Empower Large Language Models (LLM) using Knowledge Graph based Retrieval-Augmented Generation (KG-RAG) for knowledge intensive tasks

Superposition is a context-based configuration management system designed to facilitate safe and flexible rollout of config changes

🦀 Prevents outdated Rust code suggestions from AI assistants. This MCP server fetches current crate docs, uses embeddings/LLMs, and provides accurate context via a tool call.

Your proactive personal AI assistant & companion for daily productivity 🌎

Structured Data Extractor for AI Agents. Search your documents or the web for specific data and get it back in JSON or Markdown in a single tool call.

Java-Based Context-aware Recommendation Library

Contextual AI models for single-cell protein biology

AI writing studio for large-scale content.

Multimodal AI agent, an interactive data studio with on-demand ML inference, media generation, and a database explore

Context-sensitive ranking and choice in Python with PyTorch

DevContext is a cutting-edge Model Context Protocol (MCP) server designed to provide developers with continuous, project-centric context awareness. Unlike traditional context systems, DevContext continuously learns from and adapts to your development patterns and delivers highly relevant context providing a deeper understanding of your codebase.

Dynamic FastMCP extends the Model Context Protocol Python server with context-aware tools that adapt their behavior and descriptions based on user, tenant, and request context.

A Stream-based LLM Agent Framework for Continuous Context Sensing and Sharing

Open source IoT middleware suite that makes the data from just about anything usable. We believe in an open Internet of Things.

AWARE Framework for iOS

The implementation of “A Context-Aware User-Item Representation Learning for Item Recommendation”, Libing Wu, Cong Quan, Chenliang Li, Qian Wang, Bolong Zheng, Xiangyang Luo, https://dl.acm.org/citation.cfm?id=3298988

Oh-My-Papers: a Hybrid Context-aware Paper Recommendation System

AWARE Client iOS (Version2)

Semantic Labeling in VHR Images via A Self-Cascaded CNN (ISPRS JPRS, IF=6.942)

StickerSelector 是一个基于语义向量匹配的表情包选择系统，用于让 AI 在聊天中发送真正符合语境的表情包。 与传统依赖关键词或规则的方案不同，StickerSelector 会将 AI 生成的“表情包意图描述”与本地表情包进行语义匹配，从而选出在当前语境下最自然、最像真人会使用的表情。 适用于 AI 聊天机器人、QQ或微信的自动聊天、AI 女友等需要“拟人化表达”的场景。