HolyClaude: Docker Container for Claude Code with Browser UI and Headless Chromium

HolyClaude is a Docker container that packages the official Claude Code CLI from Anthropic with a browser-based interface and headless Chromium for browser automation tasks. The project addresses the challenge of running Claude Code remotely on a server with full browser functionality.

Key Features and Setup

The container includes the real Claude Code CLI from Anthropic, not a wrapper or proxy. Authentication works with Anthropic accounts the same as the desktop version, supporting Pro plans, Max plans, and API keys. Credentials are stored in a bind-mounted folder on the host machine, with the container only accessing them as Claude Code itself would.

Browser Automation Capabilities

The most complex implementation work involved configuring Chromium to run properly in Docker. The container ships with headless Chromium and Playwright pre-configured, eliminating the typical setup challenges with shared memory limits, xvfb, and sandbox flags. This enables Claude to take screenshots, run browser tests, or scrape content directly from within the container.

Additional Tools

• Gemini CLI
• OpenAI Codex
• Cursor
• Taskmaster AI

These tools allow developers to compare different AI models on the same codebase without separate environment setups.

Practical Use Cases

The creator uses HolyClaude primarily for:

• Long-running sessions on remote codebases
• Browser automation tasks
• Spinning up a second AI to double-check Claude's work

Setup and Technical Details

Setup is straightforward: docker compose up followed by opening localhost:3001. The project is free and open source under the MIT license.

Limitations and Considerations

• The web UI (cloudcli) is third-party, not developed by Anthropic
• Full image size is approximately 4GB due to Chromium and tools
• A 2GB slim version is available without the browser
• No built-in HTTPS (requires reverse proxy for external exposure)
• Not an official Anthropic product (may need updates if upstream changes occur)

This type of containerized approach is useful for developers who want to run AI coding assistants in controlled environments or on remote servers while maintaining browser automation capabilities that are typically challenging to configure in containerized setups.