RAG & Vector Memory
Retrieval-Augmented Generation pipelines, vector embeddings, and semantic memory search.
Eliminate model hallucinations by grounding LLM responses in real-time enterprise documents, codebases, and vector stores using Qdrant, Chroma, and pgvector integrations.
Available AI Systems & Skills (24)
Open in Advanced FilterA Fork of Rikkahub with an overhauled UI and feature additions
qKnow is an open-source agent platform for enterprise knowledge intelligence and industry AI. It provides knowledge graphs, RAG, and bot building, encapsulating industry AI into standardized modules for rapid deployment. Supporting unified integration of documents and data, it helps enterprises quickly build knowledge hubs and AI solutions.
Local-first Memory OS for personal AI assistants with L0-L3 memory, Wiki++ knowledge, skill routing, and TokenLess context compression.
China Unicom's Yuanjing Wanwu Agent Platform is an enterprise-grade, multi-tenant AI agent development platform. It helps users build applications such as intelligent agents, workflows, and rag, and also supports model management. The platform features a developer-friendly license, and we welcome all developers to build upon the platform.
Source-grounded public context for 立正: Superlinear posts, selected comments, YouTube, Public Axioms V1, and 《真本事》.
Self-hosted web scraper, crawler and document parser for AI agents: URLs, PDFs with OCR and Office files to clean Markdown/JSON. Firecrawl- and Jina-compatible API, MCP server, agent endpoint, Python/Node SDKs. Single Rust binary.
Learn GenAI and Agentic AI from Zero to Production
Shared Single-file memory layer for all your agents, sub mili-second RAG over text, photo and video on Apple Silicon.. No Server. No API. One File. Pure Swift
A systematic AI Agent development tutorial covering LLM agents, RAG, tool use, memory systems, multi-agent systems, LangChain, LangGraph, MCP, and agentic RL.｜从零开始学 AI Agent 开发 | 系统、全面、实战导向的 Agent 开发教程 | 每日自动追踪 arXiv 最新论文 | Learn AI Agent Development from Scratch
📚🔭 Your personal research radar — an LLM-powered tool that auto-aggregates the latest papers for your keywords across arXiv / Crossref / Semantic Scholar / GitHub / RSS.
Give every agent your team's brain on day one. The memory layer for AI agents. Everything your team knows. Cited, and always up to date.
Open-source AI real estate assistant: RAG + vector search over your own listings, 16 LLM providers, FastAPI + Next.js. Run it on your own server with Docker, or try the live demo.
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🧠 LLM-Driven Intelligent Memory & Context Management System （AI记忆管理与智能上下文感知平台） AI记忆管理平台 | 智能上下文感知 | RAG检索增强生成 | 向量检索引擎
Local-first 3-in-1 AI memory layer & MCP server for Claude Code, Codex, Grok, Gemini, VS Code and Cursor. Fuses session history, codebase indexing & concept graphs in SQLite. Enables zero-cloud, privacy-first context & instant recall, supports multi-agent swarms.
AI desktop app for chat, RAG, Skills, MCP tools, and agents. Support multiple LLMs (Anthropic, OpenAI, VertexAI, vLLM, Nvidia NIM, Gemini, OpenRouter)
The Complete AI Development Toolkit for Claude Code — 103 skills, 36 agents, 172 hooks. Production-ready patterns for full-stack development.
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.
AI Agent that handles engineering tasks end-to-end: integrates with developers’ tools, plans, executes, and iterates until it achieves a successful result.
Structural code intelligence for AI agents — semantic search, knowledge graphs, and a built-in MCP server in one Rust binary. Give Claude, Cursor, and any MCP client a deep understanding of your codebase.
A queryable second brain over your scattered notes and docs - hybrid retrieval (vector + BM25), section-level citations, and an MCP server so AI agents can use it. ~300 lines, no LangChain.
Build & Monetize for RAG & Vector Memory
Have you created an autonomous agent, MCP server, or Claude Code skill for RAG & Vector Memory? Publish it to AIStoreHub with dual-currency checkout (USD $ + INR ₹) and earn 85–90% net revenue.