KDnuggets compares LangChain, LlamaIndex and building LLM apps from scratch

LangChain offers broad integrations and agentic workflows but suffers from deep abstraction layers and rapid evolution, making debugging and upgrades harder. It excels for prototyping or projects requiring diverse tool connections. LlamaIndex focuses on retrieval-augmented generation (RAG) and provides robust evaluation tools, ideal for document-based Q&A systems. However, it struggles with non-retrieval use cases. Building from scratch grants full control over infrastructure but demands upfront effort and expertise in embedding models, vector databases, and prompt engineering. The guide suggests starting with a framework for exploration, then migrating critical logic to a custom build as requirements solidify.