AI Agent Skills

Neo4j graph construction from unstructured data using LLMs

Awesome-GraphRAG: A curated list of resources (surveys, papers, benchmarks, and opensource projects) on graph-based retrieval-augmented generation.

🕸 WebGL Graph Visualizations for React. Maintained by @goodcodeus.

A modular graph-based Retrieval-Augmented Generation (RAG) system

MineContext is your proactive context-aware AI partner（Context-Engineering+ChatGPT Pulse）

🔥 Comprehensive survey on Context Engineering: from prompt engineering to production-grade AI systems. hundreds of papers, frameworks, and implementation guides for LLM…

🧠 Make your agents learn from experience. Now available as a hosted solution at kayba.ai

Awesome resources for in-context learning and prompt engineering: Mastery of the LLMs such as ChatGPT, GPT-3, and FlanT5, with up-to-date and cutting-edge updates.

GPU-accelerated force graph layout and rendering

📰 Must-read papers and blogs on LLM based Long Context Modeling 🔥

RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context layer for L…

The Context Layer for unstructured data: typed, versioned datasets over S3, GCS, Azure

🤖 A Python library for learning and evaluating knowledge graph embeddings

pingcap/autoflow is a Graph RAG based and conversational knowledge base tool built with TiDB Serverless Vector Storage. Demo: https://tidb.ai

This repository provides an advanced Retrieval-Augmented Generation (RAG) solution for complex question answering. It uses sophisticated graph based algorithm to handle…

A 7-layer memory operating system for Hermes Agent — persistent memory with Qdrant, structured facts, fabric recall, auto-curated wiki, and surgical context injection. R…