hero-vector
hero-vector
hero-vector

We are proud to be the

Presenter sponsor

of

ETH Online 2025

​Get ready to code, create, and compete with the ETH Online 2025 kicking off with an epic 3-week hackathon.

October 10, 2025 to January 1, 1970

Virtual

Prizes

1st Prize

$3500

Cash Prize

Awarded to the team that shows the most effective and creative use of ASI:One for human–agent interaction, paired with MeTTa for structured reasoning. Judged on problem choice, solution quality, and real-world impact.

2nd Prize

$2500

Cash Prize

Awarded to the team with the most impactful, well-presented launch on Agentverse. Your listing should be easy to find via ASI:One and clearly explain how MeTTa powers your logic. Judges look for usability, discoverability, clear purpose, and adoption potential.

3rd Prize

$1750

Cash Prize

Awarded to the team that builds the most cohesive multi-agent system using Fetch.ai agents and MeTTa for shared knowledge and reasoning. We’ll evaluate how smoothly agents communicate, collaborate, and coordinate complex tasks across chains and environments.

4th Prize

$1250

Cash Prize

Awarded to the team that delivers the most innovative demonstration of agent collaboration within the ASI:One ecosystem. Judges will look for originality, technical depth, and potential for future scalability.

5th Prize

$1000

Cash Prize

Awarded to the team with the most polished user-facing experience that makes advanced human–agent interaction intuitive and engaging. Judged on design quality, accessibility, and clarity of value to end users.

Introduction

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

  • Build agents using the uAgents framework.
  • Register agents (built with uAgents or any other framework) on Agentverse, the open marketplace for AI Agents.
  • Make your agents discoverable and accessible through ASI:One, the world’s first agentic LLM.
What are AI Agents?

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

The Three Pillars of the Fetch.ai Ecosystem

  • uAgents – A Python library developed by Fetch.ai for building autonomous agents. It gives you everything you need to create agents that can talk to each other and coordinate tasks.
  • 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.

Challenge statement

Build Autonomous AI Agents with the ASI Alliance

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

Use Fetch.ai's uAgents framework or your preferred agentic stack to build agents that can interpret natural language, make decisions, and trigger real-world actions. Deploy them to Agentverse, the ASI-wide registry and orchestration layer where agents connect, collaborate, and self-organize.

Enhance your agents with structured knowledge from SingularityNET's MeTTa Knowledge Graph. For agent discovery and human interaction, integrate the Chat Protocol to make your agents accessible through the ASI:One interface.

Whether you're building in healthcare, logistics, finance, education, or DeAI-native applications—this is your launchpad. Develop agents that talk to each other. That learn and adapt. That drive real outcomes across sectors.

The future of decentralized AI isn't siloed. It's composable, cross-chain, and powered 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 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 README.md file:

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

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

Tool Stack

architecture

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

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?
  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.

Prizes

1st Prize

$3500

Cash Prize

Awarded to the team that shows the most effective and creative use of ASI:One for human–agent interaction, paired with MeTTa for structured reasoning. Judged on problem choice, solution quality, and real-world impact.

2nd Prize

$2500

Cash Prize

Awarded to the team with the most impactful, well-presented launch on Agentverse. Your listing should be easy to find via ASI:One and clearly explain how MeTTa powers your logic. Judges look for usability, discoverability, clear purpose, and adoption potential.

3rd Prize

$1750

Cash Prize

Awarded to the team that builds the most cohesive multi-agent system using Fetch.ai agents and MeTTa for shared knowledge and reasoning. We’ll evaluate how smoothly agents communicate, collaborate, and coordinate complex tasks across chains and environments.

4th Prize

$1250

Cash Prize

Awarded to the team that delivers the most innovative demonstration of agent collaboration within the ASI:One ecosystem. Judges will look for originality, technical depth, and potential for future scalability.

5th Prize

$1000

Cash Prize

Awarded to the team with the most polished user-facing experience that makes advanced human–agent interaction intuitive and engaging. Judged on design quality, accessibility, and clarity of value to end users.

Collaborators

partner-image
partner-image
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

Profile picture of Wendwossen Dufera

Wendwossen Dufera

Machine Learning Engineer

Profile picture of Nahom Senay

Nahom Senay

Machine Learning Engineer

Mentors

Profile picture of Abhi Gangani

Abhi Gangani

Developer Advocate

Profile picture of Kshipra Dhame

Kshipra Dhame

Developer Advocate

Profile picture of Rajashekar Vennavelli

Rajashekar Vennavelli

AI Engineer

Profile picture of Trung Tran

Trung Tran

Junior Software Engineer

Profile picture of Ryan Pham

Ryan Pham

Junior Software Engineer

Profile picture of Dev Chauhan

Dev Chauhan

Developer Advocate

Profile picture of Gautam Manak

Gautam Manak

Developer Advocate

Sounds exciting, right?

Schedule

Friday, October 10

17:00 BST

ETHOnline 2025 Kickoff & Summit

Online

17:00 BST

Hacking Begins!

Online

22:00 BST

Idea Brainstorming & Team Formation Session

Online

Wednesday, January 15

20:00 BST

Project Feedback Session 1

Online

Wednesday, October 22

15:00 BST

Project Feedback Session 2

Online

Sunday, October 26

16:00 BST

Project Submissions Due!

Online

Monday, October 27

16:00 BST

Judging Round 1: Asynchronous Project Judging

Online

Tuesday, October 28

16:00 BST

Judging Round 2: Live Project Judging

Online

Friday, October 31

16:00 BST

ETHOnline 2025 Finale – Finalists Announced!

Online