LangChain vs LlamaIndex: how developers choose a framework for LLM applications

In the LLM developer community, a practice has become established: projects start with raw API calls to models (OpenAI, Anthropic, Mistral) and migrate to a framework as architecture becomes more complex. This helps choose the right tool at each stage of development.

Why start with raw API

First script, first prototype — why immediately pull in a framework? Raw API is simple and clear: send JSON, get response. No abstraction layers, full control over the request, easier to debug what's going wrong. The developer immediately sees the real syntax of requests and better understands the model documentation.

When a framework is needed

But chains of requests become complex: one call depends on another, you need to manage conversation context (memory), often you need to call external tools — search, databases, third-party APIs. Here LangChain or LlamaIndex become indispensable: they abstract away routine, provide ready-made patterns, simplify testing.

• LangChain: universal framework, support for 100+ integrations, large community
• LlamaIndex: emphasis on RAG and working with documents
• Pydantic AI: minimalist, typed, for strict code requirements

What this means

Tool choice depends on scale and needs. A small bot or MVP is faster to write with raw API, enterprise system requires a framework. The main thing is not to make premature optimization and not to pull an unnecessary framework into a simple project that will become excess baggage.