The problem Meme Agent solves

Meme Agent is an AI-driven Web3 platform that simplifies and automates the creation, trading, and sniping of memecoins. By combining custom large language models (LLMs), blockchain technology, and seamless user onboarding, Meme Agent empowers users to explore the memecoin ecosystem with ease.

How It Works

1. Seamless Onboarding:

   • Users sign in through Web3Auth, allowing them to execute blockchain transactions without the complexity of setting up wallets.

2. AI-Powered Memecoin Creation:

   • Users can deploy their own memecoin using the platform's Memecoin Factory Contract, which allows customization of token name, symbol, max supply, and initial mint amount.
   • The platform automatically generates an ERC-20 token and a corresponding Uniswap Liquidity Pool (LP) pair with user-supplied liquidity in USDT.

3. Automated Trading & Sniping:

   • An AI agent assists in creating and trading memecoins, including sniping opportunities with precision.
   • Lit Protocol powers on-chain Lit Actions, enabling the AI to automate complex operations such as memecoin sniping.

4. Analytics and Insights:

   • Subgraphs are utilized to provide detailed analytics, including liquidity metrics, token creation timestamps, and trading activity, ensuring transparency and informed decision-making.

5. Cross-Chain Compatibility:

   • Meme Agent operates on multiple blockchains, including Polygon, BNB, Supra, and Base, ensuring broad accessibility and scalability.

Challenges we ran into

One significant challenge we encountered was the 30-second execution time limitation of Lit Protocol's Lit Actions. Since our project relies heavily on AI-driven bot actions to perform on-chain transactions for memecoin sniping, this limitation posed a bottleneck. The time constraint made it difficult to complete the entire process of signing and executing transactions within a single Lit Action, requiring us to explore innovative workarounds to mitigate the issue.
This challenge highlighted the need for optimization in how we structure and execute our AI-driven operations, as well as the importance of understanding protocol limitations when designing complex workflows.