klingai-api 0.1.0

Unofficial Kling AI API client, CLI and MCP server

klingai-api

Unofficial Kling AI Python SDK, CLI, and MCP server.

This package serves a dual purpose: it works as a Model Context Protocol (MCP) server that gives LLMs access to Kling AI's video generation capabilities, and as a standalone Python SDK + CLI for developers who want to integrate Kling AI into their own projects.

Quick Start (MCP)

Add to your MCP client config (Claude Desktop, Cursor, etc.):

{
  "mcpServers": {
    "klingai": {
      "command": "pipx run",
      "args": ["klingai-api", "mcp"],
      "env": {
        "ACCESS_TOKEN": "<your-access-key>",
        "SECRET_TOKEN": "<your-secret-key>"
      }
    }
  }
}

Or run it manually:

# stdio (default)
klingai mcp

# streamable HTTP
klingai mcp --http --port 8000

Get your API keys from the Kling AI Developer Platform.

Installation

pip install klingai-api

Requires Python 3.14+.

Authentication

Credentials can be provided via environment variables or CLI flags:

# Environment variables (recommended)
export ACCESS_TOKEN="your-access-key"
export SECRET_TOKEN="your-secret-key"

# CLI flags
klingai --access-token <key> --secret-token <key> <command>

Verify your credentials:

klingai test-credentials

CLI Usage

Every API method maps to a subcommand. Run klingai --help for the full list.

Text to Video

# Create a generation task
klingai text2video --prompt "A cat walking on the beach at sunset"

# With options
klingai text2video \
  --prompt "A drone shot over a mountain range" \
  --model-name kling-v2-6 \
  --mode pro \
  --aspect-ratio 9:16 \
  --duration 10

# Check task status
klingai get-text2video --task-id <task_id>

# List all tasks
klingai list-text2video --page-num 1 --page-size 10

Image to Video

# Animate an image
klingai image2video \
  --image "https://example.com/photo.jpg" \
  --prompt "Gentle wind blowing through the scene"

# With camera control
klingai image2video \
  --image "https://example.com/photo.jpg" \
  --camera-control '{"type": "simple", "config": {"zoom": 5}}'

# Check task status
klingai get-image2video --task-id <task_id>

# List all tasks
klingai list-image2video

MCP Server

# stdio transport (for MCP clients)
klingai mcp

# streamable HTTP transport
klingai mcp --http --host 0.0.0.0 --port 8000

Python SDK

from api import KlingAiAPI

api = KlingAiAPI(
    access_token="your-access-key",
    secret_token="your-secret-key",
)

with api:
    # Text to video
    task = api.text2video("A cat walking on the beach at sunset")
    print(task)

    # Check status
    status = api.get_text2video(task["data"]["task_id"])
    print(status)

    # Image to video
    task = api.image2video(
        "https://example.com/photo.jpg",
        prompt="Gentle wind blowing",
        mode="pro",
    )

Available Models

Text to Video	Image to Video
kling-v1 (default)	kling-v1 (default)
kling-v1-6	kling-v1-5
kling-v2-master	kling-v1-6
kling-v2-1-master	kling-v2-master
kling-v2-5-turbo	kling-v2-1
kling-v2-6	kling-v2-1-master
	kling-v2-5-turbo
	kling-v2-6

Support

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License

MIT