microsoft/PyRIT

Langflow pairs a drag-and-drop canvas with editable Python components, letting you build an LLM-powered agent flow and ship it as an API, MCP server, or JSON export.

LangChain is a Python framework (MIT-licensed) for assembling LLM-powered apps and agents from standard components: model wrappers, prompts, retrieval, tools, and chain/graph orchestration handed off to LangGraph. It's for developers gluing together model providers and data sources, not for a single prompt-response call.

Microsoft's introductory course on building generative AI applications walks you through 21 structured lessons created by Microsoft Cloud Advocates. The curriculum alternates between conceptual 'Learn' lessons and hands-on 'Build' lessons, progressing from LLM fundamentals through advanced topics like RAG, AI agents, and fine-tuning. Each lesson includes a written explanation, video introduction, and working code samples in both Python and TypeScript. Basic Python or TypeScript knowledge is expected. The course supports Azure OpenAI, OpenAI API, Microsoft Foundry Models, and Foundry Local for fully offline execution. With over 50 language translations maintained via automated GitHub Actions, it's accessible to a global developer audience. MIT licensed with an active Discord community and developer forum for peer support.

LLMs-from-scratch is Sebastian Raschka's GitHub companion to the Manning book Build a Large Language Model (From Scratch), ISBN 9781633437166. It walks through building, pretraining, and finetuning a GPT-style model in PyTorch across seven chapters and five appendices, plus a growing set of bonus notebooks covering newer open architectures.