Current AI agent frameworks live high up in the application layer. They are fundamentally disconnected from the bare metal. When an agent executes a multi-step loop reasoning, calling a tool, fetching vector embeddings, and generating a response, it suffers from massive data transfer overhead, unoptimized context-window memory management, and fragmented GPU utilization. In short: running complex agent hives on standard operating systems is slow, expensive, and structurally inefficient.


Kernel Bees is a low-latency software framework that sits directly on top of the operating system, acting as an execution engine for autonomous AI agents. We bring agent orchestration down to the kernel level, bare-metal close to the GPUs.

By leveraging native CUDA libraries, Kernel Bees optimizes memory allocation, parallelizes agent chain-of-thought processing, and handles asynchronous tool execution directly on tensor cores. Just like a bee colony maximizes efficiency through decentralized, specialized roles, Kernel Bees utilizes custom CUDA kernels to allow hundreds of local agents to "swarm" a workload concurrently without choking GPU VRAM.


We provide the foundational primitives: agent scheduling, fast-path memory context switching, and hardware-accelerated tool calling, that developers need to build production-grade, real-time agentic ecosystems. Kernel Bees turns raw GPU compute into high-velocity autonomous intelligence.

Kernel Bees

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Kernel Bees

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