🤖 WyseOS SDK for Python

The official Python SDK for WyseOS - Build intelligent AI-powered applications with seamless API integration, real-time WebSocket support, and simplified task execution.

🚀 New in v0.2.1: Simplified task execution interface with TaskRunner - execute complex AI tasks with just a few lines of code!

✨ What is WyseOS?

WyseOS is an advanced AI platform that enables developers to build sophisticated AI agents and workflows. The Python SDK provides:

• 🎯 Intelligent Task Execution - Run complex AI tasks with automatic plan acceptance
• 🔄 Real-time Communication - WebSocket integration for live AI interactions
• 📂 File Processing - Upload and analyze documents, images, and data files
• 🌐 Browser Automation - AI-powered web browsing and data extraction
• 👥 Team Management - Organize AI agents and workflows by teams
• 🛡️ Enterprise Ready - Type-safe, robust error handling, and comprehensive logging

🚀 Quick Start

Installation

pip install wyseos-sdk

30-Second Example

from wyseos.mate import Client
from wyseos.mate.config import load_config
from wyseos.mate.models import CreateSessionRequest
from wyseos.mate.websocket import WebSocketClient, TaskExecutionOptions

# Initialize client
client = Client(load_config("mate.yaml"))

# Create session
session = client.session.create(
    CreateSessionRequest(team_id="wyse_mate", task="Analyze market trends")
)
session_info = client.session.get_info(session.session_id)

# Execute AI task
ws_client = WebSocketClient(
    base_url=client.base_url,
    api_key=client.api_key,
    session_id=session_info.session_id
)
task_runner = ws_client.create_task_runner(client, session_info)

result = task_runner.run_task(
    task="Analyze Q4 2024 market trends in tech sector",
    team_id="wyse_mate",
    options=TaskExecutionOptions(auto_accept_plan=True)
)

if result.success:
    print(f"✅ Analysis complete: {result.final_answer}")
    print(f"⏱️ Completed in {result.session_duration:.1f} seconds")
else:
    print(f"❌ Task failed: {result.error}")

🎯 Complete Quick Start Guide →

🎮 Key Features

🤖 Simplified Task Execution

Execute complex AI workflows with minimal code:

# Automated execution - fire and forget
result = task_runner.run_task(
    task="Create a comprehensive market analysis report",
    team_id="wyse_mate",
    options=TaskExecutionOptions(auto_accept_plan=True)
)

# Interactive execution - with user input
task_runner.run_interactive_session(
    initial_task="Help me research competitors",
    team_id="wyse_mate"
)

📂 File Upload & Processing

# Upload files for AI analysis
uploaded_files = []
upload_result = client.file_upload.upload_file("data.csv")
if upload_result.get("file_url"):
    uploaded_files.append({
        "file_name": "data.csv",
        "file_url": upload_result["file_url"]
    })

# Use files in task execution
result = task_runner.run_task(
    task="Analyze this dataset and create visualizations",
    attachments=uploaded_files,
    team_id="wyse_mate"
)

⚡ Real-time WebSocket Integration

from wyseos.mate.websocket import WebSocketClient, MessageType

ws = WebSocketClient(
    base_url=client.base_url,
    api_key=client.api_key,
    session_id=session_id
)

ws.set_message_handler(lambda msg: print(f"AI Agent: {msg.get('content')}"))
ws.connect(session_id)

🔧 Flexible Configuration

# mate.yaml
mate:
  api_key: "your-api-key"
  base_url: "https://api.wyseos.com"
  timeout: 30

# Configuration options
options = TaskExecutionOptions(
    auto_accept_plan=True,           # ✅ Auto-approve AI plans
    capture_screenshots=False,        # 📸 Browser screenshots
    enable_browser_logging=True,      # 🌐 Browser activity logs
    completion_timeout=300,           # ⏱️ Task timeout
)

