agentic-rag-for-dummies and Controllable-RAG-Agent
These are complementary educational resources that address different aspects of agentic RAG development—the first provides a beginner-friendly modular introduction using LangGraph, while the second offers an advanced implementation with graph-based algorithms for handling complex question-answering scenarios.
About agentic-rag-for-dummies
GiovanniPasq/agentic-rag-for-dummies
A modular Agentic RAG built with LangGraph — learn Retrieval-Augmented Generation Agents in minutes.
This project helps developers build advanced AI assistants that can intelligently answer questions using custom data. It takes your documents (like PDFs or Markdown files) and processes them into a searchable format, then uses an AI to interpret user questions, find relevant information, and generate clear, coherent answers. It's designed for AI developers or data scientists who want to create sophisticated conversational agents.
About Controllable-RAG-Agent
NirDiamant/Controllable-RAG-Agent
This repository provides an advanced Retrieval-Augmented Generation (RAG) solution for complex question answering. It uses sophisticated graph based algorithm to handle the tasks.
This project helps people answer complex questions from their documents, like research papers or books, even when the answer isn't obvious. You provide your documents and ask a question, and it gives you a well-reasoned answer based only on your data. Anyone who needs to extract precise, detailed answers from large amounts of text, such as researchers, analysts, or educators, would find this useful.
Related comparisons
Scores updated daily from GitHub, PyPI, and npm data. How scores work