One repo, two dozen agentic AI tutorials, no framework

What it does This repository houses a growing library of standalone coding tutorials—mostly Jupyter Notebooks—covering agentic AI patterns. Each entry implements a specific technique or framework, from building memory layers with Mem0 and GBrain to orchestrating multi-agent pipelines with LangGraph, SmolAgents, and Google ADK. It is essentially a broad, unindexed curriculum in executable form.

The interesting bit Rather than building a single framework, the repo acts as a survey course of the entire agentic ecosystem. You will find side-by-side implementations of Microsoft’s agent governance toolkit, reinforcement-learning memory retrieval, cost-aware LLM routing, and even biological network modeling—suggesting the authors are trying to document the field faster than it can stabilize.

Key highlights

• Covers 20+ distinct topics including memory infrastructure (Mem0, Memori, GBrain), planning and critique loops, tool governance, and browser automation.
• Mixes major frameworks and APIs: LangGraph, SmolAgents, CAMEL, Google ADK, Groq, Gemini, and OpenAI.
• Each tutorial links to a matching Marktechpost article and provides either a .ipynb notebook or a .py script.
• Includes niche experiments like agentic UI generation, dead-code detection with Repowise, and RL-powered long-term memory retrieval.
• Several notebooks include Colab badges for immediate execution.

Caveats

• There is no unifying architecture; it is a grab bag of disconnected blog-post companions.
• Some file links still point to an older repository name (AI-Tutorial-Codes-Included), suggesting the index may not be fully maintained.
• Depth varies by tutorial, and there is no search or tagging system beyond the flat README list.

Verdict Worth bookmarking if you are evaluating agentic patterns or need a quick, runnable reference before committing to a framework. Skip it if you are looking for a single, maintained library to import into production.