Skillget

HappyHorse-1.0-API

mcp
Security Audit
Pass
Health Pass
  • License — License: MIT
  • Description — Repository has a description
  • Active repo — Last push 0 days ago
  • Community trust — 86 GitHub stars
Code Pass
  • Code scan — Scanned 4 files during light audit, no dangerous patterns found
Permissions Pass
  • Permissions — No dangerous permissions requested
Purpose
This Python package is a Model Context Protocol (MCP) server and API wrapper. It allows developers to integrate with Alibaba's video generation AI via the muapi.ai platform, enabling the creation and editing of 1080p videos from text prompts or static images.

Security Assessment
Overall Risk: Medium. The tool requires sending an API key to the third-party service muapi.ai, which means your prompts, uploaded images, and generated content will traverse and be processed on external servers. No dangerous local permissions or shell executions were detected, but users should be aware that "HappyHorse" is not an official Alibaba SDK. It is an independent wrapper routing traffic through a third-party API gateway.

Quality Assessment
The project is actively maintained (last updated today) and holds the highly permissive MIT license. It has earned 86 GitHub stars, indicating a solid baseline of community trust. A light code scan of four files found no dangerous or malicious patterns, showing the code is clean and straightforward for its intended purpose.

Verdict
Use with caution due to reliance on an unofficial third-party API gateway for authentication and data processing.
SUMMARY

Python wrapper for HappyHorse 1.0 API by Alibaba — #1 ranked AI video generator. Generate native 1080p HD videos with integrated audio from text and images.

README.md

HappyHorse 1.0 API: Python Wrapper for Alibaba's #1 AI Video Generator

PyPI version
GitHub stars
License: MIT
Python 3.7+

The most comprehensive Python wrapper for the HappyHorse 1.0 API (developed by Alibaba's Taotian Group), delivered via muapi.ai. Generate cinematic, native 1080p AI videos with integrated audio from text prompts and static images — currently ranked #1 on the Artificial Analysis Video Arena.

🚀 Why Use HappyHorse 1.0 API?

HappyHorse 1.0 is Alibaba's state-of-the-art AI video generation model, built by the Future Life Lab team at Taotian Group. It debuted anonymously on April 7, 2026, instantly claiming the top spot in both Text-to-Video and Image-to-Video categories on the Artificial Analysis leaderboard.

  • #1 Ranked: 1333 Elo in T2V, 1392 Elo in I2V — surpassing every competitor on the Artificial Analysis leaderboard.
  • Native 1080p HD: Full HD output without upscaling, powered by a 15B-parameter 40-layer Transformer architecture.
  • Integrated Audio-Video: Jointly generates video and audio in a single forward pass — no separate audio pipeline needed.
  • Blazing Fast: ~10 seconds average generation time, one of the fastest available models.
  • Developer-First: Simple Python SDK via MuAPI infrastructure.

🌟 Key Features of HappyHorse 1.0 API

  • ✅ HappyHorse 1.0 Text-to-Video (T2V): Transform descriptive prompts into stunning native 1080p HD video clips.
  • ✅ HappyHorse 1.0 Image-to-Video (I2V): Animate static images with precise motion control using images_list.
  • ✅ Integrated Audio-Video Generation: Generate synchronized audio and video jointly in one Transformer pass — include sound cues like "rain pattering" or "crowd cheering" directly in your prompt.
  • ✅ Video Extension: Seamlessly extend existing clips while maintaining consistent style and motion.
  • ✅ Video Edit: Edit existing videos using natural language prompts and reference images.
  • ✅ File Upload: Upload local images and videos directly via the upload_file method for use in generation tasks.
  • ✅ Flexible Aspect Ratios: Optimized for 16:9, 9:16 (TikTok/Reels), and 1:1.
  • ✅ Quality Tiers: 1080p (default) and 4k output support.

