Crawl4AI RAG MCP Server

A high-performance Retrieval-Augmented Generation (RAG) system using Crawl4AI for web content extraction, sqlite-vec for vector storage, and MCP integration for AI assistants.

Summary

This system provides a production-ready RAG solution that combines:

• Crawl4AI for intelligent web content extraction with markdown conversion

• SQLite with sqlite-vec for vector storage and semantic search

• RAM Database Mode for 10-50x faster query performance

• MCP Server for AI assistant integration (LM-Studio, Claude Desktop, etc.)

• REST API for bidirectional communication and remote access

• Security Layer with input sanitization and domain blocking

Quick Start

Option 1: Local Development

1. Clone and setup:

git clone https://github.com/Rob-P-Smith/mcpragcrawl4ai.git
cd mcpragcrawl4ai
python3 -m venv .venv
source .venv/bin/activate  # Linux/Mac
pip install -r requirements.txt

2. Start Crawl4AI service:

docker run -d --name crawl4ai -p 11235:11235 unclecode/crawl4ai:latest

3. Configure environment:

# Create .env file
cat > .env << EOF
IS_SERVER=true
USE_MEMORY_DB=true
LOCAL_API_KEY=dev-api-key
CRAWL4AI_URL=http://localhost:11235
EOF

4. Run MCP server:

python3 core/rag_processor.py

Option 2: Docker Server Deployment

1. Deploy full server (REST API + MCP):

cd mcpragcrawl4ai
docker compose -f deployments/server/docker-compose.yml up -d

2. Test deployment:

curl http://localhost:8080/health

See Deployment Guide for complete deployment options.

Architecture

Core Components

• MCP Server (core/rag_processor.py) - JSON-RPC 2.0 protocol handler

• RAG Database (core/data/storage.py) - SQLite + sqlite-vec vector storage with RAM mode support

• Content Cleaner (core/data/content_cleaner.py) - Navigation removal and quality filtering

• Sync Manager (core/data/sync_manager.py) - RAM database differential sync with virtual table support

• Crawler (core/operations/crawler.py) - Web crawling with DFS algorithm and content extraction

• Defense Layer (core/data/dbdefense.py) - Input sanitization and security

• REST API (api/api.py) - FastAPI server with 15+ endpoints

• Auth System (api/auth.py) - API key authentication and rate limiting

• Recrawl Utility (core/utilities/recrawl_utility.py) - Batch URL recrawling via API with concurrent processing

Database Schema

• crawled_content - Web content with markdown, embeddings, and metadata

• content_vectors - Vector embeddings (sqlite-vec vec0 virtual table with rowid support)

• sessions - User session tracking for temporary content

• blocked_domains - Domain blocklist with wildcard patterns

• _sync_tracker - Change tracking for RAM database differential sync (memory mode only)

Technology Stack

• Python 3.11+ with asyncio for concurrent operations

• SQLite with sqlite-vec extension for vector similarity search

• SentenceTransformers (all-MiniLM-L6-v2) for embedding generation

• langdetect for language detection and filtering

• FastAPI for REST API with automatic OpenAPI documentation

• Crawl4AI for intelligent web content extraction with fit_markdown

• Docker for containerized deployment

• aiohttp for async HTTP requests in utilities

Documentation

For detailed documentation, see:

• Deployment Guide - Comprehensive deployment options

• Installation Guide - Setup and configuration

• API Documentation - REST API reference

• Quick Start Guide - Get started quickly

• Troubleshooting - Common issues and solutions

• Full Documentation - Complete documentation index

Key Features

Performance

• RAM Database Mode: In-memory SQLite with differential sync for 10-50x faster queries

• Vector Search: 384-dimensional embeddings using all-MiniLM-L6-v2 for semantic search

• Batch Crawling: High-performance batch processing with retry logic and progress tracking

• Content Optimization: 70-80% storage reduction through intelligent cleaning and filtering

• Efficient Storage: fit_markdown conversion and content chunking for optimal retrieval

Functionality

• Deep Crawling: DFS-based multi-page crawling with depth and page limits

• Content Cleaning: Automatic removal of navigation, boilerplate, and low-quality content

• Language Filtering: Automatic detection and filtering of non-English content

• Semantic Search: Vector similarity search with tag filtering and deduplication

• Target Search: Intelligent search with automatic tag expansion

• Content Management: Full CRUD operations with retention policies and session management

• Batch Recrawling: Concurrent URL recrawling via API with rate limiting and progress tracking

Security

• Input Sanitization: Comprehensive SQL injection defense and input validation

• Domain Blocking: Wildcard-based domain blocking with social media and adult content filters

• API Authentication: API key-based authentication with rate limiting

• Safe Crawling: Automatic detection and blocking of forbidden content

Integration

• MCP Server: Full MCP protocol support for AI assistant integration

• REST API: Complete REST API with 15+ endpoints for all operations

• Bidirectional Mode: Server mode (host API) and client mode (forward to remote)

• Docker Deployment: Production-ready containerized deployment

Quick Usage Examples

Via MCP (in LM-Studio/Claude Desktop)

crawl_and_remember("https://docs.python.org/3/tutorial/", tags="python, tutorial")
search_memory("list comprehensions", tags="python", limit=5)
target_search("async programming best practices", initial_limit=5, expanded_limit=20)
get_database_stats()

Via REST API

# Crawl and store content
curl -X POST http://localhost:8080/api/v1/crawl/store \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"url": "https://docs.python.org/3/tutorial/", "tags": "python, tutorial"}'

# Semantic search
curl -X POST http://localhost:8080/api/v1/search \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"query": "list comprehensions", "tags": "python", "limit": 5}'

# Get database stats
curl http://localhost:8080/api/v1/stats \
  -H "Authorization: Bearer YOUR_API_KEY"

Via Python Client

from api.api import Crawl4AIClient

client = Crawl4AIClient("http://localhost:8080", "YOUR_API_KEY")
result = await client.crawl_and_store("https://example.com", tags="example")
search_results = await client.search("python tutorials", limit=10)
stats = await client.get_database_stats()

Performance Metrics

With RAM database mode enabled:

• Search queries: 20-50ms (vs 200-500ms disk mode)

• Batch crawling: 2,000+ URLs successfully processed

• Database size: 215MB (2,296 pages, 8,196 embeddings)

• Sync overhead: <100ms for differential sync (idle: 5s, periodic: 5min)

• Sync reliability: 100% success rate with virtual table support

• Memory usage: ~500MB for full in-memory database

• Storage optimization: 70-80% reduction through content cleaning