shubhamsaboo/awesome-llm-apps

Awesome LLM Apps is a hands-on cookbook of more than one hundred ready-to-run example apps built around large language models. The point is that anyone starting an AI project shouldn't have to rebuild the same underlying patterns over and over: a chat agent, a retrieval pipeline that grounds answers in your own documents (RAG, short for retrieval-augmented generation), or a setup where several agents work as a team. Each template is hand-written original code, tested end to end, and meant to be cloned, customized, and shipped. The way it works is straightforward. The repository is organized into thirteen categories, ranging from starter single-file agents through advanced multi-agent teams, voice AI agents, MCP agents, RAG tutorials, agents with memory, chat-with-X tutorials, LLM optimization tools, fine-tuning tutorials, and crash courses on agent frameworks. The README claims a project runs in three commands: clone the repo, install Python dependencies, and run a Streamlit app. Templates are provider-agnostic, so the same code can be pointed at different model providers like Claude, Gemini, GPT, Llama, Qwen, or xAI by changing configuration. Free step-by-step walkthroughs are published on a separate site called Unwind AI. You would use this if you want concrete starting points for building an AI assistant, research agent, voice bot, or RAG-style search over your own data, especially if you prefer learning by running working examples instead of from theory. The license is Apache-2.0, so projects can be forked, shipped, or sold without restriction. The full README is longer than what was provided.