VideoClaw: AI Creative Video Production Agent

简体中文 | English

Talk to OpenClaw directly: "Generate a video about X" -> done.

💥 News

• 2026/3/27: 🎬 VideoClaw was officially released, supporting full workflow automation from idea to video generation, with user intervention and adjustment available at any time.
• 2026/4/9: ♾️ VideoClaw was optimized for short dramas, adding infinite continuation and customizable plots.
• 2026/4/29: 🧩 Added three features: commentary-style short videos, action transfer, and digital human talking videos.
• 2026/5/8: ⚙️ Added WebUI configuration for APIs and default models, with one-click installation support.
• 2026/5/13: 🎞️ Integrated Pixelle-Video HTML templates into commentary-style short videos.

📖 Overview

VideoClaw is an AI director system for creative video production. You only need to provide an idea, a story outline, or even a vague concept. The system will break it down into an executable filmmaking workflow, continuously producing intermediate assets that can be reviewed, confirmed, revised, and delivered, until a complete final video is generated.

It is not a one-shot text-to-video tool. It is a full production line covering script planning -> character and scene design -> storyboard planning -> reference image generation -> video generation -> post-production editing. Instead of giving you a black-box result, VideoClaw behaves like a collaborative AI directing team: each stage informs the next, and every key node is visible, editable, and extensible.

📺 Demos

🎬 VideoClaw

Click to view WebUI UI design

Stage	Demo	Description
Home		Displays the system overview, supports viewing historical projects, creating new projects, and global configuration (API keys and default model settings). This is the starting point of the creation workflow.
Script Planning		Enter a creative title and project synopsis, and the system automatically generates a structured multi-scene script (including narration and dialogue), with support for intelligent continuation of subsequent plots.
Character/Scene Design		Automatically extracts core characteristics of characters and scenes from the script, generating stylistically consistent reference concept art as the visual basis for later storyboard generation.
Storyboard Planning		Breaks each script scene into continuous visual storyboards, specifying camera perspective, action descriptions, and reference content in detail to ensure narrative coherence.
Reference Image Generation		Generates high-quality, high-precision reference base images for each storyboard scene, controlling lighting details and composition as key visual references for video generation.
Video Generation		Calls mainstream high-performance video generation models (such as Wan and Kling) to convert storyboard images into dynamic clips.
Final Editing		Aggregates all generated video clips and exports a publishable final video with one click.

📱 Series 1: A Programmer Uses OpenClaw to Acquire His Former Company After Being Laid Off (Realistic Short Drama)

8 episodes in total, an underdog story with twists and reversal. The first 6 episodes were generated initially, followed by 2 continued episodes.

▶️ Episode 1 Laid Off	▶️ Episode 2 Late-Night Departure, First Breakthrough	▶️ Episode 3 AI Funding, Old Employer in Crisis	▶️ Episode 4 Acquiring Xingyao
▶️ Episode 5 Acquisition, Liquidation, New Life	▶️ Episode 6 New Life, Looking Back	▶️ Episode 7 Technology Backfires	▶️ Episode 8 Upholding Ethics, Getting Through Together

🖥️ Series 2: The Village Teacher (Sci-Fi Comic Drama)

5 episodes in total, a tribute to the inheritance of civilization.

▶️ Episode 1 The Last Lesson	▶️ Episode 2 The Cleansing Plan
▶️ Episode 3 A Dying Entrustment	▶️ Episode 4 Questions of Life and Death
▶️ Episode 5 Light of Civilization

🎞️ More Demos

WeChat Interaction

Feishu Interaction

✨ Features

🚀 Quick Start

Method 1: One-Click Installation (Recommended)

Linux / MacOS:

# 1. Clone the repository
git clone https://github.com/HITsz-TMG/VideoClaw.git
cd VideoClaw

# 2. Enter the app directory and run the installer
cd video-claw/video-claw
chmod +x install.sh
./install.sh

# 3. back to root dir
cd ../..

Windows:

# 1. Clone the repository
git clone https://github.com/HITsz-TMG/VideoClaw.git
cd VideoClaw

# 2. Enter the app directory and run the installer
cd video-claw\video-claw
install.bat

# 3. back to root dir
cd ../..

The installer checks Python, Node.js, npm, and ffmpeg, installs backend and frontend dependencies, copies backend/config.yaml.example to backend/config.yaml, and builds the frontend. After installation, fill in model service API keys in backend/config.yaml and confirm the main-workflow default models under models. You can also start the frontend and edit these settings from the "Settings" page at the bottom of the sidebar. Then start the services:

# Start backend
cd video-claw/video-claw/backend
uv run python api_server.py

# Start frontend in a new terminal
cd video-claw/video-claw/frontend
npm start

By default, the backend runs at http://localhost:8000, and the frontend runs at http://localhost:3000.

