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@patchy631/ai-engineering-hub

AI engineering tutorials and project collection

AI Engineering Hub is a large index of example projects for building with LLMs, RAG, agents, and MCP. Each folder is a separate tutorial or app, with notebooks, Python files, and its own setup notes.

37,473 starsโ€ข6.2k forksโ€ขJupyter Notebookโ€ขUpdated 21d ago
Who it's for

Builders who want hands-on examples for LLM apps, agent workflows, RAG systems, and MCP integrations.

What it delivers

You can jump from concept to a working example instead of starting each AI project from scratch.

What it does

Tutorial index by difficulty

The top-level README groups projects into beginner, intermediate, and advanced sections so builders can pick a starting point.

Agent and RAG examples

Several folders show agentic RAG, multi-agent research, memory, and web-backed retrieval workflows.

Fine-tuning and model-building notebooks

Projects like DeepSeek-finetuning and Build-reasoning-model include notebooks for training and reasoning-model work.

MCP and integration demos

Folders such as Multi-Agent-deep-researcher-mcp-windows-linux and agent-with-mcp-memory demonstrate MCP-based setups.

App-style examples

Many subprojects include app.py, server.py, or notebook files that you can run and adapt.

README

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AI Engineering Hub ๐Ÿš€

Welcome to the AI Engineering Hub - your comprehensive resource for learning and building with AI!

๐ŸŒŸ 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.


๐Ÿ“‹ Table of Contents


๐ŸŽฏ Getting Started

New to AI Engineering? Start here:

  1. Complete Beginners: Check out the AI Engineering Roadmap for a comprehensive learning path
  2. Learn the Basics: Start with Beginner Projects like OCR apps and simple RAG implementations
  3. Build Your Skills: Move to Intermediate Projects with agents and complex workflows
  4. Master Advanced Concepts: Tackle Advanced Projects including fine-tuning and production systems

๐Ÿ“ฌ Stay Updated with Our Newsletter!

Get a FREE Data Science eBook ๐Ÿ“– 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!

Daily Dose of Data Science Newsletter


๐ŸŽ“ Projects by Difficulty

๐ŸŸข 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

Basic RAG

Multimodal & Media

Other Tools


๐ŸŸก Intermediate Projects

Multi-component systems, agentic workflows, and advanced features for experienced practitioners.

AI Agents & Workflows

Voice & Audio

Advanced RAG

Multimodal

MCP (Model Context Protocol)

Model Comparison & Evaluation


๐Ÿ”ด Advanced Projects

Complex systems, fine-tuning, production deployments, and cutting-edge implementations.

Fine-tuning & Model Development

Advanced Agent Systems

Advanced MCP & Infrastructure

Production Systems

Learning Resources


๐Ÿ“ข 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:

  1. Fork the repository
  2. Create a new branch for your contribution
  3. Submit a Pull Request and describe the improvements

Check out our contributing guidelines for more details.


๐Ÿ“œ License

This repository is licensed under the MIT License - see the LICENSE file for details.


๐Ÿ’ฌ Connect

For discussions, suggestions, and more, feel free to create an issue or reach out directly!

Happy Coding! ๐ŸŽ‰

Files in the repo

Repository payloadโ€ข118 top-level entries
  • acp-code
  • agent-with-mcp-memory
  • agent2agent-demo
  • agentic_rag
  • agentic_rag_deepseek
  • ai_news_generator
  • ai-avatar-demo
  • ai-engineering-roadmap
  • ai-podcast-generation
  • ai-podcast-generator
  • amazon-product-analysis-server
  • art_mcp_rl
  • assets
  • audio-analysis-toolkit
  • autogen-stock-analyst
  • biotech-agentic-analyst
  • book-writer-flow
  • brand-monitoring
  • build-code-harness
  • Build-reasoning-model
  • chat-with-audios
  • chat-with-code
  • code-model-comparison
  • colbert-rag
  • Colivara-deepseek-website-RAG
  • content_planner_flow
  • context-engineering-pipeline
  • context-engineering-workflow
  • corrective-rag
  • courses
  • cursor_linkup_mcp
  • database-memory-agent
  • DeepSeek-finetuning
  • deepseek-multimodal-RAG
  • deepseek-thinking-ui
  • deploy-agentic-rag
  • document-chat-rag
  • documentation-writer-flow
  • eval-and-observability
  • eyelevel-mcp-rag
  • fastest-rag-milvus-groq
  • fastest-rag-stack
  • financial-analyst-deepseek
  • finetune-studio-mcp-app
  • firecrawl-agent
  • flight-booking-crew
  • gemma3-ocr
  • github-rag
  • gpt-oss-thinking-ui
  • gpt-oss-vs-qwen3
  • graphiti-mcp
  • groundX-doc-pipeline
  • grpo-finetuning-qwen3
  • guidelines-vs-traditional-prompt
  • hotel-booking-crew
  • hugging-face-skills
  • imagegen-janus-pro
  • kitops-mcp
  • knowledge distillation
  • LaTeX-OCR-with-Llama
  • llama-4_vs_deepseek-r1
  • llama-4-rag
  • llama-ocr
  • llamaindex-mcp
  • local-chatgpt
  • local-chatgpt with DeepSeek
  • local-chatgpt with Gemma 3
  • mcp-agentic-rag
  • mcp-agentic-rag-firecrawl
  • mcp-video-rag
  • mcp-voice-agent
  • mindsdb-mcp
  • minimaxm2-vs-sonnet4-5-vs-kimik2-vs-gemini3
  • modernbert-rag
  • motia-content-creation
  • Multi-Agent-deep-researcher-mcp-windows-linux
  • multi-modal-rag
  • multilingual-meeting-notes-generator
  • multimodal-rag-assemblyai
  • multiplatform_deep_researcher
  • notebook-lm-clone
  • o3-vs-claude-code
  • open-agent-builder
  • openai-swarm-ollama
  • openclaw-secure-deployment
  • paralegal-agent-crew
  • parlant-conversational-agent
  • pixeltable-mcp
  • qwen-2.5VL-ocr
  • qwen3_vs_deepseek-r1
  • qwen3-thinking-ui
  • rag-sql-router
  • rag-voice-agent
  • rag-with-dockling
  • real-time-voicebot
  • resources
  • sales-analytics-agent
  • sdv-mcp
  • siamese-network
  • simple-rag-workflow
  • sonnet4-vs-o4
  • sonnet4-vs-qwen3-coder
  • stagehand x mcp-use
  • stock-portfolio-analysis-agent
  • streaming-ai-chatbot
  • train-yolo26-object-detection
  • trustworthy-rag
  • ultimate-ai-assitant-using-mcp
  • video-rag-gemini
  • web-browsing-agent
  • Website-to-API-with-FireCrawl
  • Youtube-trend-analysis
  • zep-memory-assistant
  • zep-observations
  • .gitignore
  • .gitmodules
  • LICENSE
  • README.md

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