We are proud to be the
TOP sponsor
LA Hacks 2026
April 24, 2026 to April 26, 2026
Pauley Pavilion
Prizes
Best Use of Fetch.ai - 1st Prize
$2500
Cash Prize + Internship Interview Opportunity
Best Use of Fetch.ai - 2nd Prize
$1500
Cash Prize + Internship Interview Opportunity
Best Use of Fetch.ai - 3rd Prize
$1000
Cash Prize + Internship Interview Opportunity
Best OmegaClaw Skill powered by Agentverse - 1st Prize
$1500
Cash Prize
Best OmegaClaw Skill powered by Agentverse - 2nd Prize
$1000
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
Track 1 - Agentverse - Search & Discovery of Agents
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:
- Share your ASI:One Chat session URL Example
- Agent(s) URL on Agentverse Example
- Github Repo URL (Public) + demo video on Devpost
Track 2 - OmegaClaw Skill Forge powered by Agentverse
Goal:
Build a new specialist skill/capability for OmegaClaw using Agentverse.
In this track, participants will expand what OmegaClaw can do by connecting it to specialist intelligence through Agentverse. In many cases, this will mean building a specialist agent for a specific use case, registering it on Agentverse, and then creating the OmegaClaw skill or integration layer that allows OmegaClaw to discover, invoke, and use that agent when needed. If a strong existing Agentverse agent already fits the use case, participants may choose to integrate that instead.
OmegaClaw uses ASI1 as its underlying LLM, so submissions should be designed with the expectation that OmegaClaw will use ASI1 for reasoning, deciding when delegation is needed, and routing requests to the appropriate Agentverse capability. The goal is not just to build a standalone agent, but to make that capability usable by OmegaClaw through search, discovery, routing, and delegation in a real interaction flow.
Please refer this guide to start developing with Agentverse and OmegaClaw
Deliverables:
- A working specialist OmegaClaw skill that allows OmegaClaw to discover and invoke that capability
- Agent profile url on agentverse for the agent being used for the skill: Example
- a newly built agent registered on Agentverse, or
- an existing Agentverse agent successfully integrated into the workflow
- A live or recorded demo showing the full end-to-end interaction:
- a user interacts with OmegaClaw through a channel such as Telegram, IRC, or another supported interface
- OmegaClaw identifies the need for a specialist capability
- OmegaClaw invokes the Agentverse skill / specialist agent
- the result is returned back through OmegaClaw to the user
- A short technical explanation covering:
- what the capability does
- whether the team built a new agent or used an existing one
- how OmegaClaw routes the request
- how the result is returned to the user through the chosen channel
-
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
Track 1:
-
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?
Track 2:
-
Value of the Skill added to OmegaClaw (25%)
- How useful and meaningful is the skill being added to OmegaClaw?
- Does it solve a real problem or add a genuinely valuable new function?
-
Quality of the Agent registered/used on Agentverse (25%)
- How well does OmegaClaw discover, invoke, and use the Agentverse capability?
- Is the interaction flow coherent and does the delegation feel intentional rather than bolted on?
-
End-to-End User Experience (25%)
- How clearly does the project demonstrate a real user interacting with OmegaClaw through a channel such as Telegram or IRC, with OmegaClaw successfully calling the specialist capability and returning the result?
- Does the workflow feel complete and easy to follow?
-
Technical Execution (25%)
- How well is the system implemented?
- Does the integration work reliably end to end, and is the overall architecture thoughtful and functional?
Prizes
Best Use of Fetch.ai - 1st Prize
$2500
Cash Prize + Internship Interview Opportunity
Best Use of Fetch.ai - 2nd Prize
$1500
Cash Prize + Internship Interview Opportunity
Best Use of Fetch.ai - 3rd Prize
$1000
Cash Prize + Internship Interview Opportunity
Best OmegaClaw Skill powered by Agentverse - 1st Prize
$1500
Cash Prize
Best OmegaClaw Skill powered by Agentverse - 2nd Prize
$1000
Cash Prize
Collaborators
Judges
Sana Wajid
Chief Development Officer - Fetch.ai
Chief Operations Officer - Innovation Lab
Attila Bagoly
Chief AI Officer, Fetch.ai
Patrick Hammer
AGI Researcher, SingularityNET
Khellar Crawford
Chief AGI Innovation Officer, SingularityNET
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
Martin Ceballos
Junior Software Engineer
Daksha Arvind
Junior Software Engineer
Schedule
15:30 PDT
Hacker Check-In Starts
Upper Concourse
18:00 PDT
Opening Ceremony
Pauley Pavilion Floor
19:00 PDT
Hacking Starts
Pauley Pavilion
19:00 PDT
Dinner
Upper Concourse
08:00 PDT
Breakfast
Upper Concourse
10:00 PDT
Fetch.ai Workshop
Pavilion Club A
12:00 PDT
Lunch
Upper Concourse
18:00 PDT
Dinner
Upper Concourse
07:00 PDT
Hacking Ends
Pauley Pavilion Floor
08:00 PDT
Breakfast
Upper Concourse
10:00 PDT
Judging
Pauley Pavilion Floor
12:45 PDT
Top 5 Demos
Pauley Pavilion Floor
13:30 PDT
Closing Ceremony
Pauley Pavilion Floor