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Retrieval-Augmented Generation

Retrieval-augmented generation (RAG) is a technique that improves large language models by retrieving relevant information from external sources and using it to generate more accurate and context-aware responses.

A RAG system combines information retrieval with a language model. It is commonly used in AI assistants, search systems, document question answering, and applications that need access to private or frequently updated information.

Here are 41,837 public repositories matching this topic...

Build Agentic workflows, RAG pipelines, with rich AI model and tool support on one collaborative workspace. Deploy on cloud, VPC, or self-hosted, so teams move from prototype to production without rebuilding the stack.

  • Updated Aug 19, 2026
  • TypeScript

User-friendly AI Interface (Supports Ollama, OpenAI API, ...)

  • Updated Aug 19, 2026
  • Python

100+ AI Agents, Agent Skills and RAG Apps - Free and Open Source.

  • Updated Aug 17, 2026
  • Python

Turn any codebase, with its docs, SQL schemas, configs, and PDFs, into a queryable knowledge graph. A /graphify skill for Claude Code, Cursor, Codex, and Gemini CLI: local deterministic AST parsing, every edge explained, no vector store.

  • Updated Aug 19, 2026
  • Python

Persistent Context Across Sessions for Every Agent – Captures everything your agent does during sessions, compresses it with AI, and injects relevant context back into future sessions. Works with Claude Code, OpenClaw, Codex, Gemini, Hermes, Copilot, OpenCode + More

  • Updated Aug 19, 2026
  • JavaScript

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

  • Updated Aug 19, 2026
  • Go

Turn any PDF or image document into structured data for your AI. A powerful, lightweight OCR toolkit that bridges the gap between images/PDFs and LLMs. Supports 100+ languages.

  • Updated Jul 22, 2026
  • Python

🐙 Guides, papers, lessons, notebooks and resources for prompt engineering, context engineering, RAG, and AI Agents.

  • Updated Mar 11, 2026
  • MDX

📚 《从零开始构建智能体》——从零开始的智能体原理与实践教程

  • Updated Aug 18, 2026
  • Python

Compress tool outputs, logs, files, and RAG chunks before they reach the LLM. 20% fewer tokens for coding agents, 60-95% fewer tokens for JSON, same answers. Library, proxy, MCP server.

  • Updated Aug 19, 2026
  • Python

Stop renting your intelligence. Own it with AnythingLLM. Everything you need for a powerful local-first agent experience

  • Updated Aug 19, 2026
  • JavaScript

Universal memory layer for AI Agents

  • Updated Aug 18, 2026
  • Python

Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data. 🐳Docker-friendly.⚡Always in sync with Sharepoint, Google Drive, S3, Kafka, PostgreSQL, real-time data APIs, and more.

  • Updated Jul 5, 2026
  • Jupyter Notebook

LlamaIndex is the leading document agent and OCR platform

  • Updated Aug 19, 2026
  • Python

【低代码迈入v2.0时代,一句话即可生成整个系统】企业级AI低代码平台,一键生成前后端代码甚至整个系统。 AI Skills 一句话画流程、设计表单、生成报表、大屏。内置 AI应用平台涵盖:AI聊天、知识库、流程编排、MCP插件等,兼容主流大模型。引领AI低代码「Skills 生成 → 在线配置 → 代码生成 → 手工合并->AI修改」开发模式,解决 Java 项目 90% 重复工作,提高效率又不失灵活。

  • Updated Aug 14, 2026
  • Java

Milvus is a high-performance, cloud-native vector database built for scalable vector ANN search

  • Updated Aug 19, 2026
  • Go

《深入理解 AI Agent:设计原理与工程实践》(李博杰 著)开源主仓库:全书正文、编译版 PDF 与按章配套代码

  • Updated Aug 19, 2026
  • Python

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