diff --git a/nova-act/README.md b/nova-act/README.md
index 55d90f60..8f67afc8 100644
--- a/nova-act/README.md
+++ b/nova-act/README.md
@@ -12,6 +12,7 @@ Nova Act enables developers to build intelligent agents that can interact with w
 - **Integrate seamlessly** with Python code for testing, assertions, and debugging
 
 The SDK works in conjunction with **Amazon Bedrock AgentCore Browser**, a secure, cloud-based browser environment that provides:
+
 - Session isolation and security
 - Built-in observability (live viewing, CloudTrail logging, session replay)
 - Containerized ephemeral environments
@@ -38,12 +39,14 @@ Automate quality assurance testing for web applications using Nova Act and Agent
 - **Integration with pytest** for comprehensive test reporting and CI/CD pipelines
 
 **Key Benefits:**
+
 - Reduce test maintenance overhead by 40-60%
 - Execute comprehensive test suites in minutes instead of hours
 - Eliminate false negatives from UI changes
 - Scale testing across browsers, devices, and environments effortlessly
 
 **What's Included:**
+
 - Sample retail web application for testing
 - 15 ready-to-use JSON test cases
 - Pytest framework with parallel execution support
@@ -52,15 +55,44 @@ Automate quality assurance testing for web applications using Nova Act and Agent
 
 [→ Explore the QA Testing Use Case](./usecases/qa-testing/)
 
+### 💰 [Price Comparison](./usecases/price_comparison/)
+
+Automate product price comparison across multiple retailers using Nova Act. This use case showcases:
+
+- **Intelligent product search** that navigates retail websites and finds matching products
+- **Concurrent execution** across multiple retailers for faster price discovery
+- **Structured data extraction** using Pydantic schemas for reliable pricing information
+- **Captcha handling** with user prompts for manual intervention when needed
+- **CSV export** for easy analysis and comparison of results
+
+**Key Benefits:**
+
+- Compare prices across Amazon, Best Buy, Costco, Target, and more in minutes
+- Reduce manual price checking effort by automating browser navigation
+- Get structured output with product names, prices, and promotion details
+- Easily customize product searches and retailer sources via CLI arguments
+
+**What's Included:**
+
+- Ready-to-run price comparison script
+- Support for custom product SKUs and retailer sources
+- Pydantic-based data validation for extracted pricing
+- ThreadPoolExecutor for parallel retailer searches
+- Complete setup and usage instructions
+
+[→ Explore the Price Comparison Use Case](./usecases/price_comparison/)
+
 ## Why Agentic Workflows?
 
 Traditional automation approaches face significant challenges:
+
 - **High maintenance overhead**: Scripts break with UI changes, requiring constant updates
 - **Limited scalability**: Manual testing is time-intensive and incomplete
 - **Technical barriers**: Creating automation requires specialized programming knowledge
 - **Brittleness**: Automation relies on specific selectors that fail when components change
 
 Agentic AI with Nova Act addresses these challenges by:
+
 - **Adapting dynamically** to interface changes without manual updates
 - **Mimicking human interaction** patterns for realistic testing
 - **Enabling non-technical users** to create tests through simple JSON definitions
@@ -81,6 +113,7 @@ This project is licensed under the MIT-0 License. See the [LICENSE](../LICENSE)
 ## Support
 
