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January 31, 2026
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Best Overall AI Agent Solution
$750
Cash Prize
Awarded to the team that delivers the most complete end-to-end agent solution - successfully turning user intent into real-world outcomes with strong technical execution, clear monetisation approach, and meaningful user impact.
Best Multi-Agent Workflow
$500
Cash Prize
Recognises the team that designs the most effective and well-coordinated multi-agent system, demonstrating clear planning, real-time adaptation, and seamless collaboration between agents to achieve complex goals.
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
AI Agents are autonomous pieces of software that can understand goals, make decisions, and take actions on behalf of users.
Challenge statement
Build and launch AI Agents on Agentverse, discoverable via ASI:One, that turn user intent into real, executable outcomes.
Autonomous systems that understand user goals and translate them into coordinated actions across tools, data, and services.
Design a multi-agent workflow that takes natural language goals, breaks them into multi-step plans, and adapts in real time to ensure success - while including built-in monetization so you can charge for usage, outcomes or features.
Use any agentic framework like Langgraph, CrewAI, ADK, etc. of your choice to bring your idea to life.
Deploy your agents to Agentverse and implement the Chat Protocol (mandatory) & Payment Protocol (optional) to support direct ASI:One interactions and built-in monetization.
Productivity β 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 β 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 β Agents that help people learn, stay updated, and understand complex topics. Think interactive study aids, AI explainers, or research companions.
Wildcard β Got an idea that doesnβt fit neatly into the above? Go for it. As long as it uses the Fetch.ai stack and delivers real value, it belongs here.
Check out the resources to learn how to build and deploy your own AI agents.
Important links
Examples to get you started:
Code
README.mdTo achieve this, include the following badge in your agentβs
README.md

Video
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 β
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()
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Client connection URL: https://mcp.agentverse.ai/sse
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Tool Stack
Judging Criteria
Functionality & Technical Implementation (25%)
Use of Fetch.ai Technology (20%)
Innovation & Creativity (20%)
Real-World Impact & Usefulness (20%)
User Experience & Presentation (15%)
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

Dev Chauhan
Developer Advocate
Gautam Manak
Developer Advocate
11:00 EST
Opening Ceremony
Salomon DECI
12:00 EST
Lunch
Sayles 104
16:00 EST
Fetch.ai Workshop
Friedman 208
10:00 EST
Brunch
Sayles 104
11:30 EST
Hacking Ends
Online
12:00 EST
Judging Begins
Sayles Auditorium
16:30 EST
Closing Ceremony
Salomon DECI