Kling AI Python SDK

A modern, type-safe Python SDK for interacting with the Kling AI API. This SDK provides a clean, intuitive interface for all Kling AI services including video generation, image processing, and account management.

✨ Features

• Full API Coverage: Support for all Kling AI endpoints
• Type Safety: Built with Pydantic v2 for runtime type checking
• Async/Await: Native async support with HTTPX
• Automatic Retries: Configurable retry logic for transient failures
• Comprehensive Models: Strongly-typed request/response models
• Developer Experience: Detailed error messages and logging

🚀 Installation

# Using Poetry (recommended)
poetry add kling-ai-sdk

# Using pip
pip install kling-ai-sdk

📚 Quick Start

Basic Usage

from kling.client import KlingClient
from kling.api.text_to_video import TextToVideoRequest

async def main():
    # Initialize client with your API key
    client = KlingClient(api_key="your-api-key")
    
    # Create a text-to-video request
    request = TextToVideoRequest(
        prompt="A beautiful sunset over mountains",
        duration=5.0,
        resolution="1920x1080"
    )
    
    # Submit the request
    response = await client.text_to_video(request)
    print(f"Video generation started with ID: {response.task_id}")

# Run the async function
import asyncio
asyncio.run(main())

📦 API Modules

Account Information

• Query resource packages and usage
• Check account limits and quotas

from kling.api.account_information_inquiry import get_account_costs

async def check_usage():
    client = KlingClient(api_key="your-api-key")
    response = await get_account_costs(
        client=client,
        start_time=start_timestamp,
        end_time=end_timestamp
    )
    return response

Callback Protocol

• Handle asynchronous callbacks for long-running tasks
• Process task updates and completions

from kling.api.callback_protocol import CallbackRequest, register_callback_handler

@register_callback_handler
def handle_callback(callback: CallbackRequest):
    print(f"Received callback for task {callback.task_id}")
    print(f"Status: {callback.task_status}")
    if callback.task_result:
        print(f"Result URL: {callback.task_result.video_url}")

Media Generation

• Text-to-Video generation
• Image-to-Video conversion
• Multi-Image Video creation
• Video effects and enhancements
• Virtual Try-On functionality

# Text to Video
from kling.api.text_to_video import TextToVideoRequest

# Image to Video
from kling.api.image_to_video import ImageToVideoRequest

# Video Effects
from kling.api.video_effects import apply_effect

🔧 Configuration

Configure the client with environment variables or directly:

from kling.client import KlingClient

# Initialize with custom settings
client = KlingClient(
    api_key="your-api-key",
    base_url="https://api.kling.ai/v1",
    timeout=30.0,
    max_retries=3
)

Environment Variables

export KLING_API_KEY="your-api-key"
export KLING_BASE_URL="https://api.kling.ai/v1"
export KLING_TIMEOUT=30

🤝 Contributing

Contributions are welcome! Please read our contributing guidelines before submitting pull requests.

📄 License

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

Error Handling

The SDK raises KlingAPIError for API-related errors and ValidationError for request/response validation issues.

try:
    task = await video_api.create_task(request)
except KlingAPIError as e:
    print(f"API Error: {e}")
except ValidationError as e:
    print(f"Validation Error: {e}")

Contributing

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a pull request

License

MIT