📚 Documentation & Examples

Resource	Description
🚀 Quick Start Guide	Get up and running in 5 minutes
📖 Installation Guide	Detailed installation instructions
🎯 Complete Example	Full-featured example application
📋 Release Notes	Latest updates and changes

🎪 Example Applications

The examples/ directory contains real-world usage patterns:

• 📊 Data Analysis: Upload CSV files and get AI-powered insights
• 🌐 Web Research: Automated web browsing and data extraction
• 📝 Document Processing: Analyze PDFs, images, and text files
• 💬 Interactive Sessions: Build chatbot-like experiences
• 🔄 Workflow Automation: Chain multiple AI tasks together

🛠️ Development & Contributing

Development Setup

# Clone repository
git clone https://github.com/WyseOS/wyseos-sdk-python
cd wyseos-sdk-python

# Create virtual environment
python -m venv venv
source venv/bin/activate  # or `venv\Scripts\activate` on Windows

# Install in development mode
pip install -e .

# Install development tools (optional)
pip install pytest pytest-cov black isort flake8 mypy

Testing

# Run tests
pytest

# With coverage
pytest --cov=wyseos

Contributing

We welcome contributions! Please:

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

📊 Project Status

Component	Status
🔧 Core SDK	✅ Complete
🌐 WebSocket Support	✅ Complete
📁 File Upload	✅ Complete
🤖 Task Execution	✅ Complete
📚 Documentation	✅ Complete
🧪 Test Coverage	🚧 In Progress
📱 Mobile Examples	📋 Planned

🏗️ Architecture

wyseos/
├── mate/                    # Core SDK module
│   ├── client.py           # Main API client
│   ├── websocket.py        # WebSocket + TaskRunner
│   ├── models.py           # Pydantic data models
│   ├── config.py           # Configuration management
│   ├── errors.py           # Exception classes
│   └── services/           # API service modules
│       ├── user.py         # User management
│       ├── team.py         # Team operations
│       ├── agent.py        # AI agent management
│       ├── session.py      # Session handling
│       ├── browser.py      # Browser automation
│       └── file_upload.py  # File operations

🔗 API Reference

Core Classes

Class	Purpose
Client	Main API client for HTTP requests
WebSocketClient	Real-time WebSocket communication
TaskRunner	Simplified task execution interface
TaskExecutionOptions	Configuration for task execution
TaskResult	Comprehensive task execution results

Key Methods

# Client operations
client.user.list_api_keys()
client.team.get_list()
client.agent.get_list()
client.session.create()

# File operations
client.file_upload.validate_file()
client.file_upload.upload_file()

# Task execution
task_runner.run_task()              # Automated execution
task_runner.run_interactive_session()  # Interactive mode

🚨 Error Handling

The SDK provides structured error handling:

from wyseos.mate.errors import APIError, ValidationError, NetworkError

try:
    result = task_runner.run_task("Your task")
except ValidationError as e:
    print(f"Validation error: {e}")
except APIError as e:
    print(f"API error: {e.message}")
except NetworkError as e:
    print(f"Network error: {e}")

🌟 What's New in v0.2.0

• 🎯 New TaskRunner Interface: Execute AI tasks with 90% less code
• ⚡ Performance Optimizations: Screenshots disabled by default for faster execution
• 🔧 Simplified Configuration: Cleaner options with intelligent defaults
• 📚 Enhanced Documentation: Complete rewrite with practical examples
• 🏗️ Improved Architecture: Better separation of concerns and modularity

📄 License

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

🆘 Support & Community

• 🐛 Issues: GitHub Issues
• 📧 Email: support@wyseos.com
• 💬 Discussions: GitHub Discussions
• 📖 Documentation: API Docs

🔗 Related Projects

• 🌐 WyseOS Platform: wyseos.com
• 📦 PyPI Package: pypi.org/project/wyseos-sdk
• 🧪 JavaScript SDK: Coming soon
• 🔗 REST API: docs.wyseos.com

---

🚀 Ready to build the future with AI?

Get Started • View Examples • API Docs

Built with ❤️ by the WyseOS team