Fetch.ai has recently launched ASI-1 Mini, a Web3-native LLM designed for complex, autonomous workflows.
Try now
Fetch.ai has recently launched ASI-1 Mini, a Web3-native LLM designed for complex, autonomous workflows.
Try now
July 4, 2025 to July 6, 2025
Le Palais des Festivals et des Congrès de Cannes
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.
Challenge statement
Bridge Intent and Action: Build AI Agents That Think, Plan, and Deliver
We live in an age of infinite possibilities—but finite execution. People dream of financial independence, seamless automation, better health, smarter learning, or just getting more done. Yet most tools today—digital assistants, automators, and even LLMs—fall short of turning complex, human intentions into coordinated action.
This hackathon challenges you to build autonomous agents that understand what users actually want to achieve—and orchestrate the full stack of actions needed to make it happen.
Using the uAgents framework or any other agentic framework of your choice, create agents that can understand open-ended, natural language goals and break them into multi-step execution plans. These agents should verify outcomes and adapt in real time to ensure successful intent fulfillment.
Once built, register your agents on Agentverse—Fetch.ai’s open marketplace where they agents can be discovered by other agents and users. Implement the Chat Protocol to to enable seamless natural language interaction via ASI:One, allowing users to talk directly to your agents.
From optimizing a user’s portfolio to automating workflows or building playful tools that anticipate needs, your agents should go beyond commands to deliver outcomes.
This is your chance to shape the future of AI—from passive tools to autonomous, intelligent, goal-driven agents.
👉 Check out the resources to learn how to build and deploy your own AI agents.
Code
README.md file includes key details about your agents, such as their name and address, for easy reference.To achieve this, include the following badge in your agent’s README.md file:
Video
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
Functionality & Technical Implementation (25%)
Use of Fetch.ai Technology (20%)
Innovation & Creativity (20%)
Real-World Impact & Usefulness (20%)
User Experience & Presentation (15%)
ASI1 Trailblazer
$5000
Cash Prize
FetchFusion Award
$3000
Cash Prize
Best Multi-Agent System
$2000
Cash Prize
Judges
Sana Wajid
Chief Development Officer
Attila Bagoly
Chief AI Officer
Mentors
Kshipra Dhame
Developer Advocate
Abhi Gangani
Developer Advocate
Nikolay Dimitrov
Community and Developer Advocate
13:00 GMT+2
Registrations Open
Le Palais des Festivals et des Congrès de Cannes
15:00 GMT+2
ASI Workshop
Le Palais des Festivals et des Congrès de Cannes
18:30 GMT+2
Dinner
Le Palais des Festivals et des Congrès de Cannes
20:00 GMT+2
Welcome and Opening Ceremonies
Le Palais des Festivals et des Congrès de Cannes
21:00 GMT+2
Hacking Begins
Le Palais des Festivals et des Congrès de Cannes
10:00 GMT+2
Brunch
Le Palais des Festivals et des Congrès de Cannes
15:00 GMT+2
Project Feedback Session
Le Palais des Festivals et des Congrès de Cannes
16:00 GMT+2
Fun Activity
Le Palais des Festivals et des Congrès de Cannes
18:00 GMT+2
Dinner
Le Palais des Festivals et des Congrès de Cannes
08:00 GMT+2
Breakfast
Le Palais des Festivals et des Congrès de Cannes
09:00 GMT+2
Project Submissions Due
Le Palais des Festivals et des Congrès de Cannes
09:30 GMT+2
Project Judging
Le Palais des Festivals et des Congrès de Cannes
15:30 GMT+2
Closing Ceremonies
Le Palais des Festivals et des Congrès de Cannes
15:00 GMT+2
Finalist Notified
Le Palais des Festivals et des Congrès de Cannes