WorkflowStudio 🤖

WorkflowStudio is a sophisticated multi-agent AI system that coordinates multiple specialized AI agents to complete complex tasks and workflows. The system features a modern web interface for managing missions and building custom agent workflows.

🌟 Features

Core Capabilities

• Multi-Agent Coordination: Five specialized AI agents working together
• Web Search Integration: Real-time information gathering from the web
• Custom Workflows: Visual workflow builder for creating custom agent sequences
• Real-time Monitoring: Live updates on agent status and task progress
• Modern Web Interface: Clean, dark-themed web UI with real-time updates

Specialized Agents

• 🎯 Manager: Coordinates missions and manages team workflow
• 🔍 Researcher: Gathers information using web search capabilities
• 📊 Analyst: Analyzes data and provides insights
• ✍️ Writer: Creates high-quality content and documentation
• 👁️ Reviewer: Reviews work quality and validates results

🏗️ Architecture

The system is built with:

• Backend: Python with Flask and SocketIO for real-time communication
• Frontend: Modern JavaScript with WebSocket integration
• AI Engine: Ollama for local LLM inference
• Web Search: Google Search and BeautifulSoup for web research
• Communication: Real-time updates via WebSockets

📋 Prerequisites

Before running WorkflowStudio, ensure you have:

1. Python 3.8+ installed
2. Ollama installed and running locally
3. Required AI Models downloaded in Ollama:
   • llama3.2:latest (primary model)
   • qwen3:latest (alternative model)
   • Whichever model you prefer for your tasks

Installing Ollama and Models

# Install Ollama (macOS)
brew install ollama

# Start Ollama service
ollama serve

# Download required models
ollama pull llama3.2:latest
ollama pull qwen3:latest

🚀 Installation & Setup

1. Clone the Repository

git clone <repository-url>
cd WorkflowStudio

2. Install Dependencies

pip install -r requirements.txt

3. Verify Ollama Connection

Make sure Ollama is running on http://localhost:11434:

curl http://localhost:11434/api/version

4. Start the Application

# Option 1: Use the automated startup script
python start_web.py

# Option 2: Start manually
python web_server.py

The web interface will be available at http://localhost:7777

💻 Usage

Custom Workflow Builder

2. Design Your Workflow:
   • Drag agents from the palette to the canvas
   • Configure each agent's task and parameters
   • Set custom prompts for specialized behavior
   • Choose specific AI models for each agent
3. Save and Execute: Save your workflow and run it with custom missions

Pre-built Workflows

• Business Plan Development: Complete business plan creation workflow
• Content Marketing Campaign: End-to-end marketing campaign development
• Market Research Analysis: Comprehensive market research and competitor analysis
• Product Launch Strategy: Full product launch planning and execution
• Blog Post Creation: Simple content creation workflow

🔧 Configuration

Central Configuration (config.py)

The system uses a centralized configuration file that contains all shared constants and settings:

# Server configuration
SERVER_PORT = 7777
SERVER_HOST = '0.0.0.0'

# Ollama configuration
OLLAMA_BASE_URL = "http://localhost:11434"
OLLAMA_API_VERSION = "/api/version"
OLLAMA_API_TAGS = "/api/tags"
OLLAMA_TIMEOUT = 5

# Default model configuration
DEFAULT_MODEL = "llama3.2:latest"

# Web interface configuration
SECRET_KEY = 'infinitybench_ai_secret_2024'
TEMPLATES_AUTO_RELOAD = True

# Workflow storage configuration
WORKFLOWS_DIR = 'workflows'

# Logging configuration
MAX_LOGS_DISPLAY = 50

Additional Configuration (config.json)

{
  "ollama": {
    "base_url": "http://localhost:11434",
    "model": "qwen3:latest",
    "temperature": 0.7,
    "top_p": 0.9,
    "max_tokens": 1000
  },
  "agents": {
    "manager": { "enabled": true, "max_memory": 100 },
    "researcher": { "enabled": true, "max_memory": 100 },
    "analyst": { "enabled": true, "max_memory": 100 },
    "writer": { "enabled": true, "max_memory": 100 },
    "reviewer": { "enabled": true, "max_memory": 100 }
  }
}

Customizing Settings

To modify system settings, edit the config.py file:

• Server Port: Change SERVER_PORT to use a different port
• Ollama Connection: Update OLLAMA_BASE_URL for different Ollama instances
• Default Model: Modify DEFAULT_MODEL to use your preferred AI model
• Workflow Storage: Change WORKFLOWS_DIR for custom workflow location

Environment Variables

You can override configuration with environment variables:

• OLLAMA_BASE_URL: Ollama server URL
• DEFAULT_MODEL: Default AI model to use

🔍 How It Works

Standard Mission Flow

1. Planning Phase: Manager analyzes the mission and creates execution plan
2. Research Phase: Researcher gathers information using web search
3. Analysis Phase: Analyst processes data and identifies insights
4. Content Creation: Writer creates comprehensive content
5. Quality Review: Reviewer validates and provides feedback

Agent Communication

• Agents maintain memory of previous interactions
• Context is shared between agents for continuity
• Real-time status updates through WebSocket connection

Web Search Integration

The Researcher agent can:

• Perform Google web searches
• Extract and summarize web content
• Cite sources in research reports

📁 Project Structure

WorkflowStudio/
├── main.py                 # Core multi-agent system
├── web_server.py          # Flask web server
├── start_web.py           # Automated startup script
├── config.py              # Centralized configuration settings
├── config.json            # Additional configuration settings
├── requirements.txt       # Python dependencies
├── static/                # Web assets (CSS, JS)
├── templates/             # HTML templates
└── workflows/             # Pre-built workflow definitions

🛠️ API Endpoints

REST API

• POST /api/start_mission - Start a new mission
• POST /api/run_custom_workflow - Execute custom workflow
• GET /api/ollama/models - Get available AI models

WebSocket Events

• log_update - Real-time log messages
• agent_status_update - Agent status changes
• mission_completed - Mission completion
• mission_error - Error notifications

🔧 Troubleshooting

Common Issues

Ollama Connection Failed

# Check if Ollama is running
curl http://localhost:11434/api/version

# Start Ollama if not running
ollama serve

Model Not Found

# Download required models
ollama pull llama3.2:latest
ollama pull qwen3:latest

Web Search Not Working

• Ensure you have internet connectivity
• Check if Google search dependencies are installed:

pip install googlesearch-python beautifulsoup4

Port 7777 Already in Use

# Kill process using port 7777
lsof -ti:7777 | xargs kill -9

# Or modify the port in config.py
# Edit config.py and change SERVER_PORT = 7777 to your preferred port

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📜 License

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

🆘 Support

If you encounter issues or have questions:

1. Check the troubleshooting section above
2. Review the console logs for error messages
3. Ensure all prerequisites are properly installed
4. Open an issue on the repository with detailed information

🚀 What's Next?

Future enhancements planned:

• Integration with additional AI models
• Advanced workflow branching and conditionals
• Multi-language support
• Cloud deployment options
• Enhanced collaboration features
• API integrations for external services

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WorkflowStudio - Unleashing the power of coordinated AI agents for complex task completion! 🚀