Agentic-AI-Systems: A Markdown-First Curriculum for Production Agent Design
Practical system design, tools, and hands-on resources for building Gen-AI agents & agentic AI systems.
At a glance
- What is it?
- Alireza Dirafzoon's MIT-licensed repository is a structured course on agentic AI rather than an installable library. It is useful if you want a reading path through design patterns, RAG architectures and evaluation, and it is the wrong choice if you need a package to import.
- Who is it for?
- Adopt this repository as a reading and orientation resource if you are designing an agent system and need a map of patterns, RAG variants and evaluation layers before committing to a stack; it is not a dependency and should not be added to requirements.txt. Skip it if you need a maintained runtime, a stable API surface or a test harness.
- Can I use it commercially?
- Yes. MIT is a permissive licence: you can use, modify and sell software built on it, as long as you keep its copyright and licence notices.
- Is it still maintained?
- Yes. The repository last received commits 87 days ago.
- What is it written in?
- Mainly Python, according to GitHub's language statistics.
Answers come from the project's GitHub data, last synced on September 15, 2026, and from our analysis. They are not legal advice.
DEEP OPEN-SOURCE ANALYSIS
What Problem the Repository Actually Solves
The README frames the project as "a curated and evolving knowledge base and hands-on guide for learning about Agents, Agentic AI system design, and building production-ready AI agents and multi-agent systems." That sentence is the whole product definition. There is no package to install, no CLI, and no runtime. The problem it addresses is orientation: the agent tooling space spans OpenAI's Agents SDK, LangGraph, LlamaIndex, LangChain, CrewAI and Chainlit, and each has its own vocabulary for the same concepts (tools, state, handoffs, memory). A reader who wants to compare those vocabularies usually ends up reading six documentation sites in parallel. This repository puts them behind one table of contents.
The intended audience is visible in the chapter list. Chapter 6 is interview preparation, with "GenAI and agentic system design interview frameworks, prompts, and practice templates." Chapter 1 covers foundations including evaluation and modern Python. Chapter 3 covers architectures, layers and design patterns. That is a curriculum for an engineer preparing to design or be interviewed about agent systems, not a toolkit for someone shipping a feature this week. If you are looking for a library, the repository will not help you, and the README does not pretend otherwise.
How the Material Is Organised and What Each Chapter Holds
The repository is a directory tree of Markdown documents plus code samples, with six numbered top-level folders: 01_foundations, 02_frameworks, 03_system_design, 04_use_cases, 05_resources and 06_interview_prep. Each has its own README, and the root README acts as the index.
The substantive material sits in chapter 3. The README points to 03_system_design/design-patterns/agentic-ai-design-patterns.md, described as "a comprehensive overview of reusable design patterns in Gen-AI and agentic AI systems," and to 03_system_design/2026-agentic-ai-system-design.md, which the README says covers "harness engineering, long-running agents, AgentOps, context engineering, MCP/A2A, security, and cost-aware design." That second file is the most interesting item in the list, because harness engineering, long-running agents and cost-aware design are the concerns that show up after a prototype works and before it runs unattended. The RAG directory covers basic, agentic, multi-modal and advanced RAG in one place. The evaluation document at 03_system_design/evals/agentic-ai-evals.md is described as covering "evaluation layers, dimensions, methods, and frameworks."
Chapter 2 holds the framework-specific tutorials: folders for OpenAI, LangGraph, LlamaIndex, LangChain, CrewAI and Chainlit. The README links to 02_frameworks/1_openai/README.md for OpenAI API and Agents SDK material, and to 02_frameworks/4_llamaindex/llamaindex-multi-agent-workflow.md for multi-agent work with LlamaIndex and CrewAI. Chapter 5 is a single resources.md file listing courses, tool stacks and protocols. Chapter 4 is marked in the README as commented out, so the use-cases chapter is not currently surfaced on the index page even though the directory is listed in the contents table. Treat that as a sign of how the repository is maintained: the README and the tree can drift apart.
Getting Started Without an Install Step
There is no installation section in the README and no package metadata referenced in the supplied material, so the entry point is the repository itself. Clone it and read the index:
git clone https://github.com/alirezadir/Agentic-AI-Systems.git cd Agentic-AI-Systems
From there, the reading order the README implies is chapter 1 for foundations, chapter 3 for design, then chapter 2 for the framework you have chosen. The code samples live inside the framework folders. Because the repository is described as Python throughout and the tutorials target OpenAI, LangGraph, LlamaIndex, LangChain, CrewAI and Chainlit, running any sample will require installing the relevant framework and providing credentials for whichever model provider that sample uses. Those per-sample requirements are not specified in the root README; you will find them in the individual chapter and sample files, if at all.
