A comprehensive guide to building production-ready AI agents and multi-agent systems
Welcome to Agentic-AI-Systems — a curated and evolving knowledge base for mastering Agentic and Generative AI system design! This repository is a collection of resources and examples that I have found useful in my journey of building agentic AI systems.
| Section | Description |
|---|---|
| 1. Foundations | GenAI concepts and design, LLMs, evaluation, modern Python, and more. |
| 2. Frameworks | Tutorials and example codes for OpenAI, LangGraph, LlamaIndex, LangChain, CrewAI, Chainlit, and more |
| 3. System Design | Architectures, patterns, cognition layers, diagrams, and code |
| 4. Use Cases | Real-world and example agent projects and applications |
| 5. Resources | Courses, tool stacks, protocols, and list of resources. |
- AI Engineering Book Summary (Chip Huyen): GenAI system workflows and infrastructure.
- GenAI Platform Overview: Platform architecture and technical breakdowns.
- Evaluation Framework: Score-based evaluation methods and tools.
- OpenAI Agents Quickstart: Fast, example-driven intro to OpenAI API.
- LLMs: Transformers, LLMs, their training workflows, and frameworks.
- LangGraph Tutorials: Step-by-step guides for building agentic workflows with LangGraph.
- LlamaIndex Multi-Agent Systems: Building multi-agent systems using LlamaIndex.
- Agent System Design Patterns: Common architectures, cognition layers, and best practices for agentic systems.
- Real-World Use Cases: Example projects and applications of agentic AI in production.
- Resource Library: Curated list of courses, tools, protocols, and further reading.
- Practical: Real code, real projects, real-world patterns
- Curated: Only the most useful frameworks, tools, and concepts
- Reference-first: Designed for quick lookup and deep dives
- Production-ready: Tested examples with deployment guides
- Community-driven: Continuously updated with latest best practices
🔜 More coming soon — contributions welcome! You can start by forking and submitting your own code-based experiments.
Pull requests are welcome! If you'd like to add new frameworks, examples, or summaries, fork the repo and submit a PR.
Made with ❤️ by Alireza Dirafzoon.