🛠 Installation

Via Pip (Recommended)

pip install happyhorse-1-api

From Source

# Clone the HappyHorse 1.0 API repository
git clone https://github.com/Anil-matcha/HappyHorse-1.0-API.git
cd HappyHorse-1.0-API

# Install required dependencies
pip install -r requirements.txt

Configuration

Create a .env file in the root directory and add your MuAPI API key:

MUAPI_API_KEY=your_muapi_api_key_here

🤖 HappyHorse 1.0 MCP Server

Use HappyHorse 1.0 as an MCP (Model Context Protocol) server, allowing AI assistants like Claude Desktop or Cursor to directly invoke HappyHorse generation tools.

Running the MCP Server

  1. Ensure MUAPI_API_KEY is set in your environment.
  2. Run the server:
    python3 mcp_server.py
  3. To test with the MCP Inspector:
    npx -y @modelcontextprotocol/inspector python3 mcp_server.py

💻 Quick Start with HappyHorse 1.0 API (Python)

from happyhorse_api import HappyHorseAPI

# Initialize the HappyHorse 1.0 client
api = HappyHorseAPI()

# 1. Generate Video from Text (T2V)
print("Generating AI Video using HappyHorse 1.0...")
submission = api.text_to_video(
    prompt="A cinematic aerial shot of a coastal city at golden hour, waves crashing against cliffs, birds flying, 1080p",
    aspect_ratio="16:9",
    duration=10,
    quality="1080p"
)

# 2. Wait for completion
result = api.wait_for_completion(submission['request_id'])
print(f"Success! View your HappyHorse 1.0 video here: {result['outputs'][0]}")

🎵 Audio-Video Generation

HappyHorse 1.0's standout feature: jointly generating video and audio in a single Transformer forward pass.

from happyhorse_api import HappyHorseAPI

api = HappyHorseAPI()

# Generate video + audio together from text
submission = api.text_to_video_with_audio(
    prompt="A thunderstorm rolling over a mountain range, lightning flashing, thunder rumbling, rain pattering on leaves",
    aspect_ratio="16:9",
    duration=10,
    quality="1080p"
)

result = api.wait_for_completion(submission['request_id'])
print(f"Video with audio: {result['outputs'][0]}")

# Or use image-to-video with audio
submission = api.image_to_video_with_audio(
    prompt="@image1 comes alive — waves crashing, seagulls calling, ocean breeze",
    images_list=["https://example.com/beach.jpg"],
    aspect_ratio="16:9",
    duration=10,
)
result = api.wait_for_completion(submission['request_id'])
print(f"Animated image with audio: {result['outputs'][0]}")

Tip: Include explicit sound cues in your prompt (e.g. "crowd cheering", "piano melody", "engine roaring") for richer, more accurate audio generation.

📡 API Endpoints & Reference

1. HappyHorse 1.0 Text-to-Video (T2V)

Endpoint: POST https://api.muapi.ai/api/v1/happyhorse-1.0-t2v

curl --location --request POST "https://api.muapi.ai/api/v1/happyhorse-1.0-t2v" \
  --header "Content-Type: application/json" \
  --header "x-api-key: YOUR_API_KEY" \
  --data-raw '{
      "prompt": "A majestic eagle soaring over snow-capped mountains at sunrise",
      "aspect_ratio": "16:9",
      "duration": 10,
      "quality": "1080p"
  }'

2. HappyHorse 1.0 Image-to-Video (I2V)

Endpoint: POST https://api.muapi.ai/api/v1/happyhorse-1.0-i2v

Reference images with @image1, @image2, etc. in the prompt.

curl --location --request POST "https://api.muapi.ai/api/v1/happyhorse-1.0-i2v" \
  --header "Content-Type: application/json" \
  --header "x-api-key: YOUR_API_KEY" \
  --data-raw '{
      "prompt": "@image1 — the clouds drift slowly, light shifts from golden to dusk",
      "images_list": ["https://example.com/landscape.jpg"],
      "aspect_ratio": "16:9",
      "duration": 10,
      "quality": "1080p"
  }'