If you only want to install dependencies and skip the frontend build temporarily, run:

AIGC_DIRECTOR_SKIP_FRONTEND_BUILD=1 ./install.sh

Method 2: Manual Installation

# 1. Clone the repository
git clone https://github.com/HITsz-TMG/Video-Claw.git
cd Video-Claw

# 2. Configure and start the backend
cd video-claw/video-claw/backend

# Install backend dependencies
uv sync

# Configure backend YAML
cp config.yaml.example config.yaml
# Edit config.yaml, fill in API keys, and confirm main-workflow default models
# You can also use the frontend Settings page after startup

# Start backend
uv run python api_server.py
# Service runs at http://localhost:8000

# 3. Configure and start the frontend in a new terminal
cd video-claw/video-claw/frontend
npm install
npm run build
npm start
# Visit http://localhost:3000

If uv is not installed, you can also create a Python virtual environment manually and install backend dependencies with pip install -r requirements.txt.

Method 3: OpenClaw Auto Setup

Send this message to OpenClaw:

Please clone this git repository: https://github.com/HITsz-TMG/Video-Claw.git
Then recursively copy the video-claw folder inside Video-Claw to .openclaw/workspace/skills and use it as an AIGC-related skill.

When using it, it is recommended to explicitly say "use video-claw":

Use video-claw to generate a video with the content "A Dog's Purpose".

Method 4: Install via ClawHub

Make sure clawhub-cli is installed locally.

Open a terminal and run the following command. Choose yes for every prompt.

clawhub install video-claw

After installation, ClawHub will copy video-claw into workspace/skills or your specified skills directory.

Then you can follow Method 1 for one-click installation or Method 2 for manual setup, or let OpenClaw build and run the project for you.

The first time you use video-claw, if the project has not been built manually, OpenClaw will automatically build and start both the backend and frontend. This may take some time because setup involves dependency installation and compilation.

🔧 Configuration

project_name: Video-Claw

server:
  host: 127.0.0.1
  port: 8000
  debug: false

api_providers:
  common:
    print_model_input: false
    proxy: ''
  openai:
    api_key: your_openai_key
    base_url: https://api.openai.com/v1
    enable_proxy: false
  gemini:
    api_key: your_gemini_key
    base_url: https://generativelanguage.googleapis.com/v1beta
    enable_proxy: false
  deepseek:
    api_key: your_deepseek_key
    base_url: https://api.deepseek.com/v1
    enable_proxy: false
  dashscope:
    api_key: your_dashscope_key
    base_url: https://dashscope.aliyuncs.com/api/v1
    enable_proxy: false
  ark:
    api_key: your_ark_key
    base_url: https://ark.cn-beijing.volces.com/api/v3
    enable_proxy: false
  kling:
    access_key: your_kling_access_key
    secret_key: your_kling_secret_key
    enable_proxy: false

models:
  llm: qwen3.5-plus
  vlm: qwen3.5-plus
  image_t2i: doubao-seedream-5-0-260128
  image_it2i: doubao-seedream-5-0-260128
  video: wan2.7-i2v
  video_ratio: '16:9'
  eval: qwen3.5-plus

Artifacts and Storage

All tasks metadata and generated artifacts are stored under video-claw/video-claw/backend/code/.

video-claw/video-claw/backend/code/
├── data/
│   ├── tasks/                  # Pipeline task metadata (JSON)
│   └── sessions/               # AIGC-Claw session metadata (JSON)
└── result/
    ├── task/                   # Pipeline generated artifacts (by Task ID)
    │   └── <task_id>/          # e.g., 20260514_204946_961f95d9
    │       ├── audio_xx.mp3    # Audio segments
    │       ├── video_xx.mp4    # Video segments
    │       ├── storyboard.json # Storyboard data
    │       └── final.mp4       # Final combined video
    ├── image/                  # AIGC-Claw generated images
    │   └── <session_id>/       # By Session ID
    │       ├── Assets/         # Character and setting assets
    │       │   ├── characters/ # Character reference images
    │       │   └── settings/   # Setting reference images
    │       └── Scenes/         # Generated storyboard reference images
    ├── video/                  # AIGC-Claw generated videos
    │   └── <session_id>/       # By Session ID
    └── script/                 # AIGC-Claw generated script/storyboard data

🙏 Acknowledgments

The idea and design of Video-Claw were inspired by Pixelle-Video, AutoResearchClaw, huobao-drama, Flova, and libtv-skills.

📚 Related Work

Framework	Paper Information
	[SIGGRAPH Asia 2024] FilmAgent: Automating Virtual Film Production Through a Multi-Agent Collaborative Framework Zhenran Xu, Jifang Wang, Longyue Wang, Zhouyi Li, Senbao Shi, Baotian Hu, Min Zhang [Paper] [GitHub]
	[SIGGRAPH Asia 2024] Anim-Director: A Large Multimodal Model Powered Agent for Controllable Animation Video Generation Yunxin Li, Haoyuan Shi, Baotian Hu, Longyue Wang, Jiashun Zhu, Jinyi Xu, Zhen Zhao, Min Zhang [Paper] [GitHub]
	[SIGGRAPH Asia 2025] AniMaker: Multi-Agent Animated Storytelling with MCTS-Driven Clip Generation Haoyuan Shi, Yunxin Li, Xinyu Chen, Longyue Wang, Baotian Hu, Min Zhang [Paper] [GitHub]

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