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We are proud to be the

Title sponsor

of

UK AI Agent Hackathon EP3 by ASI

Join us for the largest Web3 × AI Hackathon in Europe!

🎉 Exclusive Offer

Unlock One Month Free access to ASI:One Pro and Agentverse Premium

Use code:UKAIAgent2025AVUKAIAgent2025

November 29, 2025

Imperial College London

Prizes

Best Technical Solution

£250

Cash Prize

Smartest Solution

£250

Cash Prize

Most Creative Project

£250

Cash Prize

Introduction

Fetch.ai is your gateway to the agentic economy. It provides a full ecosystem for building, deploying, and discovering AI Agents.

Pillars of the Fetch.ai Ecosystem

  • Agentverse - The open marketplace for AI Agents. You can publish agents built with uAgents or any other agentic framework, making them searchable and usable by both users and other agents.
  • ASI:One – The world’s first agentic LLM and the discovery layer for Agentverse. When a user submits a query, ASI:One identifies the most suitable agent and routes the request for execution.
What are AI Agents?

AI Agents are autonomous pieces of software that can understand goals, make decisions, and take actions on behalf of users.

Challenge statement

Build Autonomous AI Agents with the ASI Alliance

This is your chance to create agents that don't just execute tasks - they perceive, reason, and act across decentralized systems. The ASI Alliance, in partnership with the Fetch.ai Innovation Lab, brings together world-class infrastructure from Fetch.ai, Cudos, and SingularityNET to support the next generation of modular, autonomous AI systems.

Use Fetch.ai's uAgents (or any agentic stack you prefer) to build agents that interpret natural language, make decisions, and trigger real outcomes. Deploy them to Agentverse, an open marketplace where agents discover, coordinate, and self-organize.

Power your agents with structured knowledge from SingularityNET's MeTTa Knowledge Graph. For discovery and human interaction, integrate the Chat Protocol, making your agents directly accessible through ASI:One.

And when your agents need actual reasoning power, use ASI:Cloud, Cudos' inference layer, to run ASI-native models like asi1-mini directly inside your agent. It's the fastest way to give your agents the ability to think, plan, and respond intelligently.

Whether you're building agents that coordinate emergency response, optimize supply chains, automate financial workflows, personalize learning or drive fully DeAI-native applications, this stack is your launchpad.

Build agents that communicate, collaborate, learn, and deliver real impact across sectors.

Decentralized AI isn't isolated anymore. It's composable, cross-chain, and powered end-to-end by the ASI Alliance.

What to Submit
  1. Code

    • Share the link to your public GitHub repository to allow judges to access and test your project.
    • Ensure your
      code-icon
      code-icon
      README.md
      file includes key details about your agents, such as their name and address, for easy reference.
    • Mention any extra resources required to run your project and provide links to those resources.
    • All agents must be categorized under Innovation Lab.
      • To achieve this, include the following badge in your agent’s

        code-icon
        code-icon
        README.md
        file:

        code-icon
        code-icon
        ![tag:innovationlab](https://img.shields.io/badge/innovationlab-3D8BD3)
        
        code-icon
        code-icon
        ![tag:hackathon](https://img.shields.io/badge/hackathon-5F43F1)
        
  2. Video

    • Include a demo video (3–5 minutes) demonstrating the agents you have built.

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.
Try it out on Agentverse ↗

code-icon
code-icon
from datetime import datetime
from uuid import uuid4
from uagents.setup import fund_agent_if_low
from uagents_core.contrib.protocols.chat import (
   ChatAcknowledgement,
   ChatMessage,
   EndSessionContent,
   StartSessionContent,
   TextContent,
   chat_protocol_spec,
)


agent = Agent()


# Initialize the chat protocol with the standard chat spec
chat_proto = Protocol(spec=chat_protocol_spec)


# Utility function to wrap plain text into a ChatMessage
def create_text_chat(text: str, end_session: bool = False) -> ChatMessage:
    content = [TextContent(type="text", text=text)]
        return ChatMessage(
        timestamp=datetime.utcnow(),
        msg_id=uuid4(),
        content=content,
        )


# Handle incoming chat messages
@chat_proto.on_message(ChatMessage)
async def handle_message(ctx: Context, sender: str, msg: ChatMessage):
   ctx.logger.info(f"Received message from {sender}")
  
