This bootcamp covers the LangChain framework end-to-end, enabling you to build AI Agents in Python. You’ll explore prompt templates, chain composition, agent tooling, conversational memory, and document retrieval. Through interactive notebooks and detailed exercises, you’ll implement chatbots, automated workflows, question-answering systems, and custom agent chains. By course end, you’ll understand how to deploy and optimize LangChain-based agents for diverse tasks.

Lernmi is a cutting-edge application that focuses on enhancing communication abilities by incorporating role play exercises based on real-life scenarios. It combines theoretical learning with practical training, backed by personalized feedback from specialized mentors. This approach ensures that users can relate to real-world situations, practicing and refining their soft skills to become more effective communicators.

Mathify is an innovative problem-solving app designed to assist users in mastering mathematical concepts. The platform offers a variety of tools and resources that cater to different learning styles, making math accessible and engaging for everyone. With its user-friendly interface, Mathify empowers students, educators, and math enthusiasts to solve complex problems efficiently. Whether you are looking for step-by-step solutions, interactive tutorials, or additional practice exercises, Mathify has got you covered.

StudyPoint AI revolutionizes the learning experience by using advanced AI technology to provide personalized and engaging educational content. The platform adapts to each student's unique learning style and pace, ensuring that they receive the most effective instruction possible. It includes a variety of resources and tools, such as interactive lessons, practice exercises, and real-time feedback, all designed to support and motivate students throughout their educational journey. With StudyPoint AI, learning becomes more accessible, efficient, and enjoyable.

AI Agent Workshop is a comprehensive repository offering practical examples and templates for developing AI Agents with Python. The workshop includes Jupyter notebooks demonstrating agent frameworks, tool integrations (e.g., web search, file operations, database queries), memory mechanisms, and multi-step reasoning. Users learn to configure custom agent planners, define tool schemas, and implement loop-based conversational workflows. Each module presents exercises on handling failures, optimizing prompts, and evaluating agent outputs. The codebase supports OpenAI’s function calling and LangChain connectors, allowing seamless extension for domain-specific tasks. Ideal for developers seeking to prototype autonomous assistants, task automation bots, or question-answering agents, it provides a step-by-step path from basic agents to advanced workflows.

Deep Study AI Agent uses OpenAI’s GPT models to process user-provided text or documents, extract key concepts, and generate study aids. Users upload lecture notes, PDFs, or text files, and the agent produces concise summaries, sets of flashcards, multiple-choice quizzes, and targeted practice exercises. It also offers configurable difficulty settings and contextual hints. The modular design allows extensions for new content types and prompt templates, making it flexible for various academic subjects and self-study workflows.