What is awesome-ai-apps?

This repository is a curated collection of more than 80 practical code examples for building applications that use large language models (LLMs), which are AI systems trained to generate and understand text. The examples cover several categories of AI application: text-based agents, voice assistants, retrieval-augmented generation apps, and tools that connect AI to external data sources using the Model Context Protocol (MCP). Retrieval-augmented generation (RAG) is a technique where an AI looks up relevant information from a database or document set before generating a response, helping it give more accurate and specific answers. An agent is an AI system that can take actions such as searching the web, calling an API, or running code, rather than just answering questions. MCP is a protocol that lets AI systems connect to external tools and services in a standardized way. The collection is organized by complexity and type. Starter and simple agents are the quickest to run and understand, while the advanced agents section covers more involved patterns. Separate categories group voice agents (that can speak and listen), memory agents (that retain information across conversations), and RAG applications together for easier browsing. The repository also includes an 8-lesson course on building AI agents with the AWS Strands SDK. The course progresses from basic agent setup and session management through multi-agent patterns, where multiple AI agents work together, and ends with production topics like safety guardrails and observability. Most examples are written in Python. The project is aimed at developers who want working starting points for common AI application patterns rather than theory-first explanations.