GitHub repository organizing LLM, RAG, and agent implementations across beginner to advanced levels is gaining 1,191 stars per day.
The ai-engineering-hub repository has accumulated 40,446 stars and 6,500 forks, with a current velocity of 1,191 stars per day. The project organizes 93+ production-ready implementations across three difficulty tiers: 22 beginner projects, 48 intermediate projects, and 23 advanced projects. All code is written in Jupyter Notebooks and released under the MIT license.
What it covers
The repository packages working code for LLMs, Retrieval-Augmented Generation (RAG), AI agents, and multimodal systems. Beginner projects include OCR with Llama 3.2 vision models, local ChatGPT clones using DeepSeek-R1 and Gemma 3, simple RAG workflows with LlamaIndex and Ollama, and image generation with Janus-Pro. Examples include LaTeX equation extraction from images and document chat interfaces.
Intermediate projects combine multiple components: agentic RAG systems with document search and web fallback, YouTube trend analysis with CrewAI and BrightData, stock analysis with Microsoft AutoGen, and content planning pipelines. Advanced projects are also listed in the README by category.
How it is organized
The repository groups projects by difficulty level and domain. The main README links to an AI Engineering Roadmap for newcomers and directs users to start with beginner projects before progressing to intermediate and advanced work. The MIT license and Jupyter format allow you to adapt and deploy code directly into your own projects.
Who should use it
The resource targets developers and AI practitioners building beyond tutorials. If you are implementing RAG systems, deploying local language models, or orchestrating multi-step AI workflows, the repository provides concrete reference implementations rather than conceptual guides. The repository has 134 open issues, indicating active maintenance.
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