To interact with LLMs and make them more accessible for various applications, there are frameworks such as LangChain, Llama Index and frameworks for LLM serving.

LangChain provides standardized interface for interacting with multiple LLMs. It offers tools for building apps with LLMs.

Llama Index helps to organize and curate data sources for the LLMs.

LLM serving frameworks are designed to optimize the process of deploying LLMs in production environment. They handle tasks such as model loading, inference and routing requests.

Essentially, LLM frameworks are toolkits that help developers to interact with and leverage the LLMs more effectively.

There could be standardized interfaces with different LLMs (irrespective of their architecture or API). There is prompt engineering to get the desired output from an LLM. There are tools and libraries to help developers design and optimize prompts. There is performance optimization by using frameworks that provide tools to optimize LLM inference for better performance.

Some frameworks enable chaining multiple LLMs together to create more complex workflows and apps. Frameworks integrate with other development tools and libraries,

Apart from LangChain and LlamaIndex, we have OpenLLM and Ray Serve frameworks.