agent-browser

name	agent-browser
description	Browser automation using Vercel's agent-browser CLI. Use when you need to interact with web pages, fill forms, take screenshots, or scrape data. Uses Bash commands with ref-based element selection. Triggers on "browse website", "fill form", "click button", "take screenshot", "scrape page", "web automation".
allowed-tools	Bash
argument-hint	[url]

agent-browser: CLI Browser Automation

Vercel's headless browser automation CLI designed for AI agents. Uses ref-based selection (@e1, @e2) from accessibility snapshots.

CRITICAL: Use agent-browser CLI Only

DO NOT use Chrome MCP tools (mcp__claude-in-chrome__) or Playwright MCP tools (mcp__plugin_playwright_playwright__) when this skill is active.

This skill uses the agent-browser CLI exclusively. It is a Bash-based tool from Vercel that runs headless Chromium. It is NOT the same as browser automation via MCP.

Setup Check

# Check installation
command -v agent-browser >/dev/null 2>&1 && echo "Installed" || echo "NOT INSTALLED - run: npm install -g agent-browser && agent-browser install"

Install if needed

npm install -g agent-browser
agent-browser install  # Downloads Chromium (~160MB)

Core Workflow

The snapshot + ref pattern is optimal for LLMs:

1. Navigate to URL
2. Snapshot to get interactive elements with refs
3. Interact using refs (@e1, @e2, etc.)
4. Re-snapshot after navigation or DOM changes

# Step 1: Open URL
agent-browser open https://example.com

# Step 2: Get interactive elements with refs
agent-browser snapshot -i --json

# Step 3: Interact using refs
agent-browser click @e1
agent-browser fill @e2 "search query"

# Step 4: Re-snapshot after changes
agent-browser snapshot -i

Key Commands

Navigation

agent-browser open <url>       # Navigate to URL
agent-browser back             # Go back
agent-browser forward          # Go forward
agent-browser reload           # Reload page
agent-browser close            # Close browser

Snapshots (Essential for AI)

agent-browser snapshot              # Full accessibility tree
agent-browser snapshot -i           # Interactive elements only (recommended)
agent-browser snapshot -i --json    # JSON output for parsing
agent-browser snapshot -c           # Compact (remove empty elements)
agent-browser snapshot -d 3         # Limit depth

Interactions

agent-browser click @e1                    # Click element
agent-browser dblclick @e1                 # Double-click
agent-browser fill @e1 "text"              # Clear and fill input
agent-browser type @e1 "text"              # Type without clearing
agent-browser press Enter                  # Press key
agent-browser hover @e1                    # Hover element
agent-browser check @e1                    # Check checkbox
agent-browser uncheck @e1                  # Uncheck checkbox
agent-browser select @e1 "option"          # Select dropdown option
agent-browser scroll down 500              # Scroll (up/down/left/right)
agent-browser scrollintoview @e1           # Scroll element into view

Get Information

agent-browser get text @e1          # Get element text
agent-browser get html @e1          # Get element HTML
agent-browser get value @e1         # Get input value
agent-browser get attr href @e1     # Get attribute
agent-browser get title             # Get page title
agent-browser get url               # Get current URL
agent-browser get count "button"    # Count matching elements

Screenshots & PDFs

agent-browser screenshot                      # Viewport screenshot
agent-browser screenshot --full               # Full page
agent-browser screenshot output.png           # Save to file
agent-browser screenshot --full output.png    # Full page to file
agent-browser pdf output.pdf                  # Save as PDF

Wait

agent-browser wait @e1              # Wait for element
agent-browser wait 2000             # Wait milliseconds
agent-browser wait "text"           # Wait for text to appear

Semantic Locators (Alternative to Refs)

agent-browser find role button click --name "Submit"
agent-browser find text "Sign up" click
agent-browser find label "Email" fill "[e-mail]"
agent-browser find placeholder "Search..." fill "query"

Sessions (Parallel Browsers)

# Run multiple independent browser sessions
agent-browser --session browser1 open https://site1.com
agent-browser --session browser2 open https://site2.com

# List active sessions
agent-browser session list

Examples

Login Flow

agent-browser open https://app.example.com/login
agent-browser snapshot -i
# Output shows: textbox "Email" [ref=e1], textbox "Password" [ref=e2], button "Sign in" [ref=e3]
agent-browser fill @e1 "[e-mail]"
agent-browser fill @e2 "password123"
agent-browser click @e3
agent-browser wait 2000
agent-browser snapshot -i  # Verify logged in

Search and Extract

agent-browser open https://news.ycombinator.com
agent-browser snapshot -i --json
# Parse JSON to find story links
agent-browser get text @e12  # Get headline text
agent-browser click @e12     # Click to open story

Form Filling

agent-browser open https://forms.example.com
agent-browser snapshot -i
agent-browser fill @e1 "John Doe"
agent-browser fill @e2 "[e-mail]"
agent-browser select @e3 "United States"
agent-browser check @e4  # Agree to terms
agent-browser click @e5  # Submit button
agent-browser screenshot confirmation.png

Debug Mode

# Run with visible browser window
agent-browser --headed open https://example.com
agent-browser --headed snapshot -i
agent-browser --headed click @e1

JSON Output

Add --json for structured output:

agent-browser snapshot -i --json

Returns:

{
  "success": true,
  "data": {
    "refs": {
      "e1": {"name": "Submit", "role": "button"},
      "e2": {"name": "Email", "role": "textbox"}
    },
    "snapshot": "- button \"Submit\" [ref=e1]\n- textbox \"Email\" [ref=e2]"
  }
}

vs Playwright MCP

Feature	agent-browser (CLI)	Playwright MCP
Interface	Bash commands	MCP tools
Selection	Refs (@e1)	Refs (e1)
Output	Text/JSON	Tool responses
Parallel	Sessions	Tabs
Best for	Quick automation	Tool integration

Use agent-browser when:

• You prefer Bash-based workflows
• You want simpler CLI commands
• You need quick one-off automation

Use Playwright MCP when:

• You need deep MCP tool integration
• You want tool-based responses
• You're building complex automation