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The Hands-On-AI-Engineering MCP server was last committed today and has 10 contributors. Issue health: 0% of issues resolved. AgentRank freshness score reflects real-time maintenance activity.

Hands-On-AI-Engineering MCP Server

Name: Sumanth077/Hands-On-AI-Engineering
Rating: 3.4 (1 reviews)
Author: Sumanth077

Sumanth077/Hands-On-AI-Engineering

Score: 61.1 Rank #5425 Search & RAG

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A curated collection of practical AI projects implementing OCR systems, RAG, AI agents, and other AI use cases.

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Overview

Sumanth077/Hands-On-AI-Engineering is a Python tool. A curated collection of practical AI projects implementing OCR systems, RAG, AI agents, and other AI use cases. Topics: agents, ai, llms, ocr, python, rag, ai-agents, ai-engineering, generative-ai, mcp.

Ranked #5425 out of 24840 indexed tools.

Has 3,701 GitHub stars.

Has 10 contributors.

Actively maintained with commits in the last week.

Ecosystem

Python No license

agentsaillmsocrpythonragai-agentsai-engineeringgenerative-aimcp

Score Breakdown

Stars 15% 3,701

3,701 stars → well-known project

Freshness 25% today

Last commit today → actively maintained

Issue Health 25% 0%

0/9 issues closed → many open issues

Contributors 10% 10

10 contributors → active community

Dependents 25% 0

No dependents → no downstream usage

npm Downloads N/A

PyPI Downloads N/A

Forks 897

Description Good

License None

Weights: Freshness 20% · Issue Health 20% · Dependents 22% · Stars 10% · Contributors 8% · How we score →

How to Improve

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License low impact

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Issue Health high impact

You have 9 open vs 0 closed issues — triaging stale issues improves health

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Details

Stars: 3,701
Forks: 897
Open Issues: 9
Closed Issues: 0
Contributors: 10
Dependents: —
Language: Python
License: —
Last Commit: today

Matched Queries

topic:mcp

From the README

<p align="center">
  <a href="https://aiengineering.beehiiv.com/">
    
  </a>
</p>
<div align="center">

# 🚀 Hands-On AI Engineering

</div>

A curated collection of practical, production-ready AI projects across multiple modalities, including language models, multimodal models, OCR systems, RAG pipelines, and AI agents. Each project is designed to help you learn, experiment, and build real-world AI applications.

## 📋 Table of Contents

- [🎯 Why This Repository?](#-why-this-repository)
- [🗂️ Project Categories](#️-project-categories)
- [🚀 Getting Started](#-getting-started)
- [🤝 Contributing](#-contributing)
- [📜 License](#-license)

---

## 🎯 Why This Repository?

- **Learn by Doing**: Each project includes complete code, setup instructions, and documentation
- **Production-Ready**: Projects follow best practices and are ready to be adapted for real-world use
- **Diverse Use Cases**: From RAG systems to multi-agent workflows and specialized applications
- **Multiple Model Pr

Read full README on GitHub →