Dify AgentBox

A production-ready, all-in-one Docker image designed for AI agents and autonomous systems that need to execute code across multiple programming languages.

Why AgentBox?

AI agents often need to:

• Execute code in multiple languages (Python, JavaScript, Go, etc.)
• Process documents, images, and multimedia
• Interact with web browsers for automation
• Access databases and external services

Instead of managing separate containers or installing dependencies on-the-fly, AgentBox provides everything pre-configured in a single, secure image.

What's Inside

• Multi-language runtime: Python, Node.js, Go, Ruby, Rust
• Document processing: PDF, Excel, PowerPoint, Word, HTML
• Browser automation: Playwright with Chromium & Firefox
• Data tools: pandas, numpy, database clients (MySQL, PostgreSQL, SQLite)
• Media processing: ffmpeg, image manipulation
• Development tools: git, build tools, debuggers

Quick Start

docker run -it --rm -v $(pwd):/workspace langgenius/dify-agentbox:latest

Multi-arch images (amd64/arm64) are published and tagged with the full git SHA plus latest.

Use Cases

• AI Agent Execution: Run LLM-generated code safely in isolated environments
• Code Interpreters: Execute user code across multiple languages
• Document Processing: Convert, extract, and transform documents at scale
• Browser Automation: Scrape websites, test web applications, automate workflows
• CI/CD Pipelines: Universal build environment with all tools included

Managing This Repository

Project Structure

dify-agentbox/
├── versions/versions.yaml    # Version configuration for all packages
├── Dockerfile.j2              # Jinja2 template for generating Dockerfile
├── build.py                   # Build script to render and build images
└── .github/workflows/         # CI/CD automation

Making Changes

1. Update Package Versions

Edit versions/versions.yaml:

languages:
  python:
    version: "3.12"
  nodejs:
    version: "20"

python_packages:
  - name: "pandas[excel,html,xml]"
    version: "~=2.2.3"

2. Render & build locally (multi-arch)

# Install dependencies
uv sync

# Render Dockerfile
uv run python build.py

# Build multi-arch image with git SHA tag
TAG=$(git rev-parse --short HEAD)
docker buildx build --platform linux/amd64,linux/arm64 -t langgenius/dify-agentbox:${TAG} .

3. Test Changes

# Run the image
docker run -it --rm langgenius/dify-agentbox:${TAG}

# Verify installations
python --version
node --version
go version

CI/CD Workflow

Images are automatically built and pushed when:

• Push to main: Builds and tags as latest
• Create tag (e.g., v1.0.0): Builds and tags with semantic version
• Pull request: Validates build without pushing

Multi-architecture builds (amd64, arm64) are enabled by default.

Adding New Packages

System packages (apt):

system_packages:
  utilities:
    - your-package-name

Python packages:

python_packages:
  - name: "your-package"
    version: "~=1.0.0"

Node.js packages:

nodejs_packages:
  - your-global-package

After making changes, rebuild and test locally before pushing.

License

Apache 2.0