Show HN: Shumai – 用于创意工作的开源 Frame.io 替代方案
Show HN: Shumai – open-source Frame.io alternative for creative work

原始链接: https://github.com/shumaiOne/shumai

Shumai 是一个开源平台,旨在通过先进的资产管理和人工智能协作来简化创意工作流程。它提供安全且兼容 S3 的存储、精细的基于角色的访问控制,以及逐帧标注、分布式视频转码和可自定义元数据等专业级工具。 其核心人工智能功能包括:具备上下文感知能力的聊天助手、使用向量嵌入的语义搜索,以及通过 Google Gemini 实现的自动化元数据生成。该平台还提供了一个安全的沙盒环境,用于执行自定义脚本和自动化工具。 部署方式灵活,可满足不同用户的需求: * **Docker Compose**:最快捷的方法,无需手动安装即可快速设置。 * **NPM/包管理器**:适合全局安装,需要 PostgreSQL (pgvector) 数据库以及 FFmpeg 和 bubblewrap 等特定的系统依赖项。 * **源代码**:提供给希望直接构建或进行开发的贡献者。 Shumai 还配备了功能强大的命令行界面 (CLI),允许用户直接从终端管理项目、资产和版本。对于包括云存储或 Temporal 工作流编排在内的高级配置,请查阅官方文档。

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原文

Shumai Logo

An open-source platform for all your creative work.

Website | Docs | Demo

Shumai App Screenshot

  • S3-Compatible & Local Storage: Securely store and serve your creative assets using a local filesystem or any S3-compatible cloud storage (AWS S3, Cloudflare R2, MinIO, etc.).
  • Frame-by-Frame Annotations & Comments: Give precise feedback using frame-specific drawing tools and timestamped comments directly on video and image assets.
  • Secure Sharing & Collections: Create secure public share links and curated media collections to collaborate with clients and stakeholders.
  • Granular Access Control: Manage workspace permissions using team-level and project-level role-based access controls.
  • Distributed Transcoding via Temporal: Offload resource-heavy video transcoding to a background worker pool orchestrated by Temporal.
  • Custom Asset Metadata: Define and customize dynamic metadata fields tailored to your production pipeline.
  • Collaborative AI Chat: Converse with a context-aware AI agent directly within your project workspace.
  • Custom Skills & Tools: Extend the agent's capabilities by registering custom scripts, tools, and automation skills.
  • Isolated Sandbox Execution: Run agent-submitted scripts securely within a sandboxed environment.
  • AI-Powered Metadata Autofill: Automatically generate tags, descriptions, and custom metadata for new assets using Google Gemini.
  • Semantic Search: Locate assets instantly based on visual or conceptual search queries using vector embeddings.

Below is a quickstart guide for running Shumai with local storage. For advanced configuration options (including S3-compatible storage and Temporal workflow orchestration), see our Documentation.

Docker Compose is the fastest way to get Shumai running. You do not need to clone the repository or install packages manually. Ensure you have Docker and Docker Compose installed, then follow these steps:

  1. Create and navigate to a new directory for your configuration and data volumes:
    mkdir shumai && cd shumai
  2. Download the docker-compose.yaml file:
    curl -o docker-compose.yaml https://raw.githubusercontent.com/shumaiOne/shumai/main/docker-compose/local/docker-compose.yaml
  3. Configure Environment Variables (Remote Deployments only): If deploying Shumai to a remote server (e.g., AWS EC2, VPS), edit docker-compose.yaml and add AWS_ENDPOINT_URL_S3 under the environment section, setting it to your server's public IP address or domain name (e.g., http://12.34.56.78).
  4. Start the services in detached mode:
  5. Open your browser and access Shumai at http://localhost:3000 (or http://<your-server-ip>:3000 for remote deployments).

Option 2: Install via NPM / Package Manager

Shumai is published as @shumai-one/shumai on NPM. This option allows you to run Shumai globally or locally.

