576 个
🐙 AI Agent Pipeline 基于意图将查询路由至文档、天气或聊天模块，结合 LangGraph、ChromaDB 与 LangSmith，实现跨 CLI 与 UI 的模块化、可观测工作流🐙 AI Agent Pipeline routes queries by intent to docs, weather, or chat, with LangGraph, ChromaDB, and LangSmith for modular, observable workflows across CLI and UI.
一条 RAG pipeline，从 Stack Overflow 抓取问答内容并转化为 RAG 格式，存储到 huggingface 数据集中。This is RAG pipeline that take question answer from Stack Overflow and convert it to rag and get stored in the dataset in huggingface
📊 通过构建知识图谱并从文档语料中提取洞察，简化面向查询的摘要流程——基于 GraphRAG 流水线。📊 Streamline query-focused summarization by constructing knowledge graphs and extracting insights from document corpora with the GraphRAG pipeline.
AI agent memory 与基础设施全景——912 个系统 × 68 列的对比目录，覆盖记忆层、agent 框架、运行时、vector store、知识图谱、MCP server、benchmark。支持按类型化边、谱系、引用进行检索。AI agent memory & infrastructure landscape — comparative catalog of 912 systems × 68 columns covering memory layers, agent frameworks, runtimes, vector stores, knowledge graphs, MCP servers, benchmarks. Searchable with typed edges, lineages, citations.
韩国加密货币 × AI 垂直媒体与社区——alpha.moss.land：频道立场、AI 简报、RAG、8 个带业绩记录的 AI persona、12 工具 MCP server。Korean crypto × AI vertical media + community — alpha.moss.land. Channel stance, AI briefs, RAG, 8 AI personas with track records, 12-tool MCP server.
为 LLM Agent 自动化学术文献搜索、检索和管理工作流，使用面向主流数据库的集成研究 Skill。Automate academic literature search, retrieval, and management workflows for LLM agents using integrated research skills for major databases.
面向 Zed 的智能代码库搜索与索引。异步 MCP server，支持 LanceDB/BM25 混合搜索、多桶 RAG 以及自主 self-healing workflow。高性能、内存安全，可直接用于生产代码。Intelligent codebase search & indexing for Zed. Async MCP server featuring LanceDB/BM25 hybrid search, multi-bucket RAG, and autonomous self-healing workflows. High-performance, memory-safe, and ready for your production code.
在 LIBERO 与 RoboTwin2.0 上，基于状态融合解码与冻结视频骨干网络训练面向机器人操作的轻量级世界动作模型。Train lightweight world action models for robot manipulation using state-fusion decoding and frozen video backbones on LIBERO and RoboTwin2.0.
1,716 道 AI/ML 面试题（含答题框架）、69 张架构图、按角色的学习路径，以及语音驱动的模拟面试工具。覆盖 LLM、RAG、agent、MCP/A2A、系统设计、MLOps、安全与云部署。1,716 AI/ML interview questions with answer frameworks, 69 architecture diagrams, role-based study paths, and a voice-enabled mock interview simulator. Covers LLMs, RAG, agents, MCP/A2A, system design, MLOps, safety and cloud deployment.
使 AI agent 能够在任何市场上自主创建、评估与进化 Skill，无需用户干预Enable AI agents to autonomously create, evaluate, and evolve skills across any marketplace without user intervention.
本地优先的 AI 论文阅读助手 —— Agentic RAG、引文追溯、Electron 桌面应用，完全离线。Local-first AI reading assistant for academic papers — Agentic RAG, citation tracing, Electron desktop app, fully offline
🖥️ 通过终端中键盘优先的 Kanban board 简化工作流，实现快速、专注的任务管理。🖥️ Streamline your workflow with a keyboard-first Kanban board in your terminal for fast, focused task management.
使用 SQLite 和 FAISS 存储、整合与召回 coding agent 记忆，附带 provenance 追踪，实现快速、结构化的知识访问Store, consolidate, and recall coding agent memories with provenance tracking using SQLite and FAISS for fast, structured knowledge access.
AI Research Wiki 2026：通过深度引用综合自动构建知识库AI Research Wiki 2026: Auto-Building Knowledge Base with Deep Citation Syntheses
🌟 利用 GPU 加速 Apple Silicon 上的 SQLite 数据库操作，提升分析速度与数据管理效率🌟 Accelerate SQLite database operations on Apple Silicon with GPU power for faster analytics and efficient data management.
基于 LlamaIndex、Redis 与 PII 脱敏的 RAG 搜索引擎。RAG search engine using LlamaIndex, Redis, and PII masking.
智利国家机构网络安全合规扫描器，对标 21.663 号法令及 ANCI 通用指令。结合主动网络扫描与声明式问卷，生成 PDF 差距报告以及面向 CSIRT Chile 的 JSON 报告。Escáner de cumplimiento de ciberseguridad para organismos del Estado chileno, alineado a Ley 21.663; y las Instrucciones Generales de la ANCI. Combina escaneo activo de red con un cuestionario declarativo para producir un informe de brechas en PDF y un reporte JSON listo para CSIRT Chile.
使用先进的 RAG 系统发现你的下一部心仪动画，提供精准推荐与增强语义搜索。🎬 Discover your next favorite anime with this advanced Retrieval-Augmented Generation system, offering precise recommendations and enriched semantic search.
