NirDiamant/GenAI_Agents

📚 What is GenAI Agents?

A curated library of Jupyter notebooks that walk you through building generative‑AI agents with popular frameworks such as LangChain, LangGraph, AutoGen and MCP. The repo starts with very simple “talk‑to‑the‑model” bots and scales up to multi‑agent systems that can ingest documents, plan travel itineraries, grade essays, generate music, or even manage disaster‑response data streams. Every notebook is a self‑contained tutorial – code, explanations and a ready‑to‑run example – so you can copy, modify, and experiment locally.

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🎯 Core ideas the repo teaches

Level	What you learn
Beginner	Prompt‑driven conversational agents, basic question‑answering, data‑analysis notebooks using LangChain.
Framework	How to structure reusable agent workflows with LangGraph (state‑charts for LLMs) and the Model Context Protocol for external tool integration.
Educational	Multi‑agent academic assistants (paper review, note‑taking, Feynman‑style learning).
Business	Customer‑support bots, contract‑analysis helpers, project‑manager assistants, automated testing agents.
Creative	Text‑to‑GIF, TTS poetry, AI‑composed music, meme generators, interactive murder‑mystery games.
Analysis	Memory‑augmented chat, internet‑search summarizers, self‑improving agents, multi‑agent research teams, code‑healing utilities.

🛠️ What’s inside?

• 57+ notebooks (growing) covering a wide spectrum of agent types.
• Framework‑agnostic examples: most use LangChain for quick prototypes; many showcase LangGraph for more structured, stateful pipelines; a few use AutoGen for collaborative research agents.
• Featured tutorial – Document Intake Agent (LangGraph) that turns Office files into LLM‑ready markdown and answers questions grounded in the uploaded content.
• Utility notebooks that measure energy cost of “read vs. write”, demonstrate the inner loop of an agent, and show how to hook external APIs.

🚀 How to get started

1. Clone the repo and install the required Python packages (the notebooks list their requirements.txt).
2. Open any notebook in VS Code, JupyterLab, or Google Colab.
3. Follow the step‑by‑step markdown cells – they explain the theory, show the code, and include a “run it” link that executes the notebook in a hosted environment.
4. Replace the placeholder API keys with your own LLM provider (OpenAI, Anthropic, etc.) and start experimenting.

🤝 Community & support

• Contributions welcome – see CONTRIBUTING.md for guidelines on adding new agents or improving docs.
• Active Discord, Reddit (r/EducationalAI), and LinkedIn channels where the author shares updates, answers questions, and posts new video explainers.
• A newsletter (50 k+ subscribers) that announces fresh tutorials and occasional discounts on the author’s paid course.

📺 Extra learning resources

• Short YouTube explainers linked throughout the README (e.g., Why AI Uses So Much Energy, AI Agents Are Just While Loops). Each video points to a runnable notebook that reproduces the demo.
• A paid “Prompt to Production” course (optional) that expands the repo’s material into a full‑stack production pipeline.

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In a nutshell: GenAI Agents is a hands‑on, open‑source curriculum for anyone who wants to move from a single‑turn chatbot to a fully‑featured, multi‑agent AI system, with ready‑to‑run code and a supportive community.