We are proud to be the
Platinum sponsor
BeachHacks 9.0
March 21, 2026 to March 22, 2026
California State University Long Beach
Prizes
Best Use of Fetch.ai - 1st Prize
$300
Cash Prize + Internship Interview Opportunity
Best Use of Fetch.ai - 2nd Prize
$200
Cash Prize + Internship Interview Opportunity
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 Register AI Agents on Agentverse, discoverable via ASI:One, that turn user intent into real, executable outcomes.
Requirements to be eligible for a prize:
- Develop a single or multi-agent orchestration that demonstrates reasoning, tool execution, and solves a real-world problem.
- Use any agentic framework like Claude SDK, OpenAI Agent SDK, Google ADK, Langgraph, CrewAI, etc. of your choice OR simple plain python to bring your idea to life.
- Register your agents with Agentverse and implement the Chat Protocol (mandatory) & Payment Protocol (optional) to support direct ASI:One interactions and built-in monetization.
- No custom frontend is required - the use case must be demonstrated directly through ASI:One
Deliverables:
-
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()
Important links
Fetch.ai Resources




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?
- Is there a well-defined monetization approach aligned with the agent’s functionality and value delivered?
-
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 - 1st Prize
$300
Cash Prize + Internship Interview Opportunity
Best Use of Fetch.ai - 2nd Prize
$200
Cash Prize + Internship Interview Opportunity
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

Dev Chauhan
Developer Advocate
Gautam Manak
Developer Advocate

Rajashekar Vennavelli
AI Engineer

Ryan Tran
Junior Software Engineer

Daksha Arvind
Junior Software Engineer
Schedule
10:00 PDT
Check-In
Pointe, CSULB
11:00 PDT
Opening Ceremony
Pointe, CSULB
12:00 PDT
Hacking Begins
Pointe, CSULB
12:00 PDT
Fetch.ai Workshop
Pointe, CSULB
13:00 PDT
Lunch
Pointe, CSULB
19:00 PDT
Dinner
Pointe, CSULB
10:30 PDT
Venue closed for the night
Pointe, CSULB
08:30 PDT
Venue Re-opens
Pointe, CSULB
11:00 PDT
Breakfast
Pointe, CSULB
11:45 PDT
Submissions Due
Pointe, CSULB
12:00 PDT
Judging Begins
Pointe, CSULB
15:00 PDT
Lunch
Pointe, CSULB
15:30 PDT
Closing Ceremony
Pointe, CSULB