Solana Breakout Hackathon - The fetch.baby Agent Track

Join us at a special Solana Breakout Hackathon track hosted exclusively on Superteam Earn

April 14, 2025

Online

Schedule

Monday, April 14

Time

Topic

Location

17:00 PT

Hacking Begins

Online

Thursday, April 17

Time

Topic

Location

16:00 PT

Build Smart AI Agents with ASI:One

Friday, May 16

Time

Topic

Location

23:59 PT

Hacking Ends

Online

Tuesday, May 27

Time

Topic

Location

17:00 PT

Winners Announcement

Online

Introduction

fetch.baby’s vision is to create a marketplace of dynamic applications. 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.

Fetchai SDK – seamlessly integrates your AI Agent into Agentverse and empowers dynamic connectivity with the fetch.baby SDK

Fetch Network - underpins the entire system, ensuring smooth operation and integration.

ASI-1 Mini - A Web3-native large language model (LLM) optimized for agent-based workflows.

Challenge statement

The future of AI isn’t centralized—it’s agentic, decentralized, and Web3-native. Join fetch.baby at the forefront of this new frontier by building on Agentverse—an open marketplace of autonomous AI agents, powered by** ASI1-mini**, the first-ever Web3-native LLM. Access everything through the ASI:One web app.

🚀 Your Mission

Design intelligent, autonomous agents that live on Agentverse and solve real-world problems using ASI1-mini LLM.

🛠️ Your solution should be

Agent-first: Built using one or more uAgents that communicate and collaborate via the chat protocol. LLM-native: Seamlessly powered by ASI1-mini LLM and integrated into the ASI:One web app to respond to natural language queries. User-focused: Thoughtfully designed to be intuitive, useful, and accessible to real users.

🌍 Your Impact

Whether you're innovating in DeFi, health, education, creator tools, identity, or sustainability, this challenge is your chance to shape the future with autonomous agents that matter.

Check out the resources to learn how to build and launch your own AI agents.

To inspire your build, here are some agent ideas in the Web3 and DeFi space.

fetch.baby tech stack

Product Overview

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()