Contributions follow a fork-and-PR flow. The README states: "Pull requests are welcome! If you'd like to add new frameworks, examples, or summaries, fork the repo and submit a PR." There is no CONTRIBUTING file referenced, no code of conduct mentioned, and no test suite described, which matters if you plan to submit code rather than prose. The licence is MIT, so you may copy, modify and redistribute the material, including commercially, provided the copyright notice and permission notice are preserved. That is a permissive arrangement, but note that MIT covers this repository's own content; the frameworks it teaches carry their own licences, and some of those are not MIT.
Where the Repository Falls Short
The README claims "Production-ready: Tested examples with deployment guides" under its Why section. Nothing in the supplied material substantiates that claim. There are no releases retrieved, no CI configuration mentioned, no test directory described, and no version tags. A reader should treat "tested" as an assertion by the author rather than a property they can verify from the repository metadata. The honest description of the code samples is that they are illustrative examples of framework usage, and illustrative examples rot quickly when the frameworks beneath them change.
That decay is the real failure mode. The repository teaches six frameworks whose APIs move independently. A tutorial written against one LangGraph version may not match the next. Because there are no releases and no changelog in the supplied material, there is no way to tell from the root of the repository which samples are current and which are stale. The last push date, 2026-06-20, tells you the repository as a whole is active, but it says nothing about any individual file. A reader who copies a code sample without opening the file's history is guessing.
The second limitation is scope overlap. Design patterns, architectures and the 2026 system design update all cover system-level structure, and the README describes each in similar terms ("comprehensive overview"). Without reading all three, you cannot tell which is the canonical treatment and which is the older one. For a reference-first repository, that ambiguity costs time. A short "start here" note at the top of chapter 3 would fix it, and its absence is a design gap rather than a content gap.
How This Differs From an Installable Agent Framework
The natural comparison is LangGraph, which the repository itself teaches in chapter 2. The difference is categorical. LangGraph is a library you add to a project: you write graph nodes and edges, and the library executes them. Agentic-AI-Systems is a set of documents you read before deciding whether to use LangGraph, CrewAI, LlamaIndex or the OpenAI Agents SDK. One is a dependency; the other is the reasoning that precedes picking a dependency.
That distinction has practical consequences. A framework gives you a runtime, an API surface that is versioned, and a bug tracker. This repository gives you prose, diagrams and examples, and its versioning signal is a single push timestamp. If your team's problem is "we do not know which agent framework fits our workload," the repository is a reasonable first stop because it covers several in one place. If your problem is "our agent loop needs durable state across restarts," no amount of reading chapter 3 will solve it; you need the framework's persistence documentation, not a survey of design patterns.
There is a middle position worth naming. The evaluation chapter and the RAG chapter cover ground that framework documentation usually treats thinly. Framework docs explain how to call the API, not how to decide whether a multi-modal RAG variant is worth its latency. If you already have a framework chosen and a prototype running, the design and evaluation chapters are the parts of this repository that still have something to say to you.
Maintenance Cost and What to Verify Before Relying on It
The maintenance cost falls on the reader, not on a package manager. There is nothing to upgrade, so there is no dependency drift to manage, but there is also no deprecation notice when a tutorial stops matching the framework it teaches. Your cost is the time to check each file you depend on. The cheapest check is to open the file in the repository's history view and compare its last commit date against the release cadence of the framework it covers. A LangGraph tutorial from a year ago is a different asset from one written last month.
Three things are worth verifying before you build on any chapter. First, whether the sample actually runs against the framework version you have installed, since the repository ships no lockfile or requirements file that the supplied material mentions. Second, whether the chapter you are reading has been superseded, particularly in chapter 3 where design patterns, architectures and the 2026 update overlap. Third, whether the code samples carry their own licence headers, because MIT on the repository does not automatically resolve the licensing of vendored snippets from other projects.
The fork-and-PR model means the repository's quality depends on who contributes and how carefully their prose is reviewed. The README's own contribution note is a single line. If you intend to depend on this material for a team, the practical move is to pin a specific commit hash in your internal notes rather than tracking main, so that a later edit does not silently change the document your design decision was based on.
Editorial conclusion
Adopt this repository as a reading and orientation resource if you are designing an agent system and need a map of patterns, RAG variants and evaluation layers before committing to a stack; it is not a dependency and should not be added to requirements.txt. Skip it if you need a maintained runtime, a stable API surface or a test harness. Before relying on any chapter, open the specific file and check its last commit date, since the repository is a collection of independently written documents with no release tags and no retrieved releases, and the only version signal available is the last push date of 2026-06-20.
Community notes