AgenticRAG-Survey vs ragflow
AgenticRAG-Survey: This repository serves as a comprehensive survey on Agentic Retrieval-Augmented Generation (Agentic RAG), a transformative AI paradigm integrating autonomous agents into RAG pipelines. It details foundational principles, taxonomy, comparative analysis, real-world applications, challenges, and future directions for researchers and practitioners.; ragflow: RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that integrates RAG with Agent capabilities. It provides a superior context layer for LLMs and offers a streamlined RAG workflow adaptable to enterprises of any scale.
TL;DR
Enhanced medical diagnostics and information retrieval in healthcare.
- Your documents have complex layouts (PDFs, scanned files, tables, slides) and you need layout analysis, OCR, and traceable citations.
- You want an Apache-2.0 all-in-one RAG platform where you can inspect and hand-tune template-based chunks in a web UI.
- Your server has less than 4 CPU cores and 16 GB RAM; RAGFlow's multi-service stack is heavy.
- You want an autonomous web research agent that writes 2,000-word reports from live search rather than indexing a local document corpus; GPT Researcher fits better.
Side-by-Side Comparison
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