We are proud to be the

Silver sponsor

UK AI Agent Hackathon

The largest Web3 x AI Hackathon in Europe

March 15, 2025

Imperial College London

Schedule

Saturday, March 15

Time

Topic

Location

10:00 GMT

Opening Remarks

Imperial College London

10:00 GMT

Hacking Begins

Imperial College London

10:40 GMT

Keynote by Sana Wajid - Chief Development Officer Fetch.ai

Imperial College London

11:40 GMT

Coffee Break

Imperial College London

13:00 GMT

Lunch & Networking Break

Imperial College London

15:30 GMT

Afternoon Coffee Break

Imperial College London

16:50 GMT

Closing Remarks

Imperial College London

Saturday, March 22

Time

Topic

Location

12:00 GMT

Hands-On Workshop on ASI1 Mini and AI Agents

Imperial College London

Thursday, April 10

Time

Topic

Location

12:00 GMT

Hacking Ends

Imperial College London

Sunday, April 13

Time

Topic

Location

10:00 GMT

Demo Day

Imperial College London

Introduction

Fetch.ai’s vision is to create a marketplace of dynamic applications. 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.

Fetchai SDK – seamlessly integrates your AI Agent into Agentverse and empowers dynamic connectivity with the Fetch.ai SDK

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

ASI-1 Mini - A Web3-native large language model (LLM) optimized for agent-based workflows.

Challenge statement

AI is evolving beyond static models and passive automation—it's time to build AI Agents that can reason, adapt, and interact in real time. This hackathon invites you to harness the power of Fetch.ai’s LLM ASI1-Mini, alongside the uAgents framework or Fetch.ai SDK, to create truly autonomous, intelligent, and goal-driven AI Agents.

The challenge is to build AI-powered multi-agent systems that create useful, innovative, or even amusing solutions for everyday life. Your creation could help streamline tasks, deliver personalized recommendations, or even bring a bit of fun into people’s lives. But this isn’t just about individual convenience—this is about building AI Agents that benefits both individuals and society. Whether you’re tackling education, healthcare, sustainability, finance, Web3 or any other impactful domain, the goal is to demonstrate real-world use cases where AI Agents drive meaningful change.

This is your moment to code, collaborate, and create AI Agents that don’t just run—they reason, adapt, and transform lives. So rally your team, sharpen your skills, and let’s build the future of AI together!

** Got Questions?**

Join the Fetch.ai Mentorship Channel on the UK AI Agent Hackathon Discord to get expert guidance, resolve queries, and connect with mentors!

Fetch.ai tech stack

Product Overview

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()