We are proud to be the
Presenter sponsor
ETH Online 2025
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.
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.
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.
-
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:

-
-
Video
- Include a demo video (3–5 minutes) demonstrating the agents you have built.
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.
Try it out on Agentverse ↗
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()
Important links
Fetch.ai Resources




Examples to get you started:
Judging Criteria
-
Functionality & Technical Implementation (25%)
- Does the agent system work as intended?
- Are the agents properly communicating and reasoning in real time?
-
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?
-
Innovation & Creativity (20%)
- How original or creative is the solution?
- Is it solving a problem in a new or unconventional way?
-
Real-World Impact & Usefulness (20%)
- Does the solution solve a meaningful problem?
- How useful would this be to an end user?
-
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.
Judges

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

Attila Bagoly
Chief AI Officer

Wendwossen Dufera
Machine Learning Engineer

Nahom Senay
Machine Learning Engineer
Mentors

Abhi Gangani
Developer Advocate

Kshipra Dhame
Developer Advocate

Rajashekar Vennavelli
AI Engineer

Trung Tran
Junior Software Engineer

Ryan Pham
Junior Software Engineer

Dev Chauhan
Developer Advocate
Gautam Manak
Developer Advocate
Sounds exciting, right?
Schedule
17:00 BST
ETHOnline 2025 Kickoff & Summit
Online
17:00 BST
Hacking Begins!
Online
22:00 BST
Idea Brainstorming & Team Formation Session
Online
20:00 BST
Project Feedback Session 1
Online
15:00 BST
Project Feedback Session 2
Online
16:00 BST
Project Submissions Due!
Online
16:00 BST
Judging Round 1: Asynchronous Project Judging
Online
16:00 BST
Judging Round 2: Live Project Judging
Online
16:00 BST
ETHOnline 2025 Finale – Finalists Announced!
Online