📘 Explore Jupyter notebooks that teach LangGraph concepts, from basic graphs to advanced agent design, enhancing your understanding of language model control flows.
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Updated
Dec 16, 2025 - Jupyter Notebook
📘 Explore Jupyter notebooks that teach LangGraph concepts, from basic graphs to advanced agent design, enhancing your understanding of language model control flows.
🛠️ Explore hands-on notebooks to master LLMs, RAG, LangChain, CrewAI, and multi-agent systems for effective AI learning and experimentation.
Sample code and notebooks for Generative AI on Google Cloud, with Gemini on Vertex AI
Notebooks & Example Apps for Search & AI Applications with Elasticsearch
AI apps development in LangChain & LangGraph - tutorial notebooks
📁 This repository hosts a growing collection of AI blueprint projects that run end-to-end using Jupyter notebooks, MLflow deployments, and Streamlit web apps.🛠️ All projects are built using HP AI Studio with ❤️ If you find this useful, please don’t forget to star the repository ⭐ and support our work 🚀
A notebook-driven exploration of next-gen agentic RAG architectures and intelligent retrieval pipelines.
Notebooks and Code about Generative Ai, LLMs, MLOPS, NLP , CV and Graph databases
Practical course about Large Language Models.
AI Learning: A comprehensive repository for Artificial Intelligence and Machine Learning resources, primarily using Jupyter Notebooks and Python. Explore tutorials, projects, and guides covering foundational to advanced concepts in AI, ML, DL and Gen/Agentic Ai.
A complete LangGraph multi-agent system demo using SQL tools, Tavily search, MCP Toolbox, and OpenRouter models — with reproducible notebooks and a full supervisor-led agent workflow.
Educational data analysis project demonstrating BMW sales data analysis with AI-powered code assistance using Cursor IDE and Jupyter notebooks
Repository containing practical exercises and notebooks focused on AI application development and experimentation.
RAG-BluePrint is a notebook-driven mini-book that teaches RAG from the ground up. Each chapter explains one architectural component with diagrams, minimal code, and runnable examples — no heavy frameworks, no hidden abstractions. A clear, practical way to master Retrieval-Augmented Generation.
A self-correcting multi-agent system that audits and actively fixes Kaggle notebooks. Powered by LangGraph and Gemini 2.5, it uses an autonomous feedback loop to improve code quality, documentation, and reproducibility scores in minutes.
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