3 LangGraph Agencies for Document Processing

LlamaIndex is the better choice when your primary challenge is retrieval quality — getting the right chunks from a large document corpus with accurate ranking. Its purpose-built indexing abstractions, chunking strategies, and retrieval pipeline components are more mature and flexible than LangGraph's retrieval tooling. LangGraph becomes the better choice when document processing is a multi-stage workflow rather than a pure retrieval problem: when you need stateful tracking of extraction progress across a large batch, intelligent routing based on document type or extraction confidence, mandatory human review gates before downstream data writes, or reliable resumability after processing failures. Many production document pipelines use both: LlamaIndex for retrieval within a LangGraph orchestration layer.

Statefulness matters when: processing large batches where failures mid-batch should not require full reprocessing; extraction requires multiple passes over the same document (first-pass structural extraction, second-pass validation, third-pass enrichment) with intermediate state; documents must be correlated with each other (e.g., matching line items across invoice and purchase order); or the processing workflow has dynamic routing based on accumulated results from earlier stages. For single-document Q&A or simple one-pass extraction, stateless approaches are simpler and equally effective. The threshold is roughly: more than a few hundred documents in a batch, multi-pass extraction logic, or downstream data integrity requirements that demand human checkpoints.

LangGraph itself adds no licensing cost over a simpler LangChain or Assistants API approach. The cost differences come from infrastructure: LangGraph requires a checkpointing backend (PostgreSQL typically) and potentially LangSmith for observability. LLM costs are similar regardless of orchestration framework — you pay for the tokens consumed by extraction prompts. The engineering cost is higher: designing the graph, implementing node functions, and configuring state schemas takes more time than a simple prompt-and-parse approach. This investment pays off when processing volume is high enough that batch resumability, human review workflows, and extraction quality gates provide meaningful ROI. For low-volume or exploratory extraction, start with Assistants API and migrate to LangGraph when you hit its limits.

LangGraph does not inherently improve extraction accuracy over simpler frameworks — accuracy is primarily determined by the LLM, the extraction prompt, and the chunking strategy, all of which are similar regardless of orchestration layer. What LangGraph adds is the ability to implement accuracy-improving workflows: multi-pass extraction where a second agent verifies the first agent's output, confidence scoring that routes uncertain extractions to specialized re-extraction prompts, ensemble extraction where multiple prompts are compared and the consensus result is used, and human review at the confidence boundary. These workflow patterns can significantly improve the effective accuracy of a document processing system, but they require the explicit orchestration that LangGraph provides.