In the near future, we could see billions of AI agents operating simultaneously,” said Richard Blythman, Chief Scientific Officer and Co-Founder of Agentic AI startup, Naptha AI.
As AI frameworks like LangChain, AutoGen, and LlamaIndex attract growing developer communities, Naptha AI is betting on making them interoperable through a framework-agnostic platform. It allows developers to build and deploy intelligent agents within their preferred tools while ensuring seamless cooperation across systems, unlocking the full potential of multi-agent networks.
Today, Naptha AI is announcing they've secured $6 million in pre-seed funding, co-led by Arche Capital and Cyber Fund, with participation from Seed Club Ventures, the first investor in Stability AI, and other prominent industry angels and VCs. The round positions Naptha AI at the cusp of the transition from monolithic AI models to Multi-Agent Universal Intelligence (MAUI), a new paradigm where billions of adaptive agents work together in trustless environments.
“We’ve already orchestrated 50,000 agents working within a single system, and we’re on track to scale to one million soon," added Blythman. "By 2026, there could be billions of agents, collaborating to predict trends, process data, and deliver real-time insights.”
Naptha AI provides developers with a deployment framework designed to integrate specialized agents that can collaborate across industries such as software development, healthcare, and scientific research. Rather than competing with existing frameworks, the platform enables different agents to operate on heterogeneous architectures, devices, and datasets, facilitating a decentralized ecosystem that offers smarter cooperation, scalability, and economic benefits. By allowing agents to work together, Naptha AI is building the infrastructure for the Internet of Intelligence, where distributed networks of AI can drive efficiencies across industries.
Naptha’s platform supports payments through Nevermined, providing incentives for developers to build agent systems that can solve complex tasks more efficiently than traditional models. They've also benefitted from early collaborations with Nous Research, AutoGPT, and LocalAI, confirming their adoption within the open-source AI community.
