- Category
- AI Tools
- Rank
- No. 1595Tools index
- Pricing
- Open Source
- Type
- TOOL
- Builder
- Anil-matcha
- GitHub
- 10 stars
- Date
About
Python wrapper for Google DeepMind's Veo 4 — generate native 4K AI video with audio, character consistency, and camera controls.
What it does
A small Python client submits video creation and editing jobs to MuAPI, then retrieves results by polling. It accepts text, reference images, existing clips, audio cues, aspect ratios, quality settings, durations, and camera directions. An MCP server exposes the same operations to compatible AI assistants.
Why it's ranked here
The appeal is broad workflow coverage in little code: generation from text or images, audio, character references, extension, editing, uploads, and status checks. The implementation is easy to follow, but it remains a thin synchronous client around hosted MuAPI endpoints, with limited validation and basic failure handling.
What's good
The interface covers both initial creation and follow-up work, including extending and editing clips. Audio can accompany text-driven or image-driven generation. Reference photos support character continuity, while camera hints cover movements such as pans, zooms, orbits, and tracking shots. Local uploads reduce the friction of preparing source media.
Tradeoffs
Every operation depends on MuAPI infrastructure and requires its API key. The client sends values directly without checking supported durations, aspect ratios, quality tiers, file types, or reference counts. Waiting blocks the calling process, prints status messages, and uses fixed-interval polling. HTTP failures surface through the underlying request library rather than a dedicated error model.
How to use it well
It suits Python developers and MCP-based assistants that need a compact automation layer for short video experiments, content pipelines, or reference-guided iterations. Put explicit visual, sound, and camera cues in prompts, submit work asynchronously, and poll by request identifier. It does not provide a browser editor, timeline compositor, local model runtime, asset library, or durable job store.
Technical notes+
veo4_api.py defines Veo4API, loads MUAPI_API_KEY through python-dotenv, sends synchronous requests calls to https://api.muapi.ai/api/v1, uploads multipart files, and polls /predictions/{request_id}/result until completion, failure, or timeout. mcp_server.py wraps nine operations with FastMCP and returns JSON strings. setup.py and pyproject.toml both package the two modules and declare requests, python-dotenv, and mcp[cli]; .env.example documents the required credential.
Observed
- License
- MIT License
- Primary language
- Python
- Python support
- Python 3.7 or newer
- Installation
- Published package installable with pip as veo-4-api; source installation uses requirements.txt
- Interfaces
- Python library, MCP server, and documented HTTP endpoints
- Platform support
- Package metadata declares operating-system independence
- Packaging structure
- Two single-file Python modules are packaged with setuptools
- Runtime dependencies
- requests, python-dotenv, and mcp with CLI extras
Read from README.md, setup.py, pyproject.toml, requirements.txt, veo4_api.py, mcp_server.py, .env.example.
What it can do
Generate 4K video content
Text prompts or descriptions → 4K resolution video file
Generate audio for video
Video content or audio description → Synchronized audio track
Maintain character consistency across video
Character descriptions and video scenes → Video with consistent character appearance
Control camera movements and angles
Camera direction and movement parameters → Video with specified camera effects
Create AI-generated video from text
Natural language video descriptions → Complete video with visuals and audio
Tags
Tech Stack
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