Open-source browser automation API with anti-detection — Undetectable Chrome for AI agents, web scraping, and automation. REST API + MCP server + VNC debugging. Selenium/Playwright alternative that bypasses Cloudflare.

docker run -d -p 18080:18080 --name airbrowser ghcr.io/ifokeev/airbrowser-mcp:latest

# With NVIDIA GPU (recommended for anti-detection)
docker run -d -p 18080:18080 --gpus all --device /dev/dri:/dev/dri --name airbrowser ghcr.io/ifokeev/airbrowser-mcp:latest

Download and run - no Docker knowledge required:

# Linux/macOS
tar -xzf airbrowser-*.tar.gz && cd airbrowser-* && ./airbrowser

# Windows: Extract zip and double-click airbrowser.bat

git clone https://github.com/ifokeev/airbrowser-mcp.git
cd airbrowser-mcp
docker compose up --build

# With NVIDIA GPU
docker compose -f compose.gpu.yml up --build

Run natively without a container — zero container fingerprint for maximum anti-detection stealth. Tested on Ubuntu/Debian.

git clone https://github.com/ifokeev/airbrowser-mcp.git
cd airbrowser-mcp
uv run python run_local.py         # auto-installs deps + system packages
uv run python run_local.py --vnc   # with VNC viewer at http://localhost:6080/vnc.html

Requires Chrome installed on the host. See python run_local.py --help for options.

Open http://localhost:18080 - all services available:

• Undetected Chrome (SeleniumBase UC)
• 100+ concurrent browsers
• Persistent profiles & cookies
• Tab management
• Proxy per browser (DataImpulse recommended)
• MCP for AI agents
• AI vision tools (optional)

GPU passthrough enables hardware-accelerated WebGL rendering via Vulkan, making the browser fingerprint match a real desktop machine. Without it, Chrome falls back to software rendering (SwiftShader) which is easily detected by anti-bot systems.

Requirements: NVIDIA GPU + NVIDIA Container Toolkit

# Docker Compose (recommended)
docker compose -f compose.gpu.yml up

# Docker run
docker run -d -p 18080:18080 \
  --gpus all \
  --device /dev/dri:/dev/dri \
  -e NVIDIA_VISIBLE_DEVICES=all \
  -e NVIDIA_DRIVER_CAPABILITIES=all \
  ghcr.io/ifokeev/airbrowser-mcp:latest

# Portable launcher
./airbrowser --gpu

Without a GPU, Chrome uses --use-gl=swiftshader automatically. With GPU passthrough, it uses --use-gl=angle --use-angle=vulkan for real GPU rendering.

Enable AI-powered vision tools (what_is_visible, detect_coordinates) with any OpenAI-compatible vision backend. Vision turns on only when VISION_API_BASE_URL, VISION_API_KEY, and VISION_MODEL are all set.

When smart targeting is enabled per request, detect_coordinates can validate a raw vision point, optionally snap to a nearby clickable target, and return both the original click_point and a resolved_click_point with an outcome_status that tells you whether the result was confirmed, corrected, or needs inspection before clicking. Pair that with gui_click or MCP-compatible gui_click_xy to re-check coordinate clicks and request post-click feedback.

# Docker run
docker run -d -p 18080:18080 \
  -e VISION_API_BASE_URL=https://your-openai-compatible-endpoint/v1 \
  -e VISION_API_KEY=your-api-key \
  -e VISION_MODEL=your-vision-model \
  ghcr.io/ifokeev/airbrowser-mcp:latest

# Docker compose
VISION_API_BASE_URL=https://your-openai-compatible-endpoint/v1 \
VISION_API_KEY=your-api-key \
VISION_MODEL=your-vision-model \
docker compose up

Add airbrowser to your AI coding assistant:

claude mcp add airbrowser --transport http http://localhost:18080/mcp

{
  "mcpServers": {
    "airbrowser": {
      "url": "http://localhost:18080/mcp",
      "transport": "http"
    }
  }
}

{
  "mcpServers": {
    "airbrowser": {
      "url": "http://localhost:18080/mcp",
      "transport": "http"
    }
  }
}

{
  "mcpServers": {
    "airbrowser": {
      "url": "http://localhost:18080/mcp",
      "transport": "http"
    }
  }
}

Navigate to https://example.com and take a screenshot

Your AI assistant should create a browser, navigate to the URL, and return a screenshot.

Auto-generated from OpenAPI spec:

# Python
pip install airbrowser-client

# TypeScript
npm install airbrowser-client

Join our Discord server for support, feature requests, and discussion.

• docs/ - Full documentation
• examples/ - Code samples

MIT