We are proud to be the
Premium sponsor
MHacks
September 27, 2025 to September 28, 2025
University of Michigan
Prizes
Best Use of Fetch.ai
$1250
Cash Prize + Internship Interview Opportunity
Awarded to the team that demonstrates the fullest use of the Fetch.ai tech stack. To qualify, teams must register their agents on Agentverse, enable the chat protocol, and integrate ASI:One as the reasoning engine powering their agents. Judges will look for a strong end-to-end implementation that highlights how the pieces of the Fetch.ai ecosystem come together.
Best Deployment on Agentverse
$750
Cash Prize + Internship Interview Opportunity
Given to the team that publishes the highest number of useful, discoverable, and well-documented agents on Agentverse. Judges will value scale, clarity, and how easy it is for others to find and use these agents.
Best Use of ASI:One
$500
Cash Prize + Internship Interview Opportunity
Awarded to the team that shows the most effective application of ASI:One as the core reasoning and decision-making engine within their agents. The focus is on demonstrating how ASI:One can power smarter, more capable interactions.
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
🎯 Goal Build and launch AI Agents on Agentverse that turn user intent into real outcomes.
🤖 What are AI Agents? They are autonomous pieces of software that can understand goals, make decisions, and take actions on behalf of users. 🚀 Your Mission
- Design agents that take open-ended natural language goals, break them into multi-step plans, and adapt in real time to ensure success.
- Use the uAgents framework (or any agentic framework of your choice) to bring your idea to life.
- Register your agents on Agentverse, Fetch.ai’s open marketplace, and implement the Chat Protocol so users can interact with them directly through ASI:One.
- You can also build custom applications that use agents in the backend, making them accessible to end users in new and creative ways. 🌍 Inspiration
- Productivity – Tools that make daily tasks faster and smoother. Automations for schoolwork, small businesses, or niche workflows like CRM updates, email handling, or social media coordination.
- Finance – Agents that improve personal or corporate finances. From expense trackers to credit assessment or portfolio optimization, anything that helps users save, invest, or manage money.
- Education – Agents that help people learn, stay updated, and understand complex topics. Think interactive study aids, AI explainers, or research companions.
- Wildcard – Got an idea that doesn’t fit neatly into the above? Go for it. As long as it uses the Fetch.ai stack and delivers real value, it belongs here.
📚 Resources Check out the resources to learn how to build and deploy your own AI agents.
-
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()




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 Fetch.ai Technology (20%)
- Are agents registered on Agentverse?
- Is the Chat Protocol implemented for ASI:One discoverability?
-
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 solution presented clearly with a well-structured demo?
- Is there a smooth and intuitive user experience?
Prizes
Best Use of Fetch.ai
$1250
Cash Prize + Internship Interview Opportunity
Awarded to the team that demonstrates the fullest use of the Fetch.ai tech stack. To qualify, teams must register their agents on Agentverse, enable the chat protocol, and integrate ASI:One as the reasoning engine powering their agents. Judges will look for a strong end-to-end implementation that highlights how the pieces of the Fetch.ai ecosystem come together.
Best Deployment on Agentverse
$750
Cash Prize + Internship Interview Opportunity
Given to the team that publishes the highest number of useful, discoverable, and well-documented agents on Agentverse. Judges will value scale, clarity, and how easy it is for others to find and use these agents.
Best Use of ASI:One
$500
Cash Prize + Internship Interview Opportunity
Awarded to the team that shows the most effective application of ASI:One as the core reasoning and decision-making engine within their agents. The focus is on demonstrating how ASI:One can power smarter, more capable interactions.
Judges

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

Attila Bagoly
Chief AI Officer
Mentors

Abhi Gangani
Developer Advocate

Kshipra Dhame
Developer Advocate

Rajashekar Vennavelli
AI Engineer

Trung Tran
Junior Software Engineer
Schedule
09:00 EDT
Breakfast
Central Campus Classroom Building (CCCB)
09:30 EDT
Opening Ceremony
Central Campus Classroom Building (CCCB)
11:00 EDT
Team Formation / Beginner Workshop
Central Campus Classroom Building (CCCB)
11:00 EDT
Hacking Starts
North Campus Duderstadt Library, Pierpont Commons, Bob & Betty Beyster Building (BBB)
12:30 EDT
Lunch
North Campus Duderstadt Library, Pierpont Commons, Bob & Betty Beyster Building (BBB)
16:00 EDT
Fetch.ai Workshop
North Campus Duderstadt Library, Pierpont Commons, Bob & Betty Beyster Building (BBB)
19:00 EDT
Dinner
North Campus Duderstadt Library, Pierpont Commons, Bob & Betty Beyster Building (BBB)
08:00 EDT
Hacking Continues
North Campus Duderstadt Library, Pierpont Commons, Bob & Betty Beyster Building (BBB)
11:00 EDT
Brunch
North Campus Duderstadt Library, Pierpont Commons, Bob & Betty Beyster Building (BBB)
12:00 EDT
Project Submissions Due
North Campus Duderstadt Library, Pierpont Commons, Bob & Betty Beyster Building (BBB)
12:30 EDT
Judge Orientation
North Campus Duderstadt Library, Pierpont Commons, Bob & Betty Beyster Building (BBB)
13:00 EDT
Judging
North Campus Duderstadt Library, Pierpont Commons, Bob & Betty Beyster Building (BBB)
16:30 EDT
Closing Ceremony
Central Campus Classroom Building (CCCB)