RAGFlow is an open-source RAG (Retrieval-Augmented Generation) engine based on deep document understanding.

An LLM-powered knowledge curation system that researches a topic and generates a full-length report with citations.

AI orchestration framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data. With advanced retrieval methods, it's best suited for building RAG, question answering, semantic search or conversational agent chatbots.

"LightRAG: Simple and Fast Retrieval-Augmented Generation"

💡 All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows

Unified framework for building enterprise RAG pipelines with small, specialized models

Retrieval and Retrieval-augmented LLMs

The open source platform for AI-native application development.

The most advanced AI retrieval system. Containerized, Retrieval-Augmented Generation (RAG) with a RESTful API.

RAG (Retrieval Augmented Generation) Framework for building modular, open source applications for production by TrueFoundry

AutoRAG: An Open-Source Framework for Retrieval-Augmented Generation (RAG) Evaluation & Optimization with AutoML-Style Automation

Harness LLMs with Multi-Agent Programming

Generative AI reference workflows optimized for accelerated infrastructure and microservice architecture.

AI Search & RAG Without Moving Your Data. Get instant answers from your company's knowledge across 100+ apps while keeping data secure. Deploy in minutes, not months.

Fast, Accurate, Lightweight Python library to make State of the Art Embedding

⚡FlashRAG: A Python Toolkit for Efficient RAG Research

The code used to train and run inference with the ColPali architecture.

The official implementation of RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval

Ship RAG based LLM web apps in seconds.

An LLM-powered advanced RAG pipeline built from scratch