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patchy631 / Subagents

ai-engineering-hub

In-depth tutorials on LLMs, RAGs and real-world AI agent applications.

  • 38.4k estrellas
  • 6.3k forks
  • 128 issues abiertos
  • Lenguaje: Jupyter Notebook
  • Licencia: MIT
  • actualizado

Instalación

Terminal
git clone https://github.com/patchy631/ai-engineering-hub && cp ai-engineering-hub/*.md ~/.claude/agents/
  1. Clone the repository and copy the agent .md definitions into ~/.claude/agents (or .claude/agents inside a project).
  2. Start a new Claude Code session to load the agents.
  3. Delegate work to them with the Task/Agent tool or by name.

Nota editorial

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.

Items instalables

10 items en este repositorio

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.

Instalar

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Instalar

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.

Instalar

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.

Instalar

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.

Instalar

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.

Instalar

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.

Instalar

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.

Instalar

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.

Instalar

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.

Instalar
README

Resumen de Subagents

README completo en GitHub

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:

  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


📬 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!

Daily Dose of Data Science Newsletter


🎓 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
Basic RAG
Multimodal & Media
Other Tools

🟡 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

🔴 Advanced Projects

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

Fine-tuning & Model Development
Advanced Agent Systems
Topics
agentsaillmsmachine-learningmcprag
Preguntas frecuentes

Lo que la gente pregunta sobre ai-engineering-hub

¿Qué es patchy631/ai-engineering-hub?

patchy631/ai-engineering-hub es subagents para el ecosistema de Claude AI. In-depth tutorials on LLMs, RAGs and real-world AI agent applications. Tiene 38.4k estrellas en GitHub y su última actualización registrada es del 2026-10-12.

¿Cómo se instala ai-engineering-hub?

Puedes instalar ai-engineering-hub clonando el repositorio (https://github.com/patchy631/ai-engineering-hub) o siguiendo las instrucciones del README en GitHub. ClaudeWave también te ofrece bloques de instalación rápida en esta misma página.

¿Es seguro usar patchy631/ai-engineering-hub?

Nuestro agente de seguridad ha analizado patchy631/ai-engineering-hub y le ha asignado un Trust Score de 100/100 (tier: Verified). Revisa el desglose completo de comprobaciones superadas y flags en esta página.

¿Quién mantiene patchy631/ai-engineering-hub?

patchy631/ai-engineering-hub es mantenido por patchy631. La última actividad registrada en GitHub es del 2026-10-12, con 128 issues abiertos.

¿Hay alternativas a ai-engineering-hub?

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