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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 Autonomous AI Agents with the ASI Alliance
This is your chance to create agents that don't just execute tasks - they perceive, reason, and act across decentralized systems. The ASI Alliance, in partnership with the Fetch.ai Innovation Lab, brings together world-class infrastructure from Fetch.ai, Cudos, and SingularityNET to support the next generation of modular, autonomous AI systems.
Use Fetch.ai's uAgents (or any agentic stack you prefer) to build agents that interpret natural language, make decisions, and trigger real outcomes. Deploy them to Agentverse, an open marketplace where agents discover, coordinate, and self-organize.
Power your agents with structured knowledge from SingularityNET's MeTTa Knowledge Graph. For discovery and human interaction, integrate the Chat Protocol, making your agents directly accessible through ASI:One.
And when your agents need actual reasoning power, use ASI:Cloud, Cudos' inference layer, to run ASI-native models like asi1-mini directly inside your agent. It's the fastest way to give your agents the ability to think, plan, and respond intelligently.
Whether you're building agents that coordinate emergency response, optimize supply chains, automate financial workflows, personalize learning or drive fully DeAI-native applications, this stack is your launchpad.
Build agents that communicate, collaborate, learn, and deliver real impact across sectors.
Decentralized AI isn't isolated anymore. It's composable, cross-chain, and powered end-to-end by the ASI Alliance.
Important links
Examples to get you started:
Code
README.mdTo achieve this, include the following badge in your agent’s
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 ↗
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()
Agentverse MCP Server
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Agentverse MCP (Full Server)
Client connection URL: https://mcp.agentverse.ai/sse
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Client connection URL: https://mcp-lite.agentverse.ai/mcp




Tool Stack
Judging Criteria
Functionality & Technical Implementation (25%)
Use of ASI Alliance Tech (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 - Fetch.ai

Ilya Fedotov
Head of MLOps/DevOps (SingularityNET)

Rebekah Pennignton
Head of Marketing (CUDOS)
Mentors

Kshipra Dhame
Developer Advocate

Abhi Gangani
Developer Advocate

Dev Chauhan
Developer Advocate
Gautam Manak
Developer Advocate

Luke Gniwecki
Head of AI Compute Product (CUDOS)
Sounds exciting, right?
10:00 GMT
Opening Conference
Imperial College London
11:00 GMT
ASI Keynote
Imperial College London
15:30 GMT
ASI Workshop
Imperial College London
10:00 GMT
Co-working Space + Mentorship till 6th Dec
Imperial College London
10:00 GMT
Closing Ceremony and Demo Day
Imperial College London