Awesome AI Agents › Courses and Tutorials
The LLM Agents Ecosystem Handbook is a documentation-first repository that teaches the modern agent stack layer by layer and ships runnable material alongside the prose. It is organised in seven parts. Concepts describe an agent operating system: identity, memory, skills, tool calling, MCP integration, safety and observability. A provider section supplies an LLMProvider abstraction with adapters for more than 24 model providers spanning frontier APIs, fast inference services, marketplaces, enterprise clouds and local runtimes, plus a router that builds fallback chains and a matrix comparing them. A skills section gives a design guide, a taxonomy, a maturity model, a security checklist and a catalog of reusable skills. A prompt engineering section covers agent prompt patterns, instruction hierarchy, context engineering and defences against prompt injection. A coding-agent section holds repository instructions, prompts, review checklists and safe refactoring guidance for Claude Code, Cursor, Codex, Aider, Cline and custom runtimes. A design docs section covers agent and technical design documents, architecture decision records, design reviews, rollout plans and a machine-readable DESIGN.md specification. A curated catalog collects more than a hundred agent skeletons, framework comparisons, evaluation tools and tutorials. Blueprints, templates, memory and RAG tutorials, evaluation harnesses and a production readiness checklist round out the material, and an llms.txt file plus a wiki index let a coding agent read the repository directly. Entry points are mapped by reader type, from newcomers through to teams hardening safety and evaluations. The audience is engineers moving an agent from prototype to production who need one opinionated reference across model choice, orchestration, memory, guardrails and deployment rather than scattered framework documentation.
https://github.com/oxbshw/LLM-Agents-Ecosystem-Handbook
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.