Step 1: Start PostgreSQL with pgvector

Shumai requires PostgreSQL with the pgvector extension. Start a pre-configured database container using Docker:

docker run --name shumai_postgres \
  -e POSTGRES_USER=shumai \
  -e POSTGRES_PASSWORD=shumai_password \
  -e POSTGRES_DB=shumai_db \
  -p 5432:5432 \
  -d pgvector/pgvector:pg18

Step 2: Create a workspace folder

Create a dedicated directory to store your environment configuration and media files (which are saved in a ./data directory by default):

mkdir shumai && cd shumai

Step 3: Install Platform-Specific Dependencies

Make sure the following system dependencies are installed on your host machine before installing Shumai:

Package Description Ubuntu/Debian Fedora Arch macOS
ffmpeg Media transcoding and metadata extraction sudo apt install -y ffmpeg sudo dnf install -y ffmpeg sudo pacman -S --noconfirm ffmpeg brew install ffmpeg
bubblewrap Sandboxing environment for secure AI agent execution sudo apt install -y bubblewrap sudo dnf install -y bubblewrap sudo pacman -S --noconfirm bubblewrap NOT REQUIRED
socat Bidirectional socket relay for sandbox network bridging sudo apt install -y socat sudo dnf install -y socat sudo pacman -S --noconfirm socat brew install socat
ripgrep Fast search tool for workspace security policies sudo apt install -y ripgrep sudo dnf install -y ripgrep sudo pacman -S --noconfirm ripgrep brew install ripgrep

Note

Ubuntu 24.04+ Note: These releases restrict unprivileged user namespaces by default. To allow bubblewrap and the sandbox isolation layer to function, disable this restriction:

sudo sysctl -w kernel.apparmor_restrict_unprivileged_userns=0

Alternatively, configure an AppArmor profile that grants user namespace creation (userns) privileges to the relevant binaries.

Step 4: Install Shumai globally

Install Shumai globally using your preferred package manager:

# NPM
npm install -g @shumai-one/shumai

# PNPM
pnpm add -g @shumai-one/shumai

# Bun
bun add -g @shumai-one/shumai

Step 5: Configure Environment Variables

Create a .env file in your workspace folder (shumai/) and add the following configuration:

DATABASE_URL=postgresql://shumai:shumai_password@localhost:5432/shumai_db?schema=public
BETTER_AUTH_SECRET=ySxs7DxzHDZBbeeHNPEwBuspYwipBqz5Gk5XdBjNhWw=
STORAGE_BACKEND=local
SHUMAI_SERVER_PORT=3000
AWS_ENDPOINT_URL_S3=http://localhost

Note

If deploying on a remote server, update AWS_ENDPOINT_URL_S3 from http://localhost to your server's public IP address or domain name.

Important

For Bun Users: Since the published package binaries contain a #!/usr/bin/env node shebang, running the global command directly (e.g., shumai) will execute under Node.js. If you installed with Bun and want to run under the Bun runtime, you must prefix all commands with bun run --bun (e.g., bun run --bun shumai, bun run --bun shumai -d, bun run --bun shumai stop).

Start the application from your workspace folder:

Alternatively, you can run and manage Shumai in daemon mode:

  • Start in daemon mode:
  • Stop Shumai:
  • Restart Shumai:
  • Show/tail logs:

On startup, Shumai will automatically run database migrations and start the web server at http://localhost:3000.


Option 3: Run from Source (Development)

To set up Shumai locally for development:

  1. Clone the repository and install dependencies:
    git clone https://github.com/shumaiOne/shumai.git
    cd shumai
    bun install
  2. Start the pgvector database container (as described in Option 2, Step 1).
  3. Create a .env file at the root of the workspace using the configuration from Option 2, Step 5.
  4. Apply the database schema migrations:
  5. Start the local development server:

Command Line Interface (CLI)

Shumai provides a Command Line Interface (CLI) tool to manage projects, folders, and assets, upload files/folders, and create new versions directly from your terminal.

For more details on installation and usage, see the CLI Readme.

联系我们 contact @ memedata.com