Ai Engineering Hub

AI Engineering Hub

Your comprehensive resource for learning and building with AI

[](https://github.com/damn8daniel/ai-engineering-hub) [](https://opensource.org/licenses/MIT) [](http://makeapullrequest.com)

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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.

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Table of Contents

• [Getting Started](#-getting-started)
• [Projects by Difficulty](#projects-by-difficulty)
• [Beginner Projects (22)](#-beginner-projects)
• [Intermediate Projects (48)](#-intermediate-projects)
• [Advanced Projects (23)](#-advanced-projects)
• [AI Engineering Roadmap](#-ai-engineering-roadmap)
• [Contributing](#contributing)
• [License](#license)

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Getting Started

New to AI Engineering? Start here:

1. Complete Beginners: Check out the [AI Engineering Roadmap](./ai-engineering-roadmap) for a comprehensive learning path
2. Learn the Basics: Start with [Beginner Projects](#-beginner-projects) like OCR apps and simple RAG implementations
3. Build Your Skills: Move to [Intermediate Projects](#-intermediate-projects) with agents and complex workflows
4. Master Advanced Concepts: Tackle [Advanced Projects](#-advanced-projects) including fine-tuning and production systems

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Projects by Difficulty

Beginner Projects

> Simple, self-contained projects to get started with AI engineering.

OCR & Vision

| Project | Description | Tech Stack | |---------|-------------|------------| | [LaTeX OCR with Llama](./LaTeX-OCR-with-Llama) | Convert LaTeX equation images to code | Llama 3.2 Vision, Streamlit | | [Llama OCR](./llama-ocr) | 100% local OCR application | Llama 3.2, Streamlit, Ollama | | [Gemma-3 OCR](./gemma3-ocr) | Local OCR with structured text extraction | Gemma-3, Ollama | | [Qwen 2.5 OCR](./qwen-2.5VL-ocr) | Text extraction from images | Qwen 2.5 VL |

Chat Interfaces & UI

| Project | Description | Tech Stack | |---------|-------------|------------| | [Local ChatGPT with DeepSeek](./local-chatgpt-deepseek) | Mini-ChatGPT with visible reasoning | DeepSeek-R1, Chainlit | | [Local ChatGPT with Llama](./local-chatgpt-llama) | ChatGPT clone with vision | Llama 3.2, Streamlit | | [Local ChatGPT with Gemma 3](./local-chatgpt-gemma3) | Local chat interface | Gemma 3, Ollama | | [DeepSeek Thinking UI](./deepseek-thinking-ui) | ChatGPT with visible chain-of-thought | DeepSeek-R1, React | | [Qwen3 Thinking UI](./qwen3-thinking-ui) | Thinking UI with streaming | Qwen3:4B, Streamlit | | [GPT-OSS Thinking UI](./gpt-oss-thinking-ui) | Open-source GPT with reasoning viz | GPT-OSS, React | | [Streaming AI Chatbot](./streaming-ai-chatbot) | Real-time AI streaming chatbot | Motia Framework |

Basic RAG

| Project | Description | Tech Stack | |---------|-------------|------------| | [Simple RAG Workflow](./simple-rag-workflow) | Basic RAG pipeline | LlamaIndex, Ollama | | [Document Chat RAG](./document-chat-rag) | Chat with your documents | Llama 3.3, LangChain | | [Fastest RAG Stack](./fastest-rag-stack) | Optimized RAG pipeline | SambaNova, LlamaIndex, Qdrant | | [GitHub RAG](./github-rag) | Chat with GitHub repositories | LlamaIndex, Ollama | | [ModernBERT RAG](./modernbert-rag) | RAG with modern embeddings | ModernBERT, FAISS | | [Llama 4 RAG](./llama-4-rag) | RAG powered by Llama 4 | Llama 4, LangChain |

Multimodal & Media

| Project | Description | Tech Stack | |---------|-------------|------------| | [Image Generation with Janus-Pro](./imagegen-janus-pro) | Local image generation | DeepSeek Janus-Pro 7B | | [Video RAG with Gemini](./video-rag-gemini) | Chat with videos | Gemini AI, LangChain |

