The full answer
LlamaIndex is strongest on the data side of retrieval-augmented generation: loading documents from many sources, splitting and indexing them, attaching metadata and querying them in structured ways. Creuto uses it when a product must answer questions over large or varied document sets.
LangChain provides building blocks for chaining prompts, calling tools, managing conversation memory and orchestrating agents. It is useful for prototypes and for applications that combine several steps, models or data sources.
Frameworks are a means, not an end. Abstractions can hide what a model is actually being sent, which makes debugging and cost control harder. For simpler features Creuto often writes a thin service around the provider's SDK, with its own logging and evaluation hooks.
Whatever the orchestration layer, Creuto keeps the model behind an abstraction so Claude, OpenAI, Gemini, Mistral or Llama models can be swapped without rewriting the application, and embeddings are stored in pgvector, Pinecone or Weaviate depending on scale.
Every LLM application ships with an evaluation set built from real examples and run on each change. If you already have a LangChain or LlamaIndex prototype, Creuto can review it and harden it for production.