仓库/Skill 库

先记录，后整理。一款 local-first 的 Markdown 应用，借助 AI 将零散记录转化为清晰的笔记。Capture first. Organize later. A local-first Markdown app that turns scattered records into clear notes with AI.

BISHENG 是面向下一代企业 AI 应用的开放 LLM devops 平台。强大且全面的功能包括：GenAI workflow、RAG、Agent、统一模型管理、评估、SFT、数据集管理、企业级系统管理、可观测性等。BISHENG is an open LLM devops platform for next generation Enterprise AI applications. Powerful and comprehensive features include: GenAI workflow, RAG, Agent, Unified model management, Evaluation, SFT, Dataset Management, Enterprise-level System Management, Observability and more.

面向 long horizon agents 的增量引擎 🌟 喜欢的话请点 Star！Incremental engine for long horizon agents 🌟 Star if you like it!

Pocket Flow：一个仅 100 行代码的 LLM 框架。让 Agent 构建 Agent！Pocket Flow: 100-line LLM framework. Let Agents build Agents!

基于 Rust 核心的多语言文档智能：从 106 种格式、140 种文件扩展名中提取文本、元数据、图像、表格与结构化数据，并支持 371 种语言的代码智能。提供十五种语言绑定，配套 CLI、REST API 与 MCP server。Polyglot document intelligence with a Rust core: extract text, metadata, images, tables, and structured data from 106 formats across 140 file extensions, plus code intelligence for 371 languages. Fifteen bindings, with CLI, REST API, and MCP server.

Deeplake 是面向 Agent 的 AI 数据运行时，提供无服务器 postgres 与多模态 datalake，支持可扩展的检索与训练Deeplake is AI Data Runtime for Agents. It provides serverless postgres with a multimodal datalake, enabling scalable retrieval and training.

PraisonAI 🦞 — 雇佣一支 24/7 AI 团队。停止编写样板代码，开始交付自主且自我改进的 Agent，能够研究、规划、编码并执行任务。5 行代码即可部署，内置 memory、RAG，支持 100+ LLM。PraisonAI 🦞 — Hire a 24/7 AI Workforce. Stop writing boilerplate and start shipping autonomous self-improving agents that research, plan, code, and execute tasks. Deployed in 5 lines of code with built-in memory, RAG, and support for 100+ LLMs.

🧑‍🚀 全世界最好的LLM资料总结（多模态生成、Agent、辅助编程、AI审稿、数据处理、模型训练、模型推理、o1 模型、MCP、小语言模型、视觉语言模型） | Summary of the world's best LLM resources.

使用 Python 构建自主 AI Agent。Build autonomous AI agents in Python.

22 种 prompt engineering 技术，附带 Jupyter Notebook 实战教程，覆盖从基础概念到利用 LLM 的高级策略。22 prompt engineering techniques with hands-on Jupyter Notebook tutorials, from fundamental concepts to advanced strategies for leveraging LLMs.

网页解析的终结，可扩展像素原生搜索的开端。链接：https://pixelrag.ai/The end of web parsing. The beginning of scalable pixel-native search. link: https://pixelrag.ai/

ODS V3 预发布：在 V3 正式发布前进行公开测试与打磨。将你的 PC、Mac 或 Linux 机器变为私有 AI 服务器。ODS V3 Pre-Release: Public testing and refinement ahead of the official V3 launch. Turn your PC, Mac, or Linux box into a private AI server.

AI 搜索平台。The AI search platform

用于构建有状态 Agent 的 Memory 库Memory library for building stateful agents

三语（繁中 / English / 简中）Agentic AI 学习路线图：从 LLM 基础到多 Agent 系统，收录 240+ 精选资源与动手示例。中文 AI agent 學習地圖。A trilingual (繁中 / English / 简中) learning roadmap for agentic AI: from LLM basics to multi-agent systems, with 240+ curated resources and hands-on examples. 中文 AI agent 學習地圖。

用于构建 agentic apps 的开源框架，支持 JavaScript、Go、Dart 与 Python，由 Google 在生产环境中构建并使用。Open-source framework for building agentic apps in JavaScript, Go, Dart, and Python, built and used in production by Google

为 Agent 打造的快速精准代码搜索，相比 grep+read 减少约 98% 的 token 用量。Fast and Accurate Code Search for Agents. Uses 99% fewer tokens than grep+read

🐢 面向 LLM Agent 的开源评估与测试库🐢 Open-Source Evaluation & Testing library for LLM Agents

用于构建复杂创新 RAG 流水线的低代码 MCP 框架A Low-Code MCP Framework for Building Complex and Innovative RAG Pipelines

用于构建 AI agent 的企业级 AI 开发框架。它提供多 provider LLM 的统一管理、具备高精度检索的安全企业知识库、可视化工作流编排与多 agent 协作。兼容主流 Agent Skill 标准，使开发者能够高效构建生产级 [原文未完整]。An enterprise AI development framework for building AI agents. It provides unified management of multi-provider LLMs, secure enterprise knowledge bases with high-precision retrieval, visual workflow orchestration and multi-agent coordination. Compatible with mainstream Agent Skill standards, it enables developers to efficiently build production-gra

