LazyLLM
Easiest and laziest way for building multi-agent LLMs applications.
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.
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.
Easiest and laziest way for building multi-agent LLMs applications.
"Paper2Slides: From Paper to Presentation in One Click"
SimpleMem: Efficient Lifelong Memory for LLM Agents — Text & Multimodal
Portable AI agent runtime for the JVM. One @Agent class runs on Spring AI, LangChain4j, Anthropic, or 9 more behind one SPI. Token streaming, tool calls, human approvals, and governance over WebSocket, SSE, gRPC, or WebTransport/HTTP3. Speaks MCP, A2A, and AG-UI.
A C#/.NET library to run LLM (🦙LLaMA/LLaVA) on your local device efficiently.
PipesHub is an open-source platform for securely connecting enterprise knowledge to AI. Give AI agents trusted context and your team permission-aware search with verified citations across your business systems.
Run all your local AI together in one package - Ollama, Supabase, n8n, Open WebUI, and more!
A curated collection of practical AI projects implementing OCR systems, RAG, AI agents, and other AI use cases.
🔥 官方推荐 🔥 大学春招、秋招、应届项目,SpringBoot3 + Java17 + SpringCloud Alibaba + Vue3 等技术架构,完成高仿铁路 12306 用户 + 抢票 + 订单 + 支付服务,帮助学生主打就业的项目。
2026 年最新的免费编程资源大全,持续更新!🔥 覆盖各种语言和方向(Java / Python / C++ / JavaScript / TypeScript / Golang / 前端 / 后端 / AI大模型应用开发 / AI Agent开发等)的学习路线、零基础入门教程、项目实战教程、经典编程书籍、面试题合集、求职经验分享、简历模板、开源项目推荐、开发工具推荐、实用技术资源等,对程序员和计算机专业学生非常有帮助!⭐️ 学编程,先收藏这个仓库!
Open-source multimodal retrieval engine (Morphik Core). By Morphik — AI back office for skilled nursing & senior living (morphik.ai).
A collection of scientific methods, processes, algorithms, and systems to build stories & models.
A personal knowledge base that builds and maintains itself. Drop in sources — Claude (or Codex/Gemini) reads them, extracts knowledge, and maintains a persistent interlinked wiki. Works with Claude Code, Codex, OpenCode, Gemini CLI. No API key needed.
⚡FlashRAG: A Python Toolkit for Efficient RAG Research (WWW2025 Resource)
Knowhere extracts, parses, and outputs structured chunks ready for AI Agents and RAG.
ReMe: Memory Management Kit for Agents - Remember Me, Refine Me.
Memory Sparse Attention - A scalable, end-to-end trainable latent-memory framework for 100M-token contexts.
Agent Skills for NVIDIA products, install into Claude Code, Codex, and other coding agents to run Physical AI, robotics, simulation, CUDA, and RAG workflows end to end. Skill Catalog Product | Description | Skills | AIQ | NVIDIA AI-Q Blueprint - deploy local AI-Q services and run shallow or deep research workflows as agent skills.
A streamlined and customizable framework for efficient large model (LLM, VLM, AIGC) evaluation and performance benchmarking.
AnyCrawl 🚀: A Node.js/TypeScript crawler that turns websites into LLM-ready data and extracts structured SERP results from Google/Bing/Baidu/etc. Native multi-threading for bulk processing.
总结Prompt&LLM论文,开源数据&模型,AIGC应用
AI system design guide for engineers building production AI systems and evals.
A curated list of 100+ libraries and frameworks for AI engineers building with LLMs
[KDD'2026] "VideoRAG: Chat with Your Videos"