# AgentOps 🤖
[](https://badge.fury.io/py/agentops-ai)
[](https://www.python.org/downloads/)
[](https://opensource.org/licenses/MIT)
[](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.
## ✨ Features
- 🤖 **6 AI Agents** working in harmony for comprehensive analysis
- 📝 **Automated Requirements Extraction** from code changes
- 🧪 **Intelligent Test Generation** with edge case detection
- 🔄 **Delta-Based Updates** - only analyze what changed
- 📊 **Requirements Traceability** matrix for full coverage
- 🚀 **90% Time Savings** compared to manual test writing
- 🎯 **Zero Configuration** setup with sensible defaults
- 🔧 **Rich CLI** with progressive help and examples
## 🚀 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")
```
📖 **For detailed API documentation, see [docs/api-reference.md](docs/api-reference.md)**
## 🎯 What's New (v0.3.0)
### ✅ 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
### ✅ 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
### ✅ 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
### ✅ 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
## 🎯 What's New (December 2024)
### ✅ 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
### ✅ 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
### ✅ 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
## 📁 Project Structure
```
AgentOps/
├── agentops_ai/ # Main package
│ ├── agentops_agents/ # Multi-agent system
│ ├── agentops_cli/ # Command-line interface
│ ├── agentops_core/ # Core business logic
│ ├── docs/ # Documentation
│ ├── prompts/ # LLM prompt templates
│ └── .tours/ # CodeTour files
├── examples/ # Example projects
│ └── demo-project/ # Demo project
├── .github/ # CI/CD workflows
├── .private/ # Internal documentation
└── docs/ # Project documentation
```
## 🛠️ 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
```
## 📚 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
## 🎯 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
## 🔧 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
```
## 🏗️ 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
## 🛡️ 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
## 🤝 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
## 🔍 Discoverability Features
AgentOps is designed to be **highly discoverable** with multiple ways to learn and explore:
### 🎯 Interactive Onboarding
```bash
agentops welcome # Start here for new users
agentops check # Verify your system setup
agentops status # Check your project status
```
### 📚 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
```
### 🚀 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
### 📊 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
## 📄 License
MIT License - see [LICENSE](LICENSE) file for details.
---
**Built for vibe coders who want to ship fast without sacrificing quality! 🚀**
**Latest**: Multi-agent AI system with enhanced discoverability and CLI experience ✅
## 🌟 Star Us!
If AgentOps helps you ship faster, please [star us on GitHub](https://github.com/knaig/agentops_ai) ⭐
Raw data
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"description": "# AgentOps \ud83e\udd16\n\n[](https://badge.fury.io/py/agentops-ai)\n[](https://www.python.org/downloads/)\n[](https://opensource.org/licenses/MIT)\n[](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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