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.
User-friendly AI Interface (Supports Ollama, OpenAI API, ...)
The agent engineering platform.
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.
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
RAGFlow is an agent-native database: a Retrieval-Augmented Generation (RAG) engine that grew into context infrastructure.
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.
Official Crawl4AI Cloud: a hosted web scraping, extraction, and search API for AI agents. Free API key, 1,000 credits a month, no card. Fully open source: this repo is the Apache-2.0 crawler that powers it, and you can self-host it.
🐙 Guides, papers, lessons, notebooks and resources for prompt engineering, context engineering, RAG, and AI Agents.
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.
The Memory Layer for AI Agents - Drop-in memory infrastructure for AI agents and apps. Context that persists. Built for production.
Stop renting your intelligence. Own it with AnythingLLM. Everything you need for a powerful local-first agent experience
A no-code RAG server that keeps answers in sync with your documents. Works with 8 vector databases. Built on Pathway.
《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
DeepTutor: Lifelong Personalized Tutoring. https://deeptutor.info/.
Build AI Agents, Visually
LlamaIndex is the document processing platform for AI
【AI 时代的业务系统基座】企业级 AI 低代码平台：权限、多租户、工作流、报表、大屏、AI 知识库等通用能力开箱即用，AI 只需生成业务差异部分。AI Skills 一句话生成完整系统——建表、前后端代码、菜单权限、BPM 流程、表单、报表、大屏全自动。内置 AI 应用平台（聊天、知识库、流程编排、MCP），兼容主流大模型。Java + Vue3，支持单体/微服务与信创国产化。
Milvus is a high-performance, cloud-native vector database built for scalable vector ANN search