Agent Browser

agent-browser: CLI Browser Automation

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

Philosophy

• Prefer accessibility-first selectors (refs) for stability.
• Re-snapshot after navigation or DOM changes.
• Keep actions minimal and observable.

When to use

• You need to interact with live web pages or forms.
• You need screenshots or PDFs of UI states.
• You need to scrape data with deterministic steps.

Inputs

• Target URL(s).
• Desired interactions (click/fill/scroll).
• Output artifacts (screenshots, PDFs, extracted text).

Outputs

• Action results and snapshots.
• Saved artifacts (png/pdf) when requested.
• Extracted content for further processing.

Constraints / Safety

• Redact secrets, tokens, and private URLs in logs.
• Avoid interacting with sensitive accounts without explicit approval.
• Do not run destructive actions on production systems.

Variation

• Use refs for deterministic steps; use semantic locators when refs are unstable.
• Use --session for parallel flows when isolation is required.
• Prefer JSON snapshots for parsing and automation.

Anti-Patterns

• Clicking without a fresh snapshot after navigation.
• Using brittle selectors when refs are available.
• Running long sequences without verification checkpoints.

Validation

• Confirm each step with a snapshot or expected UI change.
• Fail fast: stop at the first failed step and fix before continuing.
• See references/contract.yaml (schema_version: 1) and references/evals.yaml.

Remember

The agent is capable of extraordinary work in this domain. These guidelines unlock that potential—they don't constrain it. Use judgment, adapt to context, and push boundaries when appropriate.

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

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 "user@example.com"
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 "user@example.com"
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 "john@example.com"
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

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