Image by Author   #  Introduction   Building large language model (LLM) applications is very different from using consumer-facing tools like Claude Code, ChatGPT, or Codex. Those products are great for end users, but when you want to build your own LLM system, you need a lot more control over how everything works behind the scenes. That usually means working with libraries and frameworks that help you load open-source models, build retrieval-augmented generation (RAG) pipelines, serve models through APIs, fine-tune them on your own data, create agent-based workflows, and evaluate how well everything performs. The challenge is that LLM application development is not just about prompting a model. There are a lot of moving parts, and putting them together into something reliable can get complicated fast. In this article, we will look at 10 Python libraries that make that process easier.…