A curated collection of resources, papers, tools, and best practices for Context Engineering in AI agents and Large Language Models (LLMs).

从零构建 AI Agent：LLM 大模型应用开发实践

LLM-based Multi-Agent 系统架构设计与项目代码实践

从零构建 AI Agent：LLM 大模型应用开发实战 (AI 天才研究院 )

Agentic LLM System for Practicing System Design and other technical Interviews.

A curated collection of cutting-edge AI agent projects, frameworks, and research papers. This repository aims to catalog innovative implementations, architectural patterns, and emerging best practices in agent-based systems—from autonomous LLM-powered agents to multi-agent collaboration frameworks.

Ascend Flow (智流) is a multi-agent, adaptive learning system where specialized LLM tutors collaboratively plan, teach, generate scaffolded practice, and regulate motivation to keep learners in flow—bridging the gap from “understanding” to “solving” for coding interviews and foundational math.

Theory and Application Practice of Quantitative Investment of AI Agent Based on LLM Big Model

A machine-readable, Markdown-optimized implementation of the Secure Coding Practices designed for seamless integration with Agentic AI workflows and LLM context windows.

Lit web components best practices for AI agents and LLMs. Contains 27 rules across 7 categories, prioritized by impact.

LLM-based Multi-Agent System Architecture Design Guide and Development Project Practice

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Bridging the Gap Between LLM Agents and Clinical Practice through Interactive Sequential Benchmarking

Architectural standards and best practices for building reliable AI Agents and LLM workflows. Defining the framework for AI Reliability Engineering (AIRE).

An autonomous, context-aware AI Code Review Agent for GitLab. Powered by multi-LLMs (OpenAI, Anthropic, Gemini) with a self-learning feedback loop to enforce repository best practices.

PromptWeaver: RAG Edition helps design effective prompts for Traditional, Hybrid, and Agentic RAG systems. It offers templates, system prompts, and best practices to improve accuracy, context use, and LLM reasoning.

A real-time Japanese conversational AI built with Whisper for speech recognition, an LLM for reasoning, and a TTS engine for natural voice output. The bot listens, understands, and responds instantly in Japanese — entirely locally. Perfect for language practice, demos, and experimenting with real-time AI agents.

This is personal repository for practicing LLM (building agents, including local LLM, Prompt Engineering, RAG, Multi-agent, Chat Environment, ETC).

A development guide for getting LLM based coding agents up to speed on iOS 26 Liquid Glass design paradigm and best practices.

Production-grade GEO best practices for AI agents — everything you need to optimize content for AI search engines and LLM-based answer engines

The Test Case Generation Agent is an AI-powered application that automatically generates high-quality test cases from requirements documents. It leverages advanced LLMs and search capabilities to produce comprehensive test suites in either Gherkin or Selenium format, incorporating industry best practices and edge cases.

Tutorial on how to create LLM Agent using LangChain and Gemini

AI agent skills for LLM Gateway - best practices for image generation and more

We'll practice using the Agent Developer kit to create llms that run functions

A structured repository for creating and maintaining Laravel Best Practices optimized for agents and LLMs.

This is a practice of using LLM agent in RNA-Protein interaction prediction with deep learning models

A curated survey of database systems, design patterns, and architectural practices in modern AI systems including multi-agent frameworks, RAG pipelines, and LLM applications.

Custom instructions and development workflows to help LLM agents produce reliable, modern code and avoid deprecated patterns. Includes planning guides, debugging practices, and framework migration tips.

Repository for the Complete Artificial Intelligence Training (by Prof. Fernando Amaral), covering Machine Learning, Deep Learning, LLMs, Generative AI, NLP, Agents, Computer Vision, Anomaly Detection, Genetic Algorithms, Fuzzy Logic, and more. Includes theory and hands-on practice with Python, projects, and notebooks.

Multi AI Agents is a production-grade, cloud-native AI system built using LangGraph, LangChain, and Groq LLMs, designed with CI/CD, code quality, and containerized deployment in mind. The project follows modern MLOps practices integrating SonarQube for code quality, Jenkins for CI/CD automation, and AWS Fargate for serverless container deployment.