This is a submission for the Agent.ai Challenge: Productivity-Pro Agent (See Details)
What I Built
Buying used goods just got easier with ValueMate which is a buyer agent that helps you evaluate price, negotiate and inspect the goods.
I built a shopping assistant agent designed to enhance productivity and streamline the decision-making process when browsing used goods on Facebook Marketplace. The agent automates tedious research tasks and provides actionable insights directly within the user’s workflow. Here’s how it works:
- Extracting Key Details: The agent pulls relevant product information (title, description, price) directly from a marketplace listing.
- Automated Research with Google Search: It compares prices and gathers additional details from Google, automating the otherwise time-consuming process of manual research.
- Insightful Evaluations with LLMs: Leveraging the power of LLMs, the agent evaluates the product based on the gathered data, providing users with clear recommendations and insights.
This tool fits perfectly into the competition’s focus on productivity and workflow automation. By integrating seamlessly with a Chrome extension, the agent eliminates the need for users to switch between tabs or perform manual research, making the process faster, easier, and more efficient. Whether for frequent shoppers or anyone who needs to make informed purchasing decisions, this agent is a game-changer in terms of saving time and effort.
Inspiration for the Idea
This project is very personal to me. A while back, I bought my first lawnmower—used, from Facebook Marketplace. I wanted to save some money, but I didn’t have much experience with lawnmowers. After a few days, I realized it had several defects I hadn’t noticed during the purchase. A bit of research showed these were common issues, and if I’d known about them earlier, I could have made a better decision—or at least negotiated a better deal.
That experience stayed with me, and when I saw this challenge, I realized it was the perfect opportunity to create a solution I wished I’d had back then. With the power of LLMs, we now have tools that can analyze data and provide insights almost instantly.
It’s like Carfax for used cars—but generalized for everything. Whether it’s a lawnmower, a piece of furniture, or electronics, this agent helps users evaluate second-hand products quickly and confidently. It’s also great for anyone who wants to save money or make eco-friendly choices by buying pre-owned items.
Demo
You can try out the agent manually on Agent.ai:agent link
The real magic happens when you use it with the Chrome extension. The extension pulls the listing details from Facebook Marketplace, sends them to the agent, and displays the insights directly on the marketplace page. No extra tabs or effort needed.
Check out this video demo to see it in action:
It’s a smooth and intuitive experience that keeps users focused on what matters—finding the best deals.
Agent.ai Experience
Delightful Moments
Using Agent.ai for this project was a fascinating experience. The platform provides tools that make building agents incredibly simple and powerful, including:
- LLM integrations for generating contextually aware responses.
- Google Search capabilities for real-time data retrieval.
- Webhook and WebSocket triggers for dynamic functionality.
One feature I really loved was the Google Search integration, which wasn’t just a basic search API. It’s contextually aware, providing highly relevant results for my queries. For example, when I searched for pricing and product details, it intelligently retrieved shopping-related results and filtered out unrelated noise. This saved me a lot of time.
Here’s a screenshot of how search results are displayed on the platform:
Another example of its power is the ability to extract specific data, like price comparisons:
Building the Workflow
I started by testing out individual features like webhooks, which allowed me to send data from the Chrome extension to the agent. The Developer Console was instrumental in helping me debug the process and ensure data was flowing correctly. Here’s a snapshot of the webhook setup:
Once I had all the pieces working, I integrated them into a cohesive workflow. The workflow builder on Agent.ai made this incredibly intuitive—it was easy to visualize how data moved through the agent and refine it step by step. Here’s what the final workflow looked like:
Challenges
While the platform is powerful, debugging more complex features like web sockets and advanced Lambda functions was a challenge. The Developer Console is helpful but could be expanded for better error tracking and troubleshooting. Additionally, I found myself wishing for version control, which would have made iterative development easier.
Despite these minor hurdles, the experience of building on Agent.ai was overwhelmingly positive. It’s a platform that feels ahead of its time, and I can see it being a game-changer for automating workflows in the future.
Final Thoughts
Building this agent was a fulfilling experience. Not only did I finally bring to life an idea I’ve had for a long time, but I also got to explore the possibilities of Agent.ai and its incredible tools.
This shopping assistant doesn’t just help users save money—it also encourages sustainable choices by making it easier to buy used items. I’m excited about what this technology can do and can’t wait to see where the world of AI agents goes from here.
Thanks to the agent.ai team for creating such a fantastic platform and for hosting this challenge!
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