ai-engineering-hub
In-depth tutorials on LLMs, RAGs and real-world AI agent applications.
Install / Use
claude mcp add patchy631 -- npx -y github:patchy631/ai-engineering-hubIf the server publishes to npm under a different name, use that package instead — check the repo README.
MCP Server
Model Context Protocol server
Quality Score
Category
AI & Machine LearningSupported Platforms
Tags
Our assessment of ai-engineering-hub
ai-engineering-hub scores 88/100 on our quality scale, 431st of 965 AI & Machine Learning skills we index (top 45%).
Its MCP Server is 12 KB long, well organised into 28 sections and no code examples: a thorough specification that gives an agent plenty to work with.
With 41,161 GitHub stars, it is one of the more widely adopted skills in the catalogue.
Maintenance, license and trust
- The repository was last updated 4 days ago, so ai-engineering-hub is actively maintained.
- Our last check on 2026-10-20 found the source still online.
- It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
- Its trust signals score 95/100, with 1 caution from licensing, adoption, age or documentation. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.
Safety scan
No issues foundOur scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful.
AI review by kimi-k2.7-code on 2026-09-24. Automated pattern scan on 2026-09-24. It catches known dangerous patterns, not every risk — read a skill before letting an agent act on it.
ai-engineering-hub compared with similar skills
All 4 of these similar skills score higher than ai-engineering-hub; compare them before choosing.
| Skill | Score | Stars | Updated | Format |
|---|---|---|---|---|
| ai-engineering-hub (this skill)by patchy631 | 88 | 41.2k | 4d ago | MCP Server |
| claude-memby thedotmack | 100 | 113.5k | today | CLAUDE.md |
| Agent-Reachby Panniantong | 100 | 109.2k | today | CLAUDE.md |
| Understand-Anythingby Egonex-AI | 100 | 90.9k | 10d ago | CLAUDE.md |
| headroomby headroomlabs-ai | 100 | 78.9k | today | CLAUDE.md |
Frequently asked questions
- How do I install ai-engineering-hub?
- Run
claude mcp add patchy631 -- npx -y github:patchy631/ai-engineering-hub. The install tabs above show the steps for each supported agent. - Which AI agents does ai-engineering-hub work with?
- It is written for Claude Code and Claude Desktop, as a MCP Server file. Other agents that read the same format can often use it too.
- Is ai-engineering-hub safe to use?
- Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful. It is MIT-licensed and scores 95/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
- Is ai-engineering-hub still maintained?
- The repository was last updated 4 days ago, so ai-engineering-hub is actively maintained.
Skill content
View source on GitHubAI Engineering Hub 🚀
Welcome to the AI Engineering Hub - your comprehensive resource for learning and building with AI!
🌟 Why This Repo?
AI Engineering is advancing rapidly, and staying at the forefront requires both deep understanding and hands-on experience. Here, you will find:
- 93+ Production-Ready Projects across all skill levels
- In-depth tutorials on LLMs, RAG, Agents, and more
- Real-world AI agent applications
- Examples to implement, adapt, and scale in your projects
Whether you're a beginner, practitioner, or researcher, this repo provides resources for all skill levels to experiment and succeed in AI engineering.
📋 Table of Contents
🎯 Getting Started
New to AI Engineering? Start here:
- Complete Beginners: Check out the AI Engineering Roadmap for a comprehensive learning path
- Learn the Basics: Start with Beginner Projects like OCR apps and simple RAG implementations
- Build Your Skills: Move to Intermediate Projects with agents and complex workflows
- Master Advanced Concepts: Tackle Advanced Projects including fine-tuning and production systems
📬 Stay Updated with Our Newsletter!
Get a FREE Data Science eBook 📖 with 150+ essential lessons in Data Science when you subscribe to our newsletter! Stay in the loop with the latest tutorials, insights, and exclusive resources. Subscribe now!
🎓 Projects by Difficulty
🟢 Beginner Projects
Perfect for getting started with AI engineering. These projects focus on single components and straightforward implementations.
OCR & Vision
- LaTeX OCR with Llama - Convert LaTeX equation images to code using Llama 3.2 vision
- Llama OCR - 100% local OCR app with Llama 3.2 and Streamlit
- Gemma-3 OCR - Local OCR with structured text extraction using Gemma-3
- Qwen 2.5 OCR - Text extraction using Qwen 2.5 VL model
Chat Interfaces & UI
- Local ChatGPT with DeepSeek - Mini-ChatGPT with DeepSeek-R1 and Chainlit
- Local ChatGPT with Llama - ChatGPT clone using Llama 3.2 vision
- Local ChatGPT with Gemma 3 - Local chat interface with Gemma 3
- DeepSeek Thinking UI - ChatGPT with visible reasoning using DeepSeek-R1
- Qwen3 Thinking UI - Thinking UI with Qwen3:4B and Streamlit
- GPT-OSS Thinking UI - GPT-OSS with reasoning visualization
- Streaming AI Chatbot - Real-time AI streaming with Motia framework
Basic RAG
- Simple RAG Workflow - Basic RAG with LlamaIndex and Ollama
- Document Chat RAG - Chat with documents using Llama 3.3
- Fastest RAG Stack - Fast RAG with SambaNova, LlamaIndex, and Qdrant
- GitHub RAG - Chat with GitHub repos locally
- ModernBERT RAG - RAG with ModernBert embeddings
- Llama 4 RAG - RAG powered by Meta's Llama 4
Multimodal & Media
- Image Generation with Janus-Pro - Local image generation with DeepSeek Janus-pro 7B
- Video RAG with Gemini - Chat with videos using Gemini AI
Other Tools
- Website to API with FireCrawl - Convert websites to APIs
- AI News Generator - News generation with CrewAI and Cohere
- Siamese Network - Digit similarity detection on MNIST
🟡 Intermediate Projects
Multi-component systems, agentic workflows, and advanced features for experienced practitioners.
