DemoGPT is an open-source Python project designed to demonstrate the core concepts of AI agents using OpenAI's GPT models. It implements a conversational interface with persistent memory saved in JSON files, enabling context-aware interactions across sessions. The framework supports dynamic tool execution, such as web search, calculations, and custom extensions, through a plugin-style architecture. By simply configuring your OpenAI API key and installing dependencies, users can run DemoGPT locally to prototype chatbots, explore multi-turn dialogue flows, and test agent-driven workflows. This comprehensive demo offers developers and researchers a practical foundation for building, customizing, and experimenting with GPT-powered agents in real-world scenarios.

The Hugging Face Agents Course is a comprehensive learning path that guides users through designing, implementing, and deploying autonomous AI agents. It includes code examples for chaining language models, integrating external APIs, crafting custom prompts, and evaluating agent decisions. Participants build agents for tasks like question answering, data analysis, and workflow automation, gaining hands-on experience with Hugging Face Transformers, the Agent API, and Jupyter notebooks to accelerate real-world AI development.

Agents Course by Justinvarghese511 is a structured learning program that equips developers with the skills to architect, implement, and deploy AI agents. Through step-by-step tutorials, participants learn to design agent decision flows, integrate external APIs, and manage context and memory. The course includes hands-on code examples, Jupyter notebooks, and practical exercises for building agents that automate data extraction, respond conversationally, and perform multi-step tasks. By the end, learners will have a portfolio of working AI agent projects and best practices for production deployment.

AutoGen Hands-On provides a structured environment to learn AutoGen framework usage through practical Python examples. It guides users on cloning the repository, installing dependencies, and configuring API keys to deploy multi-agent setups. Each script demonstrates key features such as defining agent roles, session memory, message routing, and task orchestration patterns. The code includes logging, error handling, and extensible hooks that allow customization of agents’ behavior and integration with external services. Users gain hands-on experience in building collaborative AI workflows where multiple agents interact to complete complex tasks, from customer support chatbots to automated data processing pipelines. The tutorial fosters best practices in multi-agent coordination and scalable AI development.

Botley the Coding Robot makes learning to code fun and engaging for young children. Designed for kids aged 5 and up, Botley offers a screen-free coding experience that encourages hands-on learning. The robot comes with an easy-to-use remote programmer, detachable robot arms, and coding cards. Botley can be programmed to navigate obstacle courses, follow looping commands, and even perform transformations, making it an exciting and educational tool for early STEM education.

CodeGuide is an innovative tool that demystifies algorithms, offering step-by-step guides tailored for learners at all levels. Whether you're a beginner seeking to grasp the fundamentals or an advanced coder looking to refine your skills, CodeGuide provides a structured approach to learning. The platform features interactive examples and quizzes, allowing for a hands-on learning experience that is both engaging and informative. Its user-friendly interface ensures easy navigation, making the journey of learning algorithms smoother and more enjoyable.

This course equips developers with step-by-step guides to implement various AI Agents using the Hugging Face ecosystem. It covers leveraging Transformers for language understanding, retrieval-augmented generation, integrating external API tools, chaining prompts, and fine-tuning agent behaviors. Learners build agents for document QA, conversational assistants, workflow automation, and multi-step reasoning. Through practical notebooks, users configure agent orchestration, error handling, memory strategies, and deployment patterns to create robust, scalable AI-driven assistants for customer support, data analysis, and content generation.

Robobloq provides educational robot kits that encourage children to engage with STEM concepts by building and programming their own robots. Featuring over 50 robot forms and 100+ electronic modules, these kits allow users to create unique robotic projects. Kids learn through play, enhancing their coding, engineering, and hands-on skills. Designed for various age groups, Robobloq's systems are perfect for both beginners and more advanced users, making robotics accessible and enjoyable.

Stemrobo is designed to promote STEM education through a hands-on approach, enabling students and educators to create, program, and control robots. It offers a user-friendly interface that guides users through various projects, providing resources and support to foster learning in science, technology, engineering, and mathematics. With features such as coding assistance, robotics simulation and real-time interaction, Stemrobo makes complex concepts accessible to learners of all ages.