TrustRAG is a RAG framework focused on reliable input and trusted output, providing complete RAG pipeline components including document parsing, chunking, retrieval, and reranking with multiple retrieval strategies and evaluation methods.
LightRAG is a simple and fast Retrieval-Augmented Generation framework using graph-enhanced retrieval, published at EMNLP 2025.
A low-code MCP framework for building complex and innovative RAG pipelines. Combines visual pipeline design with MCP protocol integration for end-to-end RAG — from data ingestion and chunking to retrieval and generation.
Opinionated RAG framework for integrating GenAI into your apps. Works with any LLM, any vectorstore, any files — so you can focus on your product instead of building RAG pipelines.
A local knowledge base RAG and Agent application platform built on Langchain with support for ChatGLM, Qwen, Llama and other LLMs, offering conversation, knowledge base management, and agent capabilities.