Havuz

Local-first markdown notes with a first-class MCP server. 16 tools + 2 resources for Claude Code, Claude Desktop, Cursor and any MCP agent.

A lightweight CLI tool for RAG over local documents

FDE 中文成长手册：24 周 Forward Deployed Engineer 教程，贯通商业决策、一线问题发现、RAG/MCP 工程交付与运营交接。

Offline AI research assistant for Android. Answers from Wikipedia and other reference packs on the phone, with citations. Gemma 4 runs on th

Swap one RAG pipeline stage, hold the rest fixed, and measure whether the gain survives composition. The framework behind "Retrieval Augment

Give any MCP-capable agent persistent memory: remember/recall over a tiered store with hybrid vector + keyword retrieval. Single Go binary,

The ultimate memory backend for OpenClaw and Claude Code. Persistent conversation memory with LLM fact extraction, foundation-model rerankin

DURU — 두루 돕는 AI · 내 PC에 설치해 내 문서를 근거로 답하는 AI 에이전트. 망분리 환경 동작, 지식베이스·에이전트를 암호화한 파일로 공유. An on-premise AI agent that answers from your own doc

Local-first RAG knowledge base for Pi agents with hybrid code/doc search, reranking, diagnostics, and large-project indexing.

AI-powered personalized IELTS corpus distillation — install-only skill (zero-backend Agent loop) + runnable backend with QMD-RAG. 7-stage pi

Knowledge graph + MCP tool server for LLM agents with hybrid retrieval, live DB sync, Korean FTS, and memory feedback.

AI Agent engineering and quantitative finance knowledge base: source-level architecture analysis, Agent-to-Agent protocol research, runnable

面向中文食谱问答的端到端 RAG 工程实践，覆盖数据清洗与索引构建、混合检索、多轮状态管理、结构化生成，以及基于真实大模型服务的 Live E2E 测评闭环。

Eight-platform full-stack AI operating system - unifies 176 LLMs via LangGraph + MCP + A2A. Multi-tenant RLS over 340 tables, RAG knowledge

Turn documents into high-quality instruction datasets with grounding, quality filtering, deduplication, and provenance.

The local-first AI knowledge workspace — an infinite canvas for research, learning, and creation (Tauri 2 + Vue 3)

Local NotebookLM for Claude Code via Google Antigravity (agy / Gemini 3.x): /agy:notebook turns a folder of documents into per-doc summaries

DR Pixel: Page-Native Deep Research with Vision-Language Models — benchmark, evaluation harness and training code

Unified multi-layer caching library for AI/agent pipelines — LangChain, LangGraph, AutoGen, CrewAI, Agno, A2A

Open-source causal graph memory for AI agents. 85.55 on LoCoMo under a frozen, reproducible protocol. MCP server with ACT-R scoring, spreadi

Self-hostable agentic OS. Build, run & audit multi-agent AI teams from signed plugins. Bring your own LLM key, own all your data, EU/GDPR-re

考研管综199历年真题题库(2009–2026)：57题/年·试卷+答案解析·结构化Markdown·附RAG切分库

A pure-Python structured memory benchmark for multi-agent LLM systems — context graph vs vector RAG vs raw history dump, five scenarios, 18

🦅 Open-source AI agent runtime in Rust — Self-hostable with persistent memory, self-improving skills, MCP support, built-in cron, multi-plat

🏛️ Memory OS for AI agents — every conversation becomes a catalog card (archive no · entities · evidence · trace back to the transcript). Fo

AI Agent／大模型应用工程师中文学习与面试手册：10 章基础、18 章进阶、110 道详解题，配图解、追问与案例、Python 实验和复习工具。支持在线阅读与章节、题解双向跳转，用工具调用、RAG、评测和故障恢复案例练习工程判断。

TypeScript knowledge graph RAG for AI agents: source evidence, hybrid retrieval, MCP integration, and a permission-aware PostgreSQL runtime.

Your agents, briefed. Not flooded. Self-hosted context compiler: git-backed team knowledge served to coding agents as token-budgeted bundles

A modular local AI agent built with Python and Ollama, featuring tool calling, memory, web search, document processing, embeddings, and RAG.

AI brain for your Paperless-ngx archive — chat with your documents via local (Ollama) or cloud LLMs, with agentic tool use, semantic search

A complete, hands-on journey from Python fundamentals to AI Engineering. Explore Data Analytics, Data Science, Machine Learning, Deep Learni

AI-powered SaaS translation platform for web novels — batch translate with RAG memory, multi-agent collaboration, Stripe billing, Chrome ext

Search knowledge by what documents mean and how they look — not one or the other.

Chat with your X bookmarks. Turn saved posts into answers, drafts, plans, and decisions

ContextAtlas — context infrastructure for AI coding agents: hybrid retrieval, project memory and retrieval observability via CLI, MCP server

croit AIplane — one plane for all your AI. Self-hosted AI infrastructure connecting applications and users with models, agents, tools and en

Dive is a secure, local-first desktop client for LllamaCCP, LmStudio, Ollama, Pi agentic AI and Cloud. Features an ultra-compact offline RAG

Privacy-first document intelligence engine — parse PDFs, DOCX, PPTX, XLSX & CSV into AI-ready chunks for RAG pipelines. Includes HITL review

Forty hands-on recipes for production-grade retrieval-augmented generation, Nebius-first and provider-agnostic.

面向"面试官 × 简历主人公"场景的个人简历 RAG 问答 Agent：把简历、证书、荣誉、项目介绍放进知识库，面试官可对简历任意提问（自我介绍、项目深挖、技术八股、HR 素质、简历细节），系统以第一人称自然作答。核心设计是"宽松推理 + 诚实标注"——硬事实（数字/日期/证书名

Local-first desktop assistant for the whole job hunt — scrape listings, match with a local LLM, generate applications in your own voice, and

目标打造全球跨境卖家的开源 AI 全能运营助手：基于 LangGraph、RAG 与多智能体协作，连接 Amazon、独立站、ERP 和广告平台，覆盖市场洞察、智能选品、竞品监控、关键词研究、Listing 优化、广告投放、销量预测、库存补货、利润核算、客服分析、合规风控、内容营

A comprehensive hands-on, AI-first Python curriculum with 80+ interactive Jupyter notebooks for AI Engineers from core basics to LLMs, RAG,

Train linear embedding adapters with triplet loss to align retrieval embeddings with your queries (RAG).

The open-source, local-first alternative to LangSmith. No cloud. No setup.

Red-team your RAG pipeline for prompt injection, source-document leakage, and cross-document smuggling, in CI. The gap between RAG eval (RAG

Vault health, connection review and Recall for Obsidian. Studio recipes and editable diagrams in the 2.3.0 release candidate.

AI Agent / LLM application engineer Chinese interview question bank: 2,038 questions with reference answers

GitaViśva (गीताविश्व): the Bhagavad Gita as a living knowledge graph. Explore all 700 verses in 3D, read 20 classical commentaries, listen t

端到端智能体平台：把本机的 AI Agent 变成人人可用的 Web 产品（Vue 3 + Flask + Go WS 网关 + Docker 部署）