AI channel
RAG

Open-source RAG frameworks

Retrieval-augmented generation (RAG) makes a language model answer from your own documents. The text is split into chunks, turned into vectors and stored in an index, and the most relevant chunks are added to the prompt when a question comes in. The projects here cover the whole pipeline or one part of it: document parsing, vector search, or a complete question-answering app.

How to choose

Answer quality in RAG depends mostly on retrieval, not on the model. Compare how each project handles difficult documents such as PDFs, tables and scans, whether it supports hybrid search that combines keywords with vectors and re-ranking, and whether it shows which sources an answer came from.

Most-starred projects

625–642 of 642
rush-db

rushdb

★ 324

RushDB is a graph + vector database and memory layer for AI agents. Push any JSON, get typed, searchable, relationship-aware records back — no schema, no migrations. Built on Neo4j.

TypeScript
★ 323

Deep Research workflow on Dify: cascaded multi-source search → outline → cited long-form report. Credited in Awesome-Dify-Workflow.

★ 323

Swift-based vector database for on-device RAG using MLTensor and MLX Embedders

Swift
★ 322

A TypeScript sample app for the Retrieval Augmented Generation pattern running on Azure, using Azure AI Search for retrieval and Azure OpenAI and LangChain large language models (LLMs) to power ChatGPT-style and Q&A experiences.

TypeScript
lightfeed

extractor

★ 321

Use LLMs to robustly extract web data

TypeScript
pmbstyle

Alice

★ 320

Alice is a voice-first desktop AI assistant application built with Vue.js, Vite, and Electron. Advanced memory system, function calling, MCP support, optional fully local use, and more.

TypeScript
wanyichen06

LLMInternSkill

★ 320

LLMInternSkill: LLM internship resume and job-search Codex Skill for resume polish, JD tailoring, evidence guard, interview grilling, and Project Scout. 大模型实习简历与求职工具箱。

Markdown
★ 314

A practical AI agents handbook covering agent systems, agentic workflows, LangGraph, MCP/A2A, context engineering, agent memory, evaluation, observability, and multi-agent architecture. Current trend focus: Gemini Interactions API and managed agents, emerging agent runtimes, and production AI workflow patterns.

MDX
holoviz

lumen

★ 312

Illuminate your data. Agent framework turning natural language into SQL, charts, dashboards and reports.

Python
★ 312

The RAG Experiment Accelerator is a versatile tool designed to expedite and facilitate the process of conducting experiments and evaluations using Azure Cognitive Search and RAG pattern.

Python
★ 312

A free, self-paced 24-week AI engineering course: Python, machine learning, LLMs, RAG, fine-tuning, agents and MCP, Azure and Vertex and Bedrock, and Databricks. 43 runnable notebooks, one continuous case study. MIT licensed, no signup. By Zorost Intelligence AI Lab.

Jupyter Notebook
★ 311

Open-source AI real estate search with RAG, vector search, multi-provider LLMs, FastAPI, Next.js, ChromaDB, and a live demo.

Python
wandb

wandbot

★ 310

wandbot is a technical support bot for Weights & Biases' AI developer tools that can run in Discord, Slack, ChatGPT and Zendesk

Python
shutootaki

bookwith

★ 309

BookWith – A New Reading Experience with AI. A next-generation conversational reading platform that goes beyond traditional e-book readers

TypeScript
zhimaAi

ChatClaw

★ 308

ChatClaw: Get OpenClaw-like knowledge base personal AI agent in 5 mins. Sandbox-secured, ultra-small 30MB installer for macOS & Windows (install in 1 min). Connects to WhatsApp, Telegram, Slack, Discord, Gmail, DingTalk, WeChat Work, QQ, Feishu. Built-in Skill Market, Knowledge Base, Memory, MCP, Scheduled Tasks. Developed in Go ,run

Go
WangQrkkk

PaperQuay

★ 306

A desktop-first literature manager for PDF reading, translation, paper overviews, and AI agent workflows.

TypeScript
★ 302

Open-sourced course notes for Artificial Intelligence and Data Science related topics, prepared in LaTeX

Jupyter Notebook
★ 291

smart-llm-loader is a lightweight yet powerful Python package that transforms any document into LLM-ready chunks. Spend less time on preprocessing headaches and more time building what matters. From RAG systems to chatbots to document Q&A, SmartLLMLoader handles the heavy lifting so you can focus on creating exceptional AI applications.

Python