ragflow and oreilly-retrieval-augmented-gen-ai
One is a comprehensive open-source RAG engine fusing RAG with Agent capabilities, while the other is a demonstration of how to augment LLMs with real-time data using RAG, Agents, and GraphRAG, making them a tool and its educational example, respectively, rather than direct competitors.
About ragflow
infiniflow/ragflow
RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context layer for LLMs
This tool helps create advanced AI assistants that can accurately answer questions using your specific business documents and data. You input various documents like PDFs, Word files, web pages, and even structured data, and it outputs a system that provides precise, traceable answers. It's designed for business leaders, knowledge managers, or AI product developers who need to build reliable question-answering systems for internal teams or customers.
About oreilly-retrieval-augmented-gen-ai
sinanuozdemir/oreilly-retrieval-augmented-gen-ai
See how to augment LLMs with real-time data for dynamic, context-aware apps - Rag + Agents + GraphRAG.
Implements end-to-end RAG workflows using vector databases (Pinecone), multiple LLM providers (OpenAI, Anthropic, Gemini, Cohere), and LangGraph for orchestration with built-in evaluation components. Covers advanced patterns including knowledge graph-based retrieval (GraphRAG with Neo4j), embedding fine-tuning with synthetic data, multimodal search, and agentic workflows with semantic re-ranking.
Related comparisons
Scores updated daily from GitHub, PyPI, and npm data. How scores work