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October 24, 2025
Palace of Fine Arts, San Francisco
Best Use of Fetch.ai
$2500
Cash Prize + Internship Interview Opportunity
To qualify, teams must register their agents on Agentverse, enable the chat protocol, and integrate Anthropic's Claude (or any LLM) as the reasoning engine powering their agents. Judges will look for well-designed innovative agents that solve real problems, take meaningful actions, deliver exceptional user experience, and demonstrate strong implementation of the Fetch.ai ecosystem.
Best Deployment of Agentverse
$1500
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
$1000
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.
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 that understand user goals & intent and take action to achieve them.
They are autonomous pieces of software that can understand goals, make decisions, and take actions on behalf of users.
Build agents that do, not just chat:
a. Agent Development: Use popular agentic frameworks like LangGraph, CrewAI, OpenAI AgentKit, Google Agent Development Kit, etc., or build your agent from scratch in Python
b. Deployment & Discovery:
c. LLM Integration: Power your agents with Anthropic's Claude (or Gemini's multimodal capabilities, OpenAI, Groq inference, etc.)
💼 Productivity & Automation – Agents that execute workflows like email management, CRM updates, document processing, social media scheduling, or project coordination.
💰 Finance & Business – Tools for expense tracking, invoice processing, investment analysis, portfolio optimization, or financial planning that help users save, invest, or manage money.
🏥 Healthcare & Wellness – Appointment coordination, medication management, symptom tracking, health data analysis, or mental wellness support agents.
📚 Education & Research – Personalized tutors, research assistants, code reviewers, study planners, or language coaches that help people learn and understand complex topics.
🎨 Creative & Content – Content generation pipelines, design assistants, video editing automation, or marketing campaign managers that create and distribute creative work.
🖼️ Multimodal Applications – Agents that process images, audio, video, or documents: receipt scanners, visual analyzers, transcription services, or document intelligence tools.
🏗️ DevOps & Infrastructure – Log analyzers, code review bots, deployment managers, or documentation generators for system monitoring and automation.
🎯 Wildcard – Legal document processors, supply chain optimizers, customer support automation, travel planners, real estate analyzers—anything that uses the Fetch.ai stack and delivers real value.
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
Learn how to deploy your first agent on Agentverse with Claude Desktop in Under 5 Minutes
Agentverse MCP (Full Server)
Client connection URL: https://mcp.agentverse.ai/sse
Agentverse MCP-Lite
Client connection URL: https://mcp-lite.agentverse.ai/mcp
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
Mike Chrabaszcz
Developer Advocate
Chayan Shah
Junior Software Engineer
Ryan Tran
Junior Software Engineer
Martin Ceballos
Junior Software Engineer
Thang Nguyen
Junior Software Engineer
Trung Tran
Junior Software Engineer
Sounds exciting, right?
19:00 PDT
Pre-Hackathon Workshop
Wheeler 204
16:00 PDT
Opening Ceremony Begins
Palace of Fine Arts
20:00 PDT
Fetch.ai Workshop
Palace of Fine Arts
20:00 PDT
Hacking Begins
Palace of Fine Arts
09:00 PDT
Hacking Continues (Rest of the day)
Palace of Fine Arts
13:00 PDT
Networking Session
Breakout 4
16:00 PDT
Coffee Chats
Breakout 1
10:30 PDT
Hacking Ends; Judging Begins
Palace of Fine Arts
13:00 PDT
Closing Ceremony
Palace of Fine Arts
17:00 PDT
CalHacks Ends
Palace of Fine Arts