Awesome AI Agents › Courses and Tutorials
Agentic-AI-Systems is a curated knowledge base and hands-on guide for learning how to design and build agentic systems with language models. It is organised as six chapters. The first covers foundations, meaning generative AI concepts, how language models behave, evaluation and the Python tooling used in the rest of the material. The second is a set of framework tutorials with runnable example code for the OpenAI Agents SDK, LangGraph, LangChain, LlamaIndex, CrewAI and Chainlit, each with its own notes and samples so the same task can be compared across libraries. The third and largest chapter is on system design: architectural layers, AI-native architectures, a step-by-step design procedure, diagrams, and a long catalogue of reusable agentic design patterns, together with material on retrieval augmented generation in its basic, agentic, multimodal and advanced forms, an overview of useful cookbooks, and a treatment of evaluation for agentic systems covering evaluation layers, dimensions, methods and tooling. A 2026 update chapter discusses harness engineering, long-running agents, agent operations, context engineering, the MCP and A2A protocols, security and cost-aware design. The fourth chapter collects example projects and applications, the fifth is a resource library of courses, tool stacks and protocol references, and the sixth is interview preparation with system design frameworks, prompts and practice templates aimed at generative AI engineering roles. The repository also summarises related reading, including an AI engineering book summary and platform architecture breakdowns. It is written and maintained by one author as a record of their own path through the subject, and it is aimed at engineers moving from single prompt applications to multi-agent systems, and at candidates preparing for agentic system design interviews. The content is documentation and example code rather than a deployable framework.
https://github.com/alirezadir/Agentic-AI-Systems
A comprehensive guide and resource hub for prompt engineering, context engineering, retrieval-augmented generation, and AI agents, offering tutorials, papers, tools, and courses to optimize the use of large language models.
Learn Claude Code is an educational project that teaches how to build modern AI coding agents like Claude Code through a progressive tutorial covering core concepts, planning, subagents, and skills mechanisms.
AI Engineering Hub is a comprehensive repository offering in-depth tutorials and practical resources on Large Language Models, Retrieval-Augmented Generation, and real-world AI agent applications for learners and practitioners.
The Hugging Face Agents Course is a comprehensive educational resource that teaches the fundamentals and advanced concepts of AI agents and large language models through structured units, practical frameworks, and community collaboration.
GenAI_Agents is a comprehensive repository offering tutorials, implementations, and community resources for building and advancing generative AI agents from basic to advanced levels.
This course provides a comprehensive project-based tutorial on Harness Engineering for building reliable AI coding agents, covering environment setup, state management, verification, and control.
An AI-powered tool that analyzes GitHub repositories and generates beginner-friendly tutorials with visualizations to simplify understanding complex codebases.
A comprehensive guide to prepare for machine learning and AI technical interviews at major tech companies, covering coding, ML fundamentals, system design, and behavioral aspects.