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introduction
AI agents are autonomous software entities that perceive their environment, make decisions, and take actions to achieve specific goals. They form the basis of modern artificial intelligence applications, from chatbots to complex multi-agent systems. Model Context Protocol (MCP) is an open standard designed to connect AI models with external tools, APIs, and data sources.
Both of these technologies are dominating the AI field, and companies are using them to automate repetitive tasks and reduce labor, as agent AI can outperform junior-level employees in some cases.
In this article, we review 10 GitHub repositories to learn the basics of AI agents and guide you in building agent-based applications. These repositories contain tutorials, code samples, hands-on projects, valuable resources, and even YouTube guides to accelerate your learning.
10 GitHub repositories for mastering agents and MCPs
1. Learn AI and LLM from scratch
Repository: ashishps1/learn-ai-engineering
This repository provides a structured path to understanding AI and large-scale language models (LLM) from scratch using only free resources. Whether you’re a beginner or want to brush up on your basics, you’ll find valuable guides and links.
2. Microsoft’s AI agent for beginners
Repository:microsoft/ai-agents-for-beginners
Get 11 hands-on lessons designed to help you build your first AI agent. Clear explanations and practical examples make it an ideal starting point for anyone wanting to understand agent systems.
3. GenAI agent tutorial and implementation
Repository: NirDiamant/GenAI_Agents
Looking for a deep dive into Generative AI Agent technology? This repository offers comprehensive tutorials and projects from basic to advanced concepts, perfect for building smart, interactive AI systems. All projects are built using Jupyter Notebooks and include detailed instructions, code, and output so you can quickly understand how each application works.
4. Complete the Agent AI Engineering course
Repository: ed-donner/agent
In the Agentic AI Engineering course, you’ll learn how to code and deploy an AI agent in six weeks. Follow code, projects, and lessons tailored to give you a solid foundation in agent design and deployment.
5. Model of system prompts and AI tools
Repository: x1xhlol/system-prompts-and-models-of-ai-tools
Curious about how popular AI tools work under the hood? This repository collects system prompts, tools, and models from applications such as Cursor, Devin, and Replit Agent. Investigate real-world agent architectures and prompt engineering strategies.
6. AI Agent Masterclass (with video guide)
Repository: coleam00/ai-agents-masterclass
This repository is a companion to the Masterclass series on YouTube and contains all the code and resources found here. Build and extend real-world agent examples with step-by-step video tutorials.
7. Amazing AI agents (curated list)
Repository: e2b-dev/awesome-ai-agents
This is the ultimate list for anyone interested in autonomous agents. Explore a curated collection of the best AI agent frameworks, libraries, and research papers to accelerate your projects and research. This list is divided into open source agents and closed source agents.
8. Awesome MCP Server
Repository: punkpeye/awesome-mcp-servers
Examine the list of Model Context Protocol (MCP) servers. The list is divided into categories such as arts and culture, browser automation, cloud platforms, and code execution. It’s maintained by the open source community, so you’ll find the latest and most popular MCP servers.
9. Great MCP client
Repository: punkpeye/awesome-mcp-clients
I checked the list of MCP servers. We are currently checking the list of top MCP clients. These clients can include Python frameworks, desktop chatbots, VSCode extensions, agent code editors, and CLI tools such as Claude Code.
10. A great LLM app with agents and RAGs
Repository: Shubhamsaboo/awesome-llm-apps
Discover apps that combine AI agents, search augmented generation (RAG), MCP servers, and cutting-edge models from OpenAI, Anthropic, Gemini, and more. After learning the basics, you can take inspiration from these projects and start building your portfolio.
final thoughts
Large language models have limitations, and we’ve seen them firsthand. We were excited about the potential of artificial general intelligence, and now we’re seeing companies manipulate benchmarks to promote new AI models. So what’s next for AI and how can we improve it?
One promising direction concerns agents and MCP servers. These agents and MCP servers provide additional functionality that helps LLM extract more information and automate workflows.
You can build applications that search for stock prices on the Internet, analyze markets and news, and buy and sell stocks in real time. People are making millions doing this.
So what are you waiting for? Learn how to build your own agent application and start using AI the right way.
Abid Ali Awan (@1abidaliawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs about machine learning and data science technology. Avid holds a master’s degree in technology management and a bachelor’s degree in telecommunications engineering. His vision is to build AI products using graph neural networks for students suffering from mental illness.
