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Agentic AI World Hackathon

​This high energy hackathon leverages the latest cutting edge tools and platforms, giving teams the opportunity to build agentic driven solutions with the potential to break out and become the next unicorns.

September 21, 2024

San Francisco, California

Schedule

Friday, September 20

17:00 PDT

Pre-Workshop Networking with pizza

Digital Garage US

18:00 PDT

Opening Ceremony with Sana Wajid

Digital Garage US

18:10 PDT

"Built on Fetch.ai" with Yuanbo Pang and Chinmay Nilesh

Digital Garage US

18:30 PDT

AI Agents Workshop with Mark Losey

Digital Garage US

19:30 PDT

Post-Workshop Networking

Digital Garage US

Saturday, September 21

11:00 PDT

Team UP: Pitch your project for hack collaborations

Digital Garage US

13:00 PDT

Pre-Workshop Networking with pizza

Digital Garage US

13:30 PDT

AI Agents Workshop with Mark Losey

Digital Garage US

Sunday, September 22

14:00 PDT

Project Submission and Presentation

Digital Garage US

17:00 PDT

Awards Ceremony and Celebration

Digital Garage US

Introduction

Fetch.ai’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:

Agents - AI Agents are independent decision-makers that connect to the network and other agents. These agents can represent data, APIs, services, ML models and people.

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.ai SDK

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

Challenge statement

Fetch offers an easy way to create your AI agent. AI agents provide a revolutionary way to interact with LLMs. Fetch empowers LLMs from simple text generation methods to a framework that can understand a complex query, dissect it into understandable steps, and execute all of them. Although extremely powerful on their own, the capabilities of AI agents can be enhanced by using other tools.

Use the following services in your uAgent code to do more with your code! If you use all these services in your project, you would be qualified to win the Top Agentified App Prize!

Good luck and code away!

Fetch.ai tech stack

architecture

Product Overview

This flowchart can get you to where you want to be:

architecture

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 ↗

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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()
Video introduction
Video 1
Introduction to agents
Video 2
On Interval
Video 3
On Event
Video 4
Agent Messages

Judging Criteria

Each row is scored 1 to 5, with a total score being your final score.
Parameters
Definition
Example
Technology
How technically sound the use of technology is?
Best case as per the Success Tree
Engagement/ Direction
How engaging the project is for community?
Use-case solving a real life problem
Efficiency
How well the project takes use of technology? Could there have been more efficient ways of doing the same solution?
Chaining of tasks
Practicality
Is the project practical from business point of view?
Implementing the travel use-case for car-hire where everything is done by simple message
Scalability
Is there a demand for this solution in the chosen market?
A solution for recruitment which would connect to linkedin for professional profile
Impact
How impactful the project is? Both options to be evaluated ( large number of people with low impact or small amount of people with profound impact)
Flights booking through multi-agent system with cost effective solution. This will impact large amount of people

Collaborators

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Support

Support will be available at the hackathon, and you can also reach out to the core dev team who will be able to support you via Discord ↗

Judges

Profile picture of Sana Wajid

Sana Wajid

CDO at Fetch.ai Innovation Lab

Profile picture of Elliot Bertram

Elliot Bertram

BD Director at Fetch.ai Innovation Lab

Profile picture of Mark Losey

Mark Losey

CTO at FlockX

Mentors

Profile picture of Sanket Shekhar Kulkarni

Sanket Shekhar Kulkarni

Intern

Profile picture of Tanay Godse

Tanay Godse

Intern

Profile picture of Chinmay Mahagaonkar

Chinmay Mahagaonkar

Intern