Other Tools

| Project | Description | Tech Stack | |---------|-------------|------------| | [Website to API with FireCrawl](./website-to-api-firecrawl) | Convert websites to structured APIs | FireCrawl, FastAPI | | [AI News Generator](./ai-news-generator) | Automated news generation | CrewAI, Cohere | | [Siamese Network](./siamese-network) | Digit similarity detection | PyTorch, MNIST |

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Intermediate Projects

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

AI Agents & Workflows

| Project | Description | Tech Stack | |---------|-------------|------------| | [YouTube Trend Analysis](./youtube-trend-analysis) | Analyze YouTube trends | CrewAI, BrightData | | [AutoGen Stock Analyst](./autogen-stock-analyst) | Advanced stock analyst agent | Microsoft AutoGen | | [Agentic RAG](./agentic-rag) | RAG with document search and web fallback | LlamaIndex, Tavily | | [Agentic RAG with DeepSeek](./agentic-rag-deepseek) | Enterprise agentic RAG | GroundX, DeepSeek | | [Book Writer Flow](./book-writer-flow) | Automated book writing | CrewAI Flow | | [Content Planner Flow](./content-planner-flow) | Content workflow automation | CrewAI Flow | | [Brand Monitoring](./brand-monitoring) | Automated brand monitoring system | CrewAI, LangChain | | [Hotel Booking Crew](./hotel-booking-crew) | Multi-agent hotel booking | DeepSeek-R1, CrewAI | | [Deploy Agentic RAG](./deploy-agentic-rag) | Private Agentic RAG API | LitServe, LlamaIndex | | [Zep Memory Assistant](./zep-memory-assistant) | AI Agent with human-like memory | Zep, LangChain | | [Agent with MCP Memory](./agent-with-mcp-memory) | Agent with persistent graph memory | Graphiti, Opik | | [ACP Code](./acp-code) | Agent Communication Protocol demo | ACP SDK | | [Motia Content Creation](./motia-content-creation) | Social media content automation | Motia Framework |

Voice & Audio

| Project | Description | Tech Stack | |---------|-------------|------------| | [Real-time Voice Bot](./real-time-voicebot) | Travel guide voice bot | AssemblyAI, ElevenLabs | | [RAG Voice Agent](./rag-voice-agent) | Real-time RAG voice agent | Cartesia, LlamaIndex | | [Chat with Audios](./chat-with-audios) | RAG over audio files | Whisper, LangChain | | [Audio Analysis Toolkit](./audio-analysis-toolkit) | Comprehensive audio analysis | AssemblyAI | | [Multilingual Meeting Notes](./multilingual-meeting-notes) | Auto meeting notes with language detection | Whisper, GPT-4 |

Advanced RAG

| Project | Description | Tech Stack | |---------|-------------|------------| | [RAG with Dockling](./rag-with-dockling) | RAG over Excel with IBM's Dockling | IBM Dockling, LangChain | | [Trustworthy RAG](./trustworthy-rag) | RAG over complex docs with TLM | Cleanlab TLM | | [Fastest RAG with Milvus and Groq](./fastest-rag-milvus-groq) | Sub-15ms retrieval latency | Milvus, Groq | | [Chat with Code](./chat-with-code) | Chat with codebase | Qwen3-Coder | | [RAG SQL Router](./rag-sql-router) | Agent with RAG and SQL routing | LangChain, PostgreSQL |

Multimodal

| Project | Description | Tech Stack | |---------|-------------|------------| | [DeepSeek Multimodal RAG](./deepseek-multimodal-rag) | MultiModal RAG | DeepSeek-Janus-Pro | | [ColiVara Website RAG](./colivara-website-rag) | MultiModal RAG for websites | ColiVara, DeepSeek | | [Multimodal RAG with AssemblyAI](./multimodal-rag-assemblyai) | Audio + vector database + CrewAI | AssemblyAI, ChromaDB |

