AI Frameworks Comparison: LangGraph, LlamaIndex, and Orchestration Trade-offs

AI framework interviews are really about engineering judgment under abstraction pressure. Interviewers ask when LangChain-style composition accelerates delivery versus obscuring control flow, and when you would drop to plain Python with explicit state machines for agent orchestration.

LangGraph and similar graph frameworks shine for cyclical agent workflows with checkpointing and human interrupts. Candidates compare graph-based orchestration to supervisor multi-agent patterns, noting debuggability, testability, and onboarding cost for new engineers.

LlamaIndex and retrieval-focused frameworks offer ingestion connectors and query engines that speed early RAG prototypes. Discuss when their defaults become constraints—chunking strategies, hybrid search wiring, and custom metadata pipelines often require escape hatches.

Framework selection should consider observability hooks, community longevity, and migration cost. Strong answers define thin domain layers you own—schemas, eval, policies—and treat frameworks as replaceable infrastructure rather than the application core.