Vayu AGI is built around a stated 2.23 trillion-parameter architecture and serves as our primary platform for researching increasingly capable AI.

Our work extends beyond model scale. We investigate reasoning, contextual understanding, memory, learning, multimodal intelligence, planning, tool use, autonomy, and agentic behavior. We evaluate how these capabilities interact and how AI can move beyond narrowly defined tasks toward more general problem-solving.

Vayu is also being used to explore AI-assisted AI development. Our research has involved using Vayu to contribute to the development of another AI system, allowing us to investigate capability transfer, model development, evaluation, optimization, and iterative improvement.

We research how AI systems can learn from interaction, retain knowledge, adapt to changing environments, and improve through structured feedback.

Vayu's capabilities are explored across multiple platforms and environments, allowing us to study how intelligence can be integrated into software, tools, agents, and real-world systems. We investigate how AI can perceive information, reason over it, use tools, execute multi-step tasks, evaluate outcomes, and adapt its approach.

A key area of research is AI-assisted self-improvement—examining how systems can evaluate their own performance, identify limitations, propose improvements, test those improvements, and contribute to subsequent development cycles.

Develop → Evaluate → Understand → Improve → Repeat

Increasing AI capability requires equally rigorous evaluation. We research AI safety, robustness, adversarial behavior, capability boundaries, and failure modes through systematic testing and red teaming.

Vayu is evaluated across reasoning, autonomy, multimodal capabilities, tool use, problem-solving, and other emerging areas of intelligence. We also investigate jailbreaks, unexpected behaviors, and system vulnerabilities to understand where increasingly capable AI systems require further improvement.

Our long-term direction is Artificial General Intelligence—systems capable of applying broad and adaptable intelligence across diverse domains rather than being limited to a single task.

We are exploring the combination of understanding, reasoning, learning, memory, adaptation, autonomy, multimodal intelligence, and iterative improvement that could contribute to increasingly general machine intelligence

Documentation