MCP - Model Context Protocol

| Project | Description | Tech Stack | |---------|-------------|------------| | [Cursor Linkup MCP](./cursor-linkup-mcp) | Custom MCP with deep web search | Linkup, Cursor | | [EyeLevel MCP RAG](./eyelevel-mcp-rag) | MCP for RAG over complex docs | EyeLevel, Claude | | [LlamaIndex MCP](./llamaindex-mcp) | Local MCP client | LlamaIndex, Ollama | | [MCP Agentic RAG](./mcp-agentic-rag) | Agentic RAG via MCP | Claude, MCP SDK | | [MCP Agentic RAG Firecrawl](./mcp-agentic-rag-firecrawl) | Web-aware agentic RAG | FireCrawl, MCP | | [MCP Video RAG](./mcp-video-rag) | Video understanding via MCP | Gemini, MCP SDK | | [MCP Voice Agent](./mcp-voice-agent) | Voice-controlled MCP agent | AssemblyAI, MCP | | [SDV MCP](./sdv-mcp) | Synthetic Data Vault via MCP | SDV, MCP SDK | | [KitOps MCP](./kitops-mcp) | ML model management via MCP | KitOps | | [Stagehand x MCP-Use](./stagehand-mcp-use) | Web automation with MCP | Stagehand, Playwright |

Model Comparison & Evaluation

| Project | Description | Tech Stack | |---------|-------------|------------| | [Evaluation and Observability](./eval-and-observability) | LLM evaluation pipeline | CometML Opik | | [Llama 4 vs DeepSeek-R1](./llama4-vs-deepseek-r1) | Model benchmark comparison | Ollama, Python | | [Qwen3 vs DeepSeek-R1](./qwen3-vs-deepseek-r1) | Reasoning model comparison | Ollama | | [O3 vs Claude Code](./o3-vs-claude-code) | Coding model comparison | OpenAI, Anthropic | | [Sonnet4 vs O4](./sonnet4-vs-o4) | Claude vs GPT comparison | Anthropic, OpenAI | | [Sonnet4 vs Qwen3-Coder](./sonnet4-vs-qwen3-coder) | Coding benchmark | Anthropic, Qwen | | [Code Model Comparison](./code-model-comparison) | Multi-model code benchmark | Various | | [GPT-OSS vs Qwen3](./gpt-oss-vs-qwen3) | Open-source model battle | GPT-OSS, Qwen3 |

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Advanced Projects

> Production-grade systems, fine-tuning, and cutting-edge AI implementations.

Fine-tuning & Model Development

| Project | Description | Tech Stack | |---------|-------------|------------| | [DeepSeek Fine-tuning](./deepseek-finetuning) | Fine-tune DeepSeek models | Unsloth, Ollama | | [Build Reasoning Model](./build-reasoning-model) | Build DeepSeek-R1-like reasoning | GRPO, Unsloth | | [Attention Is All You Need](./attention-impl) | Transformer from scratch | PyTorch |

Advanced Agent Systems

| Project | Description | Tech Stack | |---------|-------------|------------| | [NVIDIA NIM Demo](./nvidia-nim-demo) | CrewAI Flows + NVIDIA NIM | CrewAI, NVIDIA NIM | | [Documentation Writer Flow](./documentation-writer-flow) | Automated doc generation | CrewAI Flow | | [Multi-Agent Deep Researcher](./multi-agent-deep-researcher) | Multi-agent research system | MCP, CrewAI | | [Multiplatform Deep Researcher](./multiplatform-deep-researcher) | Cross-platform research | BrightData, LangChain | | [Web Browsing Agent](./web-browsing-agent) | AI web browsing automation | CrewAI, Stagehand | | [Paralegal Agent Crew](./paralegal-agent-crew) | Legal RAG paralegal agent | CrewAI, LlamaIndex | | [FireCrawl Agent](./firecrawl-agent) | Corrective RAG agent | FireCrawl, LangGraph | | [Context Engineering Workflow](./context-engineering-workflow) | Production context pipeline | TensorLake, Zep | | [Parlant Conversational Agent](./parlant-conversational-agent) | Compliance-driven chatbot | Parlant Framework | | [Stock Portfolio Analysis Agent](./stock-portfolio-agent) | Full-stack portfolio analyzer | React, CrewAI | | [Guidelines vs Traditional Prompt](./guidelines-vs-traditional-prompt) | Prompt engineering comparison | Various LLMs |

