Shumai

An open-source platform for all your creative work.

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Features

Shumai is an open-source Frame.io alternative built for creative teams to collaborate and share media with ease:

• S3-Compatible & Local Storage: Store and serve creative assets securely using the local filesystem or any S3-compatible storage service, including AWS S3, Cloudflare R2, and MinIO.
• Frame-by-Frame Annotations & Comments: Leave precise feedback with frame-specific drawing tools and timestamped comments on both video and image assets.
• Secure Sharing & Collections: Create private share links and curated media collections for clients, stakeholders, and collaborators.
• Granular Access Control: Manage workspace permissions with team-level and project-level role-based access control.
• Distributed Transcoding via Temporal: Offload resource-intensive video transcoding to a background worker pool orchestrated by Temporal.
• Custom Asset Metadata: Define dynamic metadata fields tailored to your production pipeline.
• Share Link Watermarking: Protect shared media with customizable text and image watermarks, and save watermark as reusable templates.
• Wide File Format Support: Preview common creative file formats, including images (with PSD support), videos, audio, and documents, while allowing any file type to be uploaded and stored.
• Office & Document PDF Proxying via Gotenberg: Convert Word, PowerPoint, Excel, Markdown, HTML, and CSV documents to high-quality PDF proxies for in-browser previewing using Gotenberg.

But Shumai goes beyond being a Frame.io alternative. With modern models like GPT-Image-2 and Seedance 2.5, AI can already handle a surprising amount of creative work. That means you can create or update videos and images from a simple prompt without having to jump back into Photoshop, Premiere or Houdini. That is why Shumai includes a powerful agent system:

• Agent as a Team Member: Mention a context-aware AI agent with @mentions directly in asset comments, just like a real teammate.
• Agent as a One-to-One Assistant: Chat privately with the agent and ask it to help with tasks.
• Custom Skills & Tools: Extend the agent by registering custom scripts, tools, and automation skills.
• Model Context Protocol (MCP): Connect agents to external tools and APIs through remote MCP servers, with OAuth authentication and per-agent MCP access.
• Isolated Sandbox Execution: Run agent-submitted scripts safely in a sandboxed environment.
• AI Metadata Autofill: Automatically fill in custom metadata for new assets.
• Semantic Search: Find assets by visual or conceptual meaning using vector embeddings (Gemini Embedding 2).
• Resource Quotas: Set usage limits on AI tokens, costs, and tool execution per member, role, or team-wide, with real-time tracking.

Here is a short demo video showing what the Shumai agent can do:

https://github.com/user-attachments/assets/1186067a-72c0-41b9-836f-987438f2332a

Shumai also brings project management into the creative workflow. A built-in Kanban board helps teams organize tasks, track progress, and collaborate in one place.

The ultimate goal is an agentic Kanban, where agents and team members can work together on tasks. This is still a work in progress: you can already create tasks for AI agents, but agents cannot act on those tasks yet.

Installation

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.

Option 1: Docker Compose

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 (optional):

   • SHUMAI_SERVER_PORT controls the port the Shumai server listens on. The default is 3000.

   • By default, Shumai uses the bundled local storage service in this Docker Compose setup. To use an external S3-compatible storage instead, configure the environment variables for your S3 provider. Here are examples for AWS S3 and Cloudflare R2:

     AWS S3 Example:

     STORAGE_BACKEND: s3
     S3_BUCKET: your-bucket-name
     S3_ACCESS_KEY_ID: your-access-key-id
     S3_SECRET_ACCESS_KEY: your-secret-access-key
     AWS_ENDPOINT_URL_S3: https://s3.us-east-1.amazonaws.com
     

     Cloudflare R2 Example:

     STORAGE_BACKEND: s3
     S3_BUCKET: your-bucket-name
     S3_REGION: auto
     S3_ACCESS_KEY_ID: your-access-key-id
     S3_SECRET_ACCESS_KEY: your-secret-access-key
     AWS_ENDPOINT_URL_S3: https://<account-id>.r2.cloudflarestorage.com
     

   • By default, AWS_ENDPOINT_URL_S3 is set to: http://localhost:{SHUMAI_SERVER_PORT}, if you use local storage and expose Shumai on a custom host/port combination, set AWS_ENDPOINT_URL_S3 to the external URL that browsers will use to upload files.

     For example, if you change the port mapping in docker-compose.yaml from 3000:3000 to 12345:3000 and deploy on a server with IP address 12.34.56.78, set:

     AWS_ENDPOINT_URL_S3: http://12.34.56.78:12345
     

     This value must be reachable from client browsers and should include the externally exposed port.

4. Start the services in detached mode:

   docker compose up -d
   

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

Linux

Shumai requires the following system packages on Linux:

• ffmpeg - Used for media transcoding and metadata extraction.
• poppler (poppler-utils) - Used for PDF page image extraction and PDF sprite preview generation (pdftoppm).
• imagemagick - Used for PSD format transcoding and color profile conversion to sRGB.
• bubblewrap, socat, and ripgrep - Required by the AI agent sandbox (anthropic-experimental/sandbox-runtime) for process isolation, networking, and workspace search.

Install all required packages with one command:

Ubuntu/Debian

sudo apt install -y ffmpeg poppler-utils imagemagick bubblewrap socat ripgrep

Fedora

sudo dnf install -y ffmpeg poppler-utils ImageMagick bubblewrap socat ripgrep

Arch Linux

sudo pacman -S --noconfirm ffmpeg poppler imagemagick bubblewrap socat ripgrep

macOS

Shumai requires:

• ffmpeg - Used for media transcoding and metadata extraction.
• poppler - Used for PDF page image extraction and PDF sprite preview generation (pdftoppm).
• imagemagick - Used for PSD format transcoding and color profile conversion to sRGB.
• ripgrep - Required by the AI agent sandbox (anthropic-experimental/sandbox-runtime).

Install the required packages with Homebrew:

brew install ffmpeg poppler imagemagick ripgrep

Windows (alpha)

Shumai requires:

• ffmpeg - Used for media transcoding and metadata extraction.
• imagemagick - Used for PSD format transcoding and color profile conversion to sRGB.

Install required packages using your preferred package manager:

winget

winget install Gyan.FFmpeg ImageMagick.ImageMagick

Chocolatey

choco install ffmpeg imagemagick

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:3000
# Optional: base URL for MCP OAuth redirects (defaults to BETTER_AUTH_URL)
MCP_OAUTH_REDIRECT_BASE_URL=http://localhost:3000

Step 6: Run Shumai

Start the application from your workspace folder:

shumai

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

• Start in daemon mode:

  shumai -d
  

• Stop Shumai:

  shumai stop
  

• Restart Shumai:

  shumai restart
  

• Show/tail logs:

  shumai 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. Generate i18n translation files:

   bun run i18n:compile
   

   [!TIP]
   During frontend development, you can run bun run i18n:watch in a separate terminal to automatically recompile translation files whenever you edit strings in packages/webui/messages/.

5. Apply the database schema migrations:

   bun run db:migrate
   

6. Start the local development server:

   bun run dev
   

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.