ai-engineering-hub
In-depth tutorials on LLMs, RAGs and real-world AI agent applications.
- 38.4k stars
- 6.3k forks
- 128 open issues
- Language: Jupyter Notebook
- License: MIT
- updated
Install
git clone https://github.com/patchy631/ai-engineering-hub && cp ai-engineering-hub/*.md ~/.claude/agents/- Clone the repository and copy the agent .md definitions into ~/.claude/agents (or .claude/agents inside a project).
- Start a new Claude Code session to load the agents.
- Delegate work to them with the Task/Agent tool or by name.
Editor's note
AI Engineering Hub is a collection of 93 Jupyter Notebook tutorials and projects spanning beginner, intermediate, and advanced AI engineering topics, organized across three difficulty tiers. Projects cover retrieval-augmented generation (RAG), multi-agent workflows, vision and OCR applications, fine-tuning, and production deployment patterns using frameworks including LlamaIndex, CrewAI, AutoGen, LitServe, and the Motia framework. Claude integration surfaces primarily through MCP-based memory projects such as "Agent with MCP Memory," which pairs Graphiti memory graphs with the Opik observability platform, and through general API-level agent tutorials. Other models featured include Llama, DeepSeek, Gemma, and Qwen, making this model-agnostic in practice despite the MCP topic tag. The repository targets a broad audience from beginners building simple RAG pipelines against local Ollama instances to practitioners deploying private agentic RAG APIs. With over 35,000 stars, it functions as a broad reference library rather than a focused Claude-specific toolkit.
10 items in this repository
Search the web, scrape websites, extract structured data from URLs, and automate browsers using Bright Data's Web MCP. Use when fetching live web content, bypassing blocks/CAPTCHAs, getting product data from Amazon/eBay, social media posts, or when standard requests fail.
Execute Hugging Face Hub operations using the `hf` CLI. Use when the user needs to download models/datasets/spaces, upload files to Hub repositories, create repos, manage local cache, or run compute jobs on HF infrastructure. Covers authentication, file transfers, repository creation, cache operations, and cloud compute.
Create and manage datasets on Hugging Face Hub. Supports initializing repos, defining configs/system prompts, streaming row updates, and SQL-based dataset querying/transformation. Designed to work alongside HF MCP server for comprehensive dataset workflows.
Add and manage evaluation results in Hugging Face model cards. Supports extracting eval tables from README content, importing scores from Artificial Analysis API, and running custom model evaluations with vLLM/lighteval. Works with the model-index metadata format.
This skill should be used when users want to run any workload on Hugging Face Jobs infrastructure. Covers UV scripts, Docker-based jobs, hardware selection, cost estimation, authentication with tokens, secrets management, timeout configuration, and result persistence. Designed for general-purpose compute workloads including data processing, inference, experiments, batch jobs, and any Python-based tasks. Should be invoked for tasks involving cloud compute, GPU workloads, or when users mention running jobs on Hugging Face infrastructure without local setup.
This skill should be used when users want to train or fine-tune language models using TRL (Transformer Reinforcement Learning) on Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes guidance on the TRL Jobs package, UV scripts with PEP 723 format, dataset preparation and validation, hardware selection, cost estimation, Trackio monitoring, Hub authentication, and model persistence. Should be invoked for tasks involving cloud GPU training, GGUF conversion, or when users mention training on Hugging Face Jobs without local GPU setup.
Publish and manage research papers on Hugging Face Hub. Supports creating paper pages, linking papers to models/datasets, claiming authorship, and generating professional markdown-based research articles.
Use this skill when the user wants to build tool/scripts or achieve a task where using data from the Hugging Face API would help. This is especially useful when chaining or combining API calls or the task will be repeated/automated. This Skill creates a reusable script to fetch, enrich or process data.
Track and visualize ML training experiments with Trackio. Use when logging metrics during training (Python API) or retrieving/analyzing logged metrics (CLI). Supports real-time dashboard visualization, HF Space syncing, and JSON output for automation.
Subagents overview
AI 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
What people ask about ai-engineering-hub
What is patchy631/ai-engineering-hub?
patchy631/ai-engineering-hub is subagents for the Claude AI ecosystem. In-depth tutorials on LLMs, RAGs and real-world AI agent applications. It has 38.4k GitHub stars and its last recorded update is dated 2026-10-12.
How do I install ai-engineering-hub?
You can install ai-engineering-hub by cloning the repository (https://github.com/patchy631/ai-engineering-hub) or following the README instructions on GitHub. ClaudeWave also provides quick install blocks on this page.
Is patchy631/ai-engineering-hub safe to use?
Our security agent has analyzed patchy631/ai-engineering-hub and assigned a Trust Score of 100/100 (tier: Verified). See the full breakdown of passed checks and flags on this page.
Who maintains patchy631/ai-engineering-hub?
patchy631/ai-engineering-hub is maintained by patchy631. The last recorded GitHub activity is dated 2026-10-12, with 128 open issues.
Are there alternatives to ai-engineering-hub?
Yes. On ClaudeWave you can browse similar subagents at /categories/agents, sorted by popularity or recent activity.
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