3. HappyHorse 1.0 T2V with Audio

Endpoint: POST https://api.muapi.ai/api/v1/happyhorse-1.0-t2v-audio

Jointly generate video and synchronized audio in one pass.

curl --location --request POST "https://api.muapi.ai/api/v1/happyhorse-1.0-t2v-audio" \
  --header "Content-Type: application/json" \
  --header "x-api-key: YOUR_API_KEY" \
  --data-raw '{
      "prompt": "A busy Tokyo street at night, neon signs, rain, traffic noise, jazz music drifting from a bar",
      "aspect_ratio": "16:9",
      "duration": 10,
      "quality": "1080p"
  }'

4. HappyHorse 1.0 I2V with Audio

Endpoint: POST https://api.muapi.ai/api/v1/happyhorse-1.0-i2v-audio

Animate images with jointly generated audio.

curl --location --request POST "https://api.muapi.ai/api/v1/happyhorse-1.0-i2v-audio" \
  --header "Content-Type: application/json" \
  --header "x-api-key: YOUR_API_KEY" \
  --data-raw '{
      "prompt": "@image1 — waves begin to crash, seagulls cry in the distance, wind howling",
      "images_list": ["https://example.com/ocean.jpg"],
      "aspect_ratio": "16:9",
      "duration": 10,
      "quality": "1080p"
  }'

5. Video Extension

Endpoint: POST https://api.muapi.ai/api/v1/happyhorse-1.0-extend

curl --location --request POST "https://api.muapi.ai/api/v1/happyhorse-1.0-extend" \
  --header "Content-Type: application/json" \
  --header "x-api-key: YOUR_API_KEY" \
  --data-raw '{
      "request_id": "your-completed-request-id",
      "prompt": "The eagle lands on a mountain peak, surveying the valley",
      "duration": 10,
      "quality": "1080p"
  }'

6. Video Edit

Endpoint: POST https://api.muapi.ai/api/v1/happyhorse-1.0-video-edit

curl --location --request POST "https://api.muapi.ai/api/v1/happyhorse-1.0-video-edit" \
  --header "Content-Type: application/json" \
  --header "x-api-key: YOUR_API_KEY" \
  --data-raw '{
      "prompt": "Change the weather to a dramatic thunderstorm",
      "video_urls": ["https://example.com/video.mp4"],
      "aspect_ratio": "16:9",
      "quality": "1080p"
  }'

📖 API Method Reference

Method Parameters Description
text_to_video prompt, aspect_ratio, duration, quality, with_audio Generate native 1080p video from text.
image_to_video prompt, images_list, aspect_ratio, duration, quality, with_audio Animate images. Reference with @image1, @image2, etc.
text_to_video_with_audio prompt, aspect_ratio, duration, quality T2V with jointly generated audio in one pass.
image_to_video_with_audio prompt, images_list, aspect_ratio, duration, quality I2V with jointly generated audio in one pass.
extend_video request_id, prompt, duration, quality Extend an existing HappyHorse video segment.
video_edit prompt, video_urls, images_list, aspect_ratio, quality Edit existing videos with natural language.
upload_file file_path Upload a local file (image or video) to MuAPI.
get_result request_id Check task status and retrieve outputs.
wait_for_completion request_id, poll_interval, timeout Blocking helper — polls until generation completes.

🔗 Official Resources

📄 License

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

Keywords: HappyHorse 1.0 API, Alibaba HappyHorse, AI Video Generator, Text-to-Video AI, Image-to-Video API, HappyHorse Python SDK, Alibaba Video AI, Audio Video Generation, Integrated Audio Video, MuAPI, Video Generation API, Native 1080p AI Video, AI Video Creation, HappyHorse API Documentation, HappyHorse I2V, HappyHorse T2V, AI Movie Generator, Python Video API, HappyHorse Tutorial, #1 AI Video Model.

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