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We are proud to be the

Exhibitor sponsor

of

GenAI Week 2025 Silicon Valley

At the largest GenAI event, meet the brightest minds

July 13, 2025

Santa Clara Convention Center

Introduction

Fetch.ai’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.

architecture

Tool Stack

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
Functionality
How well do your AI Agents perform their intended tasks? How effectively are APIs and frameworks integrated into your solution?
Agentverse Integration
Have you registered all your AI Agents on Agentverse?
Quantity of Agents Created
How many AI Agents have you created for this project? Does your submission demonstrate creativity and diversity in your AI Agents?
Personal Assistant Development
Does your assistant utilize the Search and Discover feature on Agentverse to dynamically connect with and coordinate tasks between multiple agents?
Innovation and Impact
Does your project address a real-world problem or introduce novel ideas?

Collaborators

partner-image

Mentors

Profile picture of Rajashekhar Vennavelli

Rajashekhar Vennavelli

AI Engineer

Profile picture of Tanay Godse

Tanay Godse

AI Engineer

Profile picture of Chinmay Mahagaonkar

Chinmay Mahagaonkar

Software Engineer

Profile picture of Mike Chrabaszcz

Mike Chrabaszcz

Developer Advocate

Profile picture of Davel Radindra

Davel Radindra

Software Engineer

Schedule

Sunday, July 13

14:00 PDT

Opening Words

Santa Clara Convention Center