name: nova-act description: Write and execute Python scripts using Amazon Nova Act for AI-powered browser automation tasks like flight searches, data extraction, and form filling. homepage: https://github.com/aws/nova-act metadata: { "openclaw": { "emoji": "🌐", "requires": { "bins": ["uv"], "env": ["NOVA_ACT_API_KEY"] }, "primaryEnv": "NOVA_ACT_API_KEY", "install": [ { "id": "uv-brew", "kind": "brew", "formula": "uv", "bins": ["uv"], "label": "Install uv (brew)", }, ], "tools": { "nova_act": { "description": "Run a browser automation task using Amazon Nova Act.", "parameters": { "type": "object", "properties": { "url": { "type": "string", "description": "Starting URL for the browser session", }, "task": { "type": "string", "description": "Natural language task description. IMPORTANT: Resolve relative dates (e.g., 'next Monday') to specific dates (e.g., '2025-03-15') in the prompt.", }, }, "required": ["url", "task"], }, "command": [ "uv", "run", "{baseDir}/scripts/nova_act_runner.py", "--url", "{{url}}", "--task", "{{task}}", ], }, }, }, }

Nova Act Browser Automation

Use Amazon Nova Act for AI-powered browser automation. The bundled script handles common tasks; write custom scripts for complex workflows.

Quick Start with Bundled Script

Execute a browser task and get results:

uv run {baseDir}/scripts/nova_act_runner.py --url "https://google.com/flights" --task "Find flights from SFO to NYC on March 15 and return the options"

The script uses a generic schema (summary + details list) to capture output.

Writing Custom Scripts

For complex multi-step workflows or specific extraction schemas, write a custom Python script with PEP 723 dependencies:

#!/usr/bin/env python3
# /// script
# requires-python = ">=3.10"
# dependencies = ["nova-act"]
# ///

from nova_act import NovaAct

with NovaAct(starting_page="https://example.com") as nova:
    # Execute actions with natural language
    # Combine steps into a single act() call to maintain context
    nova.act("Click the search box, type 'automation', and press Enter")

    # Extract data with schema
    results = nova.act_get(
        "Get the first 5 search result titles",
        schema=list[str]
    )
    print(results)

    # Take screenshot
    nova.page.screenshot(path="search_results.png")
    print(f"MEDIA: {Path('search_results.png').resolve()}")

Run with: uv run script.py

Core API Patterns

nova.act(prompt) - Execute Actions

Use for clicking, typing, scrolling, navigation. Note: Context is best maintained within a single act() call, so combine related steps.

nova.act("""
    Click the 'Sign In' button.
    Type 'hello@example.com' in the email field.
    Scroll down to the pricing section.
    Select 'California' from the state dropdown.
""")

nova.act_get(prompt, schema) - Extract Data

Use Pydantic models or Python types for structured extraction:

from pydantic import BaseModel

class Flight(BaseModel):
    airline: str
    price: float
    departure: str
    arrival: str

# Extract single item
flight = nova.act_get("Get the cheapest flight details", schema=Flight)

# Extract list
flights = nova.act_get("Get all available flights", schema=list[Flight])

# Simple types
price = nova.act_get("What is the total price?", schema=float)
items = nova.act_get("List all product names", schema=list[str])

Common Use Cases

Flight Search

with NovaAct(starting_page="https://google.com/flights") as nova:
    # Combine steps to ensure the agent maintains context through the flow
    nova.act("""
        Search for round-trip flights from SFO to JFK.
        Set departure date to March 15, 2025.
        Set return date to March 22, 2025.
        Click Search.
        Sort by price, lowest first.
    """)

    flights = nova.act_get(
        "Get the top 3 cheapest flights with airline, price, and times",
        schema=list[Flight]
    )

Form Filling

with NovaAct(starting_page="https://example.com/signup") as nova:
    nova.act("""
        Fill the form: name 'John Doe', email 'john@example.com'.
        Select 'United States' for country.
        Check the 'I agree to terms' checkbox.
        Click Submit.
    """)

Data Extraction

with NovaAct(starting_page="https://news.ycombinator.com") as nova:
    stories = nova.act_get(
        "Get the top 10 story titles and their point counts",
        schema=list[dict]  # Or use a Pydantic model
    )

Best Practices

1. Combine steps: Nova Act maintains context best within a single act() call. Combine related actions into one multi-line prompt.
2. Use specific dates: The browser agent may struggle with relative dates like "next Monday". Always calculate and provide specific dates (e.g., "March 15, 2025") in the task prompt.
3. Be specific in prompts: "Click the blue 'Submit' button at the bottom" is better than "Click submit"
4. Use schemas for extraction: Always provide a schema to act_get() for structured data
5. Handle page loads: Nova Act waits for stability, but add explicit waits for dynamic content if needed
6. Take screenshots for verification: Use nova.page.screenshot() to capture results

API Key

• NOVA_ACT_API_KEY env var (required)
• Or set skills."nova-act".apiKey / skills."nova-act".env.NOVA_ACT_API_KEY in ~/.openclaw/openclaw.json

Notes

• Nova Act launches a real Chrome browser; ensure display is available or use headless mode
• The script prints MEDIA: lines for OpenClaw to auto-attach screenshots on supported providers
• For headless operation: NovaAct(starting_page="...", headless=True)
• Access underlying Playwright page via nova.page for advanced operations