We are proud to be the
Title sponsor
KickStart UBC Biztech
November 19, 2025 to November 26, 2025
University of British Columbia, Vancouver
Prizes
Best Use of Fetch.ai
CA$250
Cash Prize
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 a Startup-Ready Ecosystem of Autonomous Agents.
🤖 What are AI Agents? They are autonomous pieces of software that can understand goals, make decisions, and take actions on behalf of users.
You’re founders with ambition. ✨
Bring your idea to life in one week by architecting multiple intelligent agents using the agentic framework of your choice, then wrap it with uAgents, which will publish to our marketplace. These agents should actively collaborate, plan, execute, adapt, and verify outcomes to fulfil user goals and unlock real value.🤖
Publish your agent suite on Agentverse and make it discoverable on ASI:One so your customers can engage with it effortlessly through natural language. Pitch your startup, demonstrate your agent ecosystem, and prove how you’ll generate real-world impact and business traction fast.
📚 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.mdfile 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.mdfile:
-
-
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
CA$250
Cash Prize
Judges

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

Attila Bagoly
Chief AI Officer
Mentors

Mike Chrabaszcz
Developer Advocate

Ryan Tran
Junior Software Engineer

Kshipra Dhame
Developer Advocate
Sounds exciting, right?
Schedule
17:30 PST
KickStart Opening Day
UBC Sauder's Henry Angus Building
10:00 PST
Validation Phase Day 1
Virtual
10:00 PST
Validation Phase Day 1
Virtual
09:00 PST
Build Day 1
UBC Sauder's Henry Angus Building
11:00 PST
Fetch.ai Workshop
UBC Sauder's Henry Angus Building
09:00 PST
Build Day 2
UBC Sauder's Henry Angus Building
17:30 PST
Final Showcase
UBC Sauder's Henry Angus Building