Open Source Jupyter Notebook Tools
Jupyter notebooks combine executable code, text, visualizations, and results in a single interactive document. They help people explore data, test ideas, explain analyses, and share reproducible workflows without separating code from its context. Notebooks are widely used for scientific computing, teaching, data analysis, and experiments with machine learning models. They can run code in different programming languages through kernels and work locally or in hosted environments.
Open source tools in this area include notebook editors, kernels, extensions, and services for sharing or running notebooks. When choosing one, consider its license, maintenance activity, language support, security, resource requirements, and compatibility with existing libraries and workflows. These tools are useful for students, researchers, educators, data analysts, and developers who want an interactive way to work with code and communicate results.
21 repositories · updated October 3, 2026
AI-Agents-Projects-Tutorials: Explore Practical AI Agent Examples
A collection of code notebooks and scripts exploring AI agents, multi-agent workflows, memory, planning, tool use, and agentic RAG. Useful for developers and learners who want runnable examples to study or adapt.
ds-cheatsheets: Find Data Science Reference Sheets
A curated collection of data science cheat sheets covering Python, R, statistics, machine learning, deep learning, big data, SQL, and visualization. Use it to quickly find practical PDF and image references across common tools and workflows.
rag-zero-to-hero-guide: Learn Retrieval-Augmented Generation
A learning guide to retrieval-augmented generation, from core concepts to evaluation and advanced approaches. It combines explanations, Jupyter notebook implementations, tool references, and survey papers for learners building RAG knowledge.
evidently: Evaluate and Monitor ML and LLM Systems
Evidently is a Python framework for evaluating, testing, and monitoring machine-learning and LLM systems, from data quality to generated text. Use it to build offline reports and regression checks or track metrics over time in a monitoring dashboard.
jsonformer: Generate Schema-Conforming JSON with Language Models
Jsonformer guides Hugging Face language models to produce JSON that matches a supplied schema by generating variable content while inserting predictable structure itself. It suits developers who need structured model output and can work within its supported JSON Schema subset.
Qwen3-VL: Understand Images, Video, and Text with Multimodal Models
Qwen3-VL is a family of multimodal language models for interpreting images, video, and text. The repository provides inference examples, deployment guidance, and cookbooks for tasks such as OCR, spatial reasoning, and visual agents.
rag-from-scratch: Learn Retrieval-Augmented Generation Step by Step
A Jupyter notebook series that teaches the building blocks of retrieval-augmented generation, from indexing and retrieval to generation. It is aimed at learners who want to understand RAG concepts through an educational progression rather than adopt a ready-made application.
co-tracker: Track Points Across Video Frames
CoTracker tracks selected or grid-sampled points through video using a transformer-based model. It offers offline and online inference, pretrained checkpoints, and tools for evaluation and training.
notebooks: Learn and Apply Computer Vision Models
Roboflow notebooks is a hands-on tutorial collection for computer vision, covering model training, inference, detection, segmentation, and related tasks. Use it to explore techniques and run examples in hosted notebook environments.
KBLaM: Add Knowledge Bases to Language Models
KBLaM is a research implementation for giving transformer language models access to external knowledge through learned adapters and special knowledge tokens. It is aimed at researchers testing knowledge-grounded answers without a separate retrieval module.
courses: Learn Claude API and Prompting Techniques
Anthropic’s courses repository offers hands-on learning materials for using Claude through its API. It suits developers learning prompting, evaluations, and tool use, especially when they want guided examples before building Claude-powered workflows.
awesome-AI-books: Find AI Books, Papers, and Learning Resources
A curated directory of books, papers, online references, and practice environments across AI and related fields. It helps learners find starting points for topics from mathematics and machine learning to reinforcement learning and quantum computing.