sglang

AWQ is a 4-bit weight quantization technique that uses activation patterns to preserve critical weights, enabling 3x faster inference with minimal accuracy loss. It's ideal for deploying large models (7B-70B) on limited GPU memory and is particularly effective for instruction-tuned and multimodal models. This skill integrates with vLLM and Marlin kernels for optimized deployment.

CrewAI is a lightweight multi-agent orchestration framework for building teams of specialized AI agents that collaborate autonomously on complex tasks. It enables role-based agent collaboration with memory and supports sequential or hierarchical workflows for production use. The framework is built without LangChain dependencies for lean, fast execution.

AutoGPT Agents is a platform for building and deploying persistent, autonomous AI agents using visual workflows or code. It's ideal for developers creating complex, multi-step automation systems that require continuous operation or external triggers. Key features include a drag-and-drop visual builder and support for deploying agents via webhooks and schedules.

The llama-cpp skill enables efficient LLM inference on CPU, Apple Silicon, and non-NVIDIA GPUs, making it ideal for edge deployment or when CUDA is unavailable. It supports GGUF quantization for reduced memory usage and offers significant speedups over PyTorch on CPU. Use this for Macs, AMD/Intel systems, or embedded devices, but choose TensorRT-LLM for NVIDIA hardware requiring maximum throughput.