GenAI Agentic AI
Learn GenAI and Agentic AI from Zero to Production
What is GenAI Agentic AI?
GenAI Agentic AI is a Model Context Protocol (MCP) server that allows AI assistants like Claude, Cursor, and VS Code to learn genai and agentic ai from zero to production
Learn GenAI and Agentic AI from Zero to Production
This server falls under the Coding Agents category on MCPgee, the world's largest MCP server directory with 33,000+ servers.
Features
- Learn GenAI and Agentic AI from Zero to Production
Use Cases
Maintainer
Works with
Installation
Manual Installation
npx genai-agenticai-from-zero-to-productionConfiguration
Configuration Details
claude_desktop_config.json
Performance
Response Metrics
Resource Usage
How to Set Up and Use GenAI Agentic AI
GenAI-AgenticAI-From-Zero-to-Production is a comprehensive Jupyter Notebook curriculum that teaches generative AI and agentic AI development from fundamentals through production deployment. The course covers text processing, embeddings, LLM prompt engineering, LangChain application development, Retrieval-Augmented Generation with hybrid search, multi-agent orchestration, the Model Context Protocol, and evaluation frameworks using tools like LangGraph, Qdrant, ChromaDB, LangSmith, DeepEval, and multiple LLM providers including OpenAI, Google Gemini, and Groq. It is intended for developers and ML engineers who want a hands-on, end-to-end path from learning AI concepts to shipping production-grade agentic systems.
Prerequisites
- Python 3.10 or later with Jupyter Notebook or JupyterLab
- API keys for at least one LLM provider: OpenAI, Google Gemini, or Groq
- Docker (optional, for running vector databases like Qdrant locally)
- Familiarity with Python programming and basic machine learning concepts
- An MCP-compatible client if exploring the MCP module (Module 08)
Clone the repository
Download the course materials to your local machine.
git clone https://github.com/bansalkanav/GenAI-AgenticAI-From-Zero-to-Production
cd GenAI-AgenticAI-From-Zero-to-ProductionCreate and activate a Python virtual environment
Isolate the course dependencies from your system Python to avoid conflicts.
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activateInstall Python dependencies
Each module may have its own requirements file. Start by installing the core dependencies, then check individual module directories for additional packages.
pip install -r requirements.txtConfigure API keys
Set your LLM provider API keys as environment variables. The notebooks reference these variables for model calls, vector database connections, and evaluation tools.
export OPENAI_API_KEY=your-openai-key
export GOOGLE_API_KEY=your-gemini-key
export GROQ_API_KEY=your-groq-key
export LANGCHAIN_API_KEY=your-langsmith-keyLaunch Jupyter and work through the modules in order
Open JupyterLab and navigate through the 11 modules sequentially — from text processing basics through agentic AI and MCP (Module 08).
jupyter labGenAI Agentic AI Examples
Client configuration
This repository is a learning resource, not a standalone MCP server. However, the MCP module (Module 08) includes notebooks demonstrating how to configure Claude Desktop with MCP servers built during the course.
{
"mcpServers": {
"course-mcp-example": {
"command": "python",
"args": ["path/to/module08/mcp_server.py"]
}
}
}Topics and exercises to explore
The curriculum covers these hands-on exercises across 11 modules.
- "Build a RAG pipeline with hybrid search using Qdrant and LangChain"
- "Create a multi-agent system with LangGraph that routes tasks between specialized agents"
- "Implement an MCP server and connect it to Claude Desktop (Module 08)"
- "Evaluate LLM outputs with DeepEval and trace agent runs with LangSmith"
- "Deploy an agentic workflow to Azure using the Foundry SDK"Troubleshooting GenAI Agentic AI
API key errors when running notebooks
Make sure environment variables are set in the same terminal session where you launched Jupyter, or add them to a .env file and use python-dotenv to load them at the top of each notebook.
Vector database connection failures
Start Qdrant or ChromaDB locally with Docker before running RAG notebooks: 'docker run -p 6333:6333 qdrant/qdrant' for Qdrant.
Module dependencies conflict with each other
Some modules require incompatible package versions. Create a separate virtual environment per module if you encounter dependency conflicts.
Frequently Asked Questions about GenAI Agentic AI
What is GenAI Agentic AI?
GenAI Agentic AI is a Model Context Protocol (MCP) server that learn genai and agentic ai from zero to production It connects AI assistants to external tools and data sources through a standardized interface.
How do I install GenAI Agentic AI?
Follow the installation instructions on the GenAI Agentic AI GitHub repository. Clone the repo, install dependencies, and add the server config to your AI client.
Which AI clients work with GenAI Agentic AI?
GenAI Agentic AI works with all major MCP-compatible AI clients including Claude Desktop, Claude Code, Cursor, VS Code (GitHub Copilot), Windsurf, and Cline.
Is GenAI Agentic AI free to use?
Yes, GenAI Agentic AI is open source and available under the MIT license. You can use it freely in both personal and commercial projects.
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Browse More Coding Agents MCP Servers
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Set Up GenAI Agentic AI in Your Editor
Choose your AI client for step-by-step setup instructions.
Quick Config Preview
Add this to your claude_desktop_config.json or .cursor/mcp.json
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