Scenario 01
If your main challenge is document parsing, table extraction and retrieval…
LlamaIndex is built around ingestion and indexing, and covers those well.
Comparison: LangChain and LangGraph vs. LlamaIndex and custom agent loops
Understand when AI frameworks speed up development, and when a small custom loop on the model provider's SDK is easier to debug and more predictable.
Decision framework
Scenario 01
LlamaIndex is built around ingestion and indexing, and covers those well.
Scenario 02
LangGraph gives you graph-based state management and checkpoints out of the box.
Scenario 03
Write a small custom loop on the provider's SDK (OpenAI or Anthropic tool calling) with structured outputs.
Trade-offs
How the two options compare on the dimensions that usually decide this choice.
| Dimension | LangChain and LangGraph | LlamaIndex and custom agent loops | Verdict |
|---|---|---|---|
| Primary strength | Multi-step agent orchestration and state persistence (LangGraph) | Retrieval: document parsing, chunking and query routing (LlamaIndex) | LlamaIndex for retrieval; LangGraph for complex agents |
| Debuggability | Several abstraction layers; tracing (for example LangSmith) helps a lot | Fewer layers and a more direct execution flow | Custom loops are the easiest to debug |
| Predictability in production | Agent graphs need explicit bounds to avoid runaway loops | Predictable when every step is backed by a structured output schema | Custom loops are the most predictable |
Questions
Yes. A common pattern uses LlamaIndex for ingestion and retrieval, with LangGraph orchestrating the conversation and tool calls around it.
Early versions wrapped simple API calls in heavy abstractions. LangGraph is more modular, but teams that value minimal dependencies often prefer the provider SDKs directly.
Share your constraints — team, traffic, budget, compliance. We'll reply within one business day, and the call is about your decision, not our preferred stack.