",
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