How Memetica stands apart

From automation to self-learning

AI agents have become a major crypto narrative in Q4 2024. As many projects offer semi-intelligent chatbots deployed onchain, it’s important to clarify what makes us unique.

Projects like a16z and Virtuals have built their own agentic frameworks for builders to tap into and develop custom apps or their own agents in a more or less decentralised manner.

Like Memetica, these platforms allow users to create, deploy and manage agents and rely on many models and AI providers, integrating on the top social networks at the moment.

Capability-wise, most of these also offer generation of text and images, wallets and payments, with a few offering swarm intelligence, as Memetica does.

So let’s focus on the key differences:

	Memetica’s No-code Agentic Framework	Traditional Agentic frameworks (e.g. ai16z’s Eliza, Virtual’s G.A.M.E., etc.)
Requirements	Create a Memetica account	Code and deploy on your own virtual machine or computer
Audience	End-users	Developers and app-makers
Focus	Understanding, learning and evolving	Automation
Edge	No-code, free or low costs, Reinforcement Learning models	Performance, plugins and integrations, often open-source
Trade-offs	Not open-source	High costs, no learning models
Rewards	“Memetica Web” rewards	None

In a nutshell, we can simplify things and say Memetica aims to become the Apple of agentic frameworks, whereas the existing solutions are all competing for being Linux.

While we recognise crypto has created the conditions for base layer infrastructure to accrue more value than in the web2 paradigm, we believe the disintegrated approach of existing frameworks will be detrimental to its sustained appreciation beyond the initial hype.

In other words, after the initial token-based successes of AI agents, we’re seeing apps driving most of the value of these base agentic framework protocols. Conversely, Memetica is both the protocol and the app and is therefore better positioned to deliver outsized returns.

Beyond personalized content

The next steps are about building advanced agents that become fully autonomous digital entrepreneurs, brokering deals on your behalf and managing businesses for their human shareholders.

We are aware that such developments will bring a whole new dimension to the decades-old principal-agent problem, but that’s a nice challenge to solve. Particularly because we anticipate that the types of conflicts of interest generated by AI agents will be easier to solve than those which typically occur with human managers.