Retrieval Augmented Generation GitHub Repositories (2026) · Open Source Tools

[MLsys2026 Best Paper]: https://arxiv.org/abs/2506.08276. RAG on Everything with LEANN. Enjoy 97% storage savings while running a fast, accurate, and 100% private RAG application on your personal device.

📑 PageIndex: Document Index for Vectorless, Reasoning-based RAG

[EMNLP2025] LightRAG: Simple and Fast Retrieval-Augmented Generation

Your Personal Librarian Agent. Ask anything on your computer, organize files, remember important info and files.

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

Open-source inference server and production cluster for all the models your agent needs.

The ultimate RAG for your monorepo. Query, understand, and edit multi-language codebases with the power of AI and knowledge graphs

~95% on SimpleQA (e.g. Qwen3.6-27B on a 3090). Supports all local and cloud LLMs (llama.cpp, Ollama, Google, ...). 10+ search engines - arXiv, PubMed, your private documents. Everything Local & Encrypted.

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

Open-source AI orchestration framework for building context-engineered, production-ready LLM applications. Design modular pipelines and agent workflows with explicit control over retrieval, routing, memory, and generation. Built for scalable agents, RAG, multimodal applications, semantic search, and conversational systems.

AI desktop app for chat, RAG, Skills, MCP tools, and agents. Support multiple LLMs (Anthropic, OpenAI, VertexAI, vLLM, Nvidia NIM, Gemini, OpenRouter)

A Fork of Rikkahub with an overhauled UI and feature additions

💡 All-in-one AI framework for semantic search, LLM orchestration and language model workflows

A unifying framework for biomedical research knowledge graphs

Self-hosted AI gateway for private RAG, natural-language data access, and tool-calling agents.

Harness LLMs with Multi-Agent Programming

NVIDIA AI Blueprint for video search and summarization (VSS) is a GPU-accelerated reference architecture for building video analytics agents with real-time verified alerts, visual Q&A, and automated reporting. The VSS Blueprint uses vision language models (VLMs) such as NVIDIA Cosmos, LLMs such as NVIDIA Nemotron, RAG, and NVIDIA NIMs.

A no-code RAG server that keeps answers in sync with your documents. Works with 8 vector databases. Built on Pathway.

Rust library for generating vector embeddings and reranking locally!

This NVIDIA RAG blueprint serves as a reference solution for a foundational Retrieval Augmented Generation (RAG) pipeline.