Langchain

If you're building anything with LLMs beyond basic API calls, this is the industry standard framework. It handles the boring stuff like swapping between OpenAI, Anthropic, and Google models, managing conversation memory, and wiring up tool-calling agents with the ReAct pattern. The RAG implementation is especially solid for question-answering systems, with built-in document loaders, text splitters, and vector store integrations. With 119k GitHub stars and 500+ integrations, you'll find examples for basically anything. It can feel heavyweight for simple tasks, and the abstractions sometimes leak, but for production chatbots or autonomous agents, it saves weeks of plumbing work.