 For questions, issues, or feature requests:
+
 - Open an issue in this repository
 - Refer to the [official Nova Act documentation](https://github.com/aws/nova-act)
 - Contact AWS Support for enterprise assistance
diff --git a/nova-act/usecases/price_comparison/README.md b/nova-act/usecases/price_comparison/README.md
new file mode 100644
index 00000000..0b8261e2
--- /dev/null
+++ b/nova-act/usecases/price_comparison/README.md
@@ -0,0 +1,162 @@
+# Price Comparison using Amazon Nova Act
+
+This project demonstrates automated price comparison across multiple retailers using Amazon Nova Act. It showcases how AI-powered browser agents can intelligently search for products, navigate retail websites, and extract pricing information to help consumers find the best deals.
+
+## Overview
+
+This demonstration implements a concurrent price comparison workflow that searches for products across major retailers (e.g. Amazon, Best Buy, Costco, Target) and compiles results into a sortable comparison table.
+
+### Key Benefits
+
+- **Automated Price Discovery**: AI agents navigate retail sites and extract current pricing
+- **Concurrent Execution**: Parallel searches across multiple retailers reduce total execution time
+- **Structured Output**: Results exported to CSV for easy analysis and comparison
+- **Flexible Configuration**: Customize product searches and retailer sources
+
+## Architecture
+
+The demo consists of two main components:
+
+1. **Nova Act Browser Agents**: Automated browser sessions that navigate retail websites, handle captchas, and extract product information
+2. **Concurrent Execution Engine**: ThreadPoolExecutor manages parallel searches across multiple retailers
+
+## Prerequisites
+
+Before running this demo, ensure you have:
+
+### Development Environment
+
+- **Python 3.11 or higher**
+- **pip** (Python package manager)
+
+### API Access
+
+- **Amazon Nova Act API Key** - Visit [Nova Act home page](https://nova.amazon.com/act) to generate your API key
+
+## Getting Started
+
+Follow these steps to set up and run the price comparison tool:
+
+### Step 1: Install Dependencies
+
+```bash
+# Navigate to the project directory
+cd nova-act/usecases/price_comparison
+
+# Create and activate virtual environment
+python -m venv venv
+source venv/bin/activate  # On Windows: venv\Scripts\activate
+
+# Install dependencies
+pip install -r requirements.txt
+```
+
+### Step 2: Configure API Key
+
+Set your Nova Act API key as an environment variable:
+
+```bash
+export NOVA_ACT_API_KEY="your-api-key-here"
+```
+
+### Step 3: Run Price Comparison
+
+```bash
+# Run with default product (iPad Pro 13-inch M4, 256GB Wi-Fi)
+python main.py
+
+# Run with custom product
+python main.py --product_name "iPhone 15 Pro Max 256GB" --product_sku "MU793LL/A"
+
+# Run with custom sources
+python main.py --sources '[("Walmart", "https://www.walmart.com"), ("Amazon", "https://www.amazon.com")]'
+
+# Run in non-headless mode (visible browser)
+python main.py --headless=False
+```
+
+## Usage Examples
+
+### Default Search
+
+```bash
+python main.py
+```
+
+Searches for "iPad Pro 13-inch (M4 chip), 256GB Wi-Fi" (SKU: MVX23LL/A) across Amazon, Best Buy, Costco, and Target.
+
+### Custom Product Search
+
+```bash
+python main.py --product_name "MacBook Air 13-inch M3" --product_sku "MRXN3LL/A"
+```
+
+### Custom Retailer Sources
+
+```bash
+python main.py --sources '[("Walmart", "https://www.walmart.com"), ("Newegg", "https://www.newegg.com")]'
+```
+
+### Debug Mode (Visible Browser)
+
+```bash
+python main.py --headless=False
+```
+
+## Output
+
+Results are saved to `price_comparison_results.csv` with the following columns:
+
+| Column            | Description                              |
+| ----------------- | ---------------------------------------- |
+| Source            | Retailer name (Amazon, Best Buy, etc.)   |
+| Product Name      | Matched product name from retailer       |
+| Product SKU       | Search SKU used                          |
+| Price             | Current price (sorted lowest to highest) |
+| Promotion Details | Any active promotions or discounts       |
+
+## Project Structure
+
+```
+price_comparison/
+├── README.md           # This file
+├── main.py             # Main application script
+└── requirements.txt    # Python dependencies
+```
+
+## How It Works
+
+1. **Initialization**: Creates Nova Act browser sessions for each retailer
+2. **Captcha Handling**: Detects captchas and prompts user to solve if needed
+3. **Product Search**: Searches using the product SKU on each retailer site
+4. **Result Extraction**: AI agent identifies the most relevant product and extracts pricing
+5. **Data Compilation**: Results are aggregated, sorted by price, and exported to CSV
+
+## Troubleshooting
+
+### Common Issues
+
+1. **Captcha Detected**
+
+   - The tool will pause and prompt you to solve the captcha manually
+   - Press Enter after solving to continue
+
+2. **Product Not Found**
+
+   - Verify the SKU is valid for the target retailers
+   - Some products may not be available at all retailers
+
+3. **Browser Session Errors**
+
+   - Ensure you have a stable internet connection
+   - Try running in non-headless mode to debug: `--headless=False`
+
+4. **API Key Issues**
+   ```bash
+   export NOVA_ACT_API_KEY="your-api-key-here"
+   ```
+
+## Additional Resources
+
+- [Amazon Nova Act](https://nova.amazon.com/act)
+- [Nova Act Documentation](https://docs.aws.amazon.com/nova/latest/userguide/what-is-nova.html)
diff --git a/nova-act/usecases/price_comparison/main.py b/nova-act/usecases/price_comparison/main.py
new file mode 100644
index 00000000..08f4747b
--- /dev/null
+++ b/nova-act/usecases/price_comparison/main.py
@@ -0,0 +1,172 @@
+# Copyright 2025 Amazon Inc
+
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Compare product prices across multiple retailers.
+
+Searches for a product across Amazon, Best Buy, Costco, and Target concurrently
+and displays pricing and promotion details in a comparison table.
+
+Usage:
