Agentic Context Engineering (ACE) framework for generating, managing, and leveraging instructive contexts in a structured way for self-improving language models. Studies how to systematically create and utilize context that guides Agent behavior, including tool usage guides and best practices.

A comprehensive curated survey on Context Engineering covering the progression from prompt engineering to production-grade AI systems. The repository contains hundreds of papers, frameworks, and implementation guides for LLMs and AI agents, serving as a centralized reference for researchers and practitioners.

VLDB 2022 paper introducing Manu, a cloud-native vector database management system designed for scalable similarity search in cloud environments with separated storage and compute architecture.

Powerful open-source object-relational database system that, with the pgvector extension, serves as a capable vector database for AI applications. Widely used from small projects to large-scale enterprise systems, and offered as managed services by major cloud providers.

GPU-accelerated platform from VAST Data that includes a native vector database, designed for enterprise AI workloads including multi-agent systems, video-reasoning, and high-volume RAG. It combines vector embeddings with structured data and metadata in unified tables, enabling hybrid queries across modalities without orchestration layers or external indexes.

GPU-accelerated server from VAST Data that combines the VAST AI OS with NVIDIA data-processing libraries and onboard NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs. Designed for enterprise AI workloads requiring high-throughput vector search, data vectorization, and inference, it leverages the NVIDIA AI Data Platform reference design.