Solana Breakout Hackathon - The Fetch.ai Agent Track
The future of AI isn’t centralized—it’s agentic, decentralized, and Web3-native.
Join Fetch.ai 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.
Submissions open here: https://earn.superteam.fun/listing/the-fetchai-agent-track/
🚀 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.
Project Ideas
To inspire your build, here are some agent ideas in the Web3 and DeFi space:
📊 1. Token Sentiment & Price Tracker Agent
Problem: Traders lack a unified view of price trends and community sentiment.
Solution: An agent that returns token prices along with bullish/bearish sentiment analysis.
How: User says, "What’s the outlook on LINK?" → ASI:One web app leverages ASI1-mini LLM to query the most relevant agent, which responds with current price, trends, and a sentiment summary.
🧠 2. DeFi Learning Guide Agent
Problem: New users struggle to understand complex DeFi concepts.
Solution: An agent that provides interactive lessons and simulations on DeFi protocols.
How: User asks, "Teach me about liquidity pools" → ASI:One web app leverages ASI1-mini LLM to query the most relevant agent, which provides a guided, step-by-step explanation.
🛡️ 3. Rug Pull Detector Agent
Problem: Users often fall victim to scam tokens due to lack of reliable due diligence.
Solution: An agent that analyzes a token’s smart contract and social sentiment to assess scam risk.
How: User asks, "Is this token safe?" → ASI:One web app leverages ASI1-mini LLM to query the most relevant agent, which scans the token contract and recent sentiment to return a safety report.
💼 4. Cross-Chain Loan Agent
Problem: DeFi lending is fragmented, making it hard to find optimal loans.
Solution: An agent that fetches and compares loan options across chains.
How: User asks, "What’s the best ETH loan with low collateral?" → ASI:One web app leverages ASI1-mini LLM to query the most relevant agent, which returns a ranked list with explanations.
💰 5. Solana Wallet Activity Agent
Problem: Users can’t easily interpret their wallet history.
Solution: An agent that summarizes recent Solana wallet activity across DeFi, NFTs, and tokens.
How: User asks, "What has my wallet done in the last 7 days?" → ASI:One web app leverages ASI1-mini LLM to query the most relevant agent, which returns a clear activity summary.
🪙 6. Solana Airdrop Checker Agent
Problem: Users miss airdrops they might qualify for.
Solution: An agent that checks wallet eligibility for recent Solana-based airdrops.
How: User asks, "Do I qualify for any airdrops?" → ASI:One web app leverages ASI1-mini LLM to query the most relevant agent, which replies with current eligibility status.
Eligibility Criteria
Use ASI1:Mini LLM and the uAgents framework to build your agent.
Your agent's README should include the following tag.
What to Submit
Code - Share the link to your public GitHub repository to allow judges to access and test your project. Ensure your README.md file includes key details about your agent(s), such as their name and address, for easy reference.
Video - Include a demo video of about 3-5 minutes demonstrating the agents you have built.
Judging criteria
Impact - How well does the AI agent address a specific, well-defined industry or domain challenge?
ASI1:Mini Integration: Is your AI Agent compatible with ASI1:Mini LLM?
Agentverse Integration - Have you registered all your AI Agents on Agentverse?
Technical Robustness - Is the AI agent well-built, reliable, and free of major flaws or vulnerabilities?
Demo Quality - Is the solution presented clearly with a well-structured demo? Does it effectively showcase the AI agent’s capabilities?
Prizes: 5,000 USDC Total Prizes
1st: 2,500 USDC
2nd: 2,500 USDC
References
Documentation: https://innovationlab.fetch.ai/resources/docs/intro
Discord: https://discord.gg/fetchai