"Hybrid Memory Models Combining Relational, Graph, and RAG Approaches" matching MCP servers:

Enables AI agents to crawl live websites, audit AEO readiness, generate Schema.org @graph JSON-LD, llms.txt, ai.txt, and robots.txt, inject structured data into HTML, validate optimizations, and retrieve framework-specific code snippets.

Fetches web content and returns relevant chunks using RAG with semantic chunking, query-based scoring, and LexRank, supporting PDFs and 50+ languages.

"primitive" RAG-like web search model context protocol server that runs locally. ✨ no APIs ✨

Implementation of an MCP server for the RAG Web Browser Actor. This Actor serves as a web browser for large language models (LLMs) and RAG pipelines, similar to a web search in ChatGPT.

Provides web crawling and RAG capabilities for AI agents, enabling scraping of websites, storing content in a vector database (Supabase), and performing semantic search over crawled data.

Crawls and indexes documentation websites to Supabase with vector embeddings for RAG, using smart sitemap discovery and Jina AI for fast content extraction with multi-project support.

Web crawling and RAG implementation that enables AI agents to scrape websites and perform semantic search over the crawled content, storing everything in Supabase for persistent knowledge retrieval.

A database-agnostic MCP server that integrates web crawling with Retrieval Augmented Generation, supporting multiple AI providers and vector database backends for flexible and intelligent content retrieval.

Enables semantic search and retrieval-augmented generation (RAG) using Qdrant vector database. Supports indexing documents from URLs and local directories, with flexible embedding options using Ollama or OpenAI.

Complete Vinted MCP and CLI Server

Provides AI agents and assistants with advanced web crawling and RAG capabilities, enabling them to scrape websites and perform semantic search over crawled content.

Provides AI agents with complete web search, crawling, and RAG capabilities through a Docker-based solution combining Model Context Protocol, Crawl4AI, SearXNG, and Supabase.

Provides AI agents with a comprehensive web intelligence stack including crawling, private search via SearXNG, and intelligent RAG capabilities for focused content extraction. It supports advanced features like semantic vector search and knowledge graph integration for code validation to enhance AI performance and reliability.

An MCP server that integrates Crawl4AI with Supabase to enable AI agents to crawl websites, store content in a vector database, and perform RAG queries.

Validates URLs and checks their reputation to help identify AI hallucinations and verify web page authenticity.

Enables AI agents to search the web, crawl websites, and perform intelligent RAG queries with semantic search capabilities. Includes integrated private search engine, vector database storage, and optional knowledge graph for AI hallucination detection in code repositories.

A local vector database RAG system that integrates with Playwright MCP for web scraping, enabling users to build searchable knowledge bases from web content with multiple embedding providers and Claude-optimized context formatting.

Enables querying a vector database of machine learning FAQs and performing web searches using Bright Data proxies through Claude Desktop with structured agent prompts.

An MCP-native pipeline for collecting structured intelligence on higher education institutions using the WHED schema, enabling scraping, extraction, validation, and saving of profiles.

A fault-tolerant, stealth-enabled Model Context Protocol (MCP) server for web searching and content fetching. Built for AI Agents (Cursor, Claude Code, OpenCode), it uses a stealth browser engine to fetch pages, dynamically handles SPAs/React, and converts bloat into token-optimized Markdown.