LLM orchestration frameworks

Once an app makes several model and tool calls in sequence, you need plumbing: something to manage prompts, pass data between steps, talk to vector stores, parse outputs, and retry on failure. Orchestration frameworks are libraries — LangChain, LlamaIndex, and others — that provide ready-made building blocks for this wiring, so you assemble a pipeline from parts instead of writing every connection by hand.

These frameworks offer abstractions for prompt templates, chains that link steps, retrievers and document loaders, memory that carries conversation state, output parsers, and agent loops that let a model pick tools. They also standardise the interface across model providers, so swapping one vendor for another is a config change rather than a rewrite. You compose these components into a graph that describes your application's flow.

A framework can accelerate a prototype, but it adds a dependency, a learning curve, and a layer of abstraction that can hide what is actually being sent to the model — which makes debugging cost and quality harder. Many teams start with a framework and later peel it back to plain API calls for the parts they need full control over. The right call depends on how much custom logic you have; the framework is a convenience, not a requirement.