面向 AI Agent 的低开销记忆后端 —— 单二进制，内存占用约 50MB，支持验证增强准确性Low-footprint memory backend for AI agents — single binary, ~50MB RAM, verify-augmented accuracy
将学术论文转换为带注释、浏览器友好的网页，逻辑配色清晰、导航便捷，由 OpenAlex 数据驱动。Convert academic papers into annotated, browser-friendly web pages with color-coded logic and easy navigation powered by OpenAlex data.
基于 TurboQuant 优化 FAISS 兼容的向量量化，实现快速、精准的向量检索。Optimize FAISS-compatible vector quantization for fast, accurate vector search with TurboQuant
🤖 生产级 AI agent 编排平台，支持实时流式输出与多工具执行🤖 Orchestrate AI agents with ease using this production-ready platform, featuring real-time streaming and multi-tool execution capabilities.
论文 "Every Answer Has Its Own Path: Agentic Routing for Retrieval-Augmented Table Question Answering" (EMNLP 2026) 的官方代码。Official code for "Every Answer Has Its Own Path: Agentic Routing for Retrieval-Augmented Table Question Answering" (EMNLP 2026)
基于 RAG、LangGraph 多 agent workflow、FastAPI 与 PostgreSQL（pgvector）构建的 AI 教育内容生成平台。生产级架构，集成 LangSmith tracing。AI-powered educational content generation platform using RAG, LangGraph multi-agent workflows, FastAPI, and PostgreSQL with pgvector. Production-ready architecture with LangSmith tracing.
一个面向 PDF 文档问答的全栈 RAG 应用：上传 PDF，将其索引到本地向量库，然后基于页面级答案进行对话，并在内置阅读器中通过可点击引用跳转到对应页面。A full-stack retrieval-augmented generation (RAG) application for question answering over PDF documents. Upload a PDF, index it into a local vector store, then chat with page-grounded answers and clickable citations that jump to the right page in the built-in viewer.
基于 FastAPI + LangChain + RAG 的 AI 智能对话助手，支持多轮对话记忆、图片分析、流式回复、知识库 RAG 检索、上传定义知识库。
AI 安全测试——发现 LLM 应用、聊天机器人、AI Agent、MCP 服务器与 RAG 系统中的安全漏洞。对齐 OWASP（LLM、Agentic、MCP）与 NIST AI RMFAI security testing — find security vulnerabilities in LLM apps, chatbots, AI agents, MCP servers, and RAG systems. Mapped to OWASP (LLM, Agentic, MCP) and NIST AI RMF.
证伪优先的引用核验:论文存在性 / 元数据 / 论点支撑三重检查,可被 Claude Code、Codex、Cursor 以 MCP 调用 · Skeptical citation auditor for agent writing workflows · PyPI: citationguard
证据约束的代码诊断工作台：确定性静态分析拥有源码事实，LLM 只提假设，resolver/validator 决定能否绑定回真实操作——证据不足保持 unknown/abstained，从架构抑制幻觉。已在真实开源项目发现 bug（stunner #89 已被上游确认修复）。Python。
自托管、100% 本地的 AI 平台——在一个 Docker 栈中集成 LLM 推理、RAG 与知识图谱。无需 API key。Self-hosted, 100% local AI platform — LLM inference, RAG, and knowledge graphs in one Docker stack. No API keys.
开放式数据库工程课程 · 14 个模块共 64 节课 · 210 小时 · 涵盖概念建模、分布式架构、运维及面向 AI 的数据恢复 · 109 项参考文献（带 ISBN、DOI 或标准号），逐课引用并在 CI 中校验 · 5 个零依赖可执行实验 · 256 题自测 📚🗄️ Programa abierto de ingeniería de bases de datos · 64 clases en 14 partes · 210 horas · Del modelado conceptual a la arquitectura distribuida, la operación y la recuperación para IA · 109 fuentes con ISBN, DOI o norma, citadas clase a clase y verificadas en CI · 5 laboratorios ejecutables sin dependencias · Autoevaluación de 256 preguntas 📚
自构建的 AI Agent 框架——多模型 ReAct + 分层缓存友好的上下文 + 异步多 Agent + XML 工作流。CLI 与 WebUI，零 LangChain。An AI agent framework that builds itself — multi-model ReAct + tiered cache-friendly context + async multi-agent + XML workflows. CLI & WebUI, zero LangChain.
基于 RAG 的文档问答机器人，可对任意 PDF/文本文件提问。使用本地 embeddings + Groq API。学习要点：embeddings、向量搜索、分块、RAG 流水线。Document Q&A bot using RAG (Retrieval-Augmented Generation). Ask questions about any PDF/text file. Uses local embeddings + Groq API. Learns: embeddings, vector search, chunking, RAG pipeline.
为 AI Agent 提供实时新闻上下文。发送一个话题和一个 cursor；可能返回空，或返回一包带引用的简明新闻。只读、无需密钥、MIT 许可。Real-time news context for AI agents. Send a topic and a cursor; get nothing, or a short pack of cited stories. Read-only, no key, MIT.
混合神经符号 AI：Llama 3.2 1B + 精确数学 + 经验证事实，807 MB 即时 CPU 原生回答，无需 GPU。封装 .aef 分发。Hybrid neuro-symbolic AI: Llama 3.2 1B + exact math + verified facts — instant CPU-native answers in 807 MB, no GPU. Sealed .aef distribution