langchain

LangChain is the kitchen-sink AI framework — 130k stars, massive ecosystem, and an abstraction layer so deep that debugging feels like archaeology. It evolved from a simple chain library into a full "agent engineering platform" with LangSmith for observability, LangServe for deployment, and LangGraph for stateful workflows.

LlamaIndex is better if your core problem is RAG and data retrieval. CrewAI is simpler for multi-agent orchestration. For direct API calls, you honestly don't need any framework — just httpx and a prompt.

Use LangChain if you need the broadest tool integration ecosystem and your team can absorb the learning curve. The LangGraph component is genuinely good for complex stateful agent workflows.

The catch: the abstraction tax is real. When something breaks inside nested chain objects, tracing the error is painful. The API has gone through breaking changes that cost teams real migration time. And for simple use cases — calling an LLM with a prompt — LangChain adds complexity without value. Start without it, add it when you actually need the orchestration.