+    python main.py [--product_name <product name>] [--product_sku <sku>] [--sources <sources>] [--headless]
+    python main.py --product_name "iPad Pro 13-inch, 256GB Wi-Fi" --product_sku "MVX23LL/A"
+    python main.py --sources '[("Walmart", "https://www.walmart.com"), ("Amazon", "https://www.amazon.com")]'
+    python main.py --headless=False
+"""
+
+from concurrent.futures import ThreadPoolExecutor, as_completed
+
+import fire
+import pandas as pd
+from nova_act import BOOL_SCHEMA, ActAgentError, NovaAct
+from pydantic import BaseModel
+
+# Global defaults
+DEFAULT_PRODUCT_NAME = "iPad Pro 13-inch (M4 chip), 256GB Wi-Fi"
+DEFAULT_PRODUCT_SKU = "MVX23LL/A"
+DEFAULT_PRODUCT_SOURCES = [
+    # ("Amazon", "https://www.amazon.com"),
+    ("Best Buy", "https://www.bestbuy.com"),
+    ("Costco", "https://www.costco.com"),
+    ("Target", "https://www.target.com"),
+]
+
+
+class ProductPricing(BaseModel):
+    product_name: str | None
+    price: float | None
+    promotion_details: str | None
+    source: str  # Target, Best Buy, or Costco
+
+
+def check_source_price(
+    product_name: str,
+    product_sku: str,
+    source: str,
+    starting_url: str,
+    headless: bool = True,
+) -> ProductPricing | None:
+    """Check price for a product at a specific source."""
+
+    try:
+        with NovaAct(starting_page=starting_url, headless=headless) as nova:
+            # Check for captcha
+            captcha_check = nova.act_get(
+                "Is there a captcha on the screen?", schema=BOOL_SCHEMA
+            )
+            if captcha_check.matches_schema and captcha_check.parsed_response:
+                input(f"Please solve the captcha for {source} and hit return when done")
+
+            # Search using product SKU
+            nova.act_get(f"Search for '{product_sku}'.")
+
+            # Extract pricing and details from the most relevant result based on product name
+            result = nova.act_get(
+                f"""
+                Close any popups if they appear.
+                Review all search results and determine which one is the most relevant to the product name '{product_name}'.
+                Select this item to navigate to the product page.
+                Then, extract the price and any promotion details from the most relevant result that matches the product name '{product_name}'.
+                If no matching product exists, return None for all fields except source. Do not attempt to re-search using a different keyword combination.
+                """,
+                schema=ProductPricing.model_json_schema(),
+            )
+
+            if not result.matches_schema:
+                print(f"Invalid JSON from {source}: {result}")
+                return None
+
+            pricing = ProductPricing.model_validate(result.parsed_response)
+            return pricing
+
+    except ActAgentError as exc:
+        print(f"Could not retrieve pricing from {source}: {exc}")
+        return None
+
+
+def main(
+    product_name: str | None = None,
+    product_sku: str | None = None,
+    sources: list[tuple[str, str]] = DEFAULT_PRODUCT_SOURCES,
+    headless: bool = True,
+) -> None:
+    """Search for product prices across multiple sources concurrently."""
+    # Validate that both product_name and product_sku are provided together
+    if (product_name is None) != (product_sku is None):
+        raise ValueError(
+            "Both product_name and product_sku must be provided together. "
+            "You cannot specify only one without the other."
+        )
+
+    # Use defaults if neither was provided
+    if product_name is None:
+        product_name = DEFAULT_PRODUCT_NAME
+        product_sku = DEFAULT_PRODUCT_SKU
+
+    all_prices = []
+
+    print(
+        f"Searching for '{product_name}' (SKU: {product_sku}) across {len(sources)} sources...\n"
+    )
+
+    with ThreadPoolExecutor() as executor:
+        future_to_source = {
+            executor.submit(
+                check_source_price,
+                product_name,
+                product_sku,
+                source_name,
+                source_url,
+                headless,
+            ): source_name
+            for source_name, source_url in sources
+        }
+
+        for future in as_completed(future_to_source.keys()):
+            source = future_to_source[future]
+            pricing = future.result()
+
+            if pricing is not None:
+                result_dict = pricing.model_dump()
+                result_dict["sku"] = product_sku
+                all_prices.append(result_dict)
+            else:
+                all_prices.append(
+                    {
+                        "source": source,
+                        "product_name": None,
+                        "sku": product_sku,
+                        "price": None,
+                        "promotion_details": None,
+                    }
+                )
+
+    # Create DataFrame and sort by price
+    df = pd.DataFrame(all_prices)
+    df = df.sort_values(by="price", na_position="last")
+
+    # Reorder columns
+    df = df[["source", "product_name", "sku", "price", "promotion_details"]]
+
+    # Rename columns for better readability in output
+    df.columns = ["Source", "Product Name", "Product SKU", "Price", "Promotion Details"]
+
+    # Write results to CSV
+    output_file = "price_comparison_results.csv"
+    df.to_csv(output_file, index=False)
+
+    print(f"\nResults written to {output_file}")
+
+
+if __name__ == "__main__":
+    fire.Fire(main)
diff --git a/nova-act/usecases/price_comparison/requirements.txt b/nova-act/usecases/price_comparison/requirements.txt
new file mode 100644
index 00000000..cfb05637
--- /dev/null
+++ b/nova-act/usecases/price_comparison/requirements.txt
@@ -0,0 +1,19 @@
+cryptography
+fire
+install_playwright
+jsonschema
+numpy<=2.2.6
+pandas
+pillow
+playwright>=1.54.0
+pydantic>=2.10.6
+requests
+retry
+pytz
+strands-agents>=0.1.9
+strands-agents-tools
+Deprecated
+boto3
+boto3-stubs
+mypy-boto3-s3
+nova-act==3.0.67.0