⚡️ 下一代个人 AI 助手，由 LLM、RAG 和 Agent 循环驱动，支持 computer-use、browser-use 和 coding agent，演示地址：https://demo.openagentai.org⚡️next-generation personal AI assistant powered by LLM, RAG and agent loops, supporting computer-use, browser-use and coding agent, demo: https://demo.openagentai.org

LLM 实战指南：从基础到使用 LLMOps 最佳实践将 LLM 和 RAG 应用部署到 AWSThe LLM's practical guide: From the fundamentals to deploying advanced LLM and RAG apps to AWS using LLMOps best practices

面向 monorepo 的终极 RAG。借助 AI 与知识图谱的能力，对多语言代码库进行查询、理解与编辑。The ultimate RAG for your monorepo. Query, understand, and edit multi-language codebases with the power of AI and knowledge graphs

构建、评估并优化 AI 系统。涵盖 evals、RAG、agents、微调、合成数据生成、数据集管理、MCP 等内容。Build, Evaluate, and Optimize AI Systems. Includes evals, RAG, agents, fine-tuning, synthetic data generation, dataset management, MCP, and more.

面向 AI 编码 agent 的首选本地优先网页工具——基于 MCP 提供搜索、抓取、爬取与研究能力。无需 API key，无需云端，$0/query。现已开放公开测试。The go-to web for your AI coding agent — local-first search, fetch, crawl & research over MCP. No API keys, no cloud, $0/query. Public beta.

终端中的 Agent，配备本地工具：编写代码、使用终端、浏览网页。在其之上构建你自己的持久化自主 Agent。Your agent in your terminal, equipped with local tools: writes code, uses the terminal, browses the web. Make your own persistent autonomous agent on top!

免费学习如何使用 LLMOps 最佳实践构建端到端生产级 LLM & RAG 系统：源码 + 12 个实操课程。🤖 𝗟𝗲𝗮𝗿𝗻 for 𝗳𝗿𝗲𝗲 how to 𝗯𝘂𝗶𝗹𝗱 an end-to-end 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻-𝗿𝗲𝗮𝗱𝘆 𝗟𝗟𝗠 & 𝗥𝗔𝗚 𝘀𝘆𝘀𝘁𝗲𝗺 using 𝗟𝗟𝗠𝗢𝗽𝘀 best practices: ~ 𝘴𝘰𝘶𝘳𝘤𝘦 𝘤𝘰𝘥𝘦 + 12 𝘩𝘢𝘯𝘥𝘴-𝘰𝘯 𝘭𝘦𝘴𝘴𝘰𝘯𝘴

开源 LLMOps 平台：集成 prompt playground、prompt 管理、LLM 评估和 LLM 可观测性。The open-source LLMOps platform: prompt playground, prompt management, LLM evaluation, and LLM observability all in one place.

针对加速基础设施和微服务架构优化的生成式 AI 参考工作流。Generative AI reference workflows optimized for accelerated infrastructure and microservice architecture.

开源 AI 销售操作系统——自托管 CRM，内置原生 AI Agent 与 WhatsApp（WAHA）。面向以聊天方式开展销售业务的 Kommo、Octadesk、Intercom 的开源替代方案。MCP-ready，多租户，LGPD 合规。Open-source AI sales OS — self-hosted CRM with native AI agents + WhatsApp (WAHA). Open alternative to Kommo, Octadesk & Intercom for any business that sells by chat. MCP-ready, multi-tenant, LGPD.

由 AI 驱动的虚拟高管团队 —— 单一连贯的高管 persona，由 8 个 Claude 专家 agent 支撑（FastAPI + Next.js）。AI-powered virtual executive team — a single coherent executive persona backed by 8 specialist Claude agents (FastAPI + Next.js).

AI Agent 的开源 context layer。PipesHub 将公司知识（Slack、Drive、Jira、GitHub、Microsoft 365 及 40+ 连接器）转化为权限感知的 workspace，Agent 可对其进行搜索、grep、导航和引用。支持 MCP、SDK 与内置 agent，可自托管。The open-source context layer for AI agents. PipesHub turns your company's knowledge (Slack, Drive, Jira, GitHub, Microsoft 365 and 40+ connectors) into a permission-aware workspace that agents can search, grep, navigate and cite. MCP, SDKs and built-in agents. Self-hosted.

JVM 的便携式 AI Agent 运行时。一个 @Agent 类可通过一套 SPI 在 Spring AI、LangChain4j、Anthropic 或另外 9 个后端上运行。支持 token 流式输出、工具调用、人工审批，并通过 WebSocket、SSE、gRPC 或 WebTransport/HTTP3 提供治理能力。兼容 MCP、A2A 与 AG-UI 协议。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.

⚡FlashRAG：面向高效 RAG 研究的 Python 工具包（WWW2025 Resource）⚡FlashRAG: A Python Toolkit for Efficient RAG Research (WWW2025 Resource)

ReMe：面向 Agent 的记忆管理工具包——Remember Me, Refine Me.ReMe: Memory Management Kit for Agents - Remember Me, Refine Me.

NVIDIA 产品的 Agent Skills — 安装到 Claude Code、Codex 和其他编码 Agent 中，端到端运行 Physical AI、机器人、仿真、CUDA 和 RAG 工作流。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.