Advanced MCP & Infrastructure

| Project | Description | Tech Stack | |---------|-------------|------------| | [MindsDB MCP](./mindsdb-mcp) | Unified MCP for all data sources | MindsDB | | [Financial Analyst DeepSeek](./financial-analyst-deepseek) | Financial analysis agent | DeepSeek, MCP | | [Graphiti MCP](./graphiti-mcp) | Persistent memory with knowledge graphs | Zep Graphiti | | [Pixeltable MCP](./pixeltable-mcp) | Multimodal data orchestration | Pixeltable | | [Ultimate AI Assistant](./ultimate-ai-assistant) | Multi-MCP server orchestration | Claude, Multiple MCPs |

Production Systems

| Project | Description | Tech Stack | |---------|-------------|------------| | [GroundX Document Pipeline](./groundx-doc-pipeline) | Production document processing | GroundX API | | [NotebookLM Clone](./notebooklm-clone) | RAG with citations and podcasts | LangChain, TTS |

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AI Engineering Roadmap

New to AI Engineering? Follow the [complete roadmap](./ai-engineering-roadmap) with 10 stages:

| Stage | Topic | Resource | |-------|-------|----------| | 1 | Master Python | Harvard CS50p | | 2 | AI with Python | DeepLearning.AI | | 3 | Maths for ML | Khan Academy | | 4 | Understanding LLMs | 3Blue1Brown | | 5 | LLM Research | Andrej Karpathy | | 6 | AI Agents | Anthropic Guide | | 7 | Applied AI | CrewAI on Coursera | | 8 | AI Protocols (MCP) | MCP Guidebook | | 9 | Project-based Learning | This Repo | | 10 | Books | AI Engineering - Chip Huyen |

Foundation     ━━▶  Master Python  ━━▶  AI with Python
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Mathematics    ━━▶  Maths for ML  ◀━━━━━━━━━━┘
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Understanding  ━━▶  Understanding LLMs  ━━▶  LLM Research
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Application    ━━▶  AI Agents  ━━▶  Applied AI  ━━▶  MCP
                                                      │
Mastery        ━━▶  Project-based Learning  ━━▶  Books

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Tech Stack Overview

| Category | Technologies | |----------|-------------| | LLMs | Llama 4, DeepSeek-R1, Gemma 3, Qwen 3, GPT-4, Claude | | RAG | LlamaIndex, LangChain, Qdrant, Milvus, ChromaDB, FAISS | | Agents | CrewAI, AutoGen, LangGraph, Swarm | | MCP | MCP SDK (Python/TypeScript), Claude, Cursor | | Voice | AssemblyAI, Cartesia, ElevenLabs, Whisper | | Fine-tuning | Unsloth, PEFT, LoRA, QLoRA | | Frontend | Streamlit, Chainlit, React, Gradio | | Infrastructure | Ollama, LitServe, Docker, FastAPI |

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Contributing

Contributions are welcome! Here's how:

1. Fork this repository
2. Create a feature branch (git checkout -b feature/amazing-project)
3. Add your project in the appropriate difficulty folder
4. Include a README.md with setup instructions
5. Submit a Pull Request

Project README Template

Each project should include:

• Description and use case
• Architecture diagram (if applicable)
• Prerequisites and setup instructions
• Step-by-step usage guide
• Example outputs/screenshots

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License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

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Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: damn8daniel
• Source: damn8daniel/ai-engineering-hub
• License: MIT

Install and usage instructions live in the source repository linked above.