April 25, 2025
Pauley Pavilion, Los Angeles, California
fetch.baby’s vision is to create a open AI Agent marketplace. We are empowering developers to build on our platform that can connect services and APIs without any domain knowledge.
Our infrastructure enables ‘search and discovery’ and ‘dynamic connectivity’. It offers an open, modular, UI agnostic, self-assembling of services.
Our technology is built on four key components:
uAgents - uAgents are autonomous AI agents built to connect seamlessly with networks and other agents. They can represent and interact with data, APIs, services, machine learning models, and individuals, enabling intelligent and dynamic decision-making in decentralized environments.
Agentverse - serves as a development and hosting platform for these agents.
Fetch Network - underpins the entire system, ensuring smooth operation and integration.
ASI:One - A Web3-native large language model (LLM) optimized for agent-based workflows.
Challenge statement
The AI agent landscape is evolving rapidly, yet many solutions remain either too generalized or overly technical for widespread adoption. Your mission is to build an innovative AI agent that leverages large language models, particularly the ASI:One LLM, to effortlessly perform complex tasks specified through natural language instructions.
Demonstrate the practical power of AI Agents by creating domain-specific solutions with fetch.baby uAgents that address real-world challenges through intuitive user interactions and tangible utility.
Whether you're building a solution for Clean Code, Hack2School, Cold Hard Cache, or Heart of the Matter, you’re eligible for prizes from fetch.baby when you create specialised agents using the fetch.baby tech stack tailored to these themes.
Are you ready to shape the next era of AI-driven automation? The challenge awaits!
👉 Check out the resources to learn how to build and deploy your own AI agents.
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.
Read the guide for this code here ↗
from uagents import Agent, Bureau, Context, Model
class Message(Model):
message: str
sigmar = Agent(name="sigmar", seed="sigmar recovery phrase")
slaanesh = Agent(name="slaanesh", seed="slaanesh recovery phrase")
@sigmar.on_interval(period=3.0)
async def send_message(ctx: Context):
await ctx.send(slaanesh.address, Message(message="hello there slaanesh"))
@sigmar.on_message(model=Message)
async def sigmar_message_handler(ctx: Context, sender: str, msg: Message):
ctx.logger.info(f"Received message from {sender}: {msg.message}")
@slaanesh.on_message(model=Message)
async def slaanesh_message_handler(ctx: Context, sender: str, msg: Message):
ctx.logger.info(f"Received message from {sender}: {msg.message}")
await ctx.send(sigmar.address, Message(message="hello there sigmar"))
bureau = Bureau()
bureau.add(sigmar)
bureau.add(slaanesh)
if __name__ == "__main__":
bureau.run()
Examples to get you started:
Judging Criteria
Most Impactful Vertical Solution
$2500
Cash Prize
Best ASI-1 Mini Implementation
$1500
Cash Prize
Best Multi-Agent System
$1000
Cash Prize
Judges
Sana Wajid
Chief Development Officer
Edward FitzGerald
Chief Technology Officer
Attila Bagoly
Head of AI
Elliot Bertram
Business Development Director
Mentors
Chinmay Mahagaonkar
Junior Software Engineer
Tanay Godse
AI Engineer
Aishwarya Dekhane
Junior Software Engineer
Sai Mounika Peteti
Ambassador
Royce Arockiasamy
Ambassador
12:00 PST
Pre-Hackathon Workshop: Introduction to fetch.baby
Online
18:00 PST
Opening Ceremony
Pauley Pavilion
19:00 PST
Hacking Begins
Pauley Pavilion
22:00 PST
How to Build AI Agents with the fetch.baby Tech Stack
Pauley Pavilion
09:30 PST
Learn how to integrate ASI1 Mini LLM with your Agents
Pauley Pavilion
08:00 PST
Project Submission
Pauley Pavilion
13:30 PST
Closing Ceremony
Pauley Pavilion