Crawl4AI RAG MCP Server

Crawl4AI RAG MCP Server provides AI agents and coding assistants with comprehensive web crawling and retrieval-augmented generation (RAG) capabilities backed by Supabase vector storage. Key capabilities include: - Web crawling and scraping to extract structured content from any website for RAG ingestion - Supabase-backed vector store for semantic search and knowledge persistence - Advanced RAG strategies including Contextual Embeddings, Hybrid Search (vector + keyword), Agentic RAG for code extraction, and cross-encoder Reranking - Knowledge Graph generation for AI hallucination detection and repository code analysis - Integration with OpenAI embeddings with planned support for Ollama and local embedding models

Crawl4AI RAG MCP Server

Web Crawling and RAG Capabilities for AI Agents and AI Coding Assistants

A powerful implementation of the Model Context Protocol (MCP) integrated with Crawl4AI and Supabase for providing AI agents and AI coding assistants with advanced web crawling and RAG capabilities.

With this MCP server, you can scrape anything and then use that knowledge anywhere for RAG.

The primary goal is to bring this MCP server into Archon as I evolve it to be more of a knowledge engine for AI coding assistants to build AI agents. This first version of the Crawl4AI/RAG MCP server will be improved upon greatly soon, especially making it more configurable so you can use different embedding models and run everything locally with Ollama.

Consider this GitHub repository a testbed, hence why I haven't been super actively address issues and pull requests yet. I certainly will though as I bring this into Archon V2!

Overview

This MCP server provides tools that enable AI agents to crawl websites, store content in a vector database (Supabase), and perform RAG over the crawled content. It follows the best practices for building MCP servers based on the Mem0 MCP server template I provided on my channel previously.

The server includes several advanced RAG strategies that can be enabled to enhance retrieval quality:

See the Configuration section below for details on how to enable and configure these strategies.

Vision

The Crawl4AI RAG MCP server is just the beginning. Here's where we're headed:

1. Integration with Archon: Building this system directly into Archon to create a comprehensive knowledge engine for AI coding assistants to build better AI agents.

2. Multiple Embedding Models: Expanding beyond OpenAI to support a variety of embedding models, including the ability to run everything locally with Ollama for complete control and privacy.

3. Advanced RAG Strategies: Implementing sophisticated retrieval techniques like contextual retrieval, late chunking, and others to move beyond basic "naive lookups" and significantly enhance the power and precision of the RAG system, especially as it integrates with Archon.

4. Enhanced Chunking Strategy: Implementing a Context 7-inspired chunking approach that focuses on examples and creates distinct, semantically meaningful sections for each chunk, improving retrieval precision.

5. Performance Optimization: Increasing crawling and indexing speed to make it more realistic to "quickly" index new documentation to then leverage it within the same prompt in an AI coding assistant.

Features

Tools

The server provides essential web crawling and search tools:

Core Tools (Always Available)

1. crawl_single_page: Quickly crawl a single web page and store its content in the vector database 2. smart_crawl_url: Intelligently crawl a full website based on the type of URL provided (sitemap, llms-full.txt, or a regular webpage that needs to be crawled recursively) 3. get_available_sources: Get a list of all available sources (domains) in the database 4. perform_rag_query: Search for relevant content using semantic search with optional source filtering

Conditional Tools

5. search_code_examples (requires USE_AGENTIC_RAG=true): Search specifically for code examples and their summaries from crawled documentation. This tool provides targeted code snippet retrieval for AI coding assistants.

Knowledge Graph Tools (requires USE_KNOWLEDGE_GRAPH=true, see below)

6. parse_github_repository: Parse a GitHub repository into a Neo4j knowledge graph, extracting classes, methods, functions, and their relationships for hallucination detection 7. check_ai_script_hallucinations: Analyze Python scripts for AI hallucinations by validating imports,