Agentic-AI-Systems: a curated guide to building AI agents, not a framework

What Agentic-AI-Systems is, and who it is written for

This repository is a knowledge base and hands-on guide, in the author's words a collection of resources found useful while building agentic AI systems. It is not a runtime. There is no package to install, no agent loop to call, and no API surface. What you get is a set of chapters under 01_foundations, 02_frameworks, 03_system_design, 04_use_cases, 05_resources and 06_interview_prep, plus an assets directory for cover images.

The audience is narrow and worth stating plainly. It suits an engineer who already writes Python and now has to make decisions about agent architecture: how to structure a multi-agent workflow, how to evaluate one, which framework to pick. It does not suit someone looking for a drop-in library, and it does not suit a reader who wants a single canonical answer. The README describes the collection as curated and evolving, which is an accurate description of a repository whose last push was on 2026-06-20.

The framing is reference-first. That matters for how you read it. The chapters are meant for lookup when a design question comes up, not for linear study. The 2026 Agentic AI System Design Update under 03_system_design is the clearest example: it covers harness engineering, long-running agents, AgentOps, context engineering, MCP/A2A, security and cost-aware design, which is a list of concerns rather than a tutorial.

The chapter structure and how the material flows

The repository is organised as six numbered directories, each with its own README, and the root README acts as a table of contents. Chapter 1, 01_foundations, covers GenAI concepts and design, LLMs, evaluation and modern Python. Chapter 2, 02_frameworks, holds tutorials and example code for OpenAI, LangGraph, LlamaIndex, LangChain, CrewAI and Chainlit. Chapter 3, 03_system_design, is the largest by topic count: architectures, layers, design patterns, RAGs, cookbooks and evals.

The dependency direction is one way. Later chapters assume the vocabulary introduced earlier, and the framework examples in 02_frameworks assume you have read enough of 01_foundations to know what an LLM call looks like. Nothing in the layout suggests generated code or a build step. The entries at the top level are directories, a LICENSE file, a README and an assets folder.

Within 03_system_design the pages split into two kinds. Some are conceptual, such as the design patterns overview and the architectures overview. Others are closer to runnable: the code examples for agentic workflows, and the multi-agent workflow write-up under 02_frameworks/4_llamaindex. The README does not document how those examples are executed or what environment they expect, so treat them as reference implementations to read and adapt rather than scripts to run unchanged.

The RAG thread is the most developed. The README points to a single overview covering basic RAGs, agentic RAGs, multi-modal RAGs and advanced RAGs, which is a useful consolidation if you are deciding which retrieval shape fits a problem.

Installing and getting a first real use out of it

There is no install. The README gives no pip command, no package name and no version, and the repository publishes no releases. The way to use it is to clone it and read, so the first command is a clone.

git clone https://github.com/alirezadir/Agentic-AI-Systems.git
cd Agentic-AI-Systems

After that, the root README is the entry point. Open it and follow the chapter table to whichever directory matches your question. If you are starting from zero on agent design, the design patterns file under 03_system_design is the natural first read, because the README describes it as an overview of reusable patterns in Gen-AI and agentic AI systems.

ls 03_system_design
ls 02_frameworks

Those two listings tell you what actually exists, which is more reliable than the README's highlights section. The highlights list a subset of files; the directories hold the full set. If a framework you care about appears in the README's framework list but has no folder, the README is ahead of the repository.

For the code examples, the repository is Python and the examples are tied to specific frameworks. The README does not state which Python version or which dependency set the examples assume, so before running anything you will need to read the example itself and infer its imports. There is no requirements file listed among the top-level entries.

Where this repository stops being the right tool

The honest limitation is that this is a guide wearing the shape of a project. It has no released artifacts, and nothing at the top level beyond a .github directory suggests a test suite or a CI pipeline you can inspect. If your goal is to ship an agent this week, reading six chapters is the wrong order of operations. Pick a framework, read its own documentation, and come back here when you hit a design question.

The README's own claims are the place to be sceptical. It lists production-ready and tested examples with deployment guides among the reasons to use the repository, but the README does not document a deployment guide, a test command or a supported environment. Those claims are not backed by anything visible in the repository layout, and a reader should treat them as the author's framing rather than verified properties.

There is also a currency problem inherent to the format. Agent frameworks change quickly, and a curated guide is only as current as its last edit. The last push was on 2026-06-20, so anything you read about a specific framework's API should be checked against that framework's own documentation before you rely on it.

Finally, the interview prep chapter is a different product from the rest. It is useful if you are preparing for GenAI system design interviews and useless if you are building. Mixing the two audiences in one repository means neither gets a clean entry point, and the README does not separate them.

How it compares with LangGraph or LlamaIndex directly

The obvious alternative is the framework documentation itself. LangGraph and LlamaIndex both appear in this repository's framework chapter, and both publish their own docs, their own examples and their own release cadence. The difference in approach is that a framework's documentation is written by the people who maintain the runtime, is versioned against that runtime, and tells you what the code does today. This repository is written by a practitioner collecting what worked, is not versioned against any runtime, and tells you how someone reasoned about a design.

That distinction decides the use case. If you need the exact signature of a graph node or a retriever, go to the framework. If you need to decide whether your problem wants a single agent with tools or a multi-agent workflow with a supervisor, the design patterns and architectures pages here are the more direct read, and the framework docs will not help you with it.

A second alternative is the OpenAI Agents SDK material, which this repository covers under 02_frameworks/1_openai with guides, tutorials and code examples. The same rule applies: the repository summarises and frames, the SDK documents.

The repository's advantage over both is breadth in one place. Comparing how a multi-agent workflow looks in LlamaIndex against how it looks in CrewAI is a reading exercise that is awkward to do across two vendor documentation sites and straightforward here, provided the examples are still current.

Maintenance, licence and what upgrades cost you

The repository is not archived, and the last push was on 2026-06-20. That is the whole of what can be said about activity from the repository itself. There are no releases, so there is no version to pin and no changelog to read. Upgrading means pulling main and diffing the chapters you rely on, which is cheap for prose and more work for code examples.

Because there is no package, there is no dependency upgrade cost in the usual sense. The cost sits in the examples: if you copied a snippet into your own codebase, that snippet is now your code and its maintenance is yours. The repository will not tell you when a framework API changes underneath it.

Licensing is MIT, which is permissive and places few conditions on reuse. The LICENSE file is at the repository root. If you copy example code into a commercial codebase, MIT permits it, but the examples may encode assumptions from the frameworks they target, and those frameworks carry their own licences. Check the licence of whichever framework an example depends on before shipping derived code. None of this is legal advice; read the LICENSE file and the framework licences yourself.

Contributions are handled by pull request. The README says to fork the repository and submit a PR if you want to add frameworks, examples or summaries.

Editorial conclusion

Adopt it as a reading and reference track if you are designing agentic AI systems and want worked examples across OpenAI, LangGraph, LlamaIndex, CrewAI and Chainlit, or if you are preparing for a GenAI system design interview. Do not adopt it as a dependency: there is no published package and the README documents no install step, so nothing here belongs in a requirements file. Before you commit time to it, open the chapter index at the repository root and check that the framework you actually use has a matching folder under 02_frameworks, because the README lists OpenAI, LangGraph, LlamaIndex, LangChain, CrewAI and Chainlit but does not promise equal depth for each.

Frequently asked questions

Official sources

1. alirezadir/Agentic-AI-Systems on GitHub
2. Issues
3. License: MIT
4. README