AI Agents & Workflows
- YouTube Trend Analysis - Analyze YouTube trends with CrewAI and BrightData
- AutoGen Stock Analyst - Advanced analyst with Microsoft AutoGen
- Agentic RAG - RAG with document search and web fallback
- Agentic RAG with DeepSeek - Enterprise agentic RAG with GroundX
- Book Writer Flow - Automated book writing with CrewAI
- Content Planner Flow - Content workflow with CrewAI Flow
- Brand Monitoring - Automated brand monitoring system
- Hotel Booking Crew - Multi-agent hotel booking with DeepSeek-R1
- Deploy Agentic RAG - Private Agentic RAG API with LitServe
- Zep Memory Assistant - AI Agent with human-like memory
- Agent with MCP Memory - Agents with Graphiti memory and Opik
- ACP Code - Agent Communication Protocol demo
- Motia Content Creation - Social media automation workflow
Voice & Audio
- Real-time Voice Bot - Conversational travel guide with AssemblyAI
- RAG Voice Agent - Real-time RAG Voice Agent with Cartesia
- Chat with Audios - RAG over audio files
- Audio Analysis Toolkit - Audio analysis with AssemblyAI
- Multilingual Meeting Notes - Auto meeting notes with language detection
Advanced RAG
- RAG with Dockling - RAG over Excel with IBM's Docling
- Trustworthy RAG - RAG over complex docs with TLM
- Fastest RAG with Milvus and Groq - Sub-15ms retrieval latency
- Chat with Code - Chat with code using Qwen3-Coder
- RAG SQL Router - Agent with RAG and SQL routing
Multimodal
- DeepSeek Multimodal RAG - MultiModal RAG with DeepSeek-Janus-Pro
- ColiVara Website RAG - MultiModal RAG for websites
- Multimodal RAG with AssemblyAI - Audio + vector database + CrewAI
MCP (Model Context Protocol)
- Cursor Linkup MCP - Custom MCP with deep web search
- EyeLevel MCP RAG - MCP for RAG over complex docs
- LlamaIndex MCP - Local MCP client with LlamaIndex
- MCP Agentic RAG - MCP-powered Agentic RAG for Cursor
- MCP Agentic RAG Firecrawl - Agentic RAG with Firecrawl
- MCP Video RAG - Video RAG using Ragie via MCP
- MCP Voice Agent - Voice agent with Firecrawl and Supabase
- SDV MCP - Synthetic Data Vault orchestration
- KitOps MCP - ML model management with KitOps
- Stagehand × MCP-Use - Web automation with Stagehand MCP
Model Comparison & Evaluation
- Evaluation and Observability - E2E RAG evaluation with CometML Opik
- Llama 4 vs DeepSeek-R1 - Compare models using RAG
- Qwen3 vs DeepSeek-R1 - Model comparison with Opik
- O3 vs Claude Code - Compare Claude 3.7 and o3
- Sonnet4 vs O4 - Code generation comparison
- Sonnet4 vs Qwen3-Coder - Coder model comparison
- Code Model Comparison - Frontier model code comparison
- GPT-OSS vs Qwen3 - Reasoning capabilities comparison
🔴 Advanced Projects
Complex systems, fine-tuning, production deployments, and cutting-edge implementations.
Fine-tuning & Model Development
- DeepSeek Fine-tuning - Fine-tune DeepSeek with Unsloth and Ollama
- Build Reasoning Model - Build DeepSeek-R1-like reasoning models
- Attention Is All You Need Implementation - Transformer architecture from scratch
Advanced Agent Systems
- NVIDIA Demo - Documentation writer with CrewAI Flows and NVIDIA NIM
- Documentation Writer Flow - Agentic documentation workflow
- Multi-Agent Deep Researcher - MCP-powered deep researcher
- Multiplatform Deep Researcher - Multi-platform research with BrightData
- Web Browsing Agent - Browser automation with CrewAI and Stagehand
- Paralegal Agent Crew - Intelligent paralegal with RAG
- FireCrawl Agent - Corrective RAG with web search fallback
- Context Engineering Workflow - Research assistant with TensorLake and Zep
- Parlant Conversational Agent - Compliance-driven conversational agent
- Stock Portfolio Analysis Agent - Portfolio analysis with React frontend
- Guidelines vs Traditional Prompt - Structured guidelines comparison
Advanced MCP & Infrastructure
- MindsDB MCP - Unified MCP for all data sources
- Financial Analyst DeepSeek - MCP financial analysis workflow
- Graphiti MCP - Persistent memory with Zep's Graphiti
- Pixeltable MCP - Unified multimodal data orchestration
- Ultimate AI Assistant - Multi-MCP server interface
Production Systems
- GroundX Document Pipeline - World-class document processing
- NotebookLM Clone - Full NotebookLM with RAG, citations, and podcasts
Learning Resources
- AI Engineering Roadmap - Complete guide from Python to production AI
📢 Contribute to the AI Engineering Hub!
We welcome contributors! Whether you want to add new tutorials, improve existing code, or report issues, your contributions make this community thrive. Here's how to get involved:
- Fork the repository
- Create a new branch for your contribution
- Submit a Pull Request and describe the improvements
Check out our contributing guidelines for more details.
📜 License
This repository is licensed under the MIT License - see the LICENSE file for details.
💬 Connect
For discussions, suggestions, and more, feel free to create an issue or reach out directly!
Happy Coding! 🎉
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Languages
Trust signals
From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.