Tools for LLM Application Frameworks

LLM Application Frameworks provide developers with structured toolkits and abstractions to build, optimize, and deploy applications powered by large language models (LLMs) such as GPT, Claude, or LLaMA. These frameworks handle complex tasks including prompt chaining, memory management, context window optimization, tool usage, agent behavior, and API orchestration. They often support plug-and-play components like vector stores, retrievers, function calling, and multi-agent systems, making it easier to build production-grade AI tools, chatbots, assistants, search systems, and agents. Many of them include robust support for observability, logging, evaluation, and prompt versioning, and are designed to be framework-agnostic or integrable with backends like LangChain, LlamaIndex, Semantic Kernel, and others.