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Agentic AI Systems

A public, MIT-licensed repository of Python-focused learning materials and code examples for agent frameworks, agentic system design, RAG, use cases and evaluation.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Agentic AI Systems — OpenViST project profile in Agent Frameworks.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when you want a reference or learning path for agent frameworks, system architecture, multi-agent examples, RAG or evaluation—not a hosted service for creating agents.

Who it is for

  • Developers learning how to build agents with frameworks such as LangGraph, LangChain, LlamaIndex, CrewAI or OpenAI tools.
  • Technical practitioners looking for architecture patterns and examples to inform agent-system design.
  • Learners seeking foundations, evaluation references or agentic-system interview preparation.

Documented uses

  • Study framework tutorials and example code for agent workflows.
  • Compare reusable patterns and architectures when planning an agentic system.
  • Review RAG approaches and evaluation methods for agent systems.
  • Find curated courses, tools, protocols and further reading.
Before you choose

Requirements & limits

The repository is public and specifies an MIT license.

  • The material spans foundations, implementation frameworks, system design, use cases, resources and interview preparation.
  • The framework coverage includes OpenAI, LangGraph, LlamaIndex, LangChain, CrewAI and Chainlit.
  • Its primary language is Python; the repository includes tutorials and code examples as well as conceptual references.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Explore agent system design patterns and architecture resources

Use the repository’s linked materials to study agent-system architectures and reusable design patterns.

Before you start

  • Access to the public GitHub repository.

Steps

  1. Open the repository README.

  2. Follow the Agentic AI Design Patterns link to review reusable patterns.

  3. Follow the Architectures link to explore architecture layers, system-design steps, and example architectures.

Expected outcome

You can consult linked materials on agent design patterns and architecture examples.

Guide limitations

  • The README identifies learning resources but does not provide a specific implementation procedure or guarantee that an example will run.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
9 October 2026
Original website title
alirezadir/Agentic-AI-Systems · Official homepage title · retained in its source language
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