AI Agent Hackathon 2.0 @ Paradox'25

Paradox'25 IITM

May 23, 2025

IIT Madras

Introduction

Fetch.ai’s vision is to create a open AI Agent marketplace. We are empowering developers to build on our platform that can connect services and APIs without any domain knowledge.

Our infrastructure enables ‘search and discovery’ and ‘dynamic connectivity’. It offers an open, modular, UI agnostic, self-assembling of services.

Our technology is built on four key components:

uAgents - uAgents are autonomous AI agents built to connect seamlessly with networks and other agents. They can represent and interact with data, APIs, services, machine learning models, and individuals, enabling intelligent and dynamic decision-making in decentralized environments.

Agentverse - serves as a development and hosting platform for these agents.

Fetch Network - underpins the entire system, ensuring smooth operation and integration.

ASI:One - A Web3-native large language model (LLM) optimized for agent-based workflows.

Challenge statement

The AI agent landscape is evolving rapidly, yet many solutions remain either too generalized or overly technical for widespread adoption. Your mission is to build an innovative AI agent that leverages large language models—particularly the ASI:One LLM—and Fetch.ai's uAgents to effortlessly perform complex tasks specified through natural language instructions.

To demonstrate the practical power of AI Agents, create domain-specific solutions that solve real-world challenges through intuitive user interactions and tangible utility. For enhancing the capabilities of your agent, participants are encouraged to integrate the Model Context Protocol (MCPs) in their solution.

Are you ready to shape the next era of AI-driven automation? The challenge awaits!

👉 Check out the resources to learn how to build and deploy your own AI agents.

Tool Stack

Quick start example

This file can be run on any platform supporting Python, with the necessary install permissions. This example shows two agents communicating with each other using the uAgent python library.
Read the guide for this code here ↗

from uagents import Agent, Bureau, Context, Model
    class Message(Model):
        message: str
    
    sigmar = Agent(name="sigmar", seed="sigmar recovery phrase")
    slaanesh = Agent(name="slaanesh", seed="slaanesh recovery phrase")
        
    @sigmar.on_interval(period=3.0)
    async def send_message(ctx: Context):
       await ctx.send(slaanesh.address, Message(message="hello there slaanesh"))
        
    @sigmar.on_message(model=Message)
    async def sigmar_message_handler(ctx: Context, sender: str, msg: Message):
        ctx.logger.info(f"Received message from {sender}: {msg.message}")
    
    @slaanesh.on_message(model=Message)
    async def slaanesh_message_handler(ctx: Context, sender: str, msg: Message):
        ctx.logger.info(f"Received message from {sender}: {msg.message}")
        await ctx.send(sigmar.address, Message(message="hello there sigmar"))
        
    bureau = Bureau()
    bureau.add(sigmar)
    bureau.add(slaanesh)
    if __name__ == "__main__":
        bureau.run()