Report Abuse

Basic Information

Agentic AI Systems is a curated, evolving knowledge base and practical guide for designing, building, and deploying agentic and generative AI systems. It aggregates foundations, frameworks, system design patterns, architectures, evaluation methods, cookbooks, use cases, and resources to help engineers and architects construct production-ready multi-agent workflows. The repository organizes content into chapters covering foundations like modern Python and GenAI concepts, framework-specific tutorials, architecture and design pattern documentation, real-world example projects, and a resource library of courses and tools. It documents reusable design patterns, retrieval-augmented generation approaches, and evaluation dimensions to support both research and engineering work. The collection emphasizes practical code examples and deployment-oriented guidance so practitioners can move from concept to working agentic systems.

Links

Categorization

App Details

Features
Structured chapter layout with Foundations, Frameworks, Agentic System Design, Use Cases, and Resources that provide targeted learning paths. Framework tutorials and code examples cover OpenAI, LangGraph, LlamaIndex, LangChain, CrewAI, Chainlit and similar tooling. Design artifacts include agentic AI design patterns, RAG architecture overviews, cookbooks for common workflows, architecture diagrams, and evaluation guides for agentic systems. The repo contains step-by-step example projects, multi-agent system walkthroughs, and a curated resource library. Emphasis is placed on practical, production-ready examples and deployment guidance. The content is community-oriented and open to contributions so users can add experiments, frameworks, or summaries.
Use Cases
This repository helps practitioners, researchers, and engineers accelerate the design and implementation of agentic AI systems by centralizing best practices and tested examples. It provides concrete tutorials and code to learn framework-specific integrations, reusable design patterns to structure multi-agent workflows, and RAG and evaluation guidance to measure system behavior. The cookbooks and architecture examples reduce time-to-prototype, while deployment notes and production-ready examples help operationalize agents. The resource library and curated references support deeper study and tool selection. Community-driven contributions enable continuous updates with new tools and patterns, making the repo a practical reference for building, evaluating, and iterating on multi-agent and generative AI solutions.

Please fill the required fields*