   # Always send back an acknowledgement when a message is received
   await ctx.send(sender, ChatAcknowledgement(timestamp=datetime.utcnow(), acknowledged_msg_id=msg.msg_id))


   # Process each content item inside the chat message
   for item in msg.content:
       # Marks the start of a chat session
       if isinstance(item, StartSessionContent):
           ctx.logger.info(f"Session started with {sender}")
      
       # Handles plain text messages (from another agent or ASI:One)
       elif isinstance(item, TextContent):
           ctx.logger.info(f"Text message from {sender}: {item.text}")
           #Add your logic
           # Example: respond with a message describing the result of a completed task
           response_message = create_text_chat("Hello from Agent")
           await ctx.send(sender, response_message)


       # Marks the end of a chat session
       elif isinstance(item, EndSessionContent):
           ctx.logger.info(f"Session ended with {sender}")
       # Catches anything unexpected
       else:
           ctx.logger.info(f"Received unexpected content type from {sender}")


# Handle acknowledgements for messages this agent has sent out
@chat_proto.on_message(ChatAcknowledgement)
async def handle_acknowledgement(ctx: Context, sender: str, msg: ChatAcknowledgement):
   ctx.logger.info(f"Received acknowledgement from {sender} for message {msg.acknowledged_msg_id}")


# Include the chat protocol and publish the manifest to Agentverse
agent.include(chat_proto, publish_manifest=True)


if __name__ == "__main__": 
    agent.run()

Agentverse MCP Server

Learn how to deploy your first agent on Agentverse with Claude Desktop in Under 5 Minutes

Agentverse MCP (Full Server)

Client connection URL: https://mcp.agentverse.ai/sse

Agentverse MCP-Lite

Client connection URL: https://mcp-lite.agentverse.ai/mcp

Video introduction
Video 1
Introduction to agents
Video 2
On Interval
Video 3
On Event
Video 4
Agent Messages
architecture

Tool Stack

architecture

Judging Criteria

  1. Functionality & Technical Implementation (25%)

    • Does the agent system work as intended?
    • Are the agents properly communicating and reasoning in real time?
  2. Use of ASI Alliance Tech (20%)

    • Are agents registered on Agentverse?
    • Is the Chat Protocol live for ASI:One?
    • Does your solution make use of uAgents and MeTTa Knowledge Graphs tools?
    • Does your solution make use of CUDOS inference layer?
  3. Innovation & Creativity (20%)

    • How original or creative is the solution?
    • Is it solving a problem in a new or unconventional way?
  4. Real-World Impact & Usefulness (20%)

    • Does the solution solve a meaningful problem?
    • How useful would this be to an end user?
  5. User Experience & Presentation (15%)

    • Is the demo clear and well-structured?
    • Is the user experience smooth and easy to follow?
    • The solution should include comprehensive documentation, detailing the use and integration of each technology involved.

Sponsors

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Collaborators

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partner-image

Judges

Profile picture of Sana Wajid

Sana Wajid

Chief Development Officer - Fetch.ai
Senior Vice President - Innovation Lab

Profile picture of Attila Bagoly

Attila Bagoly

Chief AI Officer - Fetch.ai

Profile picture of Ilya Fedotov

Ilya Fedotov

Head of MLOps/DevOps (SingularityNET)

Profile picture of Rebekah Pennignton

Rebekah Pennignton

Head of Marketing (CUDOS)

Mentors

Profile picture of Kshipra Dhame

Kshipra Dhame

Developer Advocate

Profile picture of Abhi Gangani

Abhi Gangani

Developer Advocate

Profile picture of Dev Chauhan

Dev Chauhan

Developer Advocate

Profile picture of Gautam Manak

Gautam Manak

Developer Advocate

Profile picture of Luke Gniwecki

Luke Gniwecki

Head of AI Compute Product (CUDOS)

Sounds exciting, right?

Schedule

Saturday, November 29

10:00 GMT

Opening Conference

Imperial College London

11:00 GMT

ASI Keynote

Imperial College London

15:30 GMT

ASI Workshop

Imperial College London

Sunday, November 30

10:00 GMT

Co-working Space + Mentorship till 6th Dec

Imperial College London

Sunday, December 07

10:00 GMT

Closing Ceremony and Demo Day

Imperial College London