AutoFetch

Smart AI Agent for Local Car Parking and Rentals

Inspiration

Finding affordable and reliable car rentals or convenient parking spots can be time-consuming and frustrating — especially when small local providers aren’t listed on big platforms.
AutoFetch was built to solve this problem by creating an AI-powered web platform that uses Fetch.AI’s decentralized agents to help users easily compare, locate, and reserve the best options in their area.

Our vision is to make mobility services more accessible, transparent, and convenient — all while supporting small, independent providers.

What It Does

AutoFetch is a Fetch.AI Smart Agent Demo and web solution that allows users to:

Find and compare car rentals from local providers to choose the best deal.
Compare prices and features such as availability, distance, and ratings.
Locate and reserve parking spots before reaching the destination to ensure a hassle-free experience.
Interact with providers through decentralized Fetch.AI agents for instant booking and updates.
Get quick results through an intuitive web interface built for everyday users.

By leveraging Fetch.AI’s uAgents SDK, AutoFetch bridges decentralized AI and real-world convenience, creating a smooth experience for both customers and small-scale service providers.

How We Built It

Fetch.AI Agentverse / uAgents SDK:
Built a custom autonomous agent to process user queries for nearby rentals and parking availability.
Smart Search Logic:
Designed the agent to accept a user’s location, compare prices and distances, and recommend the best options.
Python Backend:
Implemented backend logic using Python and Fetch.AI’s asynchronous agent model.
Web Interface:
Built a simple front-end to simulate real-world usage — allowing users to input their location and view results.
Version Control:
Managed and documented everything using GitHub for collaboration and open-source contribution.

Code Example

from uagents import Agent, Context

agent = Agent(name="AutoFetch")

@agent.on_message
async def find_parking(ctx: Context, sender: str, msg: str):
    location = msg  # Simulated location query
    result = f"Best parking near {location}: GreenLot Parking (0.5 miles away, $10/hr, reservable online)"
    await ctx.send(sender, result)

agent.run()

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## Challenges we ran into
Understanding the Fetch.AI agent communication model and message handling.

Setting up a working local environment with Python, VS Code, and GitHub.

Time constraints during the hackathon to demo a working prototype.

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## Accomplishments that we're proud of
How to use Fetch.AI SDKs to build and host autonomous agents.

How to integrate decentralized AI into real-world services like parking and rentals.

Collaboration and version control using Git & GitHub.

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## What we learned
Python

Fetch.AI

VS Code

GitHub

Local Web Server